Technology has made quite a chatter in schools. Some parents are heading to school board meetings to demand their kids receive minimal screen time. Other parents are finding out that their children are or will be exposed to AI technologies without their consent.

America has a teacher shortage problem that only seems to increase every year. It's a multi-pronged issue that requires a multi-pronged approach to address. Some school districts, however, are tackling the issue in ways that are making both parents and teachers uncomfortable.

Case in point? New Mexico and Portland public schools introduced an AI program called Amira Learning. She's marketed as a reading growth learning agent.

According to the National Assessment of Educational Progress (NAEP), only 33 percent of fourth graders read at or above the proficient level in 2022. School districts across the country have been looking for ways to address this reading crisis while also struggling with the teacher shortage. Amira Learning likely feels like an obvious choice for some school districts. A computer program that promises to increase reading proficiency while freeing up the teacher's time? What could possibly be the issue?

Tech policy expert Ifeoma Ozoma sees plenty. She voiced them in an interview with New Mexico's worker-owned independent civic media co-op, 505omatic.

Ozoma shares, "Amira Learning, it's an AI voice recognition, literacy-assessing program. The state and district have contracted with Amira for kindergarten through second graders, and then third grade, optional. Of the parents that I've spoken to, not one got a notification from their principal or from teachers that their children, five, six, seven, eight years old, would have their voices recorded, and none of them were given the option to opt out."

505omatic clarifies that this is not something parents are even informed of at the beginning of the year. Ozoma confirms that parents are not given any prior notice about Amira or that this AI company has access to their child's voice.

"Not only are they using the software for the beginning, middle, and end-of-year assessments, kids are talking about using it every day. Every other day. Multiple times a day. Multiple times a week. There are many issues with it replacing the engagement that kids should be having with teachers, and what that means about making a case for having fewer teachers, and for not investing more in human resources and in literacy coaches," Ozoma says.

Ozoma's concern about schools reducing the need for human educators may not be without cause. Over in New York, a school district is planning to introduce Sally, a humanoid robot. The humanoid "teacher assistant" is debuting at Salamanca City Central School District in western New York. Sally has silicone skin and long brown hair covering her machinery to help give her a more human look. She can only move her arms, hands, head, and face, not walk around the classroom.

The robot teacher's assistant will be greeting students at Salamanca High School this fall. It's a move that concerns some parents since the high school is on the Seneca Nation reservation, which serves mostly indigenous students, according to New York Focus.

The outlet highlights a response from a concerned parent, Sierra Abrams. "We already have so many issues in our community, including environmental issues," she said. "I just don't understand the concept of adding AI onto that. Bringing it into the school district is contradicting everything we believe, in my opinion."

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If you're looking for a robot companion and you have somewhere between US$ 17,000 and US$ 146,000 burning a hole in your pocket, UBTech Robotics has just what you need. The China-based tech company recently unveiled its UWorld U1 Series, a line of life-sized, hyper-realistic robot with silicone skin and human features.

UBTech is mainly known for commercial and industrial robotics, but these new humanoid robots are meant to offer companionship, including physical assistance, social assistance, lifestyle aesthetics, and emotional support.

The robots are able to recognize 20 emotional states with over 90 percent accuracy through voice, gestures, and interaction, and they are capable of exhibiting up to 90 ppercent of basic human movements.

They are for adults only, which raises some eyebrows, and come in tiers basic (Lite) to high-end (Ultra), hence the price range. The high-end models are 990,000 yuan or roughly US$ 146,000 with the cheapest at 119,800 yuan or US$ 17,600. Pre-orders opened June 30, and the first shipments are expected in September, if the company delivers.

An on-stage presentation mixed the various robotic models and real human actors, meanwhile demos use generative AI to represent the robots. Scenes show an AI model genuinely using a phone — "hit me up if you want to talk" — and holding the off-screen viewer's hand.

Another scene, also GenAI, shows a female model frolicking on a sunny beach with its owner. Although, modern robotics companies are no strangers to odd presentations, earlier this year, Unitree's robots were part of a demonstration that felt like a sci-fi nightmare or fever dream.

Collectively, the U1 Series makes use of an end-to-end technology stack, with physical hardware — like the biomimetic skin and system-level manufacturing under-the-hood — and intelligence hardware, using a proprietary operating system, and emotion-driven large language models (LLMs) for processing.

UBTech claims the intelligence powers a "biomimetic fast-and-slow brain architecture" which draws on cognitive neuroscience to enable extremely fast, 500-millisecond, response times along with deep reasoning and reactivity. There are a lot of buzzwords there, but these robots should be capable of a wide variety of applications from hospitality and customer service to elder care, to education and in-house domestic services.

Though, these aren't the first "humanoid" robots to market, they appear to be some of the first that are intelligently advanced, on the software side. The Walker C and Walker S1 are also full-size humanoid-like robots from the company, albeit meant to provide customized hospitality or commercial services. They straight-up look like robots, however. Another human-like robot that sparks the uncanny valley feel in your home is Moya, from DroidUp. Altogether, China has so many humanoid robots it had to create a unique ID system for them all.

UBTech also introduced its "Human-Robot Companionship Initiative." China has a large population of adults living in isolated conditions, over 90 million adults living alone and 118 million empty-nest seniors. In addition, 10 percent to 20 percent of them suffer from mental health disorders, such as dementia or severe depression, who may require attentive emotional and physical care.

UBTech says: "Combined with multimodal situational awareness, the robots are designed to provide structured psychological support services." The company plans to donate 100 of its U1 Series models to mental well-being programs in 2026.

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It was only a matter of time, but xAI has a new name now that it's under the SpaceX banner. Say hello to SpaceXAI. No, you don't have to give points for creativity to Elon Musk for this one — though it's a pretty obvious change, given the journey xAI has been on over the past several months.

To recap, Musk's SpaceX acquired also-Musk-owned xAI in February. It's a move Musk has become known for in recent years, having merged X (formerly Twitter) into xAI in 2025. Now, nearly all of Musk's various companies (except Tesla) are under the same company, which helped push SpaceX to a massive IPO last month that briefly made Musk a trillionaire.

In any case, SpaceXAI made things official on X this week, debuting a new logo to boot.

We are now @SpaceXAI. pic.twitter.com/ema66xDWC9

— SpaceXAI (@SpaceXAI) July 6, 2026

It's a change that we've known was coming for a little while. In May, Musk said xAI would be "dissolved as a separate company, so it will just be SpaceXAI, the AI products from SpaceX." Now, the combined rocket-slash-AI company is focused on plans to put AI data centers into orbit, with the goal of potentially demonstrating the launch of AI satellites by the end of 2027.

Recently, SpaceX has been showing off a prototype of a "handset-like device" to investors, according to a new report from the Wall Street Journal.

The device is reportedly focused on bringing AI capabilities to users, running on the company's own operating system and integrating xAI's technology to power its AI features. It's been described as having a "sleek design" and being "slimmer than an iPhone." The device is powered by Qualcomm's Snapdragon chipset.

However, Musk weighed in on the Wall Street Journal report, calling it "utterly false" on his social media platform, X, without elaborating any further.

Musk has previously denied reports from earlier this year that SpaceX was working on a phone that would connect directly to Starlink satellites.

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OpenAI has just introduced GPT-5.6, a new family of large language models led by its flagship Sol model, alongside Terra and Luna variants built for different performance and cost requirements. However, the company is limiting the initial rollout to a small group of trusted U.S.-based partners after a request from the U.S. government.

The GPT-5.6 series introduces a new naming system, with Sol representing the highest capability tier, Terra offering GPT-5.5-level performance at half the cost, and Luna targeting lower-cost, faster AI applications. OpenAI said the models will become generally available through ChatGPT, Codex, and its API in the coming weeks.

GPT-5.6 Sol also introduces a new maximum reasoning mode that gives the model more time to solve complex tasks. OpenAI is also launching an Ultra mode that uses subagents to tackle sophisticated workflows beyond the capabilities of a single AI agent.

The company said GPT-5.6 Sol delivers its strongest performance yet in coding, biology, and cybersecurity while introducing its "most robust safety stack to date."

According to OpenAI, GPT-5.6 Sol achieved a new state of the art on TerminalBench 2.1, a benchmark for command-line coding workflows. In biology, the company said the model outperformed GPT-5.5 on GeneBench v1 while using fewer output tokens.

OpenAI also highlighted gains in cybersecurity. On ExploitBench, GPT-5.6 Sol matched the performance of Anthropic’s Mythos Preview while using roughly one-third of the output tokens. On ExploitGym, developed by researchers at UC Berkeley with OpenAI and other frontier AI labs, all three GPT-5.6 models showed improved cyber capabilities as reasoning increased.

Despite those gains, OpenAI said GPT-5.6 Sol does not cross the Cyber Critical threshold under its Preparedness Framework.

"GPT-5.6 Sol is better at helping people find and fix vulnerabilities than reliably carrying out end-to-end attacks," the company said.

The company also introduced a layered safety system that combines model-level protections, real-time misuse detection, account-level monitoring, differentiated access, and extensive automated and human red-teaming. OpenAI said it dedicated more than 700,000 A100-equivalent GPU hours to automated red-teaming to uncover jailbreak techniques before release.

Unlike previous launches, GPT-5.6 will initially be available only to a select group of trusted partners.

"As part of our ongoing engagement with the U.S. government, we previewed our plans and the models’ capabilities ahead of today’s launch," OpenAI said.

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There was a time when it is easy to tell when a face was generated with artificial intelligence (AI). Whether it was a distinctive uncanny sheen, impossibly smooth skin, eyes that didn't quite make sense or a conspicuous third ear, older AI models' facsimiles of human faces were simple to spot and easy to dismiss. That's just not true anymore.

Now, AI image generators can produce portraits so convincing that even careful observers struggle to distinguish fact from figment. That's why apps such as Zoom and Tinder allow their users to submit biometric identification, such as retinal scans, to help prove that a real person exists behind a profile picture. But a new study suggests you can train your brain to get better at spotting fakes.

Past attempts to teach people to spot AI faces have focused on training viewers to look for visual glitches or statistical fingerprints left behind by a particular image generator, such as a wonky ear or an eye with two pupils. The problem is that those clues can disappear with a software update or by simply using a different prompt.

"The AI is getting too good," said Amy Dawel, an associate professor at Australian National University and the lead author on the study, in a press release. "And fraudsters may avoid using pictures with obvious flaws anyway." The result is an endless technological arms race humanity seems destined to lose.

Instead, the researchers taught the participants how to recognize broader patterns in how AI systems generate images. "Our training directs people's attention to global qualities that differ between AI and human faces," Dawel said.

Current AI image generators are themselves trained on datasets composed of millions of images. When prompted to generate a face, they don't copy specific faces, but instead compose a new face that is based in part on the mathematical patterns shared across the faces in that data set—these allow the AI to construct a "typical" human face.

The result is that AI-generated faces often drift toward statistical averages. They're not overly unrealistic, so much as a little too balanced, a little too generic, and a little too conventional. Individually, none of these traits are necessarily suspicious. But together, the whole is blander than the sum of its parts—a subtle banality humans can often implicitly sense.

"Even relatively short training sessions helped participants improve their accuracy," says Tanya George, a student researcher at Australian National University who trained the study's participants. "Research like this can help people navigate increasingly complex online environments."

Compared with real faces, AI-generated faces tend to be more symmetrical, more proportional, and more attractive—while also being less expressive, less distinctive and significantly less memorable. When the researchers trained participants to look for these six markers instead of fleeting artifacts like malformed ears or mismatched jewelry, their ability to spot the AI face almost doubled.

In other words, AI gravitates to the center. Real people do not. Our faces are shaped by countless small deviations from the norm—our subtle asymmetries, distinctive features, and expressions make us memorable. Those imperfections are not flaws. They are our signature.

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Most Robots can now pick up boxes, sort packages, and screw in bolts without breaking a sweat. Some of them can even walk and run like humans. Hand one a floppy, slippery piece of raw salmon, though, and everything starts falling apart.

A team at the Norwegian University of Science and Technology set out to solve that problem. The result is the Sashimi-Bot, a three-armed robot that can prepare sashimi from a raw salmon loin without a chef in sight.

It divides the job neatly between its three arms. The first arm stabilizes and positions the salmon on the cutting board. The second holds a chef's knife and slices. The third picks up each finished slice with chopsticks and transfers it to a serving tray.

What makes this more than just clever arm arrangement is how the robot learned to do it. Lead researcher Sverre Herland and the team trained it using deep reinforcement learning inside a virtual simulation.

The technology let the robot practice thousands of movements and learn through trial and error, without any practice on real fish.

The knife arm also carries a GelSight tactile sensor, a soft gel surface with an embedded camera that tells the robot exactly when it has reached the cutting board.

During testing, the robot cut 34 slices of salmon. It successfully grasped 26 of the 28 slices that fell onto the cutting board with chopsticks. An additional six slices that had stuck to the knife blade were retrieved directly from it.

Each cut cycle averaged 27.9 seconds. The study is published in npj Robotics (via TechXplore). While most robots do best with rigid, predictable objects, the Sashimi-Bot is more significant than its culinary application suggests.

It is an example of robots handling delicate, irregular materials by making real-time movements and adjustments.

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Retail is moving into a new phase where artificial intelligence no longer just advises, but actually acts on it. The rise of agentic AI marks a shift from systems that recommend products or insights to systems that can make decisions and complete tasks on their own within set limits.

In simple terms, agentic AI refers to AI systems that can carry out multi-step actions without constant human prompting.

In simple terms, agentic AI refers to AI systems that can carry out multi-step actions without constant human prompting.

In retail, this includes updating stock levels, adjusting prices, managing supply chain steps, and even completing parts of a customer journey such as product selection or checkout.

The key change is autonomy. AI is moving from being a support tool to becoming an operational actor.

This shift is closely tied to advances in large language models, which now serve as the foundation for more complex AI agents. These systems are increasingly being tested across e-commerce platforms, logistics networks, and marketing operations.

While many applications are still in pilot stages, the direction is consistent across the industry: more tasks are being handed over to automated decision-making systems.

For years, AI in retail has mainly focused on prediction and recommendation. It suggested what customers might buy, or helped businesses forecast demand. Agentic AI goes further by acting on those predictions.

In practical terms, an AI agent can monitor inventory levels across multiple warehouses and trigger restocking when thresholds are reached. It can adjust product listings based on real-time demand signals.

It can also support dynamic pricing, where prices shift in response to supply, demand, and competitor activity.

These systems are being explored in both online and physical retail environments. The goal is to reduce delays between insight and action. Instead of a manager reviewing data and making a decision, the system can execute the response directly, within predefined rules.

Marketing teams are also beginning to use agentic systems to manage campaign performance. AI can test different versions of adverts, adjust targeting, and reallocate budgets based on performance data.

This reduces manual workload and allows faster responses to changing customer behaviour.

One of the most important applications of agentic AI is in supply chain management. Retail supply chains involve many moving parts, including suppliers, warehouses, transport networks and stores. Small delays or forecasting errors can quickly become costly.

Agentic AI systems can track these moving parts in real time. They can identify risks such as low stock, delayed shipments or sudden spikes in demand. In some cases, they can automatically reorder products or reroute logistics flows to reduce disruption.

This type of automation is particularly valuable for global retailers operating across multiple markets. It allows them to respond more quickly to local demand changes and reduce reliance on manual coordination between teams.

However, most retailers are still in early stages of adoption. Current use is typically limited to specific functions rather than full end-to-end autonomy. The focus is on improving accuracy and efficiency while keeping human oversight in place.

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China is now a home to the world's fastest publicly listed supercomputer for the first time since 2017, according to the latest TOP500 ranking released last 23 June.

The system, called LineShine and located at the National Supercomputing Centre in the southern city of Shenzhen, displaced the US machine El Capitan from the top spot.

The TOP500 list, published twice a year, ranks the world's most powerful known supercomputers based on a standardized performance benchmark. The latest edition was unveiled at the International Supercomputing Conference (ISC) in Hamburg.

LineShine achieved a performance of 2.198 exaflops, equivalent to more than 2 quintillion calculations per second. El Capitan, located at the Lawrence Livermore National Laboratory in California, recorded 1.809 exaflops.

Supercomputers are used for applications including climate modelling, materials research, industrial development and artificial intelligence, making the rankings a closely watched indicator in the technology competition between China and the United States.

El Capitan, Frontier and Aurora, all based in the US, occupied the next three positions in the ranking. Germany's Jupiter Booster system at the Jülich Research Centre ranked fifth.

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Researchers have developed a soft robotic gripper that can inspect fruit for ripeness and harvest it without causing damage, a capability that could help reduce food waste and improve harvesting efficiency.

The device uses soft silicone and polyurethane fingers equipped with multiple sensors to measure a fruit’s size, shape, color and firmness before deciding whether it is ready to be picked. The system was tested on strawberries and can remove the fruit by twisting its stem rather than cutting it.

The gripper was developed by a team that includes Anand Mishra, assistant professor in the Department of Mechanical, Materials and Aerospace Engineering at West Virginia University. The research began at Cornell University and is continuing at WVU.

The technology addresses a major challenge in agriculture: harvesting delicate fruits at the right stage of ripeness. Fruits such as strawberries and raspberries have a narrow harvest window and can bruise easily during picking, transportation and storage.

"Our gripper’s quick, accurate inspections and harvesting can reduce spoilage of fruits and lower supply chain costs," Mishra said.

"Fruit inspections are critical for harvesting decisions that have traditionally been made by human workers. However, using human workers for harvesting involves challenges such as labor shortages, health concerns and inaccuracies in picking."

Agricultural robots have been proposed as a solution, but many existing systems are designed for controlled greenhouse environments rather than outdoor farms. Traditional robotic grippers can also damage soft fruit because of their rigid structures.

The new system combines tactile and visual sensing in a soft robotic design. Stretchable optical fibers embedded inside each finger act as tactile and curvature sensors, while a miniature camera and distance sensor are mounted in the palm.

The five-fingered gripper can identify a fruit’s shape, stiffness and ripeness while also detecting slippage during handling. Researchers reported that the device can open and close in less than two seconds, lift loads weighing up to one kilogram and achieve nearly 100 percent accuracy in shape prediction.

According to the team, the technology could be particularly useful for fruits that do not show obvious visual signs of ripeness. Avocados are one example, where growers often rely on touch rather than appearance to determine harvest timing.

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Most leaders believe their teams are using AI as a tool. The research suggests something more consequential is happening. Workers are not just using AI to work faster. They are letting it decide, and in doing so, quietly ceding the human reasoning that determines whether those decisions are any good.

A January 2026 paper from the Wharton School introduced a term for what is now documented and measurable. Researchers Steven Shaw and Gideon Nave call it cognitive surrender.

They define it as adopting AI outputs with minimal scrutiny, thereby overriding both intuition and deliberation. Their framework extends Nobel Prize-winning psychologist Daniel Kahneman's model of fast intuitive thinking and slow deliberate thinking by introducing a third system: artificial cognition that operates entirely outside the brain.

That third system, they argue, can supplement or supplant human reasoning. When it supplants it, AI stops being a thinking partner and starts being the decision-maker.

The performance implications are direct. When workers in the study consulted an AI that was correct, their accuracy rose significantly above what they achieved on their own. When the AI was wrong, their accuracy fell well below the baseline of people who had no AI access at all. The problem is that workers had no reliable way to detect the difference. They accepted incorrect AI answers 80 percent of the time. Their confidence rose either way, whether the AI had helped them or led them astray.

This dynamic is particularly consequential with the large language models now embedded in most workplace tools. LLMs do not retrieve facts. They generate plausible-sounding responses based on patterns in training data, without access to an organization's specific context, strategy, institutional knowledge, or the domain expertise of the person using them. They do not flag uncertainty. They speak with consistent confidence regardless of accuracy.

Getting strong output from an LLM requires a skilled human on the other end: one who validates what it produces, identifies what it missed, expands the ideation beyond the initial response, and applies judgment to decide. Cognitive surrender eliminates every one of those steps.

A Microsoft Research study published in April 2025 found that confidence in AI was among the strongest predictors of whether knowledge workers engaged in critical thinking at all. The higher the trust in the tool, the less scrutiny is applied to what it returned.

As researchers noted in that same study, there is a fundamental irony at the center of automation: when routine cognitive tasks are mechanized and handed to an external system, the human is deprived of the routine practice that builds and sustains judgment. The reps disappear. And so, over time, does the muscle.

A McKinsey "State of Organizations 2026" report, published in February, found that only 23 percent of organizations qualify as AI Pioneers, those actively deploying AI across most departments and functions with a clear understanding of how it will reshape their work. The vast majority are still experimenting, running isolated pilots, or deploying AI in piecemeal ways that have yet to generate measurable enterprise-wide impact.

That gap is the opportunity. Cognitive surrender is not yet the organizational norm. Supplementing human reasoning rather than supplanting it is still a choice leaders can architect into how AI gets deployed. The window to make that choice intentionally, before passive AI reliance becomes the default operating culture, is shorter than most leaders assume.

The structural interventions the research points to are specific. Leaders who build these practices into how AI gets deployed now safeguard their organizations from a culture where AI supplants human reasoning rather than supports it, and from the performance costs that follow.

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President Donald Trump has just signed a new executive order last 2 June that asks artificial intelligence companies to share advanced AI models with the federal government before public release. The move marks the White House’s latest effort to tighten national security coordination around rapidly advancing AI systems without introducing formal licensing requirements.

The order creates a voluntary framework for AI developers to work with federal agencies on evaluating models with advanced cyber capabilities. Under the proposal, companies could provide government officials with access to certain frontier AI models up to 30 days before wider deployment.

Trump signed the order privately after delaying a planned public event with technology executives several weeks ago. At the time, he told reporters he disliked parts of the original proposal.

The White House framed the directive as a balance between accelerating AI innovation and protecting critical infrastructure from cyber threats. Administration officials repeatedly stressed that the order does not establish mandatory government approval for AI releases.

The executive order directs multiple federal agencies to strengthen cyber defenses within 30 days. The Department of Homeland Security (DHS), through the Cybersecurity and Infrastructure Security Agency (CISA), must issue new operational guidance to protect federal networks and critical infrastructure systems.

The administration also ordered agencies to expand AI-driven cybersecurity programs and improve access to defensive tools for state governments, utilities, hospitals, and community banks.

Another section creates a voluntary AI cybersecurity clearinghouse led by the Treasury Department. The initiative will coordinate vulnerability scanning, software patching, and threat detection efforts alongside private industry partners.

Trump’s order repeatedly positions AI as both a national security asset and a growing cyber risk. The administration argues that advanced AI tools could help defend government systems while also creating new attack surfaces for adversaries.

A central part of the order focuses on so-called "covered frontier models," which refer to advanced AI systems with significant cyber capabilities. Federal agencies, including the National Security Agency (NSA) and the National Institute of Standards and Technology (NIST), must develop a classified benchmarking process within 60 days. That process will assess whether a model meets the threshold for additional government review.

Companies could voluntarily ask the government to evaluate models still under development. Developers may also grant federal agencies early access to those systems before releasing them to outside partners. The order additionally allows the government to collaborate with AI companies on selecting "trusted partners" that receive early model access. However, the White House included language aimed at calming industry concerns over federal overreach.

"Nothing in this section shall be construed to authorize the creation of a mandatory governmental licensing, preclearance, or permitting requirement," the order states. That provision reflects ongoing tension between Silicon Valley and Washington over how aggressively the government should regulate frontier AI systems.

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Modern computers are often thought of as operating artificial brains. That said, they’re nowhere close to being as complex or energy-efficient as human brains. AI consumes an enormous amount of electricity, and it’s constantly demanding more as it keeps endlessly morphing and advancing.

How can our power supply catch up to a future of neural networks and digitized intelligence? Well, maybe the answer lies in merging living brain cells with a programmable electronic system.

Previous attempts to use actual neurons as the brain of a computer have run into problems. 2D neural cultures—in which the flattened neurons showed abnormal interactions and gene expression — couldn’t survive for long, and these structures were ultimately unable to replicate the connections and activity that occur in vivo.

More advanced in vitro neural networks have tried to compensate for some of those problems by mirroring the structure and function of the brain with organoids. Despite some improvements, brain organoids (clumps of stem cells engineered to turn into neurons) are inconsistent and prone to both hypoxia and necrosis.

Alternative 3D neural networks known as biological neural networks (BNNs) could still be a viable option. Such a system would ideally take the form of an in vitro model that reconstructs the brain’s networks, can be reproduced, and actually lasts. It would also feature both dense and sparse neural connections (not unlike those in the hippocampus) to prevent too much data from moving around at once.

In an effort to create a fusion of biology and machinery, researchers Tian-Ming Fu, James Sturm, and Kumar Mritunjay from Princeton University used electrodes and microscopic metal wires to create a 3D polymer mesh scaffold flexible enough for tens of thousands of living neurons to grow into a network that could operate with minimal energy.

"Understanding the brain’s network structures and working principles could help in the development of general-purpose computing with improved data and energy efficiencies, as well as provide insights into the brain’s physiology and pathology," the researchers said in a study recently published in Nature Electronics.

Fu, Sturm, and Mritunjay began this experiment to gain more insight into other lingering questions about brain function, but soon saw its potential as a biological neural network, and 3D-MIND (3D Micro-Instrumented Neural network Device) was born. Taking inspiration from origami, the researchers initially created the device in two dimensions, embedding precisely enough electronic sensors to match the soma of a neuron before folding it into 3D layers. Neurons were then integrated into the system. While this hasn’t been done with human neurons yet, rat neurons from the hippocampus—which is critical for learning and memory — were extracted and cultured on the scaffold.

Finally, the entire device was covered in a thin gel coating. Protective and practical, the coating contained proteins that would provide extra support for neurons in forming strong connections with glial cells—cells that not only hold these neural structures together, but supply nutrients, perform immune functions, regulate chemistry, produce the fatty insulation for axons known as myelin, and keep the surrounding environment clear for signaling.

Eventually, the researchers observed neurons positioning themselves and forming connections in three dimensions throughout the structure. These neurons were also stable enough to be tracked for extended periods of time, and the team managed to record growth, development, and action potentials—electrical impulses that neurons use to communicate.

The researchers admit that it will be challenging to scale this system up, but it’s definitely promising, especially when compared to current energy-guzzling AI networks.

"The interfaced system can then provide a physiologically relevant understanding of the brain’s 3D network connectivity," the team wrote. "[It has] the ability to track a 3D neural network [and] could be of use in understanding the efficiency and versatility of the brain’s computational capabilities."

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Artificial intelligence or AI is already reshaping parts of the commercial aviation industry, and in the coming years, airlines are expected to expand AI systems across operations, pricing, customer service, and flight management as they look to cut costs, increase margins, and improve reliability, efficiency, and performance.

First, modern commercial aircraft generate enormous amounts of operational data through onboard sensors that monitor critical systems. Already, airlines are increasingly using AI systems to analyze that data and identify patterns associated with wear and tear or mechanical failure.

AI could help airlines shift maintenance away from reactive repairs—which usually cause ground delays because they are discovered too late—and toward predictive maintenance. We’ve all been there: Everyone boards the flight and things seem all set to go, only to have the captain come over the intercom and announce that there’s maintenance that needs to be done.

Though maintenance will still need to occur, being able to predict it better in advance could help reduce last-minute delays, which tend to be the most frustrating.

Also, many AI-driven changes on this list could benefit the passenger. However, the outlook remains uncertain when it comes to airlines using AI in its ticket pricing methods.

Airline ticket pricing has long relied on algorithms that result in different prices at different times. But, AI is expected to make pricing systems substantially more individualized, incorporating factors such as booking history, travel timing, route demand, loyalty status, and purchasing behavior to tailor fares to specific travelers.

That could mean two passengers searching for the same flight and the same time receive different ticket prices based on what the airline thinks each will pay, based on the aforementioned criteria, as well as the likelihood of purchasing upgrades, checked bags, or premium seating.

While it is easy to see how such a pricing model could benefit the airlines and their profit margins, the concept of personalized pricing raises concerns about transparency and fairness, particularly if airlines don’t reveal the exact data they are collecting and using to determine prices.

For example, if flight purchase history shows you travel at the end of every month to the same place for work (American Airlines, for example, now makes you select Business or Personal travel when booking), can they increase the price, knowing you have to go?

Lastly, Flight cancellations, weather delays, missed connections, and maintenance disruptions generate enormous customer-service demand, and we all know how that can go: Long lines at customer service desks and tortuous time listening to on-hold music on the phone.

AI might be able to help in some cases. Instead of waiting on hold for a call center representative, passengers may increasingly interact with AI assistants capable of automatically rebooking itineraries, issuing hotel or meal vouchers, translating requests across multiple languages, providing real-time updates during irregular hours, etc. The technology could prove particularly valuable during severe weather events, when airline support systems often become overwhelmed.

Faster rebooking and more proactive communication may help reduce some of the frustration traditionally associated with large-scale travel disruptions, although the reduction in human-to-human contact could create its own frustrations, as anyone who has ever called a 1-800 number and had trouble getting through to a human can understand.

Fuel remains one of the airline industry’s largest operating expenses, often accounting for roughly a quarter or more of total costs. That financial pressure, combined with growing scrutiny over aviation emissions, has made fuel efficiency a major area of AI investment.

Airlines are increasingly using AI to optimize flight routing, cruising altitudes, taxi operations, descent procedures, and aircraft loading to make small efficiency gains that could have significant effects across large fleets operating thousands of flights daily.

In theory, that could save the airlines money and perhaps reduce ticket prices, though it’s fair for consumers to be skeptical of the latter.

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The economics of space operations are unforgiving, and Canadian startup Orbit Robotics just unveiled HELIOS — a four-armed humanoid robot that ditches legs entirely for microgravity operations. This isn’t your typical terrestrial humanoid; it’s purpose-built for environments where walking becomes irrelevant, and efficiency matters most.

Cable-driven architecture prioritizes dexterity over terrestrial walking capabilities.

While Earth-bound humanoids like Boston Dynamics’ Atlas perfect their parkour routines, HELIOS takes a fundamentally different approach. You won’t find heavy torque motors or rigid actuators here. Instead, Orbit reportedly built a lightweight skeletal chassis powered by tendon-driven cables and pulleys—like a marionette designed by aerospace engineers.

The four-arm configuration isn’t just showing off. In microgravity, movement relies on grabbing handholds and surfaces rather than walking. Those extra limbs theoretically mean HELIOS can anchor itself with two arms while manipulating cargo or equipment with the other two. Motors supposedly sit near the shoulders to reduce moving mass in the limbs, while rolling-contact elbow joints promise smooth motion without the backlash that plagues traditional hinged systems.

This design philosophy directly challenges the terrestrial humanoid playbook. Companies like Agility Robotics and Sanctuary AI optimize for warehouse floors and factory lines. HELIOS optimizes for floating through space stations, where your greatest asset isn’t balance—it’s the ability to multitask while staying put.

Maintenance duties and cargo handling represent immediate deployment opportunities.

Orbit isn’t building a robot astronaut — they’re building a robotic assistant targeting specific operational inefficiencies. The company claims current crews spend significant time on maintenance tasks that could potentially be automated.

The company’s IKARUS testbed allegedly demonstrates teleoperation and imitation learning capabilities, suggesting HELIOS might learn tasks by watching human operators rather than requiring complex programming. Think of it as motion capture for space work—astronauts demonstrate procedures once, and the robot handles routine repetitions.

Industry observers suggest that humanoid platforms make sense for human-designed environments. When your workplace has racks, handrails, and hatches built for human bodies, a human-shaped robot offers more flexibility than specialized arms like the ISS’s Canadarm2 or Dextre.

The real test isn’t whether four arms work better than two — it’s whether this approach can deliver meaningful cost savings before the next generation of space stations comes online. If orbital operations become as routine as promised, even modest automation could pay for itself quickly in space’s unforgiving economics with significant benefits for workplace safety.

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AI-powered consumer agents that can independently research, compare and order products on behalf of customers are raising serious legal and privacy questions.

While AI-assisted chat advice is already relatively common in online shopping, providers are steadily expanding their agents to cover the shopping itself — all based on customer specifications, but ultimately carried out autonomously.

Amazon, for example, is moving in this direction with Alexa for Shopping (formerly Rufus). And Google recently announced at its I/O developer conference an AI agent that can not only place products from multiple platforms into a single shopping basket, but also make payments on the user's behalf.

When AI-controlled software agents act independently on behalf of individuals or companies — making decisions and even processing purchases and payments — it raises a host of questions. Chief among them: is it actually legal?

Even though the first pilot schemes for fully autonomous AI shopping, including payment, are currently limited to the US, so-called agentic commerce is likely already legally permissible in many countries. However legal experts say there are many unresolved and legally complex questions around liability, contract law, consumer protection and payments.

One thing is clear: the more rights and autonomy AI assistants are given when shopping, the more problematic their use becomes. Critics are therefore calling for decisions to always remain with the human.

Experts at German tech magazine C't experts identify three risk areas around AI shopping agents:

1. Unresolved legal questions
   Who is liable if the AI orders the wrong product or falls for a fraudulent shop?
2. Technology vulnerable to manipulation
   Particularly problematic are the extensive permissions that agents require — such as access to emails, payment systems, calendars or online storage. If those permissions are too broad, a compromised agent could cause significant harm through unwanted purchases, or follow hidden buying instructions on manipulated websites such as fake shops.
3. Data protection problems
   To function effectively, AI agents require extensive information about preferences, context and purchase history.
   This is difficult to reconcile with GDPR principles such as data minimisation, transparency and purpose limitation. Retailers could also use the data to build psychological user profiles and exploit them for price discrimination.
   Consumers should remain sceptical of AI shopping assistants and follow these principles, c't advises:
   • Always confirm purchases manually.
   • Never grant full access to bank accounts.
   • Set spending limits.

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A new study from Monash Business School has revealed that professional advisors feel offended when clients use AI to get a second opinion on their recommendations.

The research, published in Computers in Human Behaviour, found professionals become less motivated to work with clients who consult AI tools. This effect persists even when the client only uses AI for background information, or as a complementary resource rather than a replacement.

"Advisors view AI as substantially inferior to themselves; thus, being placed in the same category as an AI system feels insulting and signals disrespect, undermining advisors' willingness to engage," Associate Professor Gerri Spassova, the lead author, said.

Imagine spending an hour helping a client plan a complex trip, carefully mapping out flights, hotels, and itineraries — only for that client to take your recommendations and book everything through an AI chatbot instead.

Researchers found professionals who lost business to an AI were far less willing to work with that client again in the future.

Clients who consult AI may be seen as less competent and less warm by the advisors they approach for help.

When clients defer to AI, it prompts advisors to question the value of their own human contribution, and this may get worse as AI gets better.

Many advisors take offense at this, and it is the major reason why they pull back from clients who consult AI.

"One can only speculate," Associate Professor Spassova said. "My intuition is that the situation will not get much better. Firstly, because professional advisors’ jobs are on the line.

"Also, as AI gets better, it may threaten our sense of worth and self-regard, and so when clients defer to AI, it would prompt advisors to question the value of their human contribution."

The study suggests for new client advisor relationships, people should not disclose that they consulted AI before the meeting.

A long history of working together might weaken the negative reaction, but even then, the advisor may still feel cheated.

This applies to doctors, lawyers, and other professionals whose expertise clients might fact-check with AI tools.

A doctor who spent years training does not want to be second-guessed by a patient who spent five minutes on ChatGPT.

AI tools usually give a general overview of a situation and are very likely to make mistakes.

Its judgment is highly dependent on the amount of information you supply, and if you are not detailed enough, its response can be misleading.

Also, AI gives responses to questions based on the way it is asked, and users can easily influence an AI tool to tell them what they want to hear.

Considering these nuances, it would be unfair to judge a professional with years of study and experience based on an uncertain tool.

There is absolutely no need to throw it in the face of a professional that you have consulted AI because it creates a sense of "lack of trust".

Until professional norms adjust to the presence of AI, clients would be wise to keep their fact checking private or risk damaging professional relationships.

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AI has arrived, and writing lyrics and composing music will never be the same.

Countless generations of musicians who created things manually would roll over in their graves at the idea of conjuring strings, piano, woodwinds, brass and percussion out of thin air, not to mention the human voice as a timeless vehicle of inspired sound. Music has been a human endeavor – until now.

New tools are blowing older ones out of the water. One such catalyst is called Suno – and it is, frankly, amazing.

Plug in your written lyrics. Write a quick prompt: minor or major key, rhythm, vocal techniques, and press "Create" and a fully formed song springs instantly out of the ether, with the voice of a singer who never lived. Or, don’t write your own lyrics, just tell Suno what you want the song to be about, and the lyrics will just appear, cadenced and scanned perfectly, in verse/chorus form, like the work of an impassioned genie from Tin Pan Alley.

It’s almost, some would say, too easy, but it’s inspired by a real vision of a new world that works differently than what we had in the first quarter of the twenty-first century.

"Some of the most fun I’ve ever had was making music with my friends," Michael Shulman, the creator of Suno, said, detailing how an early version of the app on Discord came out of his realization of how universal music is as a human language.

"Every single person in the world is creative," he said. "Every single person finds enjoyment and fulfillment from making things, and being creative. Everybody loves music."

Suno, he said, is about playing with music, not just playing music. He mentioned early jam sessions in a co-founder’s basement as another part of the impetus for the model that eventually got made.

"They were bad at the beginning," he said, of Suno’s fledgling compositions. "You needed really forgiving ears to really call it music."

But, he noted, people were willing to pay for it, and the thing took off.

Ultimately, Shulman said, most users are just regular people, although the pros are also paying attention.

"We increasingly find that huge numbers of professionals also use the product," he said, citing buy-in from producers as well as songwriters.

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Over the past month, A.I. detection has been at the center of a series of controversies: Hachette pulled the horror novel "Shy Girl" by Mia Ballard after detectors flagged it as substantially A.I.-generated.

The New York Times cut ties with a freelance book critic who admitted that an A.I. editing tool had regurgitated passages from a Guardian article into his draft. The Atlantic reported that a "Modern Love" column had been flagged as more than 60 percent A.I.-generated.

In certain corners of social media, A.I.-detector screenshots are shared like mug shots, and pile-ons have the grim energy of public stonings.

This may all seem understandable—people want to know if what they’re reading was generated by a bot, and some argue they deserve to know. However, such controversy narrows the issue of A.I.’s steady encroachment to one of process, rather than impact.

Drawing a red line around using chatbots to generate prose may make it easier to ignore the way that the technology may be shaping writing before one even types a single word. And a culture of callouts, scandals, and fear may prevent media and publishing from wrestling with much thornier questions of authorship.

At the center of many of these controversies is a company called Pangram, whose CEO, Max Spero, has become the go-to authority when A.I. authorship disputes erupt. On Twitter/X, where Spero calls himself a "slop janitor," a user flagged a Guardian sports journalist’s writing as A.I.-generated. The publication responded that this was "the same style he’s used for 11 years writing for the Guardian, long before LLMs existed. The allegation is preposterous."

Spero quote-tweeted the exchange with a Pangram time-series analysis of 871 articles by the journalist: "It’s clear that he is increasingly relying on AI. In two weeks in February he churned out nine articles classified by Pangram as fully AI-generated. Receipts below."

Or take Pangram’s appearance in the Shy Girl cancellation. Readers on Reddit and YouTube had been flagging the horror novel as suspiciously A.I. for months, but then Spero ran the full manuscript and posted the result (78 percent A.I.-generated). Hachette pulled the book the day the Times piece ran. A story in the Atlantic soon followed. Spero was on LinkedIn, urging publishers to "strictly moderat[e] AI generated content" and "draft and enforce robust AI-use policy."

A pattern emerges: The crowd suspects a problem, then Pangram validates the suspicion, stokes the mob, and sells the solution. The impulse to dismiss all this as a detector company drumming up business runs into an issue—Pangram actually works way better than you might think. Brian Jabarian, a University of Chicago economist who conducted a rigorous independent evaluation of A.I. detectors, told me flatly, "This narrative that we shouldn’t use A.I. detection doesn’t seem to hold anymore."

Jabarian’s preprint, co-authored with Alex Imas and with no disclosed financial ties to the company, tested the tool across nearly 2,000 passages and found near-zero false-positive and false-negative rates on medium-to-long texts, the length of a typical op-ed or a verbose Amazon review.

Independent benchmarks confirm that Pangram outperforms every other detector tested and is robust against "humanizers," or software designed to smuggle A.I. text past detectors. So when Spero posts a time-series chart of hundreds of articles showing when a journalist’s output started sounding fishily like ChatGPT, I am inclined to believe it. That A.I. detection is finally catching up is, on balance, a Good Thing. A.I.-generated articles already far outnumber human ones. Social media is flooded with low-effort slop. According to Pangram’s own research, a fifth of peer reviews submitted to the A.I. research conference ICLR are fully A.I.-generated, and 9 percent of American newspapers contain undisclosed bot use. In this A.I.-powered asphyxiation of the information ecosystem, Spero has positioned himself on social media as a folk hero hauling in the oxygen tanks. You can tag his company’s bot on Twitter/X, and it will tell you whether a post is A.I. On Spero’s social media to-do list: a "slop hunter of the week leaderboard."

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According to the article of Interesting Engineering, the United States is currently using an AI to streamline the nuclear regulatory process. The Department of Energy used AI mapping to convert a safety analysis document required under DOE’s authorization pathway for advanced reactor demonstrations into U.S. Nuclear Regulatory Commission (NRC) licensing documents for commercial deployment.

The DOE revealed that this accomplishment shows the role AI can play in improving the efficiency and accuracy of nuclear technology licensing, and could one day help to accelerate timelines for the commercial deployment of advanced nuclear reactors.

"Now is the time to move boldly on AI-accelerated nuclear energy deployment," said Rian Bahran, Deputy Assistant Secretary for Nuclear Reactors.

"This partnership, combined with the President’s orders, represents more than incremental 'uplift' improvements. It has the potential to transform how industry prepares its regulatory submissions and deploys nuclear energy while upholding the highest standards of safety and compliance."

Everstar’s Gordian AI solution, built on the Microsoft Azure platform, was recently used to convert the Preliminary Documented Safety Analysis for DOE’s National Reactor Innovation Center’s (NRIC) Generic High Temperature Gas Reactor (HTGR) into sections equivalent to an NRC license application, according to a press release.

The DOE revealed that the final 208-page document took one day to generate. Typically, the process takes a team of people between four and six weeks to complete the same task. The AI tool also comprehensively identified missing or incomplete information needed to successfully complete an NRC application.

Gordian was engineered for nuclear-grade technical work and is equipped with physics and engineering tools, as well as the ability to understand and integrate data through semantic ontology mapping, to ensure that the final output is computed and verified, not inferred, according to the DOE.

"Nuclear is poised to solve today’s critical energy challenges," said Kevin Kong, CEO and Founder of Everstar. "We’re excited to partner with INL to meet the moment, working together to accelerate regulatory review and commercialization."

The DOE also revealed that Gordian’s output was subsequently evaluated by an expert for accuracy, missing information, consistency, as well as grammar and structure to ensure that its results were correct and adhered to rigorous professional standards. The output was found to demonstrate quality, rigor, and depth, as well as the tool’s ability to identify and qualify its own gaps in data knowledge.

"Our collaborations with DOE, INL and across the industry are demonstrating how we can effectively bring secure, scalable AI technologies to solve key energy challenges and achieve the broader national and economic security goals envisioned by the Department’s Genesis Mission," said Carmen Krueger, Corporate Vice President, US Federal, Microsoft.

The DOE also highlighted that currently, the nuclear licensing process involves multiple rounds of manual document reviews and minor clerical adjustments, which can take years to complete.

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A Shenzhen-based robotics company LimX Dynamics has officially unveiled its latest humanoid robot called Luna. The robot made its first public physical appearance at the Taobao Influencer Festival, marking the world’s first live showcase of the platform.

The unveiling suggests LimX is expanding beyond purely industrial humanoid robotics toward robots designed for broader public interaction, lifestyle, and commercial environments.

LimX’s previous flagship humanoid, OLI, was known for its industrial metallic silver design and its ability to operate in rugged environments such as construction sites and industrial facilities.

Luna, however, represents a different direction for the company, featuring what LimX describes as a more lifestyle-oriented aesthetic.

During its debut presentation, Luna performed a short catwalk demonstration and executed an illusion turn, a gymnastics-style movement used to demonstrate balance, agility, and motion control.

The demonstration highlighted the robot’s walking stability, joint coordination, and overall movement fluidity rather than industrial task performance.

According to LimX Dynamics, Luna features upgrades to its mechanical configuration and joint system, allowing the humanoid robot to achieve 33 degrees of freedom.

This level of articulation enables more complex movement patterns and a more human-like gait compared to many current-generation humanoid robots.

Although LimX has not yet released a full Luna specification sheet, the company confirmed that the robot is based on the same architecture as its OLI humanoid platform.

According to Origin of Bots, the Luna humanoid stands 165 × 55 × 30 cm and weighs about 55 kg with its battery installed, placing it close to human proportions.

The robot can walk at speeds of up to 5 km/h (1.4 m/s), and the battery system is intended to support extended research and development cycles.

Luna uses dual Intel RealSense D435i depth cameras mounted on the head and chest along with RGB cameras for object recognition and interaction tasks. The robot employs vision–LiDAR fusion and Visual SLAM for navigation in dynamic environments and crowded spaces.

On the OLI platform, LimX uses a computing backpack powered by an NVIDIA AGX Orin chip, while perception computing is handled by an Orin NX module rated at 157 tera operations per second.

The robot operates on a Linux-based software environment using ROS 2 and Python, allowing developers and researchers to build custom interaction scripts, robotics applications, and autonomous behaviors.

LimX Dynamics also stated that Luna is designed for long-term operation and research use, with battery systems intended to support extended development and testing cycles over multiple years.

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