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Transitioning from years of working in a senior technical or executive role to a leadership position is one of the most challenging phases of a STEM career. It requires moving away from making decisions on your own to mentoring others, making strategic decisions for the organization, and collaborating with coworkers from different generations.The shift in mindset is known as “legacy leadership,” a philosophy whereby success is no longer measured by personal achievements but by how effectively a senior leader empowers others.To help seasoned professionals and senior experts navigate the transition, the inaugural IEEE International Leadership Conference (ILC) will provide attendees practical advice on cultivating collaborations and guiding emerging talent.“Early in our STEM careers, we measure success by what we have achieved,” says Jeewika Ranaweera, cochair of the IEEE ILC program committee. “Later, we should measure success by what we enable, how many people we mentor, how much knowledge we transfer, and how many doors we open for the next generation.”The ILC is scheduled for 3 and 4 October in Budapest. Registration is open.Letting go of the “expert” identityFor decades, seasoned technologists have been valued primarily for their technical expertise. Shifting from that identity can feel uncomfortable, but legacy leadership requires measuring success by different standards. They include a leader’s influence on the staff, the ability to uphold the company’s mission, and empowering others to lead and succeed.The transition requires leaders to find purpose outside their corporate titles, shifting their focus to the long-term sustainability of their teams, their organization, and the broader technical community.“A professional legacy is not measured only by what we have achieved but also by sharing our knowledge, experience, and opportunities with others,” says Sudhanshu S. Jamuar, another program committee cochair. “The real transition from expert to a legacy builder happens when we stop asking, ‘What more can I accomplish myself?’ and start asking, ‘How many others can I enable to accomplish more?’”Neeli Rashmi Prasad, IEEE […]

This article is brought to you by VicOne.Robot safety has traditionally asked: Can a machine remain safe when something goes wrong? Physical AI raises a harder question: Can a machine remain safe when an attacker changes what it sees, decides, or does even when nothing appears to have failed?As AI and robotics continue to advance at an unprecedented pace, modern robots perceive through multimodal sensors, interpret context using AI models, and translate those interpretations into physical action. As they move into dynamic environments, their safety increasingly depends on the integrity of the data guiding their decisions.That dependence creates risks that conventional safety assessments may not fully capture. Recent research has demonstrated that manipulating what a robot sees, hears, or interprets can influence its behavior without requiring direct control.Such manipulation can occur anywhere across its complex sensing and decision-making system — a layered attack surface encompassing training pipelines, system infrastructure, and runtime perception.Layer One: Corrupting intelligence at its sourceIn 2017, BadNets demonstrated that a model could behave normally under most conditions, yet fail in the presence of a specific hidden trigger. In one example, a subtle pattern caused a stop sign to be misclassified as a speed limit sign without affecting the model’s behavior on other inputs.What began as a classification vulnerability has since evolved into action manipulation.At NeurIPS 2025, researchers introduced BadVLA a backdoor attack targeting Vision-Language-Action (VLA) models that allow robots to see, interpret instructions, and produce coordinated physical movement. Rather than altering a single label, the attack caused conditional deviations in the robot’s action trajectory when a trigger was present. Without the trigger, the model largely preserved normal task performance, while the backdoor remained effective under task transfers and model fine-tuning.A related study in 2025, GoBA, showed that ordinary objects such as a coffee mug could serve as a reliable trigger. The researchers reported a 97 percent attack success rate without degrading […]

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Since about 2020, AI has largely focused on training bigger and better models. Large language models (LLMs) ballooned from millions of parameters to trillions. This proved effective: The largest version of OpenAI’s GPT-3, released in 2020, correctly answered just 43.9 percent of questions on a popular knowledge-and-reasoning benchmark. Just four years later, GPT-4o reached a score of 88.7 percent on the same exam, effectively matching those of human experts.Advanced AI labs are still training ever larger models, but that training has somewhat receded to the background of the AI conversation. In 2026, inference—the use of trained models to produce code, write essays, or make images of ourselves as elves—has come to the forefront.“It’s like training is yesterday’s news,” says Matt Kimball, principal data-center analyst at Moor Insights & Strategy. “All that any chief information officer wants to talk about is inference.” Nvidia CEO Jensen Huang, speaking at the company’s GTC 2026 conference, touted this change as the “inflection point of inference.”Part of what’s caused the shift is very simple: LLMs are becoming useful, so people are using them. On top of that, many models on the market today are reasoning models. In response to a user’s query, they run inference not just once but multiple times, reprompting themselves in a process called chain of thought. Reasoning models generate longer outputs, and models with high reasoning effort can produce up to 20 times as much text as those with low or no effort. Adding even more to the world’s inference workload, the rise of agentic AI has resulted in inference running not just as a real-time response to a user’s query but also around the clock, working autonomously toward a user-defined goal. Amazon’s Trainium chip was originally designed for AI training. However, Amazon Web Services chose to break up AI inference into two parts, with Trainium running the more computationally complex portion and Cerebras’s wafer-scale engine taking on the more memory-intensive portion.AmazonThe resulting explosion in inference demand has led to unexpected alliances among tech giants. OpenAI and […]

In 2003, Martin Eberhard, a cofounder of Tesla Motors, decided it was time to start wooing investors. To do that, however, he needed an electric car. So he talked to Alan Cocconi and asked if he could borrow the tZero, the revolutionary and blazingly fast electric roadster that Cocconi had built at AC Propulsion.Eberhard’s idea was to drive the tZero up and down Sand Hill Road in the heart of Silicon Valley and do demonstrations for curious entrepreneurs and VCs. Eberhard was joined by Tom Gage, Cocconi’s partner at AC Propulsion, on many of the visits. Like Tesla, AC Propulsion was also seeking investors, but to build a considerably more utilitarian EV.Adapted with permission from The EV Guys: How Caltech Engineers Reinvented the Electric Car, by Charles J. Murray, published by Purdue University Press.In December 2003, Eberhard also proposed a demo at Buck’s of Woodside, a popular restaurant frequented by tech entrepreneurs. At 5 o’clock on any evening, Buck’s probably had more VCs per square foot than any building in the country. Eberhard’s plan was to “show off what a real electric sports car can do,” he wrote in an email to Buck’s owner, Jamis MacNiven. MacNiven happily obliged.In some ways, the tZero was a hit. When a VC would ride shotgun in the car with Eberhard at the wheel, Eberhard would implore them to touch the dashboard. As they reached forward, he’d punch the accelerator. As the car accelerated and the g forces piled up, the VC was literally unable to touch the dashboard. That was how powerful the tZero’s acceleration was, Eberhard would say.Many of the VCs were astounded. Some even questioned whether the car was really electric. Many owned Ferraris or Lamborghinis. They knew sports cars—but this? They could never have imagined it was possible to do this with an electric drivetrain. On December 13, 2003, Martin Eberhard brought AC Propulsion’s tZero electric roadster [yellow] to Buck’s of Woodside, a popular hangout for entrepreneurs and venture capitalists. Next to the tZero is a Scion xB, which AC Propulsion’s principals thought they could turn into a mass-market electric vehicle.Martin EberhardStill, the demo at […]