2026-06-23 21:00:01
By most accounts, the United States appears poised to fall woefully short of meeting new electricity demand over the next five years as data centers and domestic manufacturing proliferate.
Ian Magruder is the founder of Utilize Coalition and previously served as director of market mobilization at Rewiring America, an affordable electrification advocacy group.
Building new power plants and transmission lines may seem like the obvious solution, but there are other options, says Ian Magruder, founder of Utilize Coalition, a nonprofit based in Washington, D.C. The U.S. uses only about half of its grid capacity, and a lot more power could be tapped by deploying a spate of newly available technologies.
Backed by Google, Tesla, HVAC systems manufacturer Carrier, and several other companies, Utilize Coalition advocates for more thorough use of grid capacity through policy change and new technologies. Magruder spoke with IEEE Spectrum about those efforts.
Why does the United States use only half of its grid?
Ian Magruder: Most studies have found that average utilization rates are between 40 and 55 percent across different geographies. And the reason is that we’ve built our grid to meet peak demand. We have to ensure that on the hottest summer day or the coldest winter morning we have enough power. But in many parts of the country, we really only hit peak a few days a year, and it’s really only a few specific hours within those days.
It didn’t used to be this way. What’s changed?
Magruder: Over the last 20 years we’ve seen the gap between average use and peak use grow wider. There are a variety of reasons for that. Grid operators have become more conservative following major blackouts and reliability events. And with more variable-generation sources such as wind and solar, grid operators are building in more capacity. But this also presents us with an incredible opportunity to get more out of the grid using new technologies.
What technologies are being deployed to address the problem?
Magruder: Pairing battery storage with energy generation is a key part of this, as are other kinds of distributed energy resources, like managed [electric vehicle] charging and smart thermostats. I would also say that transmission technologies that safely maximize the current in power lines, increase conductivity, and optimize power routes all play a critical role here. And then there’s demand flexibility, which is when utility customers adapt their power use to accommodate the grid during peak hours. Some really good work is being done around flexible data centers.
Is grid underutilization also happening elsewhere in the world?
Magruder: It’s a global phenomenon, but it varies widely by country. European grids face similar dynamics as [those in] the U.S., and in some places utilization is even lower. But Australia and the United Kingdom are further ahead in measuring and managing utilization with new technologies.
What’s the downside to overbuilding our grids?
Magruder: Mainly cost. Electricity rates have gone up, and we [at Utilize Coalition] think it’s because utilization has gone down. A report that we released earlier this year shows that a 10 percent increase in grid utilization could save Americans over US $100 billion over the next decade.
2026-06-23 20:00:01
Imagine sitting down at your desk and logging in for a performance review, with an AI system analyzing the conversation. You’ve been working long hours, balancing deadlines, and your manager asks how you’re doing. You say you’re fine, and maybe even smile, but there’s a hint of hesitation and your voice wavers. As you shift your posture, your shoulders slump.
These are subtle cues that to the human eye might hint at underlying stress. But to an AI model that’s been trained only to categorize emotions as “happy” or “sad,” such nuances are likely lost. It logs the words and a smile and moves on—and unless your human manager intervenes, the fact that you’re tired, unfocused, and maybe a couple of days from burnout never enters the equation.
“Emotion AI,” which estimates how people feel based on facial expressions, voice tone, and behavior, seems to be suddenly everywhere; it’s being used in employee well-being and recruitment interviews, education platforms, and driver-monitoring systems. Technology call-center platforms such as NiCE and Genesys use AI to detect when a customer sounds frustrated and prompt agents in real time to slow down or respond with more empathy. Giant companies like Meta and startups such as Hume AI are developing more-expressive voice AI systems that can detect emotional cues in the person they’re “talking” to and adjust how they communicate.
What’s more, hundreds of companies already offer virtual AI companionship apps, a fast-growing market that may be worth an estimated US $555 billion by 2035—and robot buddies have also entered the picture. Intuition Robotics’s ElliQ, for example, is a small device vaguely resembling a white desk lamp that’s now being used to engage older adults in conversation in hopes of reducing loneliness.
But while the field of emotion AI is advancing at a rapid clip, most existing systems are focused on detecting a limited number of signals to label one specific emotion at a time—which is insufficient if you’re trying to understand the human condition. In the real world, human signals and emotions are contextual, overlapping, and constantly changing. A laugh can signal joy, nervousness, or both; a raised voice might signal enthusiasm just as easily as frustration. To make the job of emotion detection even more difficult, reactions differ greatly from one individual to the next, depending on demographics, cultural background, and countless other variables.
In other words, there’s a gap between what we’re expecting AI to pick up on and what AI can actually deliver. That’s the gap a new field of research—what we call human-context AI—is working to close. Instead of looking at just one input and labeling it, human-context AI increasingly has the capacity to take stock of an individual’s personality and character, and to track emotions in real time while combining multiple inputs, including facial dynamics, voice, tone, language, and behavior. Crucially, responses are also evaluated in the context of a specific environment, such as a performance review or professional coaching session. The result? Computers are learning to read the scene, rather than just the screen.
The story of emotion-sensing AI began almost three decades ago in the MIT Media Lab, where the American electrical engineer and computer scientist Rosalind Picard coined the term “affective computing.” Her work introduced the radical idea that computers could be taught to recognize and respond to human emotions.
Picard’s early experiments focused on single modalities: facial expressions, tone of voice, and physiological signals, such as skin conductance or heart rate. The goal was to give machines a window into human feeling, helping them become more empathetic. It was an exciting vision, but back then the science and hardware weren’t ready. Computing power was limited, sensors were crude, and datasets were narrow and biased.
Josie Norton
Over the next decades, researchers and companies got better at measuring the many ways in which humans express themselves. In the 2010s, sentiment analysis—the processing of large volumes of text to suss out emotional undertones—began to reach the mainstream. At the same time, marketing firms, including my company, Neurologyca, began using video and webcams to measure and catalogue customer reactions. Biometric devices and activity trackers, such as Fitbits and Apple watches, also became ubiquitous, generating new streams of data about people’s sleep, step counts, stress levels, and more.
Unsurprisingly, scientists soon confirmed that larger volumes of personalized data led to greater accuracy in reading human emotions. In 2019, researchers at Cornell demonstrated that combining multiple types of signals improves emotion sensing. Their system joined physiological data, such as brain activity measured by electroencephalography (EEG) and heart rate, with visual cues like facial expression, outperforming systems that relied on just one input. Around the same time, Picard and her team at MIT found that humanoid robots trained on data unique to a specific person were substantially better at reading that person’s reactions and feelings than robots acting without personalized data.
More recent studies align with these findings. In 2024, scientists in South Korea showed that fusing physiological, environmental, and personal data to recognize emotion resulted in a 32 percent error reduction. Another paper, published in 2025, demonstrated that user-specific information significantly enhances emotion recognition performance.
Today, our devices know who we are; our habits and tendencies, likes and dislikes. They’ve also gotten smaller and more efficient. Tiny, low-power cameras and microphones embedded in phones, laptops, and virtual-reality and augmented-reality devices can detect dozens of human signals simultaneously, from eye movements and micro-expressions to breathing rhythms, voice modulation, and posture. Advances in computing have also made it possible to integrate audio, video, biometric, and text data, often without even transmitting raw data to the cloud. And researchers at Stanford, Cambridge and MIT, and Kyoto University, in Japan, as well as the Software College of Northeastern University in Shenyang, China, are exploring how fusing such inputs can refine the sensitivity and accuracy of human-machine interactions.
And yet, despite so many breakthroughs, machines still can’t reliably interpret emotion or even physical stress. Just last year, a survey published in the Journal of Psychopathology and Clinical Science revealed that stress scores on smartwatches rarely, if ever, matched the level of stress that users were experiencing. In fact, a quarter of those surveyed reported feeling the direct opposite of what their smartwatches were reporting.
Why the disconnect? We’ve gotten very good at capturing signals, but not at interpreting them. A fitness tracker might infer from your heart rate that you’re stressed and recommend easing off training, but it doesn’t know if your increased heart rate is due to excitement, tiredness, or an extra cup of coffee. Gauging emotions in real-world settings is even more difficult. To solve this complex problem, machines need context.
My company, Neurologyca, was founded in Spain in 2015, and started out in neuromarketing. Working with major European brands and conglomerates, our cofounder, Juan Graña, had realized that companies lacked solid data on consumers. At the time, most customer feedback came through surveys, which posed questions such as, “On a scale of 1 to 10, how joyful does this car advertisement make you feel?” or “Which emoji best describes your mood?” Naturally, these overly simplistic tools led to high levels of self-reporting bias, as people often misjudge or misstate their own reactions.
To get around this problem, Neurologyca set up labs, using neuroscience and cognitive science to more accurately capture human responses to products, logos, advertisements, and experiences. In addition to using biometric tools such as heart monitors, eye trackers, and EEG, we recorded millions of video frames of human reactions, logging each specific context and the resulting facial and bodily movements. To do this, we mapped over 790 points of reference, including corners of the mouth, size of the eyes and pupils, blink rate, and angling of the head. All of this data was collected and stored anonymously under strict European privacy standards.
Next, we paired this information with findings from decades of neuroscience and behavioral science studies on how biometrics, speech patterns, and human movement are related to emotion—research we continue to gather from academic institutions across Europe. We also created a database of situational contexts—for example, “watching a dog food commercial” or “hearing a new song”—and the human feelings they engendered.
In our work with companies, not only did this approach allow us to recognize nuanced emotions, it also let us identify which reactions indicated positive or negative outcomes. Take, for example, the context of horror-film trailers: Our research helped us figure out that the most successful elicit a very specific mix of emotions, namely a little bit of fear, a little bit of anxiety, but also some joy. With this knowledge, we could quickly rate viewer reactions to help a film company figure out how to tweak its trailer for the desired impact.
Neurologyca
Within a few years, we discovered that a model trained on our database could accurately evaluate emotion using just a webcam. We stopped needing to host focus groups in rooms full of equipment. Instead, we were able to do such things as sending out a new perfume sample to paid participants around the world along with a link. When people opened the link, it turned on their cameras, allowing us to record their faces as they sniffed the perfume for the first time. Suddenly, we had expanded our reach: Rather than using small focus groups in one or two countries, we could quickly assess 1,000 people across the planet, comparing how someone in Japan, India, or Germany might feel about a certain product.
About four years ago, as AI was becoming pervasive, we realized that our models had applications well beyond neuromarketing. Importantly, these models are grounded in directly observed human behavior rather than inferred patterns or loosely labeled open datasets. Looking beyond brands and companies, we established that our model could be integrated into AI systems to help them understand human emotion at a much more granular level. In other words, we could provide a layer of context.
When we talk about “a layer of context,” we mean three different types of context. The first is situational or environmental context; for example, a performance review, a telemedicine session, or a horror-film viewing. The second is personal context, which includes an individual’s specific history, goals, and baseline state. The third is behavioral context, which covers the individual’s reaction over the course of the event or interaction by evaluating real-time changes in attention, confidence, engagement, and cognitive load.
Most systems today focus on only situational context, although some are starting to include personal context. Very few include behavioral context or combine all three in a meaningful way. What we’ve built at Neurologyca is a logic layer that fuses the three and translates them into structured, machine-readable information that allows AI systems and agents to respond more effectively. Our technology is being used to enhance systems in development, as well as some that have already been deployed, including driver-safety apps like Netradyne, home assistants like Amazon Alexa, and health-care AI platforms like Sully.ai.
It works as follows: Situational context is determined by the platform or application, be it a professional coaching session, a meditation app, or a driver’s safety monitor. Personal context already lives within each respective platform—or if not, it can be created through sharing of personal data or monitoring via camera. (Most wellness and professional-development apps, for example, contain each user’s profile, history, and prior sessions.) Last but not least, behavioral context is collected and analyzed in real time using our models. In the end, our logic layer fuses these three streams of information.
Our system doesn’t assign fixed weights to the three contexts. Instead, it provides a continuous calibration, with the balance shifting depending on the specific situation. For example, a pause in speech might signal uncertainty in a performance review, but something entirely different in a relaxation setting. If signals are ambiguous or overlapping, our system reflects that uncertainty through lower confidence scores rather than forcing a definitive interpretation.
What’s more, our system can work without ever sending raw data to the cloud, thereby easing privacy concerns. In many cases, video, audio, and biometric signals never leave the device. Instead, our lightweight models extract information locally and share only what’s necessary. Cloud systems, meanwhile, are used for training, pattern analysis, and model improvement. The result is a hybrid architecture: edge-based processing for speed and privacy combined with cloud-based learning for continuous improvement.
The result? By incorporating context, AI systems are beginning to interpret aspects of the human state as interactions unfold, dynamically adapting to emotions rather than reacting after the fact. The range of potential applications is broad and still evolving. Picture a professional-development platform that uses a human avatar to perform a mock interview and then provide feedback and tips on how to appear more confident, likeable, and well-informed. Or a meditation app that knows exactly how well you slept and how anxious you’re feeling, and can recommend an appropriate breathing meditation. Or a humanoid robot teacher that can tell when a student is confused or bored and step in to get them back on track.
There have long been debates about the ethics of emotion-sensing AI. Some critics question whether systems should attempt to infer human feelings from external signals at all. They argue that reducing people to measurable outputs risks oversimplifying human experience while opening the door to manipulation, surveillance, and unfair judgments in workplaces, schools, and public spaces.
We take those risks extremely seriously. In fact, our technology aims to reduce the dangers of oversimplifying human emotion. Human-context AI is not based on the assumption that a machine can definitively know what someone is feeling. Rather, it is an attempt to move beyond simplistic labels by incorporating situational, personal, and behavioral context, while explicitly representing uncertainty when signals are ambiguous or incomplete.
That said, ethical concerns regarding implementation are real and have shaped the kinds of projects we pursue. We would never, for example, accept military engagements to help with interrogations. Not only for ethical reasons: Emotion AI cannot reliably detect deception, and claiming otherwise would be overstating what the technology can actually do. And while our technology can be used to gauge crowd behavior and predict things like when a football stadium is at risk of becoming destructively rowdy, we don’t want our technology deployed for surveillance. In short, we believe that using our logic layer on anyone who hasn’t opted in would be intrusive and ethically problematic.
In Europe, our systems are designed to comply with the EU AI Act’s restrictions on emotion recognition in workplaces and schools; as we expand into the United States, we apply jurisdiction-specific guidelines while maintaining the same core ethical commitments.
We also don’t advise companies to become overly reliant on our technology. Hiring and firing decisions should not be based on our outputs alone. Instead, our logic layer is designed to support human understanding and surface emotions that might otherwise go unnoticed.
Let’s return to the scenario of the performance review. Never mind basic AI—all humans, and even great managers, miss things during conversations. There’s a lot happening at once, as people process what’s being said, how to respond, and the greater context of the situation. These days, many exchanges also occur virtually or via video, adding more distractions while shared context is stripped away.
While we would never claim that our models understand humans better than their fellow humans, we believe we can offer an added layer to help managers capture and interpret behavioral signals that might otherwise get lost, providing greater visibility into how a conversation is unfolding.
Our model can track patterns moment to moment, picking up, for example, a shift in engagement, an instance when something didn’t land, or a change in how someone is behaving. The model won’t tell the manager what these moments mean or what to do about them; it simply makes them easier to see and follow up.
Human-context AI is at an early stage. The use cases, the adoption patterns, and the actual impact are all still evolving. At the same time, emotion-sensing systems are quickly being incorporated into real products and platforms. And without context—without knowing why people feel the way they do—AI risks misunderstanding us in critical moments.
2026-06-23 02:00:01
Artificial intelligence is the transformative, strategic technology of the early 21st century. It is significantly reshaping practically every aspect of our lives, including in ways that probably no one anticipated. Its rate of adoption and impact have been unprecedented when compared with other technologies.
AI as a distinct field was formally established in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence, proposed by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. In their August 1955 proposal for the research project, the scientists introduced the term artificial intelligence and envisioned machines capable of simulating human intelligence.
AI is the “science of making machines do things that would require intelligence if done by men,” as defined by Minsky. The professor received the ACM Turing Award, which is often called the “Nobel Prize in computing.”
Since AI’s humble beginnings 70 years ago, it has evolved significantly in its capabilities, gained prominence, and earned widespread adoption across many areas including business, education, finance, health care, industry, and the military.
IEEE’s contributions to the progress and adoption of AI throughout its journey are substantial and multifaceted.
As we celebrate AI’s 70th birthday, understanding its history, current status, limitations, and concerns is key to harnessing it for good.
Although AI emerged as a distinct field in 1956, its intellectual roots extend back further. The ideas and theories that underpin AI predate modern computers such as the ENIAC, unveiled in 1946.
In 1943 Warren Sturgis McCulloch, a neurophysiologist and cybernetician, and Walter Pitts, a logician working in computational neuroscience, were inspired by the human brain. The two devised mathematical models of artificial neurons, demonstrating that artificial neural networks could perform logical computation.
Frank Rosenblatt, a Cornell psychologist, later advanced those ideas by developing the perceptron, an early neural network that laid the foundation for modern machine learning and deep learning.
A major milestone came in 1950, when celebrated computer scientist Alan Turing posed the question, “Can machines think?” In his 1950 landmark paper “Computing Machinery and Intelligence,” published in Mind, he explored the nature of machine intelligence. He introduced the “imitation game,” later known as the Turing test, as a practical means of evaluating it. The test remains an influential concept in AI and the philosophy of intelligence, as I discussed in my article “The Turing Test at 75: Its Legacy and Future Prospects,” published in IEEE Intelligent Systems.
Claude Shannon, recognized as the father of information theory, explored the potential of machines for complex reasoning tasks in his 1950 article “Programming a Computer for Playing Chess,” published in Philosophical Magazine.
In 1956 AI became a formal discipline, inspiring scientists to explore and advance it further. John McCarthy developed Lisp in 1958, and it became the dominant programming language for AI research and development. In 1959 Arthur Lee Samuel, a computer science professor at Stanford, introduced the term machine learning to describe programs that could improve their performance through experience.
In the early 1980s, renewed enthusiasm and government funding fueled the development of symbolic AI, a rule-based expert system (also known as a knowledge-based system) that encodes domain-specific knowledge as sets of rules. A notable example was MYCIN, designed to diagnose infectious diseases.
Although successful in limited domains, expert systems’ inherent limitations have restricted their broader adoption. Expert refers to a computer system that mimics human experts in a specific domain. It was popular in the early days of AI, and subsequently disappeared with advances in AI such as neural networks and machine learning.
AI’s journey was marked by periods of soaring expectations and disappointing progress, known as “AI winters,” during which funding, interest, and confidence declined. Analyses of the episodes revealed recurring causes and insightful lessons for the field.
A new phase of growth—often described as “AI spring”—emerged in the 2010s with advances in deep learning, the rise of large language models, the transformer architecture, and generative AI (GenAI).
“The imperative before us today is not only to advance AI’s capabilities but also to ensure that it remains human-centered, trustworthy, ethical, and dedicated to enhancing human well-being and societal progress.”
Unlike earlier approaches that processed information sequentially, a transformer model analyzes an entire sequence of text or audio, assessing the importance of each word or component relative to others, enabling dramatic advancements in GenAI and its applications.
Ashish Vaswani, a former computer scientist at Google, and his colleagues at Google Brain introduced the transformer architecture that underpins today’s generative AI systems in their influential 2017 paper “Attention Is All You Need.” Vaswani and Sam Altman—chief executive of OpenAI, which offers ChatGPT—are widely regarded as the masterminds behind the GenAI revolution.
AI reached new heights with the public release of ChatGPT in 2022, followed quickly by a wave of chatbots and generative AI tools that accelerated global interest.
More recently, the rise of agentic AI systems capable of increasingly autonomous operation has expanded AI’s capabilities and impact.
AI’s 70-year journey reflects an extraordinary interplay of vision, experimentation, setbacks, innovation, and impact.
For further information and diverse perspectives on AI history, check out my curated collection of articles.
AI’s pragmatic strength lies in its ability to process information, recognize patterns, and perform cognitive tasks at an unprecedented speed and scale. It can analyze vast amounts of data, extract insights, and identify trends or anomalies that are difficult for humans to detect. The programs can automate routine tasks and repetitive knowledge work, improve productivity, and reduce costs.
Chatbots and other forms of GenAI can answer queries and rapidly create text, images, videos, music, software code, educational materials, and other content on the fly in response to a user’s prompts, accelerating information-gathering, innovation, and decision-making. AI summarizes, translates, and rephrases text effectively and can assist in idea generation. It also facilitates natural-language interactions, making technology more accessible to nonexperts and the diverse global community. Its multimodal capabilities enhance its usefulness across diverse domains. Additionally, it can serve as a powerful collaborator, augmenting creativity and problem-solving capacity rather than replacing human intelligence.
AI is transitioning from standalone tools to autonomous, goal-driven systems. Agentic AI systems that can plan, act, and adapt with minimal human oversight are on the rise, enabling large-scale impact.
The 400-page AI Index 2026, published by the Stanford Institute for Human-Centered AI, reveals the technology’s enhanced capabilities and unprecedented adoption rates, outpacing those of the telephone, the television, the personal computer, and the Internet.
For a deep exposition on the current state of AI, read this analysis from IEEE Spectrum, which also published the “Great AI Reckoning” special report.
Along with its benefits, AI presents significant risks and concerns. They include biased, discriminatory, and harmful responses; a lack of transparency and explainability in decision-making; privacy violations from data collected for AI training; and cybersecurity vulnerabilities including AI-powered attacks.
AI systems can hallucinate, generating confident but incorrect or fabricated information. Moreover, AI can facilitate and amplify the spread of misinformation, deepfakes, and manipulated content, undermining public trust and driving the algorithmic manipulation of public opinion. The flattering, people-pleasing, or affirming behavior known as AI sycophancy can be harmful as well.
Overreliance on AI could erode human judgment, critical thinking, and decision-making skills. And autonomous systems can make errors with serious consequences in critical domains including defense, health care, and transportation.
The technology’s development and deployment, therefore, must be guided by informed understanding, sound judgment, and responsible governance. In assessing AI’s suitability for any application, its capabilities, advantages, limitations, and risks must be carefully and holistically considered.
IEEE has not merely documented and disseminated AI’s progress. It has actively fostered, standardized, and guided it toward further advances and responsible use for the benefit of humanity. IEEE maintains a hub for information on its AI activities that is a valuable resource for researchers, developers, regulators, and users.
IEEE publishes 11 AI-focused journals that advance the frontiers of knowledge, including IEEE Intelligent Systems. In its AI at 70 commemorative issue, Intelligent Systems identified the 10 most influential AI articles published since 2000. The magazine, produced by the IEEE Computer Society, has inducted 10 pioneers into its AI Hall of Fame, honoring their contributions and impact on technology and society.
To foster AI research and development, since 2006, the magazine has recognized the field’s rising stars through its AI’s 10 to Watch awards. The biennial awards spotlight outstanding contributions of young researchers and professionals. Nominations for this year’s awards are open until 1 July.
Since the early days of AI, the IEEE Computer, Computational Intelligence, and Systems, Man, and Cybernetics societies have been among those that have fostered AI research and practice. The Computer Society offers a guide to becoming an AI developer.
IEEE and its societies sponsor more than 100 AI conferences annually. The conference archives are available in the IEEE Xplore Digital Library.
The IEEE Learning Network offers more than 200 courses across AI-related areas.
The IEEE Standards Association has developed more than 100 AI-related standards. Its CertifAIEd program promotes ethical design and deployment of autonomous intelligent systems.
The Institute has featured several IEEE members who have developed AI-driven applications, such as Abhishek Appaji, who has created tools to help detect psychiatric disorders.
The history of AI helps us understand the motivations behind developments and inspires and guides us toward the next phase of the technology’s innovation and revolution. AI’s trajectory is bound to be shaped by the collective choices we make now and in the future.
As Turing wrote in his 1950 landmark article, “We can only see a short distance ahead, but we can see plenty there that needs to be done.”
The imperative before us today is not only to advance AI’s capabilities but also to ensure that it remains human-centered, trustworthy, ethical, and dedicated to enhancing human well-being and societal progress.
2026-06-20 21:00:01
Salome Mikadze-Struk is no stranger to adversity. The daughter of refugees, she built a software-development business as an undergraduate at the height of the COVID-19 pandemic and kept it running despite the outbreak of war in her native Ukraine. Now, she’s drawing on her experiences to mentor tech-startup founders and speak publicly about the importance of resilience in entrepreneurship.
Mikadze-Struk was studying at Georgetown University, in Washington, D.C., when COVID-19 struck. Classes went online, and she moved back to Ukraine. In the midst of that disruption she saw an opportunity to develop her business idea, called Movadex, by tapping Ukraine’s pool of talented young engineers. Then Russia invaded in early 2022, during her final semester. Taking online classes from bomb shelters and helping employees evacuate to safer parts of the country was surreal, she says, but the team kept the company afloat and she graduated later that year.
In 2023, Mikadze-Struk took a hiatus from her business to pursue an MBA at Stanford University, which she completed this year. In her precious spare time she’s been advising startups and giving talks, using her unique perspective to promote the need for resilience in entrepreneurship—something she thinks is increasingly important in the software industry as AI coding tools upend old business models.
“You need to be okay with risk, you need to be resilient. You need to be okay with disruption and okay with uncertainty,” she says, “because this is inevitably going to be part of this industry for the foreseeable future.”
Mikadze-Struk’s parents had settled in Ukraine after fleeing conflict in the Abkhazia region of Georgia in the early 1990s. “They left everything behind,” she says. “You can look on Google Maps and zoom in on where their houses were and it’s all rubble.”
Despite this backstory, Mikadze-Struk says she and her sister had a conventional middle-class upbringing in Kyiv. Her father ran a small shop and her mother was a stay-at-home mom. Her parents placed an emphasis on education and encouraged her to study hard and take part in extracurricular programs such as Ukraine’s Junior Academy of Sciences, which introduces students to research.
“They weren’t rich, so they knew that our way to make it in life was not through investments, but through merit-based accomplishments,” she says.
When Mikadze-Struk was 14, her family discovered the newly launched Ukraine Global Scholars program, a nonprofit that helps talented students secure scholarships abroad. The program helped her win a full scholarship to the Emma Willard School, a private girl’s school in Troy, N.Y.
After graduating high school in 2018, Mikadze-Struk was accepted to Georgetown to study business administration. But it was outside the classroom that her career direction began to take shape. She won a startup competition with a medical device she had developed for a school project and, while the business idea didn’t go anywhere, it sparked an interest in entrepreneurship.
Ukraine’s software industry was booming, and she began attending startup events and competitions in her home country the summer before starting college. There she met her eventual cofounder Nor Newman.
Despite both being just 18, they saw a gap in the market. The pair noticed many founders had strong ideas but lacked the technical expertise to realize them, while talented engineering students often struggled to gain real-world experience. Newman had begun informally connecting startups with his college friends, but the pair soon saw commercial potential. “We realized we could actually create our own startup studio and help startups as a team, versus just connecting people,” says Mikadze-Struk.
Then, when the COVID-19 pandemic struck in early 2020, halfway through her sophomore year, it brought both disruption and opportunity for Newman and Mikadze-Struk. While travel restrictions and lockdowns made life complicated, there was also a surge of companies looking to move their business online. “COVID really skyrocketed everything we were doing,” she says.
Sensing an opportunity, Mikadze-Struk and Newman incorporated Movadex in Ukraine in early 2020. From the start, they decided to focus on not only providing engineering talent, but also helping startups with product development. Many times, says Mikadze-Struk, a founder’s vision for the software doesn’t line up with what users actually want. “What really helped us grow is not just the engineering or quality of code, but rather a holistic approach to creating a product and actually getting into the brain of the user,” she says.
Back in Ukraine, Mikadze-Struk had to juggle this booming business with studying remotely—taking classes at night and working during the day. It was exhausting, she says, but it also allowed her to immediately apply what she learned in business classes to building her startup.
Having successfully navigated the pandemic, Mikadze-Struk was dealt another wild card. In early 2022, Russia invaded Ukraine and her life was again turned upside down. It was particularly traumatic for her family, having already been forced from their home in Georgia once by war.
In 2023, Mikadze-Struk took an extended leave from her company to pursue an MBA at Stanford.Christie Hemm Klok
“For my parents to experience their daughters going through all the same things they had gone through was really heartbreaking,” she says. “But at the same time, because I’d heard so much about their story of resilience I had power in me to not fully break down.”
On the day of the invasion the founders told employees to take the day off and emailed clients to warn of potential disruptions. The next couple of days were spent checking on staff and evacuating as many as possible to their headquarters in Lviv, in Western Ukraine.
By the following Monday the business was back up and running. Soon afterward, they partnered with the Lviv IT Cluster business association’s nonprofit arm to help resettle refugees from the eastern part of Ukraine, where strikes were focused, and offer job placements. Throughout this period, Mikadze-Struk was also completing her final year at Georgetown remotely. “Half of my senior year was actually spent in bomb shelters,” she says.
That summer, Mikadze-Struk graduated with a bachelor’s degree in business administration and learned she had been accepted onto Stanford University’s MBA program. In 2023, she took an extended leave from Movadex and moved to California. She also gave birth to her daughter in 2024.
Balancing studies and parenthood was already a full-time job, but she continued to engage with the startup ecosystem by volunteering as a startup mentor and public speaker. Now, after graduating from Stanford, she is stepping back into a more active leadership role at Movadex, where she hopes to drive the company’s expansion into the United States. She also wants to develop a stronger focus on helping customers understand and implement AI in their businesses.
While AI is undeniably disrupting the tech industry, Mikadze-Struk, now an IEEE Senior Member, is fundamentally optimistic about its impact. “The way AI democratized access to building software and to prototyping…is just mind blowing,” she says.
But it will require a significant shift in mind-set for engineers, especially junior developers hunting for jobs. They need to “fall in love with AI” and embrace it as a powerful copilot, she says. As these tools increasingly take over the nuts-and-bolts work of coding, engineers also need to nurture higher-level skills like systems thinking and architectural design.
Perhaps most importantly, given the rapid pace at which the technology is evolving, engineers need to nurture their adaptability and resilience. “It’s both exciting and scary, because you don’t know what tomorrow will bring.”
2026-06-20 02:00:01
Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.
While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising.
The LLM technology market is expected to grow by about 33 percent every year through 2030, according to MarketsandMarkets. The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists.
To use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture, a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously.
For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained.
Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.
Here are areas that integrate large language models.
Moving past basic prompts. Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories.
Fixing the “hallucination” problem. LLMs are at risk of hallucinations, which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database.
Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.
The future of collaboration. By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues.
The gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified, available through the IEEE Learning Network.
The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society, is built for people who want to understand the “how” and the “why” behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including:
Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise.
Enroll in the course program on the IEEE Learning Network.
Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.
2026-06-19 18:00:00
In 2018, Amazon brought me in as the lead UX Sound Designer for Astro, its first consumer home robot. Astro used cameras and other sensors to map and navigate your home and workplace, and could proactively patrol, check up on loved ones, and transport small items using its built-in cargo bin. While there was a well-defined feature set and form factor, initially there was no character direction. In fact, even before Astro had a name, there were two main questions—was it simply Alexa on wheels, or was it a robot with its own character?
The Astro team was divided. One option was to focus on Alexa, and treat the mobile robot simply as an added utility. Along with the majority of the UX team, I argued for Astro to not focus on Alexa. Our belief was that a thing that moves through your home and turns toward you with intent can never be just an appliance. People would ascribe character to it whether we wanted them to or not, and so the only question was whether we shaped that character or let it happen by accident.
Ultimately, Astro became Astro rather than Alexa, and user testing backed up our decision. People didn’t see the robot as Alexa. They saw it as its own character, and that’s what they wanted it to be. Alexa on the device felt somewhat strange and creepy, but building Astro its own voice was too slow and expensive in 2018. So, we settled on Alexa as a supporting character that handled any actual talking, while Astro was the main character, communicating as much as it could without words, through sound, motion, and facial expressions.
I had been brought on to the Astro team to define the robot’s sound design language and voice. But there was no one to flesh out the robot’s actual character. You cannot make a single real decision about a character without defining it first. Every choice about how Astro moved, sounded, paused, or reacted was a character choice, and those choices required all disciplines working together. As sound lead, I was weaving together sound, motion, and character, and how they played together inside each story moment. The animators, who programmed Astro’s motion and facial expressions, were extraordinary at what they did, but the emotional arc they were animating came from the sound (and therefore character) work first. So I stepped into that role, which is where my real work started. What I learned about building character for robots applies to nearly everything being built in embodied AI right now.
Developing a character for Astro meant answering questions that had never been asked about a product at Amazon: What is the emotional range of this robot’s baseline state? How does this robot communicate uncertainty without eroding trust? Where is the line between being expressive and annoying? What are the vulnerabilities of this device’s character?
These are design questions. They have real answers, and every team working on the product has to build from them. For example, Astro’s emotional range was designed to be relatively small at first. We never wanted Astro to get too sad or too angry. It could play sad, but would snap out of it quickly and end the reaction on a high note to keep things positive.
Character leaks out of every seam and can create a disjointed experience if not defined correctly. Even if it’s just animation timing that’s slightly off, or a response that’s technically correct but contextually tone-deaf, users feel every one of these inconsistencies, even if they can’t name them. Watch what happens at the beginning and end of this Sing sequence:
Astro goes from nothing, into the emotional moment, and then lands back on nothing. No buildup, no cooldown, no sense that the feeling came from somewhere or had anywhere to go. I pushed hard for better character stitching, the transitions in and out of expressive moments that make a performance feel continuous rather than assembled, but it never got implemented. The moment itself works. But without the stitching, it reads as a clip playing on a robot rather than coming from within the robot character itself.
We had decided that Astro would have no spoken dialogue, but it had something that functioned the same way: a vocabulary of sounds, tones, and rhythms that acted as its voice. This vocabulary became the leading output of the character’s personality. The robot’s motion and facial expressions were built around it.
Astro’s wake-up sequence is a great example. Waking wasn’t just a boot animation on the screen; it was an entire performance. Slow and humble at first, the robot oriented itself quietly, then stretched its screen, checked its wheels, and finally, with an upward gesture toward its telescoping mast, it popped it up slightly, and did a little dance of joy. Sound, motion, and eyes hit every beat together in full choreography.
The character’s output in that sequence was first written as a story. Astro is waking up in its new home for the first time. Its main aspiration is to be part of a family, so this is the moment it has been waiting for, this is its purpose. Being the responsible character that it is, it wants to make sure everything is good to go before it introduces itself and starts learning its new home.
This narrative came first because it drove every other decision that we made. After the story was written, sound gave that story a metaphorical voice: the excited tones, the pacing as it checked its wheels, and the bright melodic phrase as Astro looked up at its new family for the first time and introduced itself. Once the sound was laid down, the animation team did their thing with motion and facial expressions, taking cues from the emotional arc the sound had established. Motion didn’t lead—it followed the feeling of the story and the sounds, the same way an animator follows a recorded vocal take.
That wake-up sequence became one of the most-discussed moments in early user testing. People described it as “alive.” What they were responding to wasn’t any single element. It was all three channels (sound, motion, and facial expressions) expressing the same defined character in harmony.
The most compelling characters are defined not by a fixed disposition but by how they respond to their environments and the people in them. They’re still recognizably themselves even as they adapt. This is what I call contextual character. A robot living in a home doesn’t occupy a single emotional state. It moves through rooms with different energy, encounters people in different moods, operates at different times of day, and responds to an endless range of social situations it was never explicitly designed for.
We got close to a contextual character output with Astro’s sound. When a specific piece of environmental context was fed in, the system adapted beautifully, and Astro felt completely alive. But every state like this was still a prediction we made by hand—a situation we had to imagine in advance and design a response for. A random home throws more situations at a robot than anyone can possibly predict, so there was always a longer tail of moments the system was never prepared for.
The difference between a product people describe as “smart” and one they describe as “aware” often comes down to this. Smartness is capability. Awareness is context. Presence is character. And character is always in reaction to the people around it, to its environment, to its own evolving state. That’s what makes it feel like something is emotionally present with you.
This is where AI changes the game for character design in ways that go well beyond what was possible with Astro. AI-driven adaptation doesn’t require the contextual predictions that we relied on. It learns the specific rhythms, preferences, and emotional context of the people it lives and works with. The character doesn’t just respond to context. It grows into it.
The character and soul of the impending wave of embodied AI products appears to almost always be an afterthought. And character defined late is character defined by default. It becomes the sum of a thousand small decisions made by different people thinking about anything but character. People project character onto devices whether you plan for it or not, especially if those devices move—a robot that moves is already a character. If nobody has designed this character, the result will be products that feel like nothing, or worse, feel confusing and not trustworthy. Technically impressive, but lifeless.
We did not get this fully right with Astro. So many things were moving in parallel that character was rarely treated as a utility, and it made sense why. When you are building a first-of-its-kind product, the things that are the loudest are the ones that break, the deadlines, the costs, the features a customer can point to on a box. Character is quieter than all of that. It’s easy to assume it can come later. On a team as large as the Amazon Astro team, it’s lucky to get any idea onto the road map when it is competing with a hundred others that all feel more urgent in the moment. None of this came from people not caring. It came from character being the kind of thing that is hard to prioritize until you see what its absence costs you.
If you are building a product that will share physical or conversational space with people, three things are worth considering:
Define character before you define interactions. You need a defensible character with enough emotional logic to answer hard questions consistently. Find answers to character questions early, and have every discipline build from the same foundation.
Build story and sound into the character pipeline, not the production pipeline. Story and sound developed alongside character definition has the chance to inform motion, expression, and interaction logic. This requires a different kind of collaboration, and a different kind of hire.
Design for adaptation, not just consistency. A consistent character is necessary, but the products that will matter most in people’s lives are the ones that deepen through use. The infrastructure to support that is more and more accessible, but the design thinking to take advantage of it is still rare.
An expanded version of this story is available on Medium.