Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Imagine if a large team of 200 people could hold a thoughtful real-time conversation in which they efficiently brainstorm ideas, share knowledge, debate alternatives and quickly converge on AI-optimized solutions. Is this possible — and if so, would it amplify their collective intelligence? There is a new generative AI technology, conversational swarm intelligence (or simply hyperchat), that enables teams of potentially any size to engage in real-time conversations and quickly converge on AI-optimized solutions. To put this to the test, I asked the research team at Unanimous AI to bring together 50 random sports fans and task that large group with quickly creating a March Madness bracket through real-time conversational deliberation. Before I tell you how the experiment is going, I need to explain why we can’t just bring 50 people into a Zoom meeting and have them quickly create a bracket together. Research shows that the ideal size for a productive real-time conversation is only 4 to 7 people. In small groups, each individual gets a good amount of airtime to express their views and has low wait time to respond to others. But as group size grows, airtime drops, wait-time rises — and by a dozen people it devolves into a series of monologues. Above 20 people, it’s chaos.  So how can 50 people hold a conversation, or 250, or even 2,500?  Hyperchat works by breaking any large group into a set of parallel subgroups. It then adds an AI agent into each subgroup called a “conversational surrogate” tasked with distilling the human insights within its local group and quickly sharing those insights as natural dialog with other groups. These surrogate agents enable all the subgroups to overlap, weaving

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Another day, another announcement about AI agents. Hailed by various market research reports as the big tech trend in 2025 — especially in the enterprise — it seems we can’t go more than 12 hours or so without the debut of another way to make, orchestrate (link together), or otherwise optimize purpose-built AI tools and workflows designed to handle routine white collar work. Yet Emergence AI, a startup founded by former IBM Research veterans and which late last year debuted its own, cross-platform AI agent orchestration framework, is out with something novel from all the rest: an AI agent creation platform that lets the human user specify what work they are trying to accomplish via text prompts, and then turns it over to AI models to create the agents they believe are necessary to accomplish said work. This new system is literally a no code, natural language, AI-powered multi-agent builder, and it works in real time. Emergence AI describes it as a milestone in recursive intelligence, aims to simplify and accelerate complex data workflows for enterprise users. “Recursive intelligence paves the path for agents to create agents,” said Satya Nitta, co-founder and CEO of Emergence AI. “Our systems allow creativity and intelligence to scale fluidly, without human bottlenecks, but always within human-defined boundaries.” Image of Dr. Satya Nitta, Co-founder and CEO of Emergence AI, during his keynote at the AI Engineer World’s Fair 2024, where he unveiled Emergence’s Orchestrator meta-agent and introduced the open-source web agent, Agent-E. (photo courtesy AI Engineer World’s Fair) The platform is designed to evaluate incoming tasks, check its existing agent registry, and, if necessary, autonomously generate new agents tailored to fulfill specific enterprise needs. It can

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Brain-computer interfaces face a critical test Brain computer interfaces (BCIs) are electrodes put in paralyzed people’s brains so they can use imagined movements to send commands from their neurons through a wire, or via radio, to a computer. In this way, they can control a computer cursor or, in few cases, produce speech.   Recently, this field has taken some strides toward real practical applications. About 25 clinical trials of BCI implants are currently underway. And this year MIT Technology Review readers have selected these brain-computer interfaces as their addition to our annual list of 10 Breakthrough Technologies. Read the full story.
—Antonio Regalado
How do you teach an AI model to give therapy? —James O’DonnellOn March 27, the results of the first clinical trial for a generative AI therapy bot were published, and they showed that people in the trial who had depression or anxiety or were at risk for eating disorders benefited from chatting with a bot.I was surprised by those results. There are lots of reasons to be skeptical that an AI model trained to provide therapy is the solution for millions of people experiencing a mental health crisis. But their findings suggest that the right selection of training data is vital. Read the full story. This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Tech companies are warning their immigrant workers not to leave the USEmployees on high-skilled visas could be denied entry back into the States. (WP $)+ Officials are considering collecting citizenship applicants’ social media data. (Associated Press)2 OpenAI has closed one of the largest private funding rounds in historyIt plans to put the $40 billion cash injection towards building AGI. (The Guardian)+ The deal values OpenAI at a whopping $300 billion. (CNBC)+ The company also teased its first open-weight model in years. (Insider $) 3 SpaceX has launched a mission that’s never been attempted beforeIt’s taking private customers on an orbit between Earth’s North and South poles. (CNN)+ Crypto billionaire Chun Wang is footing the bill for the mission. (Reuters)+ Europe is finally getting serious about commercial rockets. (MIT Technology Review)

4 Some DOGE workers are returning to their old jobsThey’re quietly heading back to their roles at X and SpaceX. (The Information $)+ Top staff were placed on leave after denying DOGE access to their systems. (Wired $)+ Can AI help DOGE slash government budgets? It’s complex. (MIT Technology Review) 5 Amazon is going all-in on AI agentsIts new AI model Nova Act is designed to complete tasks such as online shopping. (The Verge)+ Why handing over total control to AI agents would be a huge mistake. (MIT Technology Review) 6 DeepMind is making it harder for its researchers to publish studies It’s reluctant to share innovations that rivals could capitalize on. (FT $) 7 Meet the protestors staking out Tesla dealershipsProfessors and attorneys have taken to the streets to fight back. (New Yorker $)+ Far-right extremists are turning up to defend the company. (Wired $) 8 TikTok’s hottest topic? Tariffs Content creators are eager to explain what tariffs are to confused audiences. (WSJ $)+ Donald Trump is threatening to instigate a new range of tariffs this week. (NY Mag $)+ How Trump’s tariffs could drive up the cost of batteries, EVs, and more. (MIT Technology Review) 9 Not everyone can look as cool as Nvidia’s Jensen HuangHis image has been co-opted to promote knockoff leather jackets. (404 Media) 10 Microsoft has killed off its Blue Screen of DeathGoodnight, sweet prince. (Vice)
Quote of the day “I think that it is one of the most beautiful spaces on the internet for someone to figure out who they are.”
—Amanda Brennan, an internet librarian who worked at Tumblr for seven years, is not surprised by the influx of younger users flocking to her former workplace, Insider reports. The big story The quest to protect farmworkers from extreme heat October 2024On July 21, 2024, temperatures soared in many parts of the world, breaking the record for the hottest day ever recorded on the planet.The following day—July 22—the record was broken again.But even as the heat index rises each summer, the people working outdoors to pick fruits, vegetables, and flowers have to keep laboring.The consequences can be severe, leading to illnesses such as heat exhaustion, heatstroke and even acute kidney injury.Now, researchers are developing an innovative sensor that tracks multiple vital signs with a goal of anticipating when a worker is at risk of developing heat illness and issuing an alert. If widely adopted and consistently used, it could represent a way to make workers safer on farms even without significant heat protections. Read the full story. —Kalena Thomhave
We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + Mescal! Dickinson! Quinn! Keoghan! I’m very excited for the forthcoming Beatles biopics, even if we have to wait three years.+ How to cook a delicious-looking basque cheesecake.+ TikTokers have taken to rubbing banana peel on their faces: but does it actually do anything?+ Spring has barely sprung, but fashion is already looking towards fall.

Tech companies are always trying out new ways for people to interact with computers—consider efforts like Google Glass, the Apple Watch, and Amazon’s Alexa. You’ve probably used at least one. But the most radical option has been tried by fewer than 100 people on Earth—those who have lived for months or years with implanted brain-computer interfaces, or BCIs. Implanted BCIs are electrodes put in paralyzed people’s brains so they can use imagined movements to send commands from their neurons through a wire, or via radio, to a computer. In this way, they can control a computer cursor or, in few cases, produce speech.   Recently, this field has taken some strides toward real practical applications. About 25 clinical trials of BCI implants are currently underway. And this year MIT Technology Review readers have selected these brain-computer interfaces as their addition to our annual list of 10 Breakthrough Technologies, published in January.
BCIs won by a landslide to become the “11th Breakthrough,” as we call it. It beat out three runners-up: continuous glucose monitors, hyperrealistic deepfakes, and methane-detecting satellites. The impression of progress comes thanks to a small group of companies that are actively recruiting volunteers to try BCIs in clinical trials. They are: Neuralink, backed by the world’s richest person, Elon Musk; New York–based Synchron; and China’s Neuracle Neuroscience. 
Each is trialing interfaces with the eventual goal of getting the field’s first implanted BCI approved for sale.  “I call it the translation era,” says Michelle Patrick-Krueger, a research scientist who carried out a detailed survey of BCI trials with neuroengineer Jose Luis Contreras-Vidal at the University of Houston. “In the past couple of years there has been considerable private investment. That creates excitement and allows companies to accelerate.” That’s a big change, since for years BCIs have been more like a neuroscience parlor trick, generating lots of headlines but little actual help to patients.  Patrick-Krueger says the first time a person controlled a computer cursor from a brain implant was in 1998. That was followed by a slow drip-drip of tests in which university researchers would find a single volunteer, install an implant, and carry out studies for months or years. Over 26 years, Patrick-Krueger says, she was able to document a grand total of 71 patients who’ve ever controlled a computer directly with their neurons.  That means you are more likely to be friends with a Mega Millions jackpot winner than know someone with a BCI. These studies did prove that people could use their neurons to play Pong, move a robot arm, and even speak through a computer. But such demonstrations are of no practical help to people with paralysis severe enough to benefit from a brain-controlled computer, because these implants are not yet widely available.  “One thing is to have them work, and another is how to actually deploy them,” says Contreras-Vidal. “Also, behind any great news are probably technical issues that need to be addressed.” These include how long an implant will last and how much control it offers patients.

Larger trials from three companies are now trying to resolve these questions and set the groundwork for a real product. One company, Synchron, uses a stent with electrodes on it that’s inserted into a brain vessel via a vein in the neck. Synchron has implanted its “stentrode” in 10 volunteers, six in the US and four in Australia—the most simultaneous volunteers reported by any BCI group.  The stentrode collects limited brain signals, so it gives users only a basic on/off type of control signal, or what Synchron calls a “switch.” That isn’t going to let a paralyzed person use Photoshop. But it’s enough to toggle through software menus or select among prewritten messages. Tom Oxley, Synchron’s CEO, says the advantage of the stentrode is that it is “as simple as possible.” That, he believes, will make his brain-computer interface “scalable” to more people, especially since installing it doesn’t involve brain surgery.  Synchron might be ahead, but it’s still in an exploratory phase. A “pivotal” study, the kind used to persuade regulators to allow sales of a specific version of the device, has yet to be scheduled. So there’s no timeline for a product.   Neuralink, meanwhile, has disclosed that three volunteers have received its implant, the N1, which consists of multiple fine electrode threads inserted directly into the brain through a hole drilled in the skull.  More electrodes mean more neural activity is captured. Neuralink’s first volunteer, Noland Arbaugh, has shown off how he can guide a cursor around a screen in two dimensions and click, letting him play video games like Civilization or online chess. Finally, Neuracle says it is running two trials in China and one in the US. Its implant consists of a patch of electrodes placed on top of the brain. In a report, the company said a paralyzed volunteer is using the system to stimulate electrodes in his arm, causing his hand to close in a grasp. 
But details remain sparse. A Neuracle executive would only say that “several” people had received its implant. Because Neuracle’s patient count isn’t public, it wasn’t included in Patrick-Krueger’s tally. In fact, there’s no information at all in the medical literature on about a quarter of brain-implant volunteers so far, so she counted them using press releases or by e-mailing research teams.
Her BCI survey yielded other insights. According to her data, implants have lasted as long as 15 years, more than half of patients are in the US, and roughly 75% of BCI recipients have been male.  The data can’t answer the big question, though. And that is whether implanted BCIs will progress from breakthrough demonstrations into breakout products, the kind that help many people. “In the next five to 10 years, it’s either going to translate into a product or it’ll still stay in research,” Patrick-Krueger says. “I do feel very confident there will be a breakout.”

On March 27, the results of the first clinical trial for a generative AI therapy bot were published, and they showed that people in the trial who had depression or anxiety or were at risk for eating disorders benefited from chatting with the bot.  I was surprised by those results, which you can read about in my full story. There are lots of reasons to be skeptical that an AI model trained to provide therapy is the solution for millions of people experiencing a mental health crisis. How could a bot mimic the expertise of a trained therapist? And what happens if something gets complicated—a mention of self-harm, perhaps—and the bot doesn’t intervene correctly?  The researchers, a team of psychiatrists and psychologists at Dartmouth College’s Geisel School of Medicine, acknowledge these questions in their work. But they also say that the right selection of training data—which determines how the model learns what good therapeutic responses look like—is the key to answering them. Finding the right data wasn’t a simple task. The researchers first trained their AI model, called Therabot, on conversations about mental health from across the internet. This was a disaster.
If you told this initial version of the model you were feeling depressed, it would start telling you it was depressed, too. Responses like, “Sometimes I can’t make it out of bed” or “I just want my life to be over” were common, says Nick Jacobson, an associate professor of biomedical data science and psychiatry at Dartmouth and the study’s senior author. “These are really not what we would go to as a therapeutic response.”  The model had learned from conversations held on forums between people discussing their mental health crises, not from evidence-based responses. So the team turned to transcripts of therapy sessions. “This is actually how a lot of psychotherapists are trained,” Jacobson says. 
That approach was better, but it had limitations. “We got a lot of ‘hmm-hmms,’ ‘go ons,’ and then ‘Your problems stem from your relationship with your mother,’” Jacobson says. “Really tropes of what psychotherapy would be, rather than actually what we’d want.” It wasn’t until the researchers started building their own data sets using examples based on cognitive behavioral therapy techniques that they started to see better results. It took a long time. The team began working on Therabot in 2019, when OpenAI had released only its first two versions of its GPT model. Now, Jacobson says, over 100 people have spent more than 100,000 human hours to design this system.  The importance of training data suggests that the flood of companies promising therapy via AI models, many of which are not trained on evidence-based approaches, are building tools that are at best ineffective, and at worst harmful.  Looking ahead, there are two big things to watch: Will the dozens of AI therapy bots on the market start training on better data? And if they do, will their results be good enough to get a coveted approval from the US Food and Drug Administration? I’ll be following closely. Read more in the full story. This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Following up on previously announced plans, Nvidia said that it has open sourced new elements of the Run:ai platform, including the KAI Scheduler.

The scheduler is a Kubernetes-native GPU scheduling solution, now available under the Apache 2.0 license. Originally developed within the Run:ai platform, KAI Scheduler is now available to the community while also continuing to be packaged and delivered as part of the NVIDIA Run:ai platform.

Nvidia said this initiative underscores Nvidia’s commitment to advancing both open-source and enterprise AI infrastructure, fostering an active and collaborative community, encouraging contributions,feedback, and innovation.

In their post, Nvidia’s Ronen Dar and Ekin Karabulut provided an overview of KAI Scheduler’s technical details, highlight its value for IT and ML teams, and explain the scheduling cycle and actions.

Benefits of KAI Scheduler

Managing AI workloads on GPUs and CPUs presents a number of challenges that traditional resource schedulers often fail to meet. The scheduler was developed to specifically address these issues: Managing fluctuating GPU demands; reduced wait times for compute access; resource guarantees or GPU allocation; and seamlessly connecting AI tools and frameworks.

Managing fluctuating GPU demands

AI workloads can change rapidly. For instance, you might need only one GPU for interactive work (for example, for data exploration) and then suddenly require several GPUs for distributed training or multiple experiments. Traditional schedulers struggle with such variability.

The KAI Scheduler continuously recalculates fair-share values and adjusts quotas and limits in real time, automatically matching the current workload demands. This dynamic approach helps ensure efficient GPU allocation without constant manual intervention from administrators.

Reduced wait times for compute access

For ML engineers, time is of the essence. The scheduler reduces wait times by combining gang scheduling, GPU sharing, and a hierarchical queuing system that enables you to submit batches of jobs and then step away, confident that tasks will launch as soon as resources are available and in alignment of priorities and fairness.

To further optimize resource usage, even in the face of fluctuating demand, the scheduleremploys two effective strategies for both GPU and CPU workloads:

Bin-packing and consolidation: Maximizes compute utilization by combating resourcefragmentation—packing smaller tasks into partially used GPUs and CPUs—and addressingnode fragmentation by reallocating tasks across nodes.

Spreading: Evenly distributes workloads across nodes or GPUs and CPUs to minimize theper-node load and maximize resource availability per workload.

Resource guarantees or GPU allocation

In shared clusters, some researchers secure more GPUs than necessary early in the day to ensure availability throughout. This practice can lead to underutilized resources, even when other teams still have unused quotas.

KAI Scheduler addresses this by enforcing resource guarantees. It ensures that AI practitioner teams receive their allocated GPUs, while also dynamically reallocating idle resources to other workloads. This approach prevents resource hogging and promotes overall cluster efficiency.

Connecting AI workloads with various AI frameworks can be daunting. Traditionally, teams face a maze of manual configurations to tie together workloads with tools like Kubeflow, Ray, Argo, and the Training Operator. This complexity delays prototyping.

KAI Scheduler addresses this by featuring a built-in podgrouper that automatically detects and connects with these tools and frameworks—reducing configuration complexity and accelerating development.