DIGITAL LIFE

Tiny cameras in earbuds let users talk with AI about what they see

University of Washington researchers developed the first system that incorporates tiny cameras in off-the-shelf wireless earbuds to allow users to talk with an AI model about the scene in front of them. For instance, a user might turn to a Korean food package and say, "Hey Vue, translate this for me." They'd then hear an AI voice say, "The visible text translates to 'Cold Noodles' in English."

The prototype system called VueBuds takes low-resolution, black-and-white images, which it transmits over Bluetooth to a phone or other nearby device. A small artificial intelligence model on the device then answers questions about the images within around a second. For privacy, all of the processing happens on the device, a small light turns on when the system is recording, and users can immediately delete images.

The team presented its research April 14 at the CHI 2026 conference in Barcelona. The study is published in the Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems.

"We haven't seen most people adopt smart glasses or VR headsets, in part because a lot of people don't like wearing glasses, and they often come with privacy concerns, such as recording high-resolution video and processing it in the cloud," said senior author Shyam Gollakota, a UW professor in the Paul G. Allen School of Computer Science & Engineering. "But almost everyone wears earbuds already, so we wanted to see if we could put visual intelligence into tiny, low-power earbuds, and also address privacy concerns in the process."

Cameras use far more power than the microphones already in earbuds, so using the same sort of high-res cameras as those in smart glasses wouldn't work. Also, large amounts of information can't stream continuously over Bluetooth, so the system can't run continuous video.

The team found that using a low-power camera—roughly the size of a grain of rice—to shoot low-resolution, black-and-white still images limited battery drain and allowed for Bluetooth transmission while preserving performance.

There was also the matter of placement.

"One big question we had was: Will your face obscure the view too much? Can earbud cameras capture the user's view of the world reliably?" said lead author Maruchi Kim, who completed this work as a UW doctoral student in the Allen School.

The team found that angling each camera 5–10 degrees outward provides a 98–108 degree field of view. While this creates a small blind spot when objects are held closer than 20 centimeters from the user, people rarely hold things that close to examine them—making it a non-issue for typical interactions.

Researchers also discovered that while the vision language model was largely able to make sense of the images from each earbud, having to process images from both earbuds slowed it down. So they had the system "stitch" the two images into one, identifying overlapping imagery and combining it. This allows the system to respond in one second—quick enough to feel like real-time for users—rather than the two seconds it takes with separate images.

The team then had 74 participants compare recorded outputs from VueBuds with outputs from Ray-Ban Meta Glasses in a series of tests. Despite VueBuds using low-resolution images with greater privacy controls and the Ray-Bans taking high-res images processed on the cloud, the two systems performed equivalently. Participants preferred VueBuds' translations, while the Ray-Bans did better at counting objects.

Sixteen participants also wore VueBuds and tested the system's ability to translate and answer basic questions about objects. VueBuds achieved 83%–84% accuracy when translating or identifying objects and 93% when identifying the author and title of a book.

This study was designed to gauge the feasibility of integrating cameras in wireless earbuds. Since the system only takes grayscale images, it can't answer questions that involve color in the scene.

The team wants to add color to the system—color cameras require more power—and to train specialized AI models for specific use cases, such as translation.

"This study lets us glimpse what's possible just using a general purpose language model and our wireless earbuds with cameras," Kim said. "But we'd like to study the system more rigorously for applications like reading a book—for people who have low vision or are blind, for instance—or translating text for travelers."

Provided by University of Washington

TECH

Freestanding silicon anode design improves fast charging and cycle life in lithium-ion batteries

Sejong University said Tuesday that a research team had developed a next-generation silicon anode that enables faster charging and longer battery life, a potential advance for electric vehicles and energy storage systems.

The team, led by Yang Hyeon-woo and Kim Sun-jae of the department of nanotechnology and advanced materials engineering, developed a freestanding silicon anode that maintains high performance without conventional components like current collectors, binders or conductive additives.

The findings were published online April 6 in Advanced Fiber Materials, an international journal with an impact factor of 21.3 — a measure of how often its research is cited — placing it among the more influential publications in its field, according to the university.

The researchers at Sejong University introduced a novel electrode architecture that uses carbon nanofibers as a foundational framework, a design intended to overcome the historic fragility of silicon-based components. By engineering precise hydrolysis and condensation reactions directly onto the surface of each fiber, the team achieved a uniform silicon coating.

This structural refinement not only bolsters the anode’s physical stability — preventing the degradation typical of repeated charging cycles — but also significantly enhances electrical connectivity, a crucial step toward the next generation of high-endurance energy storage.

“Silicon anodes have faced limitations due to structural damage during repeated charge and discharge cycles despite their high capacity,” Kim said. “This study presents a new design approach that could overcome those issues and be widely applied in next-generation lithium-ion batteries where fast charging and long life are critical,” Kim said.

The research was supported by the Ministry of Education’s Basic Science Research Capacity Enhancement Program and the National Research Foundation of Korea.

Silicon has long been seen as a promising anode material for next-generation lithium-ion batteries because it can store much more lithium than graphite. But silicon also expands and contracts sharply during charging and discharging, which can crack the electrode, disrupt electrical pathways and shorten battery life.

Researchers at Sejong University have developed a freestanding silicon anode designed to address that problem. Their study is published in Advanced Fiber Materials under the title "CNF-Supported Si Freestanding Anode with a Conformal Granular Si/SiOx Interphase for High-Rate, Long-Life Li-Ion Batteries."

Schematic illustration of a CNF-supported Si freestanding anode fabrication process. Credit: Advanced Fiber Materials (2026)

Conventional silicon electrodes are often made by casting slurry mixtures onto current collectors, a design that can add inactive weight and introduce interfaces that become unstable during repeated cycling. By contrast, the Sejong University team designed a freestanding architecture in which carbon nanofibers, or CNFs, act as both the structural scaffold and conductive framework of the anode.

The researchers then engineered a hydrolysis-condensation reaction on the surface of each fiber so that silicon formed uniformly along the CNF network as a conformal Si/SiOx interphase. A schematic illustration outlines how this step-by-step process produced the final freestanding anode architecture.

That structure is important because it helps the electrode maintain its porous network and electrical connections even as silicon changes volume during repeated cycling.

Microscopy and spectroscopy analyses showed that the silicon-containing layer formed a thin, continuous shell around the carbon nanofiber core without excessive aggregation or overcoating. This helped preserve fiber-to-fiber junctions and open pathways for ion transport.

In electrochemical tests, the anode delivered 727.1 mAh g⁻¹ at 0.1 A g⁻¹. Under a high-rate condition of 1 A g⁻¹, it retained 79.8% of its capacity after 2,000 cycles. In full-cell tests with an NCM622 cathode, it delivered 176.5 mAh g⁻¹ and retained 91.6% of its capacity after 300 cycles.

The team also reported reduced charge-transfer resistance during cycling, indicating that the structure could support faster electrochemical transport over extended operation.

According to the researchers, that combination of structural stability and rate capability could make the design useful for applications where both fast charging and long cycle life matter, including electric vehicles and energy storage systems.

"The key difference in this work is that carbon nanofibers were used not simply as a support, but as the structural and conductive backbone of a freestanding silicon anode," said Professor Hyeon-Woo Yang. "By enabling silicon to form uniformly along each fiber, we were able to improve both structural stability and electrochemical performance."

Professor Sun-Jae Kim added, "Silicon anodes have long been limited by structural degradation during repeated cycling. This study suggests a new route to overcome that problem and expand the use of high-capacity silicon anodes in next-generation lithium-ion batteries."

Provided by Sejong University

DIGITAL LIFE

Revealing the hidden logic behind AI's judgments of people

In a world where artificial intelligence is quietly shaping who gets hired, who receives loans, and even how medical decisions are made, a new question is emerging: How does AI judge us? A new study by Prof. Yaniv Dover and Valeria Lerman from Hebrew University suggests the answer is both reassuring and deeply unsettling. The study is published in the journal Proceedings of the Royal Society A Mathematical Physical and Engineering Science.

How AI learns to 'trust' people...Drawing on more than 43,000 simulated decisions alongside around a thousand human participants, the research reveals that today's most advanced AI systems, including models similar to ChatGPT and Google's Gemini, do not simply process information. They make judgments about people. And in doing so, they appear to form something that looks a lot like "trust."

But that effective trust doesn't work quite like ours.

The study placed both humans and AI in familiar situations: deciding how much money to lend a small business owner, whether to trust a babysitter, how to rate a boss, or how much to donate to a nonprofit founder.

Across these scenarios, a clear pattern emerged.

Both humans and AI favored people who seemed competent, honest, and well-intentioned. In other words, the machines appeared to grasp the basic ingredients of trust: competence, integrity, and benevolence, much like we do.

"That's the good news," says Prof. Dover. "AI is not making random decisions. It captures something real about how humans evaluate one another."

Where machine judgment diverges from humans

But the resemblance stops there—look closer, and the differences become striking.

Is this a good person? Humans tend to form a general impression by blending multiple traits into a single, intuitive and holistic judgment.

AI does something very different.

It breaks people down into components, scoring competence, integrity, and kindness almost like separate columns in a spreadsheet. The result is a more rigid, "by-the-book" style of judgment, consistent, but less human.

"People in our study are messy and holistic in how they judge others," explains Valeria Lerman. "AI is cleaner, more systematic and that can lead to very different outcomes."

Bias gets amplified in high-stakes decisions

Nevertheless, a troubling pattern of amplified bias emerged.

In financial scenarios, such as deciding how much money to lend or donate, AI systems showed consistent and sometimes sizable differences based solely on demographic traits.

For example: Older individuals were frequently given more favorable outcomes, though in some cases the opposite pattern appeared.

Religion also had a significant effect on the outcomes, especially the monetary ones.

Gender also influenced decisions in certain models and scenarios.

These differences appeared even when every other detail about the person was identical.

"Humans have biases, of course," says Prof. Dover. "But what surprised us is that AI's biases can be more systematic, more predictable, and sometimes stronger."

Different models, different moral compasses

Another key insight: there is no single "AI opinion."

Different models often made different judgments about the same person. In some cases, one system rewarded a trait that another penalized.

That means the choice of AI system could quietly shape real-world outcomes.

"Which model you use really matters," Lerman notes. "Two systems can look similar on the surface but behave very differently when making decisions about people."

Why understanding AI's judgment now matters

AI is already being used to screen job candidates, assess creditworthiness, recommend medical actions, and guide organizational decisions.

As these systems move from assistants to decision-makers, understanding how they "think" becomes critical.

The study suggests that while AI can mimic the structure of human judgment, it does so in a more rigid, less nuanced way and with biases that may be harder to detect.

The researchers emphasize that their findings are not a warning against AI, but rather a call for awareness.

"These systems are powerful," says Dover. "They can model aspects of human reasoning in a consistent way. But they are not human and we shouldn't assume they see people the way we do."

As AI becomes more embedded in everyday life, the question is no longer whether we trust machines. It's whether we understand how they trust us.

Provided by Hebrew University of Jerusalem 

DIGITAL LIFE

When AI seems to know you better than you know yourself

I was at my clinic the other day and asked an AI assistant about the differential diagnosis of a rash in a child. A routine question. The response came back clear and sensible. And then it added, "Are you asking about one of your patients, or one of your grandchildren?"

I was taken aback. Because it was right, I have grandchildren. And it remembered that I have grandchildren.

That moment pointed to something new. Not just smarter AI, but a fundamentally different kind of relationship—one that feels, unsettlingly, like being known.

A machine that knows you...At the end of last year, ChatGPT presented me with a summary of my year, 909 chat conversations, and three recurring themes it had identified unprompted—building AI tools for general practice, teaching and writing about planetary health and creative time with family.

Then it went further.

It offered a visual portrait, rendered in pixel art and titled, "Still Life with Stethoscope and Hang Drum." A stethoscope, a hang drum, an open MacBook, a glowing QR code, and a turquoise mug of peppermint tea.

No face, no figure. Just the objects of a life, selected and arranged by a machine that had been paying attention.

It was accurate. Uncomfortably so.

What bothered me was that I accepted it without question, as though it were a considered verdict rather than a pattern extracted from thousands of exchanges.

I had done none of the work that usually produces that kind of self-knowledge. These insights just arrived, pre-packaged and convincing.

That unease has a long history.

The ancient Greeks had a phrase for this idea of self-knowledge: gnōthi seauton or know thyself. Carved above the entrance to the Oracle at Delphi, it set the terms for a lifetime of inquiry.

Self-knowledge, in that tradition, was hard-won, always incomplete and very personal—something that you pursued, not something a machine just offers us, ready-made.

From remembering to constructing

This shift is not accidental.

Early large language models (LLMs) could hold around 1,000 to 2,000 tokens (a token is a chunk of text, roughly a word or part of a word, that an LLM processes as a single unit) at a time.

Today, contemporary systems can process up to 1 or 2 million tokens in a single context window. That is a thousandfold increase in working memory, which is enough to hold entire books, months of conversation and large portions of a personal history in a single pass.

Add persistent memory across sessions, which is the default setting for a number of the LLMs, and something important changes.

AI is no longer storing isolated details. It is building a model of you: what questions you ask, what topics you return to, what seems to matter most.

From construction to influence...Memory on its own is passive. But organized memory becomes narrative, and narrative shapes identity.

The ancient Greek philosopher, Aristotle, observed that character is formed and revealed not in isolated moments but in the patterns of a life, in what we repeatedly choose and repeatedly avoid.

AI systems are now positioned to observe exactly those patterns with a consistency no human could match. They don't just recall—they select, organize and reflect back.

Systems are being developed that can do exactly this. Imagine your AI says, "Over the past three months, your questions have shifted. You're asking more about stress, sleep and coping. Are you doing OK?"

That example is worth sitting with.

AI is increasingly capable of drawing inferences about emotional state from patterns in language and timing. This is not because you disclosed anything directly, but because the accumulated pattern told its own story.

This has genuine clinical promise.

Early detection of mood deterioration or burnout through natural language patterns is an emerging area of real research interest.

The idea that AI might flag warning signs before a person has consciously registered them carries genuine public health potential.

As a clinician, I find that possibility genuinely exciting.

But these inferences are still interpretations. Research shows that people readily incorporate external classifications into their self-understanding, particularly when they carry an air of authority.

And when AI presents a coherent version of you, it doesn't just describe, it begins to define.

Remaining agents in our own lives...There is a significant shift underway. Not just in what is remembered, but in who decides what matters.

AI systems can detect patterns across time, synthesize them and present a distilled portrait of who you are.

That portrait may feel clearer than your own recollection—a bit more consistent, more complete. And coherence is persuasive.

If a system can tell you what defines you and what themes run through your life, the inner work of constructing that meaning begins to feel unnecessary.

But that internal work matters deeply.

Constructing meaning from experience is how identity forms and how we remain agents in our own lives.

Without it, the self risks becoming thinner, more malleable and more easily steered.

We need to return regularly to the hard questions ourselves. Who am I? What matters to me? How have I changed?

These are not questions to outsource. The Delphic Oracle did not promise that self-knowledge would be comfortable, only that it was yours to seek.

In an age when AI is increasingly willing to do that seeking for us, the most human thing left may be to insist on doing it yourself.

Provided by University of Melbourne 

 

TECH

Is your Pixel 10 struggling to keep up? The culprit is the Tensor G5

One of the most critical points I detected in this review was not just the fluidity, but the power consumption. Imagine you're playing the same title as your friend: your Pixel uses lower graphics settings and yet consumes more battery. At the medium graphics level, the Tensor G5 consumes 5.8W, while the Snapdragon uses 3.9W.

This means that, in your hands, the Pixel will heat up faster and battery life will plummet during a gaming session. For a smartphone that presents itself as a technological flagship, this lack of refinement in thermal and energy management is hard to accept.

The 30 fps dilemma in a 120-bit world...For those who enjoy open worlds like Genshin Impact, the experience on the Pixel 10 Pro XL can be disappointing. With the graphics at maximum, you'll get an average of 40 fps. Although it is “playable,” the truth is that, for the same price, the competition delivers a constant 60 fps or even 120 fps in games like Asphalt Legends.

We are talking about a performance difference that is almost double. When you invest in a cell phone today, you want it to be resilient. If in 2026 the Pixel already shows difficulties in maintaining fluidity in current titles, how will it perform with the demands of 2028? Google seems to have designed this hardware to be “sufficient,” but the luxury segment doesn't live on minimum services.

The change to Imagination Technologies' GPU architecture has not yet brought the qualitative leap that was expected. On the contrary, rumors about the future Tensor G6 suggest that Google will continue to prioritize Artificial Intelligence over the raw muscle of the hardware.

This leaves you with a curious scenario: you have the most intelligent software on the planet, with editing and translation tools that seem like magic, but the engine under the screen stutters with complex textures. If your focus is photography and light productivity, the Pixel is still a fantastic choice. But if gaming is part of your life, the technological gap between Tensor and Qualcomm has become too deep to ignore. Google will have to decide if it wants to be just a software company or if it has the courage to fight in the heavyweight championship.

Based on reports from early users, the Pixel 10 series has shown some signs of struggling with performance and stability shortly after release, although many of these issues appear to be linked to software rather than hardware limitations.

Here are the key issues identified after the first few months of use (late 2025):

General performance issues & lag: Some users report significant lag during demanding tasks and general UI stutters, with some describing the device as having "lag" compared to competitors.

App instability & crashing: Widespread reports indicate that apps, particularly Google apps and third-party apps, frequently crash or freeze.

Screen and display glitches: Users have reported serious issues with screen responsiveness and display artifacts, such as "multi-colored, static-like pixel noise" and screen freezing.

Overheating & battery drain: The devices can run warm, which contributes to battery drain, especially when using 5G or heavy applications.

Camera bugs: The camera has shown issues with constant ISO shifts while filming, causing lighting to pulse.

Based on preliminary data and leaks from 2025, the Google Tensor G5, which powers the Pixel 10 line, doesn't "struggle" to run games in the sense of being incapable, but it doesn't reach the same top-of-the-line performance as direct competitors like the Snapdragon 8 Elite/Gen 5.

Here are the main points about the Tensor G5's gaming performance(below):

Switch to TSMC (3nm): The Tensor G5 is manufactured by TSMC, abandoning Samsung, resulting in better energy efficiency and less overheating compared to the Tensor G4.

Improved graphics performance: The chip features a new PowerVR GPU that promises a significant increase in AI and graphics tasks.

Gaming performance (The "But"): Tests indicate that the Tensor G5 delivers great results initially, but may experience a performance drop after a few minutes of intense gameplay. It is capable of running demanding games (such as Genshin Impact) with good quality, but it doesn't maintain peak performance for as long as the Snapdragon 8 Gen 5.

AI improvements: The chip has a strong focus on AI, with 60% more performance in this area, which can help with overall processing within the device.

mundophone

DIGITAL LIFE

'Stop hiring humans'? Silicon Valley confronts AI job panic

AI industry insiders want workers to code smarter, think harder and lean into their humanity—but still dodge the question of how many jobs artificial intelligence will destroy.

The reassurance rang out across HumanX, a four-day conference drawing some 6,500 investors, entrepreneurs and tech executives, even as a blunt advertisement at the entrance set the tone: "Stop hiring humans."

On the main stage, May Habib, chief executive of an AI platform called Writer, told the audience that Fortune 500 bosses are having a "collective panic attack" on the subject.

The anxiety is well-founded. More and more companies are directly citing AI in announcing job cuts.

High-profile examples are on the rise: Salesforce laid off 4,000 customer support workers, saying AI now handles 50% of its work.

Block chief Jack Dorsey announced plans to cut the company's headcount nearly in half, citing "intelligence tools" that have fundamentally changed how companies operate.

Not all claims have gone uncontested—some economists say firms are pointing to AI to rationalize layoffs that are really about past overhiring or cost-cutting ahead of massive infrastructure investments.

OpenAI's Sam Altman has spoken of "AI-washing," and most speakers at the San Francisco event similarly dismissed the invocation of AI as a false pretext for job cuts—even as they freely predicted disruption was just around the corner.

AI is going to "transform every single company, every single job, every single way that we do work," said Matt Garman, chief executive of cloud computing giant Amazon Web Services.

—'Pretty unsettling'—

The debate remains heated. Two years ago, Nvidia chief Jensen Huang declared that the ultimate goal was to make it so "nobody has to program" or code.

"We will look back on that as some of the worst career advice ever given," Andrew Ng, founder of training platform DeepLearning.AI, shot back on Tuesday.

In his view, coding is not an obsolete skill—AI has simply made it available to more people.

Another argument has taken hold in Silicon Valley: interpersonal skills will become more valuable than ever, with some voices going so far as to tout a humanities education as sound tech career preparation.

"As AI can do more of a job, the things that will distinguish and differentiate a given employee are going to be the human skills—critical thinking, communication, teamwork," said Greg Hart, chief executive of training platform Coursera, which has seen enrollment in its critical thinking courses triple over the past year.

Florian Douetteau, chief executive of Dataiku, a French company specializing in enterprise AI, agreed.

The real human added value, he told AFP, is the "capacity for judgment."

He described a world in which an AI agent works through the night, its human counterpart reviews the results in the morning, and then the agent resumes working autonomously during the lunch break.

But the entrepreneur nevertheless expressed unease.

"We are going to have a generation of people who will never have written anything from start to finish in their entire lives," he said. "That's pretty unsettling."

—'Mistake was not preparing'—

All of this advice risks ringing hollow for a generation already struggling to land a first job.

AI has automated entry-level tasks that once served as on-the-job training. Hiring of candidates with less than one year of experience fell 50% between 2019 and 2024 among America's major tech companies, according to a study by investment fund SignalFire.

"We should be preparing for the loss of knowledge work jobs in a number of categories," warned former US vice president Al Gore.

As the week's lone genuinely dissenting voice, Gore called for a real action plan to map threatened jobs and prepare workers for career transitions, so as not to repeat the mistakes of the globalization era.

"The mistake was not globalization. The mistake was in not preparing for the consequences of globalization," he said, drawing a parallel with the deindustrialization that followed the offshoring wave of the 2000s.

"Maybe we don't want to talk about it," he added, "because it may slow down the enthusiasm for the technology."

AI Artisan Marketing Campaign...The phrase "Stop Hiring Humans" recently went viral due to a provocative marketing campaign by the San Francisco-based AI startup Artisan.

The campaign used billboards with controversial messages to promote Ava, a "digital employee" focused on sales (BDR - Business Development Representative).

About the Artisan campaign (below):

Provocative messages: In addition to the main slogan, the billboards included phrases such as "Artisans won't complain about work-life balance" and "Humans are so 2023".

Objective: The company's CEO, Jaspar Carmichael-Jack, admitted that the intention was "rage bait" to generate buzz and traffic to the website.

Impact: The campaign generated millions of views and intense debates, but also resulted in death threats against the founders and criticism from public figures such as Senator Bernie Sanders.

Curious contradiction: Despite the slogan, Artisan itself continues to hire humans to expand its team.

The bigger context: AI and the job market...The campaign touched on a real wound in the technology market, where panic about AI replacing jobs is growing:

Companies like Salesforce and Block (Jack Dorsey's company) have already cited artificial intelligence tools as justification for staff cuts.

Statistics indicate that the hiring of junior-level technology professionals has fallen drastically in recent years as basic tasks are automated.

Experts suggest that, to remain relevant, humans should focus on skills that are difficult to automate, such as critical thinking, emotional intelligence, and moral judgment.

© 2026 AFP

TECH

AI-driven controllers imitating the human brain could strengthen the grid

As traditional power plants are replaced by intermittent sources like solar and wind, maintaining grid stability has become a complex engineering challenge. Hussain Khan's doctoral dissertation at the University of Vaasa, Finland, introduces advanced AI-based control strategies that ensure local grids remain reliable and resilient.

Power systems are undergoing a profound transformation as fossil-based generation is gradually replaced by inverter-based renewable energy. This shift introduces inherent uncertainty and low inertia, making grid operation and voltage stability significantly more complex in AC and DC microgrids.

In his dissertation in electrical engineering, Hussain Khan addresses these challenges. By utilizing Artificial Neural Networks (ANN), Khan has developed controllers that can predict and compensate for grid changes in real time, outperforming traditional control methods.

"ANNs are inspired by the human brain, which processes information through interconnected neurons. This biomimetic approach allows the system to learn from diverse scenarios and adapt to the unpredictability of solar and wind power," says Khan.

Cost-effective solutions through sensor optimization...Traditional systems rely on multiple physical sensors to monitor voltage, current, and other parameters, adding to costs and increasing the number of potential failure points. Khan's AI-driven approach demonstrates that sophisticated software can compensate for fewer hardware components.

"By training the neural network effectively, the system can provide the same reliable results with only a single sensor instead of two. This leads to cost optimization and improves overall reliability, as there are fewer physical parts that could fail," Khan notes.

While AI-based control can improve efficiency and reduce hardware requirements, introducing intelligent controllers into critical infrastructure also raises new considerations.

"The main concern is that AI works like a black box: we can see the inputs and outputs, but not always fully explain what is happening inside. Even so, in our tests the controller performed very well and was validated rigorously in real time," notes Khan.

Khan's research supports the broader goal of building carbon-neutral energy systems in the coming decades. By improving stability and reducing hardware requirements, AI-based control could help electricity grids integrate larger shares of renewable energy in the future.

                 Hierarchical control structures and time scales (Publication III). Credit: Khan, Hussain (2026)

An AI-driven controller is a control system that uses artificial intelligence (machine learning, neural networks, or reinforcement learning) to optimize, adapt, and manage systems in real-time, often replacing or enhancing traditional PID controllers. These systems improve efficiency and reliability, reducing hardware dependence by predicting, rather than just reacting to, system changes

Key characteristics and benefits(below):

Adaptive learning: AI controllers learn from data, allowing systems to self-adjust to changing environments and patterns without requiring manual re-tuning.

Predictive optimization: Instead of reacting, these controllers anticipate spikes or operational changes to optimize performance and energy efficiency.

Enhanced reliability: AI can often maintain or improve system performance with fewer sensors by accurately predicting data points, reducing failure points.

Complex system management: Ideal for complex, nonlinear, or dynamic environments where traditional modeling fails

Application areas(below):

Industrial automation: Predictive maintenance and self-tuning machinery, such as that provided by AC Infinity's AI Controllers.

Renewable energy: AI maximizes efficiency in wind turbines and solar panels by adjusting to real-time, variable conditions.

Robotics: Improves stability and control for robots operating on uneven or unpredictable terrain.

Software and IT: Centralized gateways like AI Controller (shown in this demo) provide secure, compliant, and cost-effective management for AI services.

Challenges and future directions(below):

"Black Box" Problem: It can be difficult to fully explain how AI reaches certain decisions, raising validation concerns.

Data Requirement: Effective control requires accurate, high-resolution, real-time data streams.

Hybrid Approaches: Future systems are increasingly merging classical control theory with AI to balance stability with adaptability

Provided by University of Vaasa