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Humans make mistakes all the time. All of us do, every day, in tasks both new and routine. Some of our mistakes are minor and some are catastrophic. Mistakes can break trust with our friends, lose the confidence of our bosses, and sometimes be the difference between life and death.

Over the millennia, we have created security systems to deal with the sorts of mistakes humans commonly make. These days, casinos rotate their dealers regularly, because they make mistakes if they do the same task for too long. Hospital personnel write on limbs before surgery so that doctors operate on the correct body part, and they count surgical instruments to make sure none were left inside the body. From copyediting to double-entry bookkeeping to appellate courts, we humans have gotten really good at correcting human mistakes.

Humanity is now rapidly integrating a wholly different kind of mistake-maker into society: AI. Technologies like large language models (LLMs) can perform many cognitive tasks traditionally fulfilled by humans, but they make plenty of mistakes. It seems ridiculous when chatbots tell you to eat rocks or add glue to pizza. But it’s not the frequency or severity of AI systems’ mistakes that differentiates them from human mistakes. It’s their weirdness. AI systems do not make mistakes in the same ways that humans do.

Much of the friction—and risk—associated with our use of AI arise from that difference. We need to invent new security systems that adapt to these differences and prevent harm from AI mistakes.

Human Mistakes vs AI Mistakes

Life experience makes it fairly easy for each of us to guess when and where humans will make mistakes. Human errors tend to come at the edges of someone’s knowledge: Most of us would make mistakes solving calculus problems. We expect human mistakes to be clustered: A single calculus mistake is likely to be accompanied by others. We expect mistakes to wax and wane, predictably depending on factors such as fatigue and distraction. And mistakes are often accompanied by ignorance: Someone who makes calculus mistakes is also likely to respond “I don’t know” to calculus-related questions.

To the extent that AI systems make these human-like mistakes, we can bring all of our mistake-correcting systems to bear on their output. But the current crop of AI models—particularly LLMs—make mistakes differently.

AI errors come at seemingly random times, without any clustering around particular topics. LLM mistakes tend to be more evenly distributed through the knowledge space. A model might be equally likely to make a mistake on a calculus question as it is to propose that cabbages eat goats.

And AI mistakes aren’t accompanied by ignorance. A LLM will be just as confident when saying something completely wrong—and obviously so, to a human—as it will be when saying something true. The seemingly random inconsistency of LLMs makes it hard to trust their reasoning in complex, multi-step problems. If you want to use an AI model to help with a business problem, it’s not enough to see that it understands what factors make a product profitable; you need to be sure it won’t forget what money is.

How to Deal with AI Mistakes

This situation indicates two possible areas of research. The first is to engineer LLMs that make more human-like mistakes. The second is to build new mistake-correcting systems that deal with the specific sorts of mistakes that LLMs tend to make.

We already have some tools to lead LLMs to act in more human-like ways. Many of these arise from the field of “alignment” research, which aims to make models act in accordance with the goals and motivations of their human developers. One example is the technique that was arguably responsible for the breakthrough success of ChatGPT: reinforcement learning with human feedback. In this method, an AI model is (figuratively) rewarded for producing responses that get a thumbs-up from human evaluators. Similar approaches could be used to induce AI systems to make more human-like mistakes, particularly by penalizing them more for mistakes that are less intelligible.

When it comes to catching AI mistakes, some of the systems that we use to prevent human mistakes will help. To an extent, forcing LLMs to double-check their own work can help prevent errors. But LLMs can also confabulate seemingly plausible, but truly ridiculous, explanations for their flights from reason.

Other mistake mitigation systems for AI are unlike anything we use for humans. Because machines can’t get fatigued or frustrated in the way that humans do, it can help to ask an LLM the same question repeatedly in slightly different ways and then synthesize its multiple responses. Humans won’t put up with that kind of annoying repetition, but machines will.

Understanding Similarities and Differences

Researchers are still struggling to understand where LLM mistakes diverge from human ones. Some of the weirdness of AI is actually more human-like than it first appears. Small changes to a query to an LLM can result in wildly different responses, a problem known as prompt sensitivity. But, as any survey researcher can tell you, humans behave this way, too. The phrasing of a question in an opinion poll can have drastic impacts on the answers.

LLMs also seem to have a bias towards repeating the words that were most common in their training data; for example, guessing familiar place names like “America” even when asked about more exotic locations. Perhaps this is an example of the human “availability heuristic” manifesting in LLMs, with machines spitting out the first thing that comes to mind rather than reasoning through the question. And like humans, perhaps, some LLMs seem to get distracted in the middle of long documents; they’re better able to remember facts from the beginning and end. There is already progress on improving this error mode, as researchers have found that LLMs trained on more examples of retrieving information from long texts seem to do better at retrieving information uniformly.

In some cases, what’s bizarre about LLMs is that they act more like humans than we think they should. For example, some researchers have tested the hypothesis that LLMs perform better when offered a cash reward or threatened with death. It also turns out that some of the best ways to “jailbreak” LLMs (getting them to disobey their creators’ explicit instructions) look a lot like the kinds of social engineering tricks that humans use on each other: for example, pretending to be someone else or saying that the request is just a joke. But other effective jailbreaking techniques are things no human would ever fall for. One group found that if they used ASCII art (constructions of symbols that look like words or pictures) to pose dangerous questions, like how to build a bomb, the LLM would answer them willingly.

Humans may occasionally make seemingly random, incomprehensible, and inconsistent mistakes, but such occurrences are rare and often indicative of more serious problems. We also tend not to put people exhibiting these behaviors in decision-making positions. Likewise, we should confine AI decision-making systems to applications that suit their actual abilities—while keeping the potential ramifications of their mistakes firmly in mind.

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Brain-inspired Computing Is Ready for the Big Time

Neuromorphic pioneer Steve Furber says it's just awaiting a killer app

6 min read

Steve Temple (left) holding a SpiNNaker chip with Steve Furber (right) in front of a labelled plot of the chip.

Steve Furber

neuromorphic computing spinnaker artificial intelligence neural networks spiking neural networks

Efforts to build brain-inspired computer hardware have been underway for decades, but the field has yet to have its breakout moment. Now, leading researchers say the time is ripe to start building the first large-scale neuromorphic devices that can solve practical problems.

The neural networks that have powered recent progress in artificial intelligence are loosely inspired by the brain, demonstrating the potential of technology that takes its cues biology. But the similarities are only skin deep and the algorithms and hardware behind today’s AI operate in fundamentally different ways to biological neurons.

Neuromorphic engineers hope that by designing technology that more faithfully replicates the way the brain works, we will be able to mimic both its incredible computing power and its energy efficiency. Central to this approach is the use of spiking neural networks, in which computational neurons mimic their biological cousins by communicating using spikes of activity, rather than the numerical values used in conventional neural networks. But despite decades of research and increasing interest from the private sector, most demonstrations remain small scale and the technology has yet to have a commercial breakout.

In a paper published in Nature in January, some of the field’s leading researchers argue this could soon change. Neuromorphic computing has matured from academic prototypes to production-ready devices capable of tackling real-world challenges, they argue, and is now ready to make the leap to large-scale systems. IEEE Spectrum spoke to one of the paper’s authors, Steve Furber, the principal designer of the ARM microprocessor—the technology that now powers most cellphones—and the creator of the SpiNNaker neuromorphic computer architecture.

Steve Furber on...

In the paper you say that neuromorphic computing is at a critical juncture. What do you mean by that?

Steve Furber: We’ve demonstrated that the technology is there to support spiking neural networks at pretty much arbitrary scale and there are useful things that can be done with them. The criticality of the current moment is that we really need some demonstration of a killer app.

The SpiNNaker project started 20 years ago with a focus on contributing to brain science, and neuromorphics is an obvious technology if you want to build models of brain cell function. But over the last 20 years, the focus has moved to engineering applications. And to really take off in the engineering space, we need some demonstrations of neuromorphic advantage.

In parallel over those 20 years, there’s been an explosion in mainstream AI based on a rather different sort of neural network. And that’s been very impressive and obviously had huge impacts, but it’s beginning to hit some serious problems, particularly in the energy requirements of large language models (LLMs). And there’s now an expectation that neuromorphic approaches may have something to contribute, by significantly reducing those unsustainable energy demands.

The SpiNNaker team assembles a million-core neuromorphic system.SpiNNaker

We are close to having neuromorphic systems at a scale sufficient to support LLMs in neuromorphic form. I think there are lots of significant application developments at the smaller end of the spectrum too. Particularly close to sensors, where using something like an event-based image sensor with a neuromorphic processing system could give a very low energy vision system that could be applied in areas such as security and automotive and so on.

When you talk about achieving a large-scale neuromorphic computer, how would that compare to systems that already exist?

Furber: There are lots of examples out there already like the large Intel Loihi 2 system, Hala Point. That’s a very dense, large-scale system. The SpiNNaker 1 machine that we’ve been running a service on [at the University of Manchester, UK] since 2016 had half a million ARM cores in the system, expanding to a million in 2018. That’s reasonably large scale. Our collaborators on SpiNNaker 2 [SpiNNcloud Systems, based in Dresden, Germany] are beginning to market systems at the 5 million core level, and they will be able to run quite substantial LLMs.

Now, how much those will need to evolve for neuromorphic platforms is a question yet to be answered. They can be translated in a fairly simplistic way to get them running, but that simple translation won’t necessarily get the best energy performance.

So is the hardware not really the issue, it’s working out how to efficiently build something on top of it?

Furber: Yes, I think the last 20 years has seen proof-of-concept hardware systems emerge at the scales required. It’s working out how to use them to their best advantage that is the gap. And some of that is simply replicating the efficient and useful software stacks that have been developed for GPU-based machine learning.

It is possible to build applications on neuromorphic hardware, but it’s still unreasonably difficult. The biggest missing components are the high-level software design tools along the lines of TensorFlow and PyTorch that make it straightforward to build large models without having to go down to the level of describing every neuron in detail.

There’s quite a diversity of different neuromorphic technologies, which can sometimes make it hard to translate findings between different groups. How can you break down those silos?

Furber: Although the hardware implementation is often quite different, the next level up there is quite a lot in common. All neuromorphic platforms use spiking neurons and the neurons themselves are similar. You have a diversity of details at the lower levels, but that can be bridged by implementing a layer of software that matches those lower level hardware differences to higher level commonalities.

We’ve made some progress on that front, because within the EU’s Human Brain Project, we have a group that’s been developing the PyNN language. It is supported by both SpiNNaker, which is a many core neuromorphic system, and the University of Heidelberg’s BrainScaleS system, which is an analog neural model.

But it is the case that a lot of neuromorphic systems are developed in a lab and used only by other people within that lab. And therefore they don’t contribute to the drive towards commonality. Intel has been trying to contribute through building the Lava software infrastructure on their Loihi system and encouraging others to participate. So there are moves in that direction but it’s far from complete.

A member of the SpiNNaker team checks on the company’s million-core machine.Steve Furber

Opinions differ on how biologically plausible neuromorphic technology needs to be. Does the field need to develop some consensus here?

Furber: I think the diversity of the hardware platforms and of the neuron models that are used is a strength in the research domain. Diversity is a mechanism for exploring the space and giving you the best chance of finding the best answers for developing serious, large-scale applications. But once you do, yes, I think you need to reduce the diversity and focus more on commonality. So if neuromorphic is about to make the transition from a largely research-driven territory to a largely application-driven territory, then we’d expect to see that kind of thing changing.

If the field wants to achieve scale will it have to sacrifice a bit of biological plausibility?

Furber: There is a trade-off between biological fidelity and engineering controllability. Replicating the extremely simple neural models that are used in LLMs does not require a lot of biological fidelity. Now, it’s arguable that if you could incorporate a bit more of the biological detail and functionality, you could reduce the number of neurons required for those models by a significant factor. If that’s true, then it may well be worth ultimately incorporating those more complex models. But it is still big research problem to prove that this is the case.

In recent years there’s been a lot of excitement about memristors—memory devices that mimic some of the functionality of neurons. Is that changing the way people are approaching neuromorphic computing?

Furber: I do think that the technologies that are being developed have the potential to be transformative in terms of improving hardware efficiency at the very low levels. But when I look at the UK neuromorphic research landscape, a very significant proportion of it is focused on novel device technologies. And arguably, there’s a bit too much focus on that, because the systems problems are the same across the board.

Unless we can make progress on the systems level issues it doesn’t really matter what the underpinning technology is, and we already have platforms that will support progress on the systems level issues.

The paper suggest that the time is ripe for large-scale neuromorphic computing. What has changed in recent years that makes you positive about this, or is it more a call to arms?

Furber: It’s a bit in-between. There is evidence it’s happening, there are a number of interesting startups in the neuromorphic space who are managing to survive. So that’s evidence that people with significant available funds are beginning to be prepared to spend on neuromorphic technology. There’s a belief in the wider community that neuromorphic’s time is coming. And of course, the huge problems facing mainstream machine learning on the energy front, that is a problem which is desperate for a solution. Once there’s a convincing demonstration that neuromorphics can change the equation, then I think we’ll see things beginning to turn.

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Latest Qualcomm RB3 Gen 2 Developer Kit Unlocks AI Computing for IoT Edge Innovation

New kits put advanced AI edge computing power into the hands of developers everywhere

8 min read

Qualcomm Technologies

This is a sponsored article brought to you by Qualcomm.

In a move set to transform the Internet of Things( IoT) landscape, Qualcomm Technologies, Inc. has introduced its Qualcomm RB3 Gen 2 developer kits, designed to put advanced AI edge computing power into the hands of developers everywhere. This kit is available as Qualcomm RB3 Gen 2, based on the Qualcomm QCS6490, or the Qualcomm RB3 Gen 2 Lite, based on the Qualcomm QCS5430.

Both QCS6490 and QCS5430 processors provide efficient, high-performance, AI enhanced solutions for applications in robotics, AI vision, industrial automation, retail, smart security, precision agriculture, smart metering, predictive maintenance and personal health. By empowering developers with robust tools for edge computing, Qualcomm Technologies is encouraging a broader range of innovators—from tech companies to startups and students—to bring cutting-edge IoT solutions to life.

Nadim Ferzli, Staff Manager, Product Marketing for Qualcomm Technologies, emphasized the importance of edge computing as a critical factor in the kit’s development. “AI-enabled edge computing has a lot of benefits, including faster response times, on-device decision making and enhanced security, as well as reduced cost,” Ferzli explained, noting that processing data locally enables faster decision-making and reduces dependency on cloud-based processing. This local computing power is essential for applications that require real-time responses like robotics, security and industrial automation.

“AI-enabled edge computing has a lot of benefits, including faster response times, on-device decision making and enhanced security, as well as reduced cost” —Nadim Ferzli, Qualcomm Technologies

The Qualcomm RB3 Gen 2 Kits feature a modular design based on the 96Board compact, credit card- sized form factor and specifications. The kit includes numerous connection options, such as multiple USB, ethernet, camera, and display ports, as well as access to various GPIOs for low-speed communication protocols like SPI, UART, and I2C, and high-speed connections like PCIE, USB, and MIPI. The kits also come with Wi-Fi 6E, Bluetooth 5.2, and optional 5G connectivity through additional modules. Qualcomm Technologies has a dedicated resource page detailing the hardware and connections. The kits can be expanded with the addition of mezzanine boards, keeping their compact size, which is beneficial for rapid prototyping and proof-of-concept projects where users can add their own attachments and integrate the kit into their preferred robot, camera, or other hardware platform. Qualcomm Technologies also provides a template that developers can take to quickly create their own mezzanine cards.

The Power of AI-Enhanced Edge Computing at the Core

Central to the appeal of the Qualcomm RB3 Gen 2 is the edge-focused approach. The QCS6490 and QCS5430 processors are engineered to handle substantial computing loads at the device level. Equipped with a multi-core CPU (up to 8 cores), GPU and AI engine (NPU & DSP) producing up to 12 dense TOPS (trillions of operations per second), these microprocessors enable devices to perform complex data processing at the edge, making them ideal for high compute applications like autonomous robotics and smart vision solutions. The processors offer a combination of high-performance compute, connectivity, and energy efficiency in one package.

Qualcomm AI Hub: The platform for on-device AI

To facilitate and accelerate the development and adoption of AI processing at the edge, Qualcomm Technologies created the Qualcomm AI Hub, a comprehensive platform designed to facilitate the deployment of AI models directly onto edge devices, enabling efficient on-device processing for applications in vision, audio, and speech and integrates with cloud-based tools like Amazon SageMaker for end-to-end AI solutions.

Developers can utilize pre-optimized models or integrate their own, with support for multiple runtimes such as TensorFlow Lite and ONNX Runtime. It offers a streamlined workflow that allows developers to compile, profile, and run AI models on actual hardware in the cloud, ensuring optimized performance and reduced latency. The combination of hardware capabilities and AI tools expands the capabilities of the device to support complex edge processing like SLM (Small Language Model), sensor fusion and autonomous machinery.

Visit the Qualcomm AI Hub to learn more →

This edge-first design not only improves processing speed but also enhances data security by keeping sensitive information on the device rather than transferring it to the cloud. For developers working in applications like smart security, personal health or industrial automation, this means critical data stays closer to its source, enabling faster, more secure responses in real-time scenarios.

Edge AI Vision and Real-Time Decisions

One of the standout features of the Qualcomm RB3 Gen 2 developer kit is the Vision Mezzanine option, which includes Qualcomm Technologies’ AI-driven image recognition capabilities. Equipped with dual cameras covering high-definition and low-definition camera support, the kits allow for real-time object detection, making it suitable for security systems, autonomous drones, and smart vision prototyping. “With our kits and enablement tools, engineers are able to accelerate the prototyping and development of AI solutions,” Ferzli explained, envisioning scenarios where edge AI is essential, such as search-and-rescue or industrial inspection. The kit can be further expanded with additional cameras that are available as optional accessories.

Qualcomm Technologies

Qualcomm Technologies’ advanced AI processing on the Edge technology allows the Qualcomm RB3 Gen 2 kits to recognize and process visual data on-device, a capability that significantly reduces latency and enhances operational efficiency. In practical terms, this means that a robot equipped with the Qualcomm RB3 Gen2 can navigate a warehouse, recognize obstacles, and make real-time decisions autonomously, without needing a cloud connection. “AI on the Edge enables these devices to analyze and make decisions instantaneously,” Ferzli shared, highlighting the power of Qualcomm Technologies’ processors in real-time applications.

Qualcomm Technologies

This local AI capability is also useful in AI-powered security systems. For example, a smart camera could be deployed to monitor a construction site, using the Qualcomm RB3 Gen 2 capabilities to detect unauthorized entry or potential hazards, and issue immediate alerts. Qualcomm Technologies’ focus on robust, high-efficiency AI computing at the device level enables devices to perform complex tasks, such as analyzing footage or identifying specific objects in high detail, directly at the edge.

Ferzli highlighted a customer project involving an inspection robot for railway safety, where a company switched from a more power-hungry, costly device to the QCS6490 solution. The switch cut memory usage by 68 percent in addition to the embedded Wi-Fi connectivity provided an efficient system that reduced costs while maintaining the same accuracy. This success story exemplifies how Qualcomm Technologies’ focus on powerful compute, exceptional connectivity and power efficiency can enhance productivity and reduce operational costs.

Edge Efficiency for Robotics and Autonomous Applications

The Qualcomm RB3 Gen 2 developer kit’s efficiency makes it a strong choice for autonomous applications, where power consumption, connectivity and computational power are vital factors. With an emphasis on low power consumption, Qualcomm Technologies’ edge computing solutions enable battery-powered devices to operate longer between charges.

According to Ferzli, Qualcomm Technologies’ DNA translates directly into these processors, offering “high compute performance, exceptional connectivity, and energy efficiency” while utilizing less memory compared to alternatives. This balance of power and efficiency allows developers to use their kit in battery-dependent applications like mobile robots and drones, where extended operation time is critical.

Qualcomm Technologies

Another example involves a lab using Qualcomm Technologies’ vision technology to automate bacteria colony counting, a process critical in food safety and medical diagnostics. Traditionally, lab technicians manually reviewed growth colonies in petri dishes, but with Qualcomm Technologies’ edge AI, the process was automated to deliver results instantly. “Qualcomm Technologies’ edge processing brings efficiency by reducing the need for human interaction and minimizing inaccuracies,” Ferzli explained, underscoring how their technology can simplify and accelerate workflows in various industries.

Developer-First Approach: Open Access and Long-Term Support

As part of its efforts to deliver an exceptional user experience for the IoT mass market, Qualcomm Technologies decided to cater more to the needs of small players by providing more open access, easier to use tools, and providing support for multiple operating systems.

Qualcomm Technologies’ commitment to democratizing edge computing is clear in its developer-focused approach. The Qualcomm RB3 Gen 2 developer kits are designed to be accessible to a wide audience, from professional engineers to hobbyists, with a competitive pricing model and comprehensive support. “Our goal is to make this product available to everyone,” Ferzli said, highlighting that Qualcomm Technologies’ open-access approach enables developers to purchase the kit and begin innovating without a lengthy or exclusive onboarding process.

The kits are able to support multiple OS including Linux, Android, Ubuntu, and Windows. Besides the Qualcomm Linux OS that is pre-loaded the kits will soon support Linux Ubuntu which may be attractive to the community of smaller developers, including an upcoming version that includes support for Ubuntu Desktop. In addition, Qualcomm Technologies’ recent push into the Windows laptop space is also fueling support for an upcoming Windows OS release that runs on the kit for the industrial market segment typically dominated by x86 based devices running Windows. The kit will also run Android OS.

The kits are supported by software development kits (SDKs) tailored for multimedia and robotics, providing developers with sample applications and demos to build and test products faster. “We created the Qualcomm AI Hub where you can bring your models or pick one of the pre-trained models, optimize them, and test them on our products,” Ferzli said, referring to Qualcomm Technologies’ dedicated Qualcomm AI Hub platform where developers can experiment with over 125 AI models on devices hosted on the cloud before deploying it on physical devices. The Qualcomm Developer Portal and Qualcomm Developer Network YouTube channel are consistently updated with training and tutorials designed to educate and support developers throughout their product development journey.

Qualcomm Technologies has also established a public community forum to address inquiries. This forum is supported by dedicated internal Qualcomm Technologies’ experts who will promptly respond to questions and provide recommendations.

To support developers further, Qualcomm Technologies has created a longevity program, guaranteeing up to 15 years of hardware and software support. This commitment is particularly valuable for industries that require reliable long-term solutions, such as industrial automation, medical devices, and smart infrastructure. “Our goal is to service all developers, from hobbyists and students to global enterprises,” Ferzli said, underscoring Qualcomm Technologies’ commitment to building a comprehensive ecosystem for edge computing.

Qualcomm Technologies

Enabling Small and Large Developers Alike

Qualcomm Technologies’ vision for democratizing edge-AI is reflected in the Qualcomm RB3 Gen 2 versatile design, which can serve both small developers and large enterprises. Whether a developer is working on a project for a large multinational or a startup exploring innovative applications, the Qualcomm RB3 Gen 2 kit provides the tools to develop high-performance, IoT-enabled products without needing an extensive engineering team. For example, a small business developing a fleet management system could use the Qualcomm RB3 Gen2 kit to build a proof of concept for smart dashcams capable of processing data locally, providing immediate feedback on road conditions, driver behavior, and vehicle health.

Meanwhile, larger enterprises can use Qualcomm Technologies’ kits for more complex applications, such as industrial robotics and automated quality control. Qualcomm Technologies’ edge technology allows companies to streamline operations by reducing the dependency on centralized cloud systems, thereby minimizing latency and enhancing data privacy. Ferzli noted that even as Qualcomm Technologies serves large clients, the Qualcomm RB3 Gen 2 kits are built to cater to developers of all sizes: “If you’re a college student building a fighting robot, a startup developing a drone, or a multinational designing a worker safety monitoring system, this kit will support your developer journey in the edge-AI transformation.”

Qualcomm Technologies’ Vision: Accelerating IoT Adoption with Edge Computing

The Qualcomm RB3 Gen 2 developer kit is more than a powerful tool—it’s a vision for the future of IoT and edge computing. By prioritizing on-device processing, Qualcomm Technologies is pushing efficient AI Edge processing in IoT, where real-time response, enhanced privacy, and high-compute are paramount. With the Qualcomm RB3 Gen 2 developer kits, Qualcomm Technologies is making advanced IoT technology available to a broad range of innovators, from established enterprises to individual developers.

As IoT continues to evolve, Qualcomm Technologies’ edge-AI focused approach is set to make a significant impact on industries ranging from smart infrastructure to robotics and autonomous vehicles. Ferzli summarized the company’s ambition: “We want to educate developers to utilize AI and IoT products better. Our technology spans the spectrum of IoT and AI, and with our developer-first approach, we’re ready to support developers in shaping the future of edge computing.”

With the Qualcomm RB3 Gen 2 developer kit, Qualcomm Technologies is setting a new standard for IoT innovation at the edge, encouraging developers to harness the power of real-time, on-device intelligence to create a more connected, efficient, and intelligent world.

Snapdragon and Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. The registered trademark Linux is used pursuant to a sublicense from the Linux Foundation, the exclusive licensee of Linus Torvalds, owner of the mark on a worldwide basis.

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Ansys SimAI Software Predicts Fully Transient Vehicle Crash Outcomes

Crash Test Prediction at the Speed of AI

1 min read

The Ansys SimAI™ cloud-enabled generative artificial intelligence (AI) platform combines the predictive accuracy of Ansys simulation with the speed of generative AI. Because of the software’s versatile underlying neural networks, it can extend to many types of simulation, including structural applications.
This white paper shows how the SimAI cloud-based software applies to highly nonlinear, transient structural simulations, such as automobile crashes, and includes:

Vehicle kinematics and deformation
Forces acting upon the vehicle
How it interacts with its environment
How understanding the changing and rapid sequence of events helps predict outcomes

These simulations can reduce the potential for occupant injuries and the severity of vehicle damage and help understand the crash’s overall dynamics. Ultimately, this leads to safer automotive design.

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IEEE Unveils the 2025–2030 Strategic Plan

The new strategic goals bolster IEEE’s long-standing mission

2 min read

Illustration of planet Earth, surrounded by icons representing the six core values of IEEE’s strategic plan.

IEEE Brand Experience; iStock

careers ieee member news ieee news ieee strategic plan type:ti

IEEE’s 2020–2025 strategic plan set direction for the organization and informed its efforts over the last four years. The IEEE Board of Directors, supported by the IEEE Strategy and Alignment Committee, has updated the goals of the plan, which now covers 2025 through 2030. Even though the goals have been updated, IEEE’s mission and vision remain constant.

The 2025–2030 IEEE Strategic Plan’s six new goals focus on furthering the organization’s role as a leading trusted source, driving technological innovation ethically and with integrity, enabling interdisciplinary opportunities, inspiring future generations of technologists, further engaging the public, and empowering technology professionals throughout their careers.

Together with IEEE’s steadfast mission, vision, and core values, the plan will guide the organization’s priorities.

“The IEEE Strategic Plan provides the ‘North Star’ for IEEE activities,” says Kathleen Kramer, 2025 IEEE president and CEO. “It offers aspirational, guiding priorities to steer us for the near future. IEEE organizational units are aligning their initiatives to these goals so we may move forward as One IEEE.”

Input from a variety of groups

To gain input for the new strategic plan from the IEEE community, in-depth stakeholder interviews were conducted with the Board of Directors, senior professional staff leadership, young professionals, students, and others. IEEE also surveyed more than 35,000 individuals including volunteers; members and nonmembers; IEEE young professionals and student members; and representatives from industry. In-person focus groups were conducted in eight locations around the globe.

The goals were ideated through working sessions with the IEEE directors, directors-elect, senior professional staff leadership, and the IEEE Strategy and Alignment Committee, culminating with the Board approving them at its November 2024 meeting.

These six new goals will guide IEEE through the near future:

Advance science and technology as a leading trusted source of information for research, development, standards, and public policy
Drive technological innovation while promoting scientific integrity and the ethical development and use of technology
Provide opportunities for technology-related interdisciplinary collaboration, research, and knowledge sharing across industry, academia, and government
Inspire intellectual curiosity and support discovery and invention to engage the next generation of technology innovators
Expand public awareness of the significant role that engineering, science, and technology play across the globe
Empower technology professionals in their careers through ongoing education, mentoring, networking, and lifelong engagement

Work on the next phase is ongoing and is designed to guide the organization in cascading the goals into tactical objectives to ensure that organizational unit efforts align with the holistic IEEE strategy. Aligning organizational unit strategic planning with the broader IEEE Strategic Plan is an important next step.

In delivering on its strategic plan, IEEE will continue to foster a collaborative environment that is open, inclusive, and free of bias. The organization also will continue to sustain its strength, reach, and vitality of our organization for future generations and ensure our role as a 501(c)(3) public charity.

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"Thinking" Visually Boosts AI Problem Solving

Giving language models a “mind’s eye” helps them tackle spatial reasoning tasks

4 min read

Side by side comparison of an 8-bit gaming model where an elf has to navigate a small grid with obstacles.

The model's "mind's eye" visualization of the scene (right) matched the reality of a maze-like game.

Chengzu Li, Wenshan Wu et al./Gymnasium

artificial intelligence neural networks multimodal robots

When humans try to solve problems, they often visualize the tasks in their heads. New research suggests that enabling artificial intelligence to do the same could boost performance on spatial reasoning challenges.

While large language models excel at many text-based tasks, they often struggle with those that require more complex reasoning. One of the most promising approaches for boosting their performance on these kinds of problems is a technique known as “chain-of-thought” (CoT) prompting, where users ask the model to “think” through them step-by-step.

This can lead to significant improvements on various reasoning tasks, especially in mathematics, coding, and logic. But the language-focused technique has proved less effective for problems requiring spatial or visual reasoning. To try and close that gap, researchers at the University of Cambridge and Microsoft Research have developed a new approach that lets AI “think” in both text and images.

The technique enables multimodal large language models, which can process both image and text data, to generate visual representations of their intermediate reasoning steps. In non-peer reviewed research posted to arXiv, the researchers report that when they tested the approach on spatial reasoning challenges involving 2D mazes, they saw significant improvements over the typical CoT technique on the most challenging scenarios.

“Spatial relations and layouts and also some geometric features are very hard to describe with pure text,” says co-lead author Chengzu Li, a Ph.D. student at Cambridge. “That’s why we think that reasoning with pure text would limit the performance of the model in spatial tasks. And that’s the main motivation for introducing visual ‘thoughts,’” he says.

How AI Visual Reasoning Works

This is not the first attempt to allow AI to reason visually. But Li says previous approaches have either involved extracting information from images and converting it to text before reasoning with it, or have relied on external software tools or specialized vision models to enable visual reasoning.

The new approach enables a single multimodal model to generate both visual and text reasoning steps itself. This work only recently became feasible, says Li, thanks to the development of more powerful multimodal AI. Older models could interpret images and text, but could only generate text outputs. For these experiments, the researchers used a model called Anole that can respond in either modality.

This model is an open-source extension of Meta’s Chameleon multimodal model: the researchers behind Anole retrained it to generate sequences of text interleaved with images. For instance, it can generate a step-by-step recipe with an image for each step. Li and colleagues took this pre-trained model and fine-tuned it on text and image data from three maze-like games with different levels of complexity. They called their fine-tuned version Multimodal Visualization of Thought (MVoT).

The researchers tested the new technique (bottom), which generates both visual and verbal thoughts, against one that reasons only in text (middle) and one that skips reasoning and jumps straight to the answer.Chengzu Li, Wenshan Wu et al.

The goal for the model was to work out what would happen if it took a pre-determined series of actions in each maze. During training, the model was shown examples that included images of the starting position in the maze and a textual description of the task, a series of reasoning steps featuring text descriptions of actions and images of where the player is on the map, and finally an answer as to what the outcome would be for those actions, such as reaching the desired destination or falling down a hole. During testing the model was only given the starting image and a sequence of actions to perform. It then generated image and text reasoning steps followed by a prediction of what would happen.

The researchers compared MVoT to four other models, three of which they fine-tuned themselves. The first two versions of the model were trained only on text data regarding the maze: One model jumped straight from a prompt to generating a final answer, the other used textual CoT reasoning. Another model was trained on examples of both image and text reasoning, but then did its own reasoning purely in text. Finally, they compared MVoT’s performance on the maze tasks to that of the GPT-4o model from OpenAI, which is the company’s most advanced multimodal model.

They found that on all three games, the MVoT model significantly outperformed all models apart from the one using traditional text CoT. That model actually did slightly better on the two simpler mazes, successfully predicting the outcome 98 percent of the time on both, compared to MVoT’s scores of 93 percent and 95 percent. But the traditional text CoT model did much worse on the most complicated game, scoring just 61 percent compared to MVoT’s 86 percent. They tested both models on progressively larger mazes and while MVoT’s performance remained stable, the other model’s performance plummeted as maze size increased.

The researchers say this outcome is likely because CoT relies on accurate textual descriptions of the environment, which get harder the more complex the mazes become. In contrast, the inclusion of images in the reasoning process appears to make MVoT much better at dealing with more challenging environments.

Applications for AI Visual Reasoning

While the tests the researchers used are simple, Li says extending this approach into more complex domains could have broad applications. One of the most compelling is robotics, where the approach could help machines reason more effectively about the visual input they get from the environment. It could also be help AI tutors better illustrate and explain ideas, particularly in areas like geometry. More broadly, he says the approach can boost model interpretability by giving humans a clear picture of what the model is thinking about in spatial tasks.

One potential gap, admits Li, is that the model has no mechanism for deciding when to reason visually or when to reason via text. At present, the model simply alternates between the two, which works well for these maze navigation challenges that have discrete steps but may be less appropriate for more complex spatial reasoning tasks.

“We haven’t really touched on when is the appropriate time to do a visual reasoning process or not,” Li says. “But I think it’s definitely one of the very interesting directions to further explore.” One possibility, he adds, would be to generate reasoning sequences with both visual and text descriptions at each step, and then get humans to provide feedback on which is more expressive. This feedback could then be used to train the model to pick the best option at each reasoning step.

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Packaging and Robots

How AI and sustainability are transforming the journey from click to delivery at Amazon

8 min read

Amazon

This is a sponsored article brought to you by Amazon.

The journey of a package from the moment a customer clicks “buy” to the moment it arrives at their doorstep is one of the most complex and finely tuned processes in the world of e-commerce. At Amazon, this journey is constantly being optimized, not only for speed and efficiency, but also for sustainability. This optimization is driven by the integration of cutting-edge technologies like artificial intelligence (AI), machine learning (ML), and robotics, which allow Amazon to streamline its operations while working towards minimizing unnecessary packaging.

The use of AI and ML in logistics and packaging is playing an increasingly vital role in transforming the way packages are handled across Amazon’s vast global network. In two interviews — one with Clay Flannigan, who leads manipulation robotics programs at Amazon, and another with Callahan Jacobs, an owner of the Sustainable Packaging team’s technology products — we gain insights into how Amazon is using AI, ML, and automation to push the boundaries of what’s possible in the world of logistics, while also making significant strides in sustainability-focused packaging.

The Power of AI and Machine Learning in Robotics

One of the cornerstones of Amazon’s transformation is the integration of AI and ML into its robotics systems. Flannigan’s role within the Fulfillment Technologies Robotics (FTR) team, Amazon Robotics, centers around manipulation robotics — machines that handle the individual items customers order on amazon.com. These robots, in collaboration with human employees, are responsible for picking, sorting, and packing millions of products every day. It’s an enormously complex task, given the vast diversity of items in Amazon’s inventory.

“Amazon is uniquely positioned to lead in AI and ML because of our vast data,” Flannigan explained. “We use this data to train models that enable our robots to perform highly complex tasks, like picking and packing an incredibly diverse range of products. These systems help Amazon solve logistics challenges that simply wouldn’t be possible at this scale without the deep integration of AI.”

At the core of Amazon’s robotic systems is machine learning, which allows the machines to “learn” from their environment and improve their performance over time. For example, AI-powered computer vision systems enable robots to “see” the products they are handling, allowing them to distinguish between fragile items and sturdier ones, or between products of different sizes and shapes. These systems are trained using expansive amounts of data, which Amazon can leverage due to its immense scale.

One particularly important application of machine learning is in the manipulation of unstructured environments. Traditional robotics have been used in industries where the environment is highly structured and predictable. But Amazon’s warehouses are anything but predictable. “In other industries, you’re often building the same product over and over. At Amazon, we have to handle an almost infinite variety of products — everything from books to coffee makers to fragile collectibles,” Flannigan said.

“There are so many opportunities to push the boundaries of what AI and robotics can do, and Amazon is at the forefront of that change.” —Clay Flannigan, Amazon

In these unstructured environments, robots need to be adaptable. They rely on AI and ML models to understand their surroundings and make decisions in real-time. For example, if a robot is tasked with picking a coffee mug from a bin full of diverse items, it needs to use computer vision to identify the mug, understand how to grip it without breaking it, and move it to the correct packaging station. These tasks may seem simple, but they require advanced ML algorithms and extensive data to perform them reliably at Amazon’s scale.

Sustainability and Packaging: A Technology-Driven Approach

While robotics and automation are central to improving efficiency in Amazon’s fulfillment centers, the company’s commitment to sustainability is equally important. Callahan Jacobs, product manager on FTR’s Mechatronics & Sustainable Packaging (MSP) team, is focused on preventing waste and aims to help reduce the negative impacts of packaging materials. The company has made significant strides in this area, leveraging technology to improve the entire packaging experience.

Amazon

“When I started, our packaging processes were predominantly manual,” Jacobs explained. “But we’ve moved toward a much more automated system, and now we use machines that custom-fit packaging to items. This has drastically reduced the amount of excess material we use, especially in terms of minimizing the cube size for each package, and frees up our teams to focus on harder problems like how to make packaging out of more conscientious materials without sacrificing quality.”

Since 2015, Amazon has decreased its average per-shipment packaging weight by 43 percent, which represents more than 3 million metric tons of packaging materials avoided. This “size-to-fit” packaging technology is one of Amazon’s most significant innovations in packaging. By using automated machines that cut and fold boxes to fit the dimensions of the items being shipped, Amazon is able to reduce the amount of air and unused space inside packages. This not only reduces the amount of material used but also optimizes the use of space in trucks, planes, and delivery vehicles.

“By fitting packages as closely as possible to the items they contain, we’re helping to reduce both waste and shipping inefficiencies,” Jacobs explained.

Advanced Packaging Technology: The Role of Machine Learning

AI and ML play a critical role in Amazon’s efforts to optimize packaging. Amazon’s packaging technology doesn’t just aim to prevent waste but also ensures that items are properly protected during their journey through the fulfillment network. To achieve this balance, the company relies on advanced machine learning models that evaluate each item and determine the optimal packaging solution based on various factors, including the item’s fragility, size, and the route it needs to travel.

“We’ve moved beyond simply asking whether an item can go in a bag or a box,” said Jacobs. “Now, our AI and ML models look at each item and say, ‘What are the attributes of this product? Is it fragile? Is it a liquid? Does it have its own packaging, or does it need extra protection?’ By gathering this information, we can make smarter decisions about packaging, helping to result in less waste or better protection for the items.”

“By fitting packages as closely as possible to the items they contain, we’re helping to reduce both waste and shipping inefficiencies.” —Callahan Jacobs, Amazon

This process begins as soon as a product enters Amazon’s inventory. Machine Learning models analyze each product’s data to determine key attributes. These models may use computer vision to assess the item’s packaging or natural language processing to analyze product descriptions and customer feedback. Once the product’s attributes have been determined, the system decides which type of packaging is most suitable, helping to prevent waste while ensuring the item’s safe arrival.

“Machine learning allows us to make these decisions dynamically,” Jacobs added. “For example, an item like a t-shirt doesn’t need to be packed in a box—it can go in a paper bag. But a fragile glass item might need additional protection. By using AI and ML, we can make these decisions at scale, ensuring that we’re always prioritizing for the option that aims to benefits the customer and the planet.”

Dynamic Decision-Making With Real-Time Data

Amazon’s use of real-time data is a game-changer in its packaging operations. By continuously collecting and analyzing data from its fulfillment centers, Amazon can rapidly adjust its packaging strategies, optimizing for efficiency at scale. This dynamic approach allows Amazon to respond to changing conditions, such as new packaging materials, changes in shipping routes, or feedback from customers.

“A huge part of what we do is continuously improving the process based on what we learn,” Jacobs explained. “For example, if we find that a certain type of packaging isn’t satisfactory, we can quickly adjust our criteria and implement changes across our delivery network. This real-time feedback loop is critical in making our system more resilient and keeping it aligned with our team’s sustainability goals.”

This continuous learning process is key to Amazon’s success. The company’s AI and ML models are constantly being updated with new data, allowing them to become more accurate and effective over time. For example, if a new type of packaging material is introduced, the models can quickly assess its effectiveness and make adjustments as needed.

Jacobs also emphasized the role of feedback in this process. “We’re always monitoring the performance of our packaging,” she said. “If we receive feedback from customers that an item arrived damaged or that there was too much packaging, we can use that information to improve model outputs, which ultimately helps us continually reduce waste.”

Robotics in Action: The Role of Gripping Technology and Automation

One of the key innovations in Amazon’s robotic systems is the development of advanced gripping technology. As Flannigan explained, the “secret sauce” of Amazon’s robotic systems is not just in the machines themselves but in the gripping tools they use. These tools are designed to handle the immense variety of products Amazon processes every day, from small, delicate items to large, bulky packages.

A photo of a robot. Amazon

“Our robots use a combination of sensors, AI, and custom-built grippers to handle different types of products,” Flannigan said. “For example, we’ve developed specialized grippers that can handle fragile items like glassware without damaging them. These grippers are powered by AI and machine learning, which allow them to plan their movements based on the item they’re picking up.”

The robotic arms in Amazon’s fulfillment centers are equipped with a range of sensors that allow them to “see” and “feel” the items they’re handling. These sensors provide real-time data to the machine learning models, which then make decisions about how to handle the item. For example, if a robot is picking up a fragile item, it will use gentler strategy, whereas it might optimize for speed when handling a sturdier item.

Flannigan also noted that the use of robotics has significantly improved the safety and efficiency of Amazon’s operations. By automating many of the repetitive and physically demanding tasks in fulfillment centers, Amazon has been able to reduce the risk of injuries among its employees while also increasing the speed and accuracy of its operations. It also provides the opportunity to focus on upskilling. “There’s always something new to learn,” Flannigan said, “there’s no shortage of training and advancement options.”

Continuous Learning and Innovation: Amazon’s Culture of Growth

Both Flannigan and Jacobs emphasized that Amazon’s success in implementing these technologies is not just due to the tools themselves but also the culture of innovation that drives the company. Amazon’s engineers and technologists are encouraged to constantly push the boundaries of what’s possible, experimenting with new solutions and improving existing systems.

“Amazon is a place where engineers thrive because we’re always encouraged to innovate,” Flannigan said. “The problems we’re solving here are incredibly complex, and Amazon gives us the resources and freedom to tackle them in creative ways. That’s what makes Amazon such an exciting place to work.”

Jacobs echoed this sentiment, adding that the company’s commitment to sustainability is one of the things that makes it an attractive place for engineers. “Every day, I learn something new, and I get to work on solutions that have a real impact at a global scale. That’s what keeps me excited about my work. That’s hard to find anywhere else.”

The Future of AI, Robotics, and Innovation at Amazon

Looking ahead, Amazon’s vision for the future is clear: to continue innovating in the fields of AI, ML, and robotics for maximum customer satisfaction. The company is investing heavily in new technologies that are helping to progress its sustainability initiatives while improving the efficiency of its operations.

“We’re just getting started,” Flannigan said. “There are so many opportunities to push the boundaries of what AI and robotics can do, and Amazon is at the forefront of that change. The work we do here will have implications not just for e-commerce but for the broader world of automation and AI.”

Jacobs is equally optimistic about the future of the Sustainable Packaging team. “We’re constantly working on new materials and new ways to reduce waste,” she said. “The next few years are going to be incredibly exciting as we continue to refine our packaging innovations, making them more scalable without sacrificing quality.”

As Amazon continues to evolve, the integration of AI, ML, and robotics will be key to achieving its ambitious goals. By combining cutting-edge technology with a deep commitment to sustainability, Amazon is setting a new standard for how e-commerce companies can operate in the 21st century. For engineers, technologists, and environmental advocates, Amazon offers an unparalleled opportunity to work on some of the most challenging and impactful problems of our time.

Learn more about becoming part of Amazon’s Team.

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Transformative Power of GenAI in Securing Autonomous Systems and Edge Robotics

Unlocking the future: Enhancing security and resilience in edge robotics with generative AI

1 min read

Rapid advances in autonomous systems and edge robotics have unlocked unprecedented opportunities in industries from manufacturing and transportation to healthcare and exploration.

Increasing complexity and connectivity have also introduced new security, resilience, and safety challenges. As edge robots integrate into our daily lives and critical infrastructures, developing innovative approaches to improve these systems' trustworthiness and reliability is mandatory.

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Dual-Arm HyQReal Puts Powerful Telepresence Anywhere

IIT’s hydraulic quadruped can carry a pair of massive arms

5 min read

A large quadrupedal robot with two large blue and black arms attached to its head with tools on the ends of them stands in a lab.

With two big arms and telepresence control, HyQReal is designed to work in dangerous environments.

IIR

quadruped robots disaster robots iit inail robotics telepresence robots

In theory, one of the main applications for robots should be operating in environments that (for whatever reason) are too dangerous for humans. I say “in theory” because in practice it’s difficult to get robots to do useful stuff in semi-structured or unstructured environments without direct human supervision. This is why there’s been some emphasis recently on teleoperation: Human software teaming up with robot hardware can be a very effective combination.

For this combination to work, you need two things. First, an intuitive control system that lets the user embody themselves in the robot to pilot it effectively. And second, a robot that can deliver on the kind of embodiment that the human pilot needs. The second bit is the more challenging, because humans have very high standards for mobility, strength, and dexterity. But researchers at the Italian Institute of Technology (IIT) have a system that manages to check both boxes, thanks to its enormously powerful quadruped, which now sports a pair of massive arms on its head.

“The primary goal of this project, conducted in collaboration with INAIL, is to extend human capabilities to the robot, allowing operators to perform complex tasks remotely in hazardous and unstructured environments to mitigate risks to their safety by exploiting the robot’s capabilities,” explains Claudio Semini, who leads the Robot Teleoperativo project at IIT. The project is based around the HyQReal hydraulic quadruped, the most recent addition to IIT’s quadruped family.

Hydraulics have been very visibly falling out of favor in robotics, because they’re complicated and messy, and in general robots don’t need the absurd power density that comes with hydraulics. But there are still a few robots in active development that use hydraulics specifically because of all that power. If your robot needs to be highly dynamic or lift really heavy things, hydraulics are, at least for now, where it’s at.

IIT’s HyQReal quadruped is one of those robots. If you need something that can carry a big payload, like a pair of massive arms, this is your robot. Back in 2019, we saw HyQReal pulling a three-tonne airplane. HyQReal itself weighs 140 kilograms, and its knee joints can output up to 300 newton-meters of torque. The hydraulic system is powered by onboard batteries and can provide up to 4 kilowatts of power. It also uses some of Moog’s lovely integrated smart actuators, which sadly don’t seem to be in development anymore. Beyond just lifting heavy things, HyQReal’s mass and power make it a very stable platform, and its aluminum roll cage and Kevlar skin ensure robustness.

The HyQReal hydraulic quadruped is tethered for power during experiments at IIT, but it can also run on battery power.IIT

The arms that HyQReal is carrying are IIT-INAIL arms, which weigh 10 kg each and have a payload of 5 kg per arm. To put that in perspective, the maximum payload of a Boston Dynamics Spot robot is only 14 kg. The head-mounted configuration of the arms means they can reach the ground, and they also have an overlapping workspace to enable bimanual manipulation, which is enhanced by HyQReal’s ability to move its body to assist the arms with their reach. “The development of core actuation technologies with high power, low weight, and advanced control has been a key enabler in our efforts,” says Nikos Tsagarakis, head of the HHCM Lab at IIT. “These technologies have allowed us to realize a low-weight bimanual manipulation system with high payload capacity and large workspace, suitable for integration with HyQReal.”

Maximizing reachable space is important, because the robot will be under the remote control of a human, who probably has no particular interest in or care for mechanical or power constraints—they just want to get the job done.

To get the job done, IIT has developed a teleoperation system, which is weird-looking because it features a very large workspace that allows the user to leverage more of the robot’s full range of motion. Having tried a bunch of different robotic telepresence systems, I can vouch for how important this is: It’s super annoying to be doing some task through telepresence, and then hit a joint limit on the robot and have to pause to reset your arm position. “That is why it is important to study operators’ quality of experience. It allows us to design the haptic and teleoperation systems appropriately because it provides insights into the levels of delight or frustration associated with immersive visualization, haptic feedback, robot control, and task performance,” confirms Ioannis Sarakoglou, who is responsible for the development of the haptic teleoperation technologies in the HHCM Lab. The whole thing takes place in a fully immersive VR environment, of course, with some clever bandwidth optimization inspired by the way humans see that transmits higher-resolution images only where the user is looking.

HyQReal’s telepresence control system offers haptic feedback and a large workspace.IIT

Telepresence Robots for the Real World

The system is designed to be used in hazardous environments where you wouldn’t want to send a human. That’s why IIT’s partner on this project is INAIL, Italy’s National Institute for Insurance Against Accidents at Work, which is understandably quite interested in finding ways in which robots can be used to keep humans out of harm’s way.

In tests with Italian firefighters in 2022 (using an earlier version of the robot with a single arm), operators were able to use the system to extinguish a simulated tunnel fire. It’s a good first step, but Semini has plans to push the system to handle “more complex, heavy, and demanding tasks, which will better show its potential for real-world applications.”

As always with robots and real-world applications, there’s still a lot of work to be done, Semini says. “The reliability and durability of the systems in extreme environments have to be improved,” he says. “For instance, the robot must endure intense heat and prolonged flame exposure in firefighting without compromising its operational performance or structural integrity.” There’s also managing the robot’s energy consumption (which is not small) to give it a useful operating time, and managing the amount of information presented to the operator to boost situational awareness while still keeping things streamlined and efficient. “Overloading operators with too much information increases cognitive burden, while too little can lead to errors and reduce situational awareness,” says Yonas Tefera, who lead the development of the immersive interface. “Advances in immersive mixed-reality interfaces and multimodal controls, such as voice commands and eye tracking, are expected to improve efficiency and reduce fatigue in the future.”

There’s a lot of crossover here with the goals of the DARPA Robotics Challenge for humanoid robots, except IIT’s system is arguably much more realistically deployable than any of those humanoids are, at least in the near term. While we’re just starting to see the potential of humanoids in carefully controlled environment, quadrupeds are already out there in the world, proving how reliable their four-legged mobility is. Manipulation is the obvious next step, but it has to be more than just opening doors. We need it to use tools, lift debris, and all that other DARPA Robotics Challenge stuff that will keep humans safe. That’s what Robot Teleoperativo is trying to make real.

You can find more detail about the Robot Teleoperativo project in this paper, presented in November at the 2024 IEEE Conference on Telepresence, in Pasadena, Calif.

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Rubidium Can Be More Than a Lithium Cast-Off

New extraction techniques makes the element—essential in high-tech timekeeping—easier to mine

4 min read

Close-up of rubidium ore, the new target metal of a mine in Western Australia.

Rubidium is often an unwanted by-product of lithium mining and extraction—but it has its own high-tech uses.

Everest Metals

mining minerals rubidium lithium

Lithium has been the apple of the mining industry’s eye as demand for electric vehicle batteries has skyrocketed. Now, the investment in lithium extraction is spilling over to include rubidium, another element on the leftmost column of the periodic table. Rubidium is found in the earth’s crust alongside lithium and is used in technologies such as atomic clocks, motion sensors, signal generators, and GPS equipment—technologies that all rely on the hyperfine transition of electrons in rubidium atoms to keep time.

Rubidium, like lithium, is mined from rock or pumped to the surface in briny groundwater. Previously, rubidium was often seen as an impurity that needed to be removed from a lithium deposit. Now, new research on extracting rubidium is opening the possibility of a greater supply of the once-overlooked metal, which could then be put to wider use in high-tech industries.

Researchers from Tianjin University in China have developed a technique to extract rubidium from solid potassium chloride salt, which forms after brine has dried. They detail their results in a study published in November in Nature Sustainability. By extracting rubidium from solid salts and limiting the amount of water in the process, the researchers report using 98 percent less energy than the techniques that extract rubidium directly from watery brine.

But, similar to lithium, rubidium exists in much higher concentrations in chunks of a rock called pegmatite than in brine water, says Brent Elliott, an economic geologist at the University of Texas at Austin who was not involved in the research.

A mining company in western Australia claims to have hit the rubidium jackpot while searching for lithium about 400 kilometers northeast of Perth, Western Australia (and around 700 kilometers north of the world’s largest hard-rock lithium mine). Researchers at Edith Cowan University (ECU) have teamed up with the company Everest Metals Corporation to pull rubidium from the rocky samples collected at the Mt. Edon pegmatite field. And, by using a new direct rubidium extraction technique, the team reported in December recovering 91 percent of the rubidium from rock samples.

Mined pegmatite is later processed to extract rubidium and lithium from the mineral. Everest Metals

The technique recycles water through the process, says Amir Razmjou, the lead investigator on the project and an associate professor at ECU. Water recycling is what makes the extraction method more sustainable than other extraction techniques. It’s an adaptation of membrane technology used for water desalination, which Razmjou, whose background is in chemical engineering, specialized in before refocusing on minerals.

A Easier Way to Extract Rubidium

Scant information is available about the specific chemicals used in the extraction technique—Everest Metals and ECU are in the process of filing a patent—but, in general, the method follows three main steps: crushing the rock samples, dissolving the mineral in acid, and purifying rubidium and lithium from the acidic slurry.

Adam Simon, a professor of earth and environmental sciences from the University of Michigan and uninvolved in the work, pieced together what he believes is the most likely methodology.

Lithium and rubidium are both naturally bonded to oxygen within pegmatite. Acids can dissolve the pegmatite, similar to how hot water dissolves sugar in a cup of coffee, Simon says. The acid solution is passed through an ion exchanger, which is a column lined with a resin to which the element of interest (rubidium, in this case) will stick. Then, dilute acid is poured through the column in order to pull the rubidium off the resin, flushing a solution of only rubidium out the other side of the column.

This process is not necessarily new. “We’ve done this for decades” to separate out uranium, Simon says.

The unique, patentable aspect of the project, Simon says, might be the use of a weakly acidic solution that pulls out only rubidium, or rubidium plus lithium. Sulfuric acid is the most commonly used acid in extraction, which is safe but requires a lot of storage and cleanup at industrial scales.

But a less acidic solution could minimize the cost to neutralize water and recycle it, Simons says. Reducing the amount of acid needed would be great for the mining industry. Simon is not aware of anyone else doing this for rubidium.

“I’m intrigued by the process if they can demonstrate that it works for rubidium and lithium,” Simon says. “It has the potential to work for other metals in other minerals.”

What’s the Market for Rubidium, Anyway?

As of now, there are no active mining sites of rubidium, according to the latest data from the U.S. Geological Survey, published in 2024. But China is a blind spot on the world’s mining map because it is so difficult to obtain information, says Candice Tuck, a mineral commodity specialist for the USGS’ National Minerals Information Center, who wrote the latest rubidium report. While there are indications that rubidium is being produced in China, there is no definitive evidence, she says.

Everest Metals, however, seems to think that demand will rise: The company expects the rubidium market to grow from 6.36 tonnes in 2023 to 7.94 tonnes in 2028.

This is the chicken-or-the-egg problem of the mining industry, says Gavin Mudd, the director of the Critical Minerals Intelligence Centre at the British Geological Survey. Demand for rubidium, as of now, is low, and low demand spurs little action from mining companies. A mine near Cornwall in the United Kingdom, run by the company Cornish Lithium, for example, throws out the rubidium and caesium that it extracts along with lithium. But sometimes a new, consistent supply of one element creates demand, Mudd says.

In September 2023, the price for one vial containing a solution of 1 gram of rubidium was going for US $121, and a vial of 100 grams in solution went for $2,160, a nearly 20 percent jump up from 2022, according to the 2024 USGS report.

“That is a lot of money for a little vial of rubidium oxide,” says Elliott, of the University of Texas. Given the potential profit, it makes sense for lithium mining companies to include another output to an existing mining operation.

“I think we are going to see a lot more happening only because the lithium extraction technologies are getting better and it just makes sense to have another stream to get another commodity out that you can sell,” Elliott says.

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NYU Researchers Develop New Real-Time Deepfake Detection Method

Chinmay Hegde is exploring challenge-response systems for detecting audio and video deepfakes

5 min read

A photo of a face on a computer monitor with a series of lines on the face.

Deepfake video and audio is powerful in the hands of bad actors. NYU Tandon researchers are developing new techniques to combat deepfake threats.

NYU Tandon

This sponsored article is brought to you by NYU Tandon School of Engineering.

Deepfakes, hyper-realistic videos and audio created using artificial intelligence, present a growing threat in today’s digital world. By manipulating or fabricating content to make it appear authentic, deepfakes can be used to deceive viewers, spread disinformation, and tarnish reputations. Their misuse extends to political propaganda, social manipulation, identity theft, and cybercrime.

As deepfake technology becomes more advanced and widely accessible, the risk of societal harm escalates. Studying deepfakes is crucial to developing detection methods, raising awareness, and establishing legal frameworks to mitigate the damage they can cause in personal, professional, and global spheres. Understanding the risks associated with deepfakes and their potential impact will be necessary for preserving trust in media and digital communication.

That is where Chinmay Hegde, an Associate Professor of Computer Science and Engineering and Electrical and Computer Engineering at NYU Tandon, comes in.

A photo of a smiling man in glasses. Chinmay Hegde, an Associate Professor of Computer Science and Engineering and Electrical and Computer Engineering at NYU Tandon, is developing challenge-response systems for detecting audio and video deepfakes.NYU Tandon

“Broadly, I’m interested in AI safety in all of its forms. And when a technology like AI develops so rapidly, and gets good so quickly, it’s an area ripe for exploitation by people who would do harm,” Hegde said.

A native of India, Hegde has lived in places around the world, including Houston, Texas, where he spent several years as a student at Rice University; Cambridge, Massachusetts, where he did post-doctoral work in MIT’s Theory of Computation (TOC) group; and Ames, Iowa, where he held a professorship in the Electrical and Computer Engineering Department at Iowa State University.

Hegde, whose area of expertise is in data processing and machine learning, focuses his research on developing fast, robust, and certifiable algorithms for diverse data processing problems encountered in applications spanning imaging and computer vision, transportation, and materials design. At Tandon, he worked with Professor of Computer Science and Engineering Nasir Memon, who sparked his interest in deepfakes.

“Even just six years ago, generative AI technology was very rudimentary. One time, one of my students came in and showed off how the model was able to make a white circle on a dark background, and we were all really impressed by that at the time. Now you have high definition fakes of Taylor Swift, Barack Obama, the Pope — it’s stunning how far this technology has come. My view is that it may well continue to improve from here,” he said.

Hegde helped lead a research team from NYU Tandon School of Engineering that developed a new approach to combat the growing threat of real-time deepfakes (RTDFs) – sophisticated artificial-intelligence-generated fake audio and video that can convincingly mimic actual people in real-time video and voice calls.

High-profile incidents of deepfake fraud are already occurring, including a recent $25 million scam using fake video, and the need for effective countermeasures is clear.

In two separate papers, research teams show how “challenge-response” techniques can exploit the inherent limitations of current RTDF generation pipelines, causing degradations in the quality of the impersonations that reveal their deception.

In a paper titled “GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response” the researchers developed a set of eight visual challenges designed to signal to users when they are not engaging with a real person.

“Most people are familiar with CAPTCHA, the online challenge-response that verifies they’re an actual human being. Our approach mirrors that technology, essentially asking questions or making requests that RTDF cannot respond to appropriately,” said Hegde, who led the research on both papers.

Challenge frame of original and deepfake videos. Each row aligns outputs against the same instance of challenge, while each column aligns the same deepfake method. The green bars are a metaphor for the fidelity score, with taller bars suggesting higher fidelity. Missing bars imply the specific deepfake failed to do that specific challenge.NYU Tandon

The video research team created a dataset of 56,247 videos from 47 participants, evaluating challenges such as head movements and deliberately obscuring or covering parts of the face. Human evaluators achieved about 89 percent Area Under the Curve (AUC) score in detecting deepfakes (over 80 percent is considered very good), while machine learning models reached about 73 percent.

“Challenges like quickly moving a hand in front of your face, making dramatic facial expressions, or suddenly changing the lighting are simple for real humans to do, but very difficult for current deepfake systems to replicate convincingly when asked to do so in real-time,” said Hegde.

Audio Challenges for Deepfake Detection

In another paper called “AI-assisted Tagging of Deepfake Audio Calls using Challenge-Response,” researchers created a taxonomy of 22 audio challenges across various categories. Some of the most effective included whispering, speaking with a “cupped” hand over the mouth, talking in a high pitch, pronouncing foreign words, and speaking over background music or speech.

“Even state-of-the-art voice cloning systems struggle to maintain quality when asked to perform these unusual vocal tasks on the fly,” said Hegde. “For instance, whispering or speaking in an unusually high pitch can significantly degrade the quality of audio deepfakes.”

The audio study involved 100 participants and over 1.6 million deepfake audio samples. It employed three detection scenarios: humans alone, AI alone, and a human-AI collaborative approach. Human evaluators achieved about 72 percent accuracy in detecting fakes, while AI alone performed better with 85 percent accuracy.

The collaborative approach, where humans made initial judgments and could revise their decisions after seeing AI predictions, achieved about 83 percent accuracy. This collaborative system also allowed AI to make final calls in cases where humans were uncertain.

“The key is that these tasks are easy and quick for real people but hard for AI to fake in real-time” —Chinmay Hegde, NYU Tandon

The researchers emphasize that their techniques are designed to be practical for real-world use, with most challenges taking only seconds to complete. A typical video challenge might involve a quick hand gesture or facial expression, while an audio challenge could be as simple as whispering a short sentence.

“The key is that these tasks are easy and quick for real people but hard for AI to fake in real-time,” Hegde said. “We can also randomize the challenges and combine multiple tasks for extra security.”

As deepfake technology continues to advance, the researchers plan to refine their challenge sets and explore ways to make detection even more robust. They’re particularly interested in developing “compound” challenges that combine multiple tasks simultaneously.

“Our goal is to give people reliable tools to verify who they’re really talking to online, without disrupting normal conversations,” said Hegde. “As AI gets better at creating fakes, we need to get better at detecting them. These challenge-response systems are a promising step in that direction.”

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Rivian Flexes Software Power: What VW Gets for $5.7B

Struggling to crack the code on EVs, VW bets big on Rivian

5 min read

X-ray image of two SUVs with electronic control units highlighted.

Left to right: A first generation Rivian with 17 unique ECUs and the second generation with 7 unique ECUs.

Rivian

evs software volkswagen rivian

Whoa, Nellie: With three electric motors and 625 kilowatts (850 horsepower), the 2025 Rivian R1S that I’m driving storms to 100 kilometers per hour in about 2.9 seconds. The Illinois-built SUV handles more like a sport sedan than the 3,100 kilogram (6,800-pound) brute that it is. Move off-road, and an adaptive air suspension can hoist the R1S to a Hummer-esque 37.8 centimeters of ground clearance, enough for leisurely dips in 1.1-meter-deep water; its 149-kWh battery snugged in carbon fiber, aluminum and high-strength steel.

You’ll need to dig even deeper to understand why Volkswagen is investing US $5.7 billion in Rivian, through a joint partnership that gives VW a 50-percent stake in the California-based builder of adventure trucks. VW may be the world’s second-largest automaker, behind Toyota. But like many legacy makers, it has struggled mightily with software. That’s a problem in the era of the so-called “Software Defined Vehicle”: cars are fast becoming smartphones on wheels, ideally less obsolescent, with centralized software replacing balkanized hardware and controls that can’t play nicely together or be updated over-the-air.

Serial missteps at VW’s in-house Cariad software unit hastened CEO Herbert Diess’ ouster in 2022, with key models such as the Porsche Macan EV and Audi Q6 E-Tron delayed for a year or more. Glitchy software and vexing screen interfaces led to a poor critical and sales reception for the ID.4, an electric SUV that VW touted as a revolution on par with the original Beetle.

The new joint venture is called Rivian and VW Group Technology. Its goal is to meld Rivian’s software expertise with VW’s global scale, speeding development of EVs with innovative features and functions. Those include the VW’s relaunch of the long-defunct Scout off-road brand, with a charmingly retro Traveler SUV and Terra pickup scheduled to arrive in 2027 from a South Carolina factory. Models from VW, Audi and Porsche will be underpinned by Rivian’s “zonal architecture” and software stack in the 2025 R1S and R1T pickup; as will a downsized R2 model that Rivian intends to build in Illinois in 2026. The money-shedding Rivian gains a financial lifeline from VW, after pressing pause on construction of a second Georgia factory, now scheduled for 2028 — and backed by a $6.5 billion Department of Energy loan approved in January.

Rivian Thinks Outside the Boxes

So what exactly is VW getting? For one, a company that literally thinks outside the box, eliminating the proliferating control boxes that are a key flaw in typical domain architectures. In the domain approach, which is commonly used by traditional automakers, every functional element of the vehicle—whether powertrains, safety systems or infotainment—is managed by its own domain controller.

In modern cars that handle increasingly complex tasks, those domains have led to redundant connections from power sources to electronic control units (ECUs), and an unwieldy octopus of wiring that stretches to all four corners of the vehicle. Whether they’re powered by electricity or gasoline, cars from legacy brands may carry as many as 150 separate ECUs.

“The old model for legacy manufacturers would be, you want an active suspension system, you add a box,” says Kyle Lobo, Rivian’s director of electrical systems. “You want fancier headlights? Another box for that.”

“Our approach is, no, let’s create these zonal controllers instead, and let’s scale them to the feature set,” Lobo says.

Rivian’s three-zone architecture—east, west, and south—links nodes that are in physical proximity, but independent of functions they provide. Zones link to each other and a central computing node via fast Ethernet, reducing latency. Lobo cites the Rivian’s adaptive suspension as an example: The south zone interfaces with rear actuation components, with the west zone linked to a proximate front suspension. The suspension is then networked over a bus.

“That’s a break from what a legacy OEM would have done, where they’d have a single suspension controller with everything connected to it,” Lobo says.

The Rivian R1S’s touchscreen dashboard comes with plenty of bells and whistles.Rivian

A New Manufacturing Model to Handle Higher Complexity

The approach delivers reductions in cost, mass, and manufacturing complexity, and could make for easier, less-costly repairs. Compared with first-generation Rivians, the zonal architecture reduces the number of electronic control units (ECUs) from 17 to seven more-powerful units, including controllers for infotainment, autonomy, motor drive units, and battery management. The architecture saves 1.6 miles of internal wiring and 44 pounds of weight, with a claimed 20-percent material cost reduction and 15-percent lower carbon emissions.

Company engineers say the approach demands the vertical integration Rivian specializes in: Elegant software and hardware, developed in-house from the start of a new design, rather than contracted from hundreds of separate suppliers. It’s the strategy favored by Tesla and now Chinese makers such as BYD, as they unlock design and manufacturing efficiencies—and attendant profits—that have stymied legacy automakers. That poses another massive challenge to companies such as VW, Toyota or GM, whose empires are built on relationships with global suppliers large and small, and the components they develop: Electronics from Bosch, say, or transmissions from ZF.

Just as importantly, Rivian’s scalable system allows comprehensive over-the-air updates—not just for infotainment or creature comforts, but performance, safety, advanced driver assistance systems( ADAS), or subscription services. For Rivian’s R1S and R1T pickup, an upgraded hardware set includes 11 cameras and five radars that can perform over 250 trillion operations per second, which Rivian claims as industry-leading computing power. Last week, Rivian founder and CEO RJ Scaringe announced the company would roll out a hands-free driving assist system later this year, akin to GM’s SuperCruise, and update that with Level 3, eyes-off-the-road capability in 2026.

On a more-whimsical note, Rivian can offer feature updates like their recent “Halloween costumes.” Using the Rivian mobile app, owners can turn interior displays into uncanny simulations of K.I.T.T. from the old Knight Rider TV series—Hasselhoff!—or Doc Brown’s DeLorean from Back to the Future, along with added exterior lighting effects. Pedestrians captured by safety cameras can be rendered as zombies onscreen, with cyclists and motorcycles appearing as headless horsemen. My personal favorite? The selectable “Owl” chirp that hooted when I locked the R1S’s doors, one of several “chirp” options for these outdoorsy trucks. Frivolous? Perhaps. But many owners love these add-ons, whether they’re video games or new apps.

Vivek Surya, director of product management, says “One of the things we have always heard from customers is that every month, they feel like they’re getting a new vehicle, and that is what we are striving for,” including ongoing development of AI functions and voice controls.

And there’s nothing frivolous about the Rivian’s design or breathtaking performance, with the R1S and R1T among the global benchmarks for electric SUVs and pickups, including up to 676 kilometers (420 miles) of driving range. Now, if Rivian would only ditch those annoying, digital vent controls that require poking a touchscreen menu to adjust....

Lobo says that “Software Defined Vehicle” may be the new industry watchword, but ultimately falls short as a descriptor.

“Internally, we call it the ‘software updatable vehicle’ rather than the software-defined vehicle. Because that’s really where the magic comes in.”

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Celebrating Steve Jobs’s Impact on Consumer Tech and Design

A look back at his career on what would have been his 70th birthday

5 min read

Steve Jobs smiling while holding up a 2008 MacBook Air.

Steve Jobs introduced the MacBook Air in 2008.

David Paul Morris/Getty Images

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Although Apple cofounder Steve Jobs died on 5 October 2011 at age 56, his legacy endures. His name remains synonymous with innovation, creativity, and the relentless pursuit of excellence. As a pioneer in technology and design, Jobs dared to imagine the impossible, transforming industries and reshaping human interaction with technology. His work continues to inspire engineers, scientists, and technologists worldwide. His contributions to technology, design, and human-centric innovation shape the modern world.

On the eve of what would have been his 70th birthday, 24 February, I examine his legacy, its contemporary relevance, and the enduring lessons that can guide us toward advancing technology for the benefit of humanity.

Jobs’s lasting impact: A revolution in technology

Jobs was more than a successful tech entrepreneur; he was a visionary who changed the world through his unyielding drive for innovation. He revolutionized many areas including computing, telecommunications, entertainment, and design. The products and services he pioneered have become integral to modern life and form the foundation for further technological advancements.

Celebrated for his vision, he also was criticized for his short temper, impatience, and lack of empathy. His autocratic, demanding leadership alienated colleagues and caused conflicts. But those traits also fueled innovation and offered lessons in both leadership pitfalls and aspirations.

Here are some of what I consider to be his most iconic innovations and contributions.

The Macintosh, the iPhone, the iPad, and more

The Macintosh, introduced in 1984, was the first commercially successful personal computer to feature a graphical user interface, built-in screen, and mouse. It made computers that followed it more accessible and user-friendly, and it sparked a revolution in personal and business computing.

Pixar Animation Studios, launched in 1986, became a creative powerhouse, revolutionizing animated storytelling with films such as Toy Story and Finding Nemo.

The iPod—which came out in 2001—and its accompanying iTunes store transformed the music industry by offering a seamless, legal way to purchase songs and albums and then digitally store them. It redefined music consumption. By combining hardware innovation with a revolutionary ecosystem, Jobs proved that technology could disrupt established industries and create value for creators and users.

The iPhone, which was launched in 2007, integrated a telephone, a music player, and connectivity to the Internet. It revolutionized mobile phone technology and reshaped global communication. The device set the minimum standard for smartphones that other manufacturers have now adopted.

The iPad, introduced in 2010, pioneered a new era in mobile computing, enhancing content consumption, creativity, and productivity.

Apple Park redefined the high-tech corporate campus. One of the final projects he proposed was the construction of a circular corporate campus in Cupertino, Calif. Nicknamed The Spaceship when it opened in 2017, the facility housed 12,000 employees in a four-story building with underground parking. A whopping 80 percent of the grounds were landscaped. “It’s curved all the way around,” Jobs said. “There’s not a straight piece of glass in this building.”

As Simon Sadler, a design professor at the University of California, Davis, outlined in a Places Journal article, Jobs also was an architect.

Jobs demonstrated the value of amalgamating technology, art, and user-centric design. His legacy and philosophy exemplify simplicity, elegance, and functionality.

Five core lessons from Jobs’s life and work

As outlined in my 2021 article in The Institute about lessons I’ve learned from him, Jobs’s life and career offer valuable insights for technologists, developers, marketers, and business leaders. In today’s rapidly evolving technological landscape, five key lessons from his legacy remain particularly relevant.

1. Innovation requires bold vision and risk-taking. Jobs created products people didn’t even realize they needed until they experienced them. He famously said, “It’s not the customer’s job to know what they want.” His work demonstrates that innovation can come from taking calculated risks and pushing boundaries. Further, Jobs fostered continuous innovation rather than resting on successful products, and he pushed Apple to keep improving and reinventing. I learned to envision possibilities beyond current limitations and create solutions that shape the future.

2. Simplicity is the ultimate sophistication. Jobs championed minimalism and clarity in design and user interface, recognizing that simplicity enhances usability. His approach underscores the importance of user experience. Regardless of technological sophistication, a product’s success depends on its accessibility, intuitive design, and value to users. His lesson for technologists and developers is to strip away complexity and focus on what truly matters.

3. Passion and persistence drive success. Jobs’s career was marked by major setbacks and unequivocal triumphs. A thought-provoking question remains: Why did Apple’s board fire him in 1985 despite his immense potential? As Michael Schlossberg explains, the reasons are complex but, in essence, it boils down to a significant disagreement between Jobs, CEO John Sculley, whom Jobs hired, and the board. As Schlossberg underscores, the episode serves as an excellent case study in corporate governance.

After being ousted from Apple, Jobs founded NeXT and led Pixar before returning to Apple in 1997 to orchestrate one of history’s most remarkable corporate turnarounds.

The story highlights the value of resilience and passion. For engineers and scientists, perseverance is crucial, as failure often precedes research, development, and innovative breakthroughs.

4. Technology must serve the users. Jobs was committed to creating technology that seamlessly integrates into human life. His principle was that technology must serve a purpose that meets human needs. It’s a goal that motivates engineers and technologists to focus their innovations in AI, robotics, biotechnology, and other areas, addressing human needs while considering ethical and societal implications.

5. Challenge conventional thinking. Apple’s Think Different campaign encapsulated Jobs’s philosophy: Challenge norms, question limitations, and pursue unconventional ideas that can change the world. His vision encourages researchers and engineers to push boundaries and explore new frontiers.

Jobs’s early insights on AI

Decades before artificial intelligence became mainstream, Jobs anticipated its transformative potential. In a 1983 speech at the International Design Conference in Aspen, Colo., he predicted AI-driven systems would fundamentally reshape daily life. His vision aligns closely with today’s advancements in generative AI.

Jobs saw books as a powerful but static medium that lacked interactivity. He envisioned interactive tools that would allow deeper engagement with the text—asking questions and exploring the author’s thoughts beyond the written words.

In 1985, Jobs envisioned the creation of a new kind of interactive tool, what we consider today to be artificial intelligence.

In the video, he said: “Someday, a student will be able to not only read Aristotle’s words but also ask Aristotle a question and receive an answer.”

Beyond interactivity, Jobs anticipated advances in brain-inspired AI systems. He believed computing, which was facing roadblocks, would evolve by understanding the brain’s architecture, and he predicted imminent breakthroughs. His early advocacy for AI-driven technologies—such as speech recognition, computer vision, and natural language processing—culminated in Apple’s 2010 acquisition of Siri, bringing AI-powered personal assistance into daily life.

With AI-driven chatbots and Apple Intelligence, Jobs’s vision of seamless, user-centric AI has become a reality. He saw computers as “bicycles for the mind”—tools to amplify human capabilities. If he were still alive, he likely would be at the forefront of AI innovation, ensuring it enhances creativity, decision-making, and human intelligence.

“As a pioneer in technology and design, Steve Jobs dared to imagine the impossible, transforming industries and reshaping human interaction with technology.”

Jobs’s approach to AI would extend far beyond functionality. I think he would prioritize humanizing AI—infusing it with emotional intelligence to understand and respond to human emotions authentically. Whether through AI companions for the elderly or empathetic customer service agents, I believe he would have pushed AI to foster more meaningful connections with users.

Furthermore, he likely would have envisioned AI seamlessly integrated into daily life, not as a detached digital assistant but as an adaptive and intuitive extension of user needs. Imagine an AI-driven device that learns about its user while safeguarding privacy—automatically adjusting its interface based on mood, location, and context to create effortless and natural interactions.

Jobs’s enduring legacy in shaping personal computing suggests that had he lived to witness the ongoing AI revolution, he would have played a pivotal role in shaping it as a tool for human advancement and creativity. He would have championed AI as a tool for empowerment rather than alienation.

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Sydney’s Tech Super-Cluster Propels Australia’s AI Industry Forward

With significant AI research and commercialization, Sydney emerges as a leader in the global AI landscape

4 min read

A photo of a smiling man next to a smiling robot.

The AI Institute at UNSW Sydney is “a front door to industry and government, to help translate the technology out of the laboratory and into practice,” says Toby Walsh, Scientia Professor of Artificial Intelligence at the University of New South Wales (UNSW Sydney).

UNSW

This is a sponsored article brought to you by BESydney.

Australia has experienced a remarkable surge in AI enterprise during the past decade. Significant AI research and commercialization concentrated in Sydney drives the sector’s development nationwide and influences AI trends globally. The city’s cutting-edge AI sector sees academia, business and government converge to foster groundbreaking advancements, positioning Australia as a key player on the international stage.

Sydney – home to half of Australia’s AI companies

Sydney has been pinpointed as one of four urban super-clusters in Australia, featuring the highest number of tech firms and the most substantial research in the country.

The Geography of Australia’s Digital Industries report, commissioned by the National Science Agency, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Tech Council of Australia, found Sydney is home to 119,636 digital professionals and 81 digital technology companies listed on the Australian Stock Exchange with a combined worth of A$52 billion.

AI is infusing all areas of this tech landscape. According to CSIRO, more than 200 active AI companies operate across Greater Sydney, representing almost half of the country’s 544 AI companies.

“Sydney is the capital of AI startups for Australia and this part of Australasia”
—Toby Walsh, UNSW Sydney

With this extensive AI commercialization and collaboration in progress across Sydney, AI startups are flourishing.

“Sydney is the capital of AI startups for Australia and this part of Australasia,” according to Professor Toby Walsh, Scientia Professor of Artificial Intelligence at the Department of Computer Science and Engineering at the University of New South Wales (UNSW Sydney).

He cites robotics, AI in medicine and fintech as three areas where Sydney leads the world in AI innovation.

“As a whole, Australia punches well above its weight in the AI sector,” Professor Walsh says. “We’re easily in the top 10, and by some metrics, we’re in the top five in the world. For a country of just 25 million people, that is quite remarkable.”

Sydney’s universities at the forefront of AI research

A key to Sydney’s success in the sector is the strength of its universities, which are producing outstanding research.

In 2021, the University of Sydney (USYD), the University of New South Wales (UNSW Sydney), and the University of Technology Sydney (UTS) collectively produced more than 1000 peer-reviewed publications in artificial intelligence, contributing significantly to the field’s development.

According to CSIRO, Australia’s research and development sector has higher rates of AI adoption than global averages, with Sydney presenting the highest AI publishing intensity among Australian universities and research institutes.

Professor Aaron Quigley, Science Director and Deputy Director of CSIRO’s Data61 and Head of School in Computer Science and Engineering at UNSW Sydney, says Sydney’s AI prowess is supported by a robust educational pipeline that supplies skilled graduates to a wide range of industries that are rapidly adopting AI technologies.

“Sydney’s AI sector is backed up by the fact that you have such a large educational environment with universities like UTS, USYD and UNSW Sydney,” he says. “They rank in the top five of AI locations in Australia.”

UNSW Sydney is a heavy hitter, with more than 300 researchers applying AI across various critical fields such as hydrogen fuel catalysis, coastal monitoring, safe mining, medical diagnostics, epidemiology and stress management.

UNSW Sydney has more than 300 researchers applying AI across various critical fields such as hydrogen fuel catalysis, coastal monitoring, safe mining, medical diagnostics, epidemiology, and stress management.UNSW

UNSW Sydney’s AI Institute also has the largest concentration of academics working in AI in the country, adds Professor Walsh.

“One of the main reasons the AI Institute exists at UNSW Sydney is to be a front door to industry and government, to help translate the technology out of the laboratory and into practice,” he says.

Likewise, the Sydney Artificial Intelligence Centre at the University of Sydney, the Australian Artificial Intelligence Institute at UTS, and Macquarie University’s Centre for Applied Artificial Intelligence are producing world-leading research in collaboration with industry.

Alongside the universities, the Australian Government’s National AI Centre in Sydney, aims to support and accelerate Australia’s AI industry.

Synergies in Sydney: where tech titans converge

Sydney’s vortex of tech talent has meant exciting connections and collaborations are happening at lightning speed, allowing simultaneous growth of several high-value industries.

The intersection between quantum computing and AI will come into focus with the April 2024 announcement of a new Australian Centre for Quantum Growth at the University of Sydney. This centre will aim to build strategic and lasting relationships that drive innovation to increase the nation’s competitiveness within the field. Funded under the Australian Government’s National Quantum Strategy, it aims to promote the industry and enhance Australia’s global standing.

“There’s nowhere else in the world that you’re going to get a quantum company, a games company, and a cybersecurity company in such close proximity across this super-cluster arc located in Sydney”
—Aaron Quigley, UNSW Sydney

“There’s a huge amount of experience in the quantum space in Sydney,” says Professor Quigley. “Then you have a large number of companies and researchers working in cybersecurity, so you have the cybersecurity-AI nexus as well. Then you’ve got a large number of media companies and gaming companies in Sydney, so you’ve got the interconnection between gaming and creative technologies and AI.”

“So it’s a confluence of different industry spaces, and if you come here, you can tap into these different specialisms,” he adds “There’s nowhere else in the world that you’re going to get a quantum company, a games company, and a cybersecurity company in such close proximity across this super-cluster arc located in Sydney.”

A global hub for AI innovation and collaboration

In addition to its research and industry achievements in the AI sector, Sydney is also a leading destination for AI conferences and events. The annual Women in AI Asia Pacific Conference is held in Sydney each year, adding much-needed diversity to the mix.

Additionally, the prestigious International Joint Conference on Artificial Intelligence was held in Sydney in 1991.

Overall, Sydney’s integrated approach to AI development, characterized by strong academic output, supportive government policies, and vibrant commercial activity, firmly establishes it as a leader in the global AI landscape.

To discover more about how Sydney is shaping the future of AI download the latest eBook on Sydney’s Science & Engineering industry at besydney.com.au

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Top 100 Global Innovators 2023

Download the report to see who made the list and more

1 min read

How we will live in the 2030s is being defined now. Our health, our prosperity and our very world are built on the ideas created today. At Clarivate, our focus is to pore over what humanity knows today and put forward the insight that explores all possible horizons – horizons that enable transition and transformation.

For 12 years, Clarivate has identified the companies and institutions whose research and innovation do not just sit on the edge of possibility but define it. Today, we recognize the Top 100 Global Innovators 2023, companies who chose to lead and create their own horizons.

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Shipt’s Algorithm Squeezed Gig Workers. They Fought Back

When their pay suddenly dropped, delivery drivers audited their employer

11 min read

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Shipt’s Algorithm Squeezed Gig Workers. They Fought Back

Mike McQuade

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In early 2020, gig workers for the app-based delivery company Shipt noticed something strange about their paychecks. The company, which had been acquired by Target in 2017 for US $550 million, offered same-day delivery from local stores. Those deliveries were made by Shipt workers, who shopped for the items and drove them to customers’ doorsteps. Business was booming at the start of the pandemic, as the COVID-19 lockdowns kept people in their homes, and yet workers found that their paychecks had become…unpredictable. They were doing the same work they’d always done, yet their paychecks were often less than they expected. And they didn’t know why.

On Facebook and Reddit, workers compared notes. Previously, they’d known what to expect from their pay because Shipt had a formula: It gave workers a base pay of $5 per delivery plus 7.5 percent of the total amount of the customer’s order through the app. That formula allowed workers to look at order amounts and choose jobs that were worth their time. But Shipt had changed the payment rules without alerting workers. When the company finally issued a press release about the change, it revealed only that the new pay algorithm paid workers based on “effort,” which included factors like the order amount, the estimated amount of time required for shopping, and the mileage driven.

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Developing the Next Generation of AI Assistant

NYU Tandon researchers built visual analytics system to support the creation of advanced intelligent agents

6 min read

A man wearing VR googles spreads nut butter on a tortilla as part of an experiment in a test kitchen lab.

In building the technology, Silva\u2019s team turned to a specific task that required a lot of visual analysis, and could benefit from a checklist based system: cooking.

NYU Tandon

This sponsored article is brought to you by the NYU Tandon School of Engineering.

If you’ve ever learned to cook, you know how daunting even simple tasks can be at first. It’s a delicate dance of ingredients, movement, heat, and techniques that newcomers need endless practice to master.

But imagine if you had someone – or something – to assist you. Say, an AI assistant that could walk you through everything you need to know and do to ensure that nothing is missed in real-time, guiding you to a stress-free delicious dinner.

Claudio Silva, director of the Visualization Imaging and Data Analytics (VIDA) Center and professor of computer science and engineering and data science at the NYU Tandon School of Engineering and NYU Center for Data Science, is doing just that. He is leading an initiative to develop an artificial intelligence (AI) “virtual assistant” providing just-in-time visual and audio feedback to help with task execution.

And while cooking may be a part of the project to provide proof-of-concept in a low-stakes environment, the work lays the foundation to one day be used for everything from guiding mechanics through complex repair jobs to combat medics performing life-saving surgeries on the battlefield.

“A checklist on steroids”

The project is part of a national effort involving eight other institutional teams, funded by the Defense Advanced Research Projects Agency (DARPA) Perceptually-enabled Task Guidance (PTG) program. With the support of a $5 million DARPA contract, the NYU group aims to develop AI technologies to help people perform complex tasks while making these users more versatile by expanding their skillset — and more proficient by reducing their errors.

Portrait of NYU researcher Claudio Silva smiling at the camera Claudio Silva is the co-director of the Visualization Imaging and Data Analytics (VIDA) Center and professor of computer science and engineering at the NYU Tandon School of Engineering and NYU Center for Data Science.NYU Tandon

The NYU group – including investigators from NYU Tandon’s Department of Computer Science and Engineering, the NYU Center for Data Science (CDS) and the Music and Audio Research Laboratory (MARL) – have been performing fundamental research on knowledge transfer, perceptual grounding, perceptual attention and user modeling to create a dynamic intelligent agent that engages with the user, responding to not only circumstances but the user’s emotional state, location, surrounding conditions and more.

Dubbing it a “checklist on steroids” Silva says that the project aims to develop Transparent, Interpretable, and Multimodal Personal Assistant (TIM), a system that can “see” and “hear” what users see and hear, interpret spatiotemporal contexts and provide feedback through speech, sound and graphics.

While the initial application use-cases for the project for evaluation purposes focus on military applications such as assisting medics and helicopter pilots, there are countless other scenarios that can benefit from this research — effectively any physical task.

“The vision is that when someone is performing a certain operation, this intelligent agent would not only guide them through the procedural steps for the task at hand, but also be able to automatically track the process, and sense both what is happening in the environment, and the cognitive state of the user, while being as unobtrusive as possible,” said Silva.

The project brings together a team of researchers from across computing, including visualization, human-computer interaction, augmented reality, graphics, computer vision, natural language processing, and machine listening. It includes 14 NYU faculty and students, with co-PIs Juan Bello, professor of computer science and engineering at NYU Tandon; Kyunghyun Cho, and He He, associate and assistant professors (respectively) of computer science and data science at NYU Courant and CDS, and Qi Sun, assistant professor of computer science and engineering at NYU Tandon and a member of the Center for Urban Science + Progress will use the Microsoft Hololens 2 augmented reality system as the hardware platform test bed for the project.

The project uses the Microsoft Hololens 2 augmented reality system as the hardware platform testbed. Silva said that, because of its array of cameras, microphones, lidar scanners, and inertial measurement unit (IMU) sensors, the Hololens 2 headset is an ideal experimental platform for Tandon’s proposed TIM system.

In building the technology, Silva’s team turned to a specific task that required a lot of visual analysis, and could benefit from a checklist based system: cooking. NYU Tandon

“Integrating Hololens will allow us to deliver massive amounts of input data to the intelligent agent we are developing, allowing it to ‘understand’ the static and dynamic environment,” explained Silva, adding that the volume of data generated by the Hololens’ sensor array requires the integration of a remote AI system requiring very high speed, super low latency wireless connection between the headset and remote cloud computing.

To hone TIM’s capabilities, Silva’s team will train it on a process that is at once mundane and highly dependent on the correct, step-by-step performance of discrete tasks: cooking. A critical element in this video-based training process is to “teach” the system to locate the starting and ending point — through interpretation of video frames — of each action in the demonstration process.

The team is already making huge progress. Their first major paper “ARGUS: Visualization of AI-Assisted Task Guidance in AR” won a Best Paper Honorable Mention Award at IEEE VIS 2023. The paper proposes a visual analytics system they call ARGUS to support the development of intelligent AR assistants.

The system was designed as part of a multi year-long collaboration between visualization researchers and ML and AR experts. It allows for online visualization of object, action, and step detection as well as offline analysis of previously recorded AR sessions. It visualizes not only the multimodal sensor data streams but also the output of the ML models. This allows developers to gain insights into the performer activities as well as the ML models, helping them troubleshoot, improve, and fine tune the components of the AR assistant.

“It’s conceivable that in five to ten years these ideas will be integrated into almost everything we do.”

ARGUS, the interactive visual analytics tool, allows for real-time monitoring and debugging while an AR system is in use. It lets developers see what the AR system sees and how it’s interpreting the environment and user actions. They can also adjust settings and record data for later analysis.NYU Tandon

Where all things data science and visualization happens

Silva notes that the DARPA project, focused as it is on human-centered and data-intensive computing, is right at the center of what VIDA does: utilize advanced data analysis and visualization techniques to illuminate the underlying factors influencing a host of areas of critical societal importance.

“Most of our current projects have an AI component and we tend to build systems — such as the ARt Image Exploration Space (ARIES) in collaboration with the Frick Collection, the VisTrails data exploration system, or the OpenSpace project for astrographics, which is deployed at planetariums around the world. What we make is really designed for real-world applications, systems for people to use, rather than as theoretical exercises,” said Silva.

“What we make is really designed for real-world applications, systems for people to use, rather than as theoretical exercises.” —Claudio Silva, NYU Tandon

VIDA comprises nine full-time faculty members focused on applying the latest advances in computing and data science to solve varied data-related issues, including quality, efficiency, reproducibility, and legal and ethical implications. The faculty, along with their researchers and students, are helping to provide key insights into myriad challenges where big data can inform better future decision-making.

What separates VIDA from other groups of data scientists is that they work with data along the entire pipeline, from collection, to processing, to analysis, to real world impacts. The members use their data in different ways — improving public health outcomes, analyzing urban congestion, identifying biases in AI models — but the core of their work all lies in this comprehensive view of data science.

The center has dedicated facilities for building sensors, processing massive data sets, and running controlled experiments with prototypes and AI models, among other needs. Other researchers at the school, sometimes blessed with data sets and models too big and complex to handle themselves, come to the center for help dealing with it all.

The VIDA team is growing, continuing to attract exceptional students and publishing data science papers and presentations at a rapid clip. But they’re still focused on their core goal: using data science to affect real world change, from the most contained problems to the most socially destructive.

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Artificial Intelligence Orchestrating Incremental $250 Billion Revenue Shift

MarketsandMarkets expects AI to open massive revenue opportunities by 2026

1 min read

With innovations in AI impacting a multitude of industries, incremental $250 billion of potential opportunities are expected to open by 2026 in adjacent and interconnected markets.

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Advanced Magnet Manufacturing Begins in the United States

Rare-earths maker MP Materials leads a tiny charge against the Chinese colossus

5 min read

A gloved lab worker using two fingers to hold up a small silvery rectangular object.

A lab worker inspecting a rare earth magnet at MP Materials' new manufacturing facility in Fort Worth, Texas.

Business Wire

rare earth metals neodymium mp materials permanent magnets rare earth production

In mid-January, a top United States materials company announced that it had started to manufacture rare earth magnets. It was important news—there are no large U.S. makers of the neodymium magnets that underpin huge and vitally important commercial and defense industries, including electric vehicles. But it created barely a ripple during a particularly loud and stormy time in U.S. trade relations.

The press release, from MP Materials, was light on details. The company disclosed that it had started producing the magnets, called neodymium-iron-boron (NdFeB), on a “trial” basis and that the factory would begin gradually ramping up production before the end of this year. According to MP’s spokesman, Matt Sloustcher, the facility will have an initial capacity of 1,000 tonnes per annum, and has the infrastructure in place to scale up to 2,000 to 3,000 tonnes per year. The release also said that the facility, in Fort Worth, Texas, would supply magnets to General Motors and other U.S. manufacturers.

NdFeB magnets are the most powerful and valuable type. They are used in motors for electric vehicles and for heating, ventilating, and cooling (HVAC) systems, in wind-turbine generators, in tools and appliances, and in audio speakers, among other gear. They are also critical components of countless military systems and platforms, including fighter and bomber aircraft, submarines, precision guided weapons, night-vision systems, and radars.

A magnet manufacturing surge fueled by Defense dollars

MP Materials’ has named its new, state-of-the-art magnet manufacturing facility Independence.Business Wire

The Texas facility, which MP Materials has named Independence, is not the only major rare-earth-magnet project in the U.S. Most notably, Vacuumschmelze GmbH, a magnet maker based in Hanau, Germany, has begun constructing a plant in South Carolina through a North American subsidiary, e-VAC Magnetics. To build the US $500 million factory, the company secured $335 million in outside funds, including at least $100 million from the U.S. government. (E-VAC, too, has touted a supply agreement with General Motors for its future magnets.)

In another intriguing U.S. rare-earth magnet project, Noveon Magnetics, in San Marcos, Texas, is producing what it claims are 2,000 tonnes of NdFeB magnets per year. The company is making some of the magnets in the standard way, starting with metal alloys, and others in a unique process based on recycling the materials from discarded magnets. USA Rare Earth announced on 8 January that it had manufactured a small amount of NdFeB magnets at a plant in Stillwater, Oklahoma.

Yet another company, Quadrant Magnetics, announced in January, 2022, that it would begin construction on a $100 million NdFeB magnet factory in Louisville, Kentucky. However, 11 months later, U.S. federal agents arrested three of the company’s top executives, charging them with passing off Chinese-made magnets as locally produced and giving confidential U.S. military data to Chinese agencies.

The multiple US neodymium-magnet projects are noteworthy but even collectively they won’t make a noticeable dent in China’s dominance. “Let me give you a reality check,” says Steve Constantinides, an IEEE member and magnet-industry consultant based in Honeoye, N.Y. “The total production of neo magnets was somewhere between 220 and 240 thousand tonnes in 2024,” he says, adding that 85 percent of the total, at least, was produced in China. And “the 15 percent that was not made in China was made in Japan, primarily, or in Vietnam.” (Other estimates put China’s share of the neodymium magnet market as high as 90 percent.)

But look at the figures from a different angle, suggests MP Materials’s Sloustcher. “The U.S. imports just 7,000 tonnes of NdFeB magnets per year,” he points out. “So in total, these [U.S.] facilities can supplant a significant percentage of U.S. imports, help re-start an industry, and scale as the production of motors and other magnet-dependent industries” returns to the United States, he argues.

And yet, it’s hard not to be a little awed by China’s supremacy. The country has some 300 manufacturers of rare-earth permanent magnets, according to Constantinides. The largest of these, JL MAG Rare-Earth Co. Ltd., in Ganzhou, produced at least 25,000 tonnes of neodymium magnets last year, Constantinides figures. (The company recently announced that it was building another facility, to begin operating in 2026, that it says will bring its installed capacity to 60,000 tonnes a year.)

That 25,000 tonnes figure is comparable to the combined output of all of the rare-earth magnet makers that aren’t in China. The $500-million e-VAC plant being built in South Carolina, for example, is reportedly designed to produce around 1,500 tonnes a year.

But even those numbers do not fully convey China’s dominance of permanent magnet manufacturing. Where ever a factory is, making neodymium magnets requires supplies of rare-earth metal, and that nearly always leads straight back to China. “Even though they only produce, say, 85 percent of the magnets, they are producing 97 percent of the metal” in the world, says Constantinides. “So the magnet manufacturers in Japan and Europe are highly dependent on the rare-earth metal coming from China.”

MP’s Mine-to-Manufacturing strategy

And there, at least, MP Materials may have an interesting edge. Hardly any firms, even in China, do what MP is attempting: produce finished magnets starting with ore that the company mines itself. Even large companies typically perform just one or at most two of the four major steps along the path to making a rare-earth magnet: mining the ore, refining the ore into rare-earth oxides, reducing the oxides to metals, and then, finally, using the metals to make magnets. Each step is an enormous undertaking requiring entirely different equipment, processes, knowledge, and skill sets.

The rare earth metal produced at MP Materials’ magnet manufacturing facility in Fort Worth, Texas, consists of mostly neodymium and praseodymium.Business Wire

“The one advantage they get from [doing it all] is that they get better insights into how different markets are actually growing,” says Stan Trout, a magnet industry consultant in Denver, Colorado. “Getting the timing right on any expansion is important,” Trout adds. “And so MP should be getting that information as well as anybody, with the different plants that they have, because they interact with the market in several different ways and can really see what demand is like in real time, rather than as some projection in a forecast.”

Still, it’s going to be an uphill climb. “There are a lot of both hard and soft subsidies in the supply chain in China,” says John Ormerod, an industry consultant based in Knoxville, Tenn. “It’s going to be difficult for a US manufacturer to compete with the current price levels of Chinese-made magnets,” he concludes.

And it’s not going to get better any time soon. China’s rare-earth magnet makers are only using about 60 percent of their production capacity, according to both Constantinides and Ormerod—and yet they are continuing to build new plants. “There’s going to be roughly 500,000 tonnes of capacity by the end of this year,” says Ormerod, citing figures gathered by Singapore-based analyst Thomas Kruemmer. “The demand is only about 50 percent of that.”

The upshot, all of the analysts agree, will be downward price pressure on rare earth magnets in the near future, at least. At the same time, the U.S. Department of Defense has made it a requirement that rare-earth magnets for its systems must be produced entirely, starting with ore, in “friendly” countries—which does not include China. “The DoD will need to pay a premium over cheaper imported magnets to establish a price floor enabling domestic U.S. producers to successfully and continuously supply the DoD,” says Constantinides.

But is what’s good for America good for General Motors, in this case? We’re all going to find out in a year or two. At the moment, few analysts are bullish on the prospect.

“The automotive industry has been extremely cost-conscious, demanding supplier price reductions of even fractions of a cent per piece,” notes Constantinides. And even the Trump administration’s tariffs are unlikely to alter the basic math of market economics, he adds. “The application of tariffs to magnets in an attempt to ‘level the playing field’ incentivizes companies to find work-arounds, such as exporting magnets from China to Malaysia or Mexico, then re-exporting from there to the USA. This is not theoretical, these work-arounds have been used for decades to avoid even the past or existing low tariff rates of about 3.5 percent.”

Correction, 12 February 2025: An earlier version of this article stated that Noveon Magnetics was producing rare-earth magnets only from materials reclaimed from discarded magnets. In fact, Noveon is producing magnets from recycled materials and also from “virgin” alloys.

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Supersonic Passenger Jet Prototype Surpasses Mach 1

The Overture could be the first commercial supersonic plane since the Concorde

4 min read

An XB-1 supersonic jet illuminated by sunshine while flying above dense clouds.

Boom Supersonic's XB-1 jet broke the sound barrier in a flight test over California's Mojave Desert.

Boom Supersonic

supersonic boom supersonic aviation aircraft

Boom Supersonic’s prototype passenger jet, the XB-1, has officially gone supersonic. The human-piloted demonstrator hit Mach 1.122 (or 1,385 kilometers per hour) at a 10.7-kilometer altitude over the Mojave Desert on 28 January—marking a major step in Boom’s plans to market commercial aircraft flying roughly twice as fast as today’s subsonic airliners by 2030.

In addition to proving XB-1 could break the sound barrier, the test validated critical technologies that will be scaled up for Overture, Boom’s Mach 1.7 jet. Designed to carry 64 to 80 passengers at altitudes up to 18 kilometers, Overture could cut flight times in half for many common transoceanic routes. Boom is eyeing hundreds of “supersonically viable” options, such as Tokyo to Seattle (reducing an 8.5-hour flight to about 4.5), Los Angeles to Sydney (from 14.5 to 8.5 hours), and New York to London (from 6.5 to 3.5 hours). Over land, the aircraft could fly about 20 percent faster than current commercial jets, at Mach 0.94.

Boom tells IEEE Spectrum it’s preparing for a second supersonic test on 10 February (weather permitting) at California’s Mojave Air and Space Port, during which the company is partnering with NASA for specialized photography. The mission aims to break the sound barrier again and continue collecting data on XB-1’s performance and handling qualities, which affect the pilot’s ability to control the aircraft under various flight conditions.

The successful test followed a carefully structured program to accelerate through subsonic, transonic, and supersonic speeds. Jeff Mabry, Boom’s vice president of XB-1 and Overture, says this involved checking handling qualities, flying qualities, and aircraft systems operation at incrementally increasing speeds and altitudes to ensure safety and performance. “It is always in our hearts and minds that a human pilot is in XB-1, not a drone,” Mabry says.

XB-1 has completed 12 test flights since March 2024, each evaluating critical components and capabilities such as the landing gear, environmental control system, and operating altitudes. Engineers also tested and modified the flutter excitation system, which intentionally vibrates the aircraft to assess its structural response.

- YouTube Happening Now. Watch XB-1’s supersonic test flight in real-time. Join us and see XB-1 break the sound barrier from the viewpoint ...

Engineering a Supersonic Jet

XB-1’s supersonic flight marks the first independently developed jet to break the sound barrier and the first human-piloted civil supersonic flight in decades.

If successful, Overture could become the first commercial supersonic airliner since the Concorde, a Mach 2 aircraft developed in the 1960s by the British Aircraft Corporation and France’s Sud Aviation. The Concorde, which entered service in 1976, could cross the Atlantic in under three hours, but high operational costs, inefficiency, and excessive noise made it economically impractical—ultimately leading to its retirement in 2003.

“As we saw with Concorde, we expect strong demand from North America to/from Europe, but what is unique to Overture is its global operability, with meaningful time savings across routes in all regions of the world,” Mabry says.

Boom says its Symphony turbofan engine will be quieter than Concorde’s deafening afterburners were.Boom Supersonic

XB-1 and Overture incorporate several advancements missing from Concorde: Instead of Concorde’s droop nose design for runway visibility, Boom uses a head-worn augmented reality vision system with external cameras and sensors. Overture’s medium-bypass turbofan engine, Symphony, will run on sustainable aviation fuel and produce noise levels comparable to subsonic jets, unlike Concorde’s deafening, CO₂-emitting afterburners.

Supersonic jet design presents unique engineering challenges, particularly in heat management and structural integrity. Concorde’s airframe could expand by up to 25 centimeters during flight due to Mach 2’s extreme temperatures. The craft was coated with a white paint designed to accommodate that stretching.

Mabry says Overture’s structure will experience thermal stresses, especially where composite and metallic parts meet. “The fuselage length will not grow like Concorde during cruise, but metallic parts will want to expand a bit at high temperatures or shrink at low temperatures, compared with the adjacent composite structural elements,” Mabry says. “These stresses are being considered [in the design] and will be verified during testing.”

Most of the structure uses lightweight carbon fiber composites, which exhibit lower thermal expansion than Concorde’s aluminum alloys. Composites enhance aerodynamics and fatigue resistance, a reason why they’re used in modern airframes like Boeing’s 787 (50 percent of the main structure) and Airbus’s A350 (53 percent).

Boom has optimized Overture for Mach 1.7, a speed intended to balance performance and material constraints. According to Mabry, the structure’s maximum temperatures during Mach 1.7 cruise allow for the use of lightweight composites, which are easier to shape into a low-drag, aerodynamic design than aluminum.

Next Steps

Boom sees a significant unmet market for supersonic transoceanic travel, claiming Overture could open 600 routes to hundreds of millions of travelers while being profitable for airlines at fares similar to first- and business-class tickets. (Rates will ultimately be left up to the airlines.)

Boom’s supersonic jets won’t rely on a droop-nose design like the Concorde had.Boom Supersonic

The company aims to secure U.S. Federal Aviation Administration and European Union Aviation Safety Agency certification by the end of the decade, clearing Overture to carry passengers. Orders currently total 130 aircraft, including from American Airlines, United Airlines, and Japan Airlines. While the company didn’t disclose specific cost estimates for Overture, it confirmed its previous projection of US $200 million per unit remains unchanged.

“Our goal is to roll out the first Overture in three years and be flight testing in four,” says Boom CEO Blake Scholl. In about 18 months, Boom plans to start producing aircraft at a new superfactory in North Carolina. The facility will initially output 33 units annually, then scale up to 66.

Meanwhile, about 50 engineers, technicians, support staff, and pilots are working on XB-1’s flight test program. Scholl expects to begin engine core tests on Symphony by late 2025 to analyze the performance of the compressor, combustor, and turbine section.

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Deploying Data Science and AI to Fight Wildlife Trafficking

NYU Tandon’s Juliana Freire is leading a team aimed at using data science to bring down criminals trafficking humans and exotic animals

5 min read

Wildlife trafficking has an unexpected new foe: computer science, data science, and machine learning.

Shutterstock

This is a sponsored article brought to you by NYU Tandon School of Engineering.

Wildlife trafficking is a lucrative market. While it’s hard to tell exactly how much money it brings in, the U.S. government estimates it’s in the billions of dollars a year. Animals and their parts are traded much like firearms or narcotics — through complex networks of suppliers, dealers, and buyers, who leave a bloody path in their wake. The destruction speaks for itself; species decimated, environments degraded, and innocent people victimized.

Wildlife trafficking concerns both conservation and global security, with significant effects across the international community. It presents a serious threat to biodiversity, and has had substantial human cost and detrimental effects including fueling crime, violence and environmental destruction. The COVID-19 pandemic, likely caused by a virus that jumped from wildlife to humans, has brought attention to the fact that wildlife trade can also have serious public health and safety implications.

Juliana Freire is a Professor of Computer Science and Data Science at New York University, and co-founder and Director of the Visualization Imaging and Data Analysis Center (VIDA) at NYU Tandon. Her recent research focuses on big-data analysis and visualization, large-scale information integration, provenance management, and computational reproducibility.

NYU Tandon

Traffickers increasingly make use of technology to streamline their activities and, at the same time, to evade detection. Internet platforms provide an easy mechanism for globalized buying and selling, which has put new pressure on wild populations of endangered and threatened species. While this creates challenges, it also opens new opportunities. As criminals use technology, complex trafficking networks leave traces of their activity on the web, and by identifying and connecting these fingerprints, researchers can obtain insights into how the trafficking networks work as well as how they can be detected and disrupted. And that’s where data scientists like Juliana Freire come in.

“Animal trafficking has many dangers, not least to the animals,” says Freire, a Professor of Computer Science and Data Science at the NYU Tandon School of Engineering, as well as the co-Director of the Visualization Imaging and Data Analysis Center (VIDA) at NYU and a member of the NYU Center for Data Science. “Ninety percent of the creatures involved die. So preventing or circumventing trafficking is an important goal to protect these animals and the environments that rely on them. And we can use data science to help fight this criminal enterprise.”

Data Defenders

Freire has spent her career creating methods and systems that empower a range of users — not just experts in data and computer science — to obtain trustworthy insights from data. This spans topics in large-scale data analysis and integration, visualization, machine learning, and web information discovery. The VIDA Center that she directs brings together a group of NYU Tandon researchers working in different areas of computer science to bring insights into everything from criminal justice, to urban life, to healthcare, with the intention to use data to produce better outcomes for society at large. Freire’s work in particular has focused on practical and societally important problems, from criminal justice, to urban congestion, to computer reproducibility, to art archives.

Even for data scientists, animal trafficking is a tricky problem to crack. “Most people who actually capture the animals are doing so out of convenience,” says Freire. “You might capture a rare monkey in a trap for another animal, and through local contacts, know that it could fetch a good price on the black market.” These people — mostly impoverished — are doing their best to live off the land, and are not the right targets for law enforcement. “It’s the middlemen — the people who buy the animals and then sell them to the highest bidder, that really drive the market.”

That makes it more difficult for law enforcement, who have to track international illicit markets which largely operate in darker corners of the internet, from popular social media sites and eBay, to sites law enforcement haven’t heard of, often using codes and ciphers they haven’t uncovered. That’s where the data comes in.

Assembling the Team

Freire has teamed up with a number of specialists to take on this challenge. She is joining together with Jennifer Jacquet, Associate Professor of Environmental Studies at NYU College of Arts and Science; Gohar Petrossian, Associate Professor in the Department of Criminal Justice at CUNY; and Sunandan Chakraborty, Assistant Professor of Data Science at Indiana University–Purdue University Indianapolis. Between the four of them, their expertise in crime, the environment and data combine to be a potent force against trafficking. And they’ve been awarded a total of $994,000 from the National Science Foundation to help take these criminals down.

The struggle they face is to find, extract, integrate and analyze information to figure out how traffickers coordinate online. For most law enforcement agencies, with budgets stretched thin and forced to prioritize other crimes, there’s simply no bandwidth to track these criminals. The goal of Freire and her team is to make it easier to keep eyes on the traffickers, by unveiling where and how they carry out their activities

“At VIDA, while our work is in foundational computer science and math, it has real-world implications”
—Juliana Freire

The approach marries data analysis, machine learning, and predictive models to help uncover the hiding holes that criminals use to huddle online. Freire and her colleagues can use a starting point — a known website where traffickers congregate to trade tips and opportunities — and spin that out to a network of unseen deep-web pockets of criminal activities.

The algorithms they’re developing will be able to track users to other sites, developing a complex web of the places where traffickers are known to communicate. And by utilizing machine learning, the model will constantly improve itself, learning exactly what’s relevant among the traffickers’ web activity, and producing an ever-more accurate portrait of the networks criminals use to trade in wildlife. The result will be a specialized search engine that will go deeper than Google could dream of, bringing leads to the fingers of law enforcement that would have previously required huge amounts of manpower.

For Freire, this is not a new problem to solve. She previously worked on DARPA’s Memex program, a three-year research effort to develop software to enable domain-specific indexing of open, public web content and domain-specific search capabilities, with a focus on Memex to combatting different kinds of crime, including human trafficking. Freire and colleagues, including Ari Juels from Cornell Tech and Torsten Suel, a professor in NYU Tandon’s Department of Computer Science and Engineering, worked on techniques to address the shortcomings of traditional search engines for specific information needs. Memex technology has been used by law-enforcement nationwide, including the New York District Attorney’s Office, to help curb human trafficking and bring justice to victims.

A Center for All Things Data Science

Freire’s work fits squarely within VIDA’s mission, which utilizes advanced data analysis and visualization to illuminate the underlying factors influencing a host of social ills. Along with Freire and co-founder Claudio Silva, VIDA comprises five full-time faculty members focused on applying data science to solve varied data-related issues including quality, efficiency, reproducibility, and legal and ethical implications.

One of VIDA’s projects in SONYC — which involves large-scale noise monitoring across New York City – leverages the latest in machine learning technology, big data analysis, and citizen science reporting to more effectively monitor, analyze, and mitigate urban noise pollution.

NYU Visualization Imaging and Data Analysis Center (VIDA)

These faculty, along with their researchers and students, are helping provide key insights to all sorts of societal problems where big data can illuminate unseen elements.

VIDA researcher and Institute Professor Guido Gerig and his collaborators are applying novel image analysis methodologies to analyze magnetic resonance imaging (MRI) of infants at risk or later diagnosed with ASD to develop tools for early identification and more timely and effective interventions for autism and related conditions. Above are MRI images taken at ages 6, 12, 24 months and 6-8 years.

Guido Gerig

“At VIDA, while our work is in foundational computer science and math, it has real-world implications,” says Freire. “We take our academic work seriously, but we also utilize education, advisory roles, and legislative and public outreach strategies to make sure that our research in data science can truly make a difference for people.”

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New IEEE Standard for Securing Biomedical Devices and Data

It covers electronic health records, devices, and hospital systems

4 min read

Florence Hudson smiling and posing with James E. Matthews the third and Yatin Trivedi while she holds her framed 2024 IEEE Standards Association Emerging Technology Award.

From left: IEEE Standards Association President James E. Matthews III joins IEEE 2933 Working Group Chair Florence Hudson and IEEE SA Awards and Recognition Committee Chair Yatin Trivedi at the ceremony where Hudson accepted the 2024 IEEE SA Emerging Technology Award.

Klein and Ulmes

biomedical cybersecurity ieee engineering in medicine and biology society ieee products and services ieee standards association standards type:ti

If you have an implanted medical device, have been hooked up to a machine in a hospital, or have accessed your electronic medical records, you might assume the infrastructure and data are secure and protected against hackers. That isn’t necessarily the case, though. Connected medical devices and systems are vulnerable to cyberattacks, which could reveal sensitive data, delay critical care, and physically harm patients.

The U.S. Food and Drug Administration, which oversees the safety and effectiveness of medical equipment sold in the country, has recalled medical devices in the past few years due to cybersecurity concerns. They include pacemakers, DNA sequencing instruments, and insulin pumps.

In addition, hundreds of medical facilities have experienced ransomware attacks, in which malicious people encrypt a hospital’s computer systems and data and then demand a hefty ransom to restore access. Tedros Adhanom Ghebreyesus, the World Health Organization’s director-general, warned the U.N. Security Council in November about the “devastating effects of ransomware and cyberattacks on health infrastructure.”

To help better secure medical devices, equipment, and systems against cyberattacks, IEEE has partnered with Underwriters Laboratories, which tests and certifies products, to develop IEEE/UL 2933, Standard for Clinical Internet of Things (IoT) Data and Device Interoperability with TIPPSS (Trust, Identity, Privacy, Protection, Safety, and Security).

“Because most connected systems use common off-the-shelf components, everything is now hackable, including medical devices and their networks,” says Florence Hudson, chair of the IEEE 2933 Working Group. “That’s the problem this standard is solving.”

Hudson, an IEEE senior member, is executive director of the Northeast Big Data Innovation Hub at Columbia. She is also founder and CEO of cybersecurity consulting firm FDHint, also in New York.

A framework for strengthening security

Released in September, IEEE 2933 covers ways to secure electronic health records, electronic medical records, and in-hospital and wearable devices that communicate with each other and with other health care systems. TIPPSS is a framework that addresses the different security aspects of the devices and systems.

“If you hack an implanted medical device, you can immediately kill a human. Some implanted devices, for example, can be hacked within 15 meters of the user,” Hudson says. “From discussions with various health care providers over the years, this standard is long overdue.”

More than 300 people from 32 countries helped develop the IEEE 2933 standard. The working group included representatives from health care–related organizations including Draeger Medical Systems, Indiana University Health, Medtronic, and Thermo Fisher Scientific. The FDA and other regulatory agencies participated as well. In addition, there were representatives from research institutes including Columbia, European University Cyprus, the Jožef Stefan Institute, and Kingston University London.

“Because most connected systems use common off-the-shelf components, everything is now hackable, including medical devices and their networks.”

The working group received an IEEE Standards Association Emerging Technology Award last year for its efforts.

IEEE 2933 was sponsored by the IEEE Engineering in Medicine and Biology Society because, Hudson says, “it’s the engineers who have to worry about ways to protect the equipment.”

She says the standard is intended for the entire health care industry, including medical device manufacturers; hardware, software, and firmware developers; patients; care providers; and regulatory agencies.

Six security measures to reduce cyberthreats

Hudson says that security in the design of hardware, firmware, and software needs to be the first step in the development process. That’s where TIPPSS comes in.

“It provides a framework that includes technical recommendations and best practices for connected health care data, devices, and humans,” she says.

TIPPSS focuses on the following six areas to secure the devices and systems covered in the standard.

Trust. Establish reliable and trustworthy connections among devices. Allow only designated devices, people, and services to have access.
Identity. Ensure that devices and users are correctly identified and authenticated. Validate the identity of people, services, and things.
Privacy. Protect sensitive patient data from unauthorized access.
Protection. Implement measures to safeguard devices from cyberthreats and protect them and their users from physical, digital, financial, and reputational harm.
Safety. Ensure that devices operate safely and do not pose risks to patients.
Security. Maintain the overall security of the device, data, and patients.

TIPPSS includes technical recommendations such as multifactor authentication; encryption at the hardware, software, and firmware levels; and encryption of data when at rest or in motion, Hudson says.

In an insulin pump, for example, data at rest is when the pump is gathering information about a patient’s glucose level. Data in motion travels to the actuator, which controls how much insulin to give and when it continues to the physician’s system and, ultimately, is entered into the patient’s electronic records.

“The framework includes all these different pieces and processes to keep the data, devices, and humans safer,” Hudson says.

Four use cases

Included in the standard are four scenarios that outline the steps users of the standard would take to ensure that the medical equipment they interact with is trustworthy in multiple environments. The use cases include a continuous glucose monitor (CGM), an automated insulin delivery (AID) system, and hospital-at-home and home-to-hospital scenarios. They include devices that travel with the patient, such as CGM and AID systems, as well as devices a patient uses at home, as well as pacemakers, oxygen sensors, cardiac monitors, and other tools that must connect to an in-hospital environment.

The standard is available for purchase from IEEE and UL (UL2933:2024).

On-demand videos on TIPPSS cybersecurity

IEEE has held a series of TIPPSS framework workshops, now available on demand. They include IEEE Cybersecurity TIPPSS for Industry and Securing IoTs for Remote Subject Monitoring in Clinical Trials. There are also on-demand videos about protecting health care systems, including the Global Connected Healthcare Cybersecurity Workshop Series, Data and Device Identity, Validation, and Interoperability in Connected Healthcare, and Privacy, Ethics, and Trust in Connected Healthcare.

IEEE SA offers a conformity assessment tool, the IEEE Medical Device Cybersecurity Certification Program. The straightforward evaluation process has a clear definition of scope and test requirements specific to medical devices for assessment against the IEEE 2621 test plan, which helps manage cybersecurity vulnerabilities in medical devices.

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