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AI agents have evolved beyond passive chatbots.

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. We’ve entered the era of AI malaise AI is spreading everywhere, and it is not going away. But what will it do? What effect will it have on our society? Will it make life better, or worse? How will we know? What’s the plan? This technology may very well take our jobs—or just crash the economy instead. Our apps are all getting injections of AI, like it or not. And it is increasingly impossible to tell whether we are relying too much on AI or not using it enough. We’re all sitting uncomfortably with AI right now. Read our essay on the strange, uncertain mood of the moment. The era of AI malaise is an essay written by our editor-in-chief Mat Honan. It accompanies MIT Technology Review’s 10 Things That Matter in AI Right Now, our list of the big ideas, trends, and advances in the field that are driving progress today—and will shape what’s possible tomorrow. Here’s how technology transformed babymaking Technology is changing the way we make babies. Clinicians have improved hormonal treatments. Embryologists have devised ways to culture embryos in the lab for longer. IVF clinics today offer multiple genetic tests for embryos. The technology has also had a huge social impact, allowing for changes in the structure of families and providing more reproductive choices for would-be parents. Now, AI and robots are set to usher in another new era for IVF. Here’s how technology is reshaping babymaking. —Jessica Hamzelou This story is from The Checkup, our weekly newsletter giving you the inside track on all things biotech. Sign up to receive it in your inbox every Thursday. How robots learn: a brief, contemporary history For decades, researchers have been inspired by science fiction robots that can move through the world, adapt to different environments, and interact with people. But bringing these devices into the messiness of the real world has proved incredibly difficult. Now, advances in AI are changing that. Instead of relying on rigid rules, robots are learning through trial and error, simulations, and huge amounts of real-world data. The progress represents a revolution in how machines interact with their surroundings. It also means that Silicon Valley roboticists are dreaming big again. Here’s how we got here.  —James O’Donnell This story is from the latest issue of our print magazine, which is all about nature. Subscribe now to read it in full. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 ICE plans to develop its own smart glassesThe “ICE Glasses” would identify people in real time. (404 Media)+ ICE already uses an app with facial recognition to track citizens. (NYT $)+ A new lawsuit wants to stop ICE using DNA to track critics. (Ars Technica) 2 AI is distorting key economic signalsIt makes growth look better and the job market look worse. (WSJ $)+ Welcome to the economic singularity. (MIT Technology Review) 3 A cyberattack paralyzed thousands of schoolsAnd stole 275 million people’s data from edtech platform Canvas. (NYT $)+ The digital learning software is used across the US. (CNN)+ It’s the worst case scenario from an attack on one education platform. (Wired $) 4 The US suspects Nvidia chips were smuggled to Alibaba via ThailandSuper Micro servers containing Nvidia chips were allegedly smuggled. (Bloomberg $)+ Through a firm linked to Thailand’s national AI initiative. (Reuters $) 5 China’s affordable AI models are increasingly worrying Silicon ValleyThey’re often cheaper and more adaptable than US rivals. (Bloomberg $)+ China is betting big on open source. (MIT Technology Review) 6 Scientists developed a new energy storage system inspired by sunburnIt stores solar energy by mimicking molecular changes in damaged DNA. (BBC)+ Solar and wind with battery storage are becoming cost-competitive. (Reuters $)+ Here are three other breakthrough climate technologies. (MIT Technology Review) 7 Russia’s internet crackdown is hobbling small businessesApp restrictions and internet outages are causing headaches. (Reuters $) 8 Younger researchers are more likely to produce “disruptive” scienceA new study found more experience led to fewer breakthroughs. (Nature) 9 Why Richard Dawkins was mistaken to believe Claude has feelingsBut his line of inquiry wasn’t altogether foolish. (The Atlantic $)+ Why it’ll be hard to tell if AI ever becomes conscious. (MIT Technology Review) 10 The Golden Globes have new AI rules (and they’re looser than the Oscars’)AI is permitted as an enhancement, but not as a replacement. (Gizmodo)+ Last week, the Oscars banned AI actors and writing. (NPR) Quote of the day “When I am talking to these astonishing creatures, I totally forget that they are machines. I treat them exactly as I would treat a very intelligent friend.”  —Evolutionary biologist Richard Dawkins reflects on his interactions with advanced AI systems in an essay published in Unherd. One More Thing VIRGINIA HANUSIK How to stop a state from sinking In a 10-month span in 2020 and 2021, southwest Louisiana saw five climate-related disasters, including two destructive hurricanes and flash floods. But there could be a better way to protect the area: elevation. The $6.8 billion Southwest Coastal Louisiana Project is betting that raising buildings while restoring coastal boundary lands that have long acted as natural barriers can preserve this slice of coastline.  Here’s how officials hope to protect vulnerable communities by lifting homes out of the floodplain. —Xander Peters 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.) + Soaking in a hot tub might offer even more health benefits than a sauna.+ A court has officially protected America’s largest rainforest from future logging.+ Experience the majesty of the world’s largest owl collection through these intimate, high-detail portraits.+ A dad has turned his toddler’s random stories into high-production pop songs that are surprisingly catchy.

Evaluating AI models trained on brain signals has long been a messy, inconsistent topic. Different research groups use different preprocessing pipelines, train models on different datasets, and report results on a narrow set of tasks — making it nearly impossible to know which model actually works best, or for what. A new framework from Meta AI team is designed to fix that. Meta Researchers have released NeuralBench, a unified, open-source framework for benchmarking AI models of brain activity. Its first release, NeuralBench-EEG v1.0, is the largest open benchmark of its kind: 36 downstream tasks, 94 datasets, 9,478 subjects, 13,603 hours of electroencephalography (EEG) data, and 14 deep learning architectures evaluated under a single standardized interface. https://ai.meta.com/research/publications/neuralbench-a-unifying-framework-to-benchmark-neuroai-models/ The Problem NeuralBench Solves The broader field of NeuroAI where deep learning meets neuroscience has exploded in recent years. Self-supervised learning techniques originally developed for language, speech and images are now being adapted to build brain foundation models: large models pretrained on unlabeled brain recordings and fine-tuned for downstream tasks ranging from clinical seizure detection to decoding what a person is seeing or hearing. But the evaluation landscape has been badly fragmented. Existing benchmarks like MOABB cover up to 148 brain-computer interfacing (BCI) datasets but limit evaluation to just 5 downstream tasks. Other efforts — EEG-Bench, EEG-FM-Bench, AdaBrain-Bench — are each constrained in their own ways. For modalities like magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), there is no systematic benchmark at all. The result — claims about foundation models being “generalizable” or “foundational” often rest on cherry-picked tasks with no common reference point. What is NeuralBench? NeuralBench is built on three core Python packages that form a modular pipeline. NeuralFetch handles dataset acquisition, pulling curated data from public repositories including OpenNeuro, DANDI, and NEMAR. NeuralSet prepares data as PyTorch-ready dataloaders, wrapping existing neuroscience tools like MNE-Python and nilearn for preprocessing, and HuggingFace for extracting stimulus embeddings (for tasks involving images, speech, or text). NeuralTrain provides modular training code built on PyTorch-Lightning, Pydantic, and the exca execution and caching library. Once installed via pip install neuralbench, the framework is controlled via a command-line interface (CLI). Running a task is as simple as three commands: download the data, prepare the cache, and execute. Every task is configured through a lightweight YAML file that specifies the data source, train/validation/test splits, preprocessing steps, target processing, training hyperparameters, and evaluation metrics. https://ai.meta.com/research/publications/neuralbench-a-unifying-framework-to-benchmark-neuroai-models/ What NeuralBench-EEG v1.0 Covers The first release focuses on EEG and spans eight task categories: cognitive decoding (image, sentence, speech, typing, video, and word decoding), brain-computer interfacing (BCI), evoked responses, clinical tasks, internal state, sleep, phenotyping, and miscellaneous. Three classes of models are compared: Task-specific architectures (~1.5K–4.2M parameters, trained from scratch): ShallowFBCSPNet, Deep4Net, EEGNet, BDTCN, ATCNet, EEGConformer, SimpleConvTimeAgg, and CTNet. EEG foundation models (~3.2M–157.1M parameters, pretrained and fine-tuned): BENDR, LaBraM, BIOT, CBraMod, LUNA, and REVE. Handcrafted feature baselines: sklearn-style pipelines using symmetric positive definite (SPD) matrix representations fed into logistic or Ridge regression. All foundation models are fine-tuned end-to-end using a shared training recipe — AdamW optimizer, learning rate of 10⁻⁴, weight decay of 0.05, cosine-annealing with 10% warmup, up to 50 epochs with early stopping (patience=10). The sole exception is BENDR, for which the learning rate is lowered to 10⁻⁵ and gradient clipping is applied at 0.5 to obtain stable learning curves. This intentional standardization otherwise removes model-specific optimization tricks — such as layer-wise learning rate decay, two-stage probing, or LoRA — so that architecture and pretraining methodology are what actually gets evaluated. Data splitting is handled differently per task type to reflect real-world generalization constraints: predefined splits where provided by dataset research team, leave-concept-out for cognitive decoding tasks (all subjects seen in training, but a held-out set of stimuli used for testing), cross-subject splits for most clinical and BCI tasks, and within-subject splits for datasets with very few participants. Each model is trained three times per task using three different random seeds. Evaluation metrics are standardized by task type: balanced accuracy for binary and multiclass classification, macro F1-score for multilabel classification, Pearson correlation for regression, and top-5 accuracy for retrieval tasks. All results are additionally reported as normalized scores (s̃), where 0 corresponds to dummy-level performance and 1 corresponds to perfect performance, enabling fair cross-task comparisons regardless of metric scale. One important methodological note: some EEG foundation models were pretrained on datasets that overlap with NeuralBench’s downstream evaluation sets. Rather than discarding these results, the benchmark flags them with hashed bars in result figures so readers can identify potential pretraining data leakage — no strong trend suggesting leakage inflates performance was observed, but the transparency is preserved. The benchmark offers two variants: NeuralBench-EEG-Core v1.0, which uses a single representative dataset per task for broad coverage, and NeuralBench-EEG-Full v1.0, which expands to up to 24 datasets per task to study within-task variability across recording hardware, labs, and subject populations. A Kendall’s τ of 0.926 (p < 0.001) between Core and Full rankings confirms that the Core variant is a reliable proxy — though a few model positions do shift, including CTNet overtaking LUNA when more datasets are included. https://ai.meta.com/research/publications/neuralbench-a-unifying-framework-to-benchmark-neuroai-models/ Two Key Findings Finding 1: Foundation models only marginally outperform task-specific models. The top-ranked models overall are REVE (69.2M parameters, mean normalized rank 0.20), LaBraM (5.8M, rank 0.21), and LUNA (40.4M, rank 0.30). But several task-specific models trained from scratch — CTNet (150K parameters, rank 0.32), SimpleConvTimeAgg (4.2M, rank 0.35), and Deep4Net (146K, rank 0.43) — trail closely behind. CTNet actually overtakes the LUNA foundation model to rank third in the Full variant, despite having roughly 270× fewer parameters. This shows the gap between task-specific and foundation models is narrow enough that expanding dataset coverage alone is sufficient to change global rankings. Finding 2: Many tasks remain genuinely hard. Cognitive decoding tasks — recovering dense representations of images, speech, sentences, video, or words from brain activity — are particularly challenging, with even the best models scoring well below ceiling. Tasks like mental imagery, sleep arousal, psychopathology decoding, and cross-subject motor imagery and P300 classification

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Forty-eight years ago this July, Louise Joy Brown became the world’s first person born with the help of in vitro fertilization. Millions more IVF babies have entered the world since then. And that’s partly thanks to advances in technology that have made IVF safer and more effective. But it’s still not perfect. The process can be slow, painful, and expensive—and that’s for the lucky people who are able to access it in the first place. And by at least one measure, IVF success rates have been declining in recent years. Reproduction is complex, and there’s a lot that embryologists and gynecologists still don’t know and can’t control. They don’t know why many healthy-looking embryos don’t “stick” in the uterus, for example. They don’t always have an explanation for why their patients can’t get pregnant. And they can’t always account for vast differences in IVF success rates between individuals and between fertility clinics. Scientists are working on all those questions and more. They’re wrestling with complex ethical questions about how new genetic tools will be used to analyze or even alter embryos. Meanwhile, technologies designed to standardize treatment, eliminate human error, boost success rates, and make IVF more accessible are already beginning to usher in a new era for assisted reproduction—one aided by AI and robots. 1. Helping embryos stick Some of those technologies are being developed at the Carlos Simon Foundation in Valencia, Spain. When I visited in March, researchers gave me a tour of the labs and showed me a device that had been used to keep a human uterus alive outside the body for the first time. While some members of the team dream of building artificial uteruses that might one day be able to carry a fetus to term, they first want to use such devices to learn more about implantation—the moment at which a fertilized egg makes contact with the lining of the uterus, burrows inside, and essentially “hatches,” triggering the start of a pregnancy. Despite decades of advances in IVF, that process is still poorly understood. Even healthy-looking embryos stick no more than 40% to 60% of the time. In IVF techniques used today, clinics can create early-stage embryos and wait until the uterus is deemed most receptive, but once they insert the embryo into the uterus, it’s on its own. Xavier Santamaria, senior clinical scientist at the Carlos Simon Foundation, and his colleagues are trialing a different approach. They’ve developed a device that, at the press of a button, injects the embryo into the uterine lining. JESS HAMZELOU / MITTR In a demonstration I watched with a prototype, Santamaria picked up his speculum and turned to face the vaginal opening of his “patient,” which in this case was just a model of the real thing—a plastic bottom with labia, a vagina, a uterus, and ovaries, two short stumps representing what would normally be a pair of legs held in stirrups. He hunched over and peered inside. “Embryo,” he called. His colleague Maria Pardo, an embryologist, passed him a thin needle containing a mouse embryo she had recently collected from a petri dish. Santamaria’s device allows for the embryo-containing needle to be connected to a delivery tube. This tube also has a camera, a light, and a sensor that lets the doctor know when the needle reaches the uterine lining. Once it has been fed into the uterus, the gynecologist can see the inside of the organ and direct the tube to the lining. JESS HAMZELOU / MITTR “When everything is ready, you just press the button,” Santamaria said as he activated it using a foot pedal, allowing the embryo to be injected. “There it goes.” The team has just started a trial of the device; so far, fewer than 10 women have undergone the procedure, and none of those have become pregnant. But foundation director Carlos Simon is hopeful, noting that the inventors of IVF had to perform over 160 cycles before Louise Brown was born (between 1969 and 1978, that team performed 457 cycles in 250 people, resulting in only two live births). “The trial is ongoing,” he says. 2. Picking the “best” eggs, sperm, and embryos One long-running challenge of IVF has been selection. Say you manage to collect 10 eggs from one partner and a decent-looking semen sample from the other. How do you choose which cells to use? The same question comes up once the resulting embryos have been cultured in a dish for a few days: Which should you transfer to the uterus? Traditionally, these judgments have been made by eye. Embryologists literally pick the ones that look the best in terms of their shape or, in the case of sperm, how they move. But scientists have been working on alternatives. And over the last decade or so, many have turned to genetic testing to hint at which embryos have the best chances of creating a healthy baby. The most commonly used test is called PGT-A, which stands for preimplantation genetic testing for aneuploidy. Aneuploidy essentially means having an “incorrect” number of chromosomes, and it is thought that embryos with such characteristics are more likely to be lost through miscarriage or potentially develop into babies with genetic conditions. Once embryologists have created embryos in the lab, they can pinch off a few cells and test them for aneuploidies. The tests are especially beneficial for women over the age of 38, says Alan Penzias, a reproductive endocrinologist at Boston IVF. “You start to see an improvement: more babies and fewer miscarriages,” he says. The tests can shorten the time to pregnancy. This type of genetic testing is possible thanks to multiple advances in technology—not just in genomics, but also in the ability to keep embryos alive in a dish for five to six days and the technique of freezing embryos while

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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. What’s next for IVF IVF has brought millions of babies into the world over the last four decades. But the process can still be slow, painful, and expensive—and far from guaranteed to work. Now, a wave of new technologies aims to change that.  Researchers are using AI to identify promising sperm and embryos, developing robotic systems that could automate parts of the IVF process, and even exploring controversial genetic editing techniques designed to prevent inherited disease. The technologies could make IVF more effective and accessible. But they’re also raising difficult ethical questions about how far reproductive medicine should go. Find out what’s next for IVF. —Jessica Hamzelou This story is from MIT Technology Review’s What’s Next series, which looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. The balcony solar boom is coming to the US Dozens of US states are considering legislation to allow people to install plug-in solar systems, often called balcony solar. These small arrays require little to no setup and could help cut emissions and power bills. Proponents say the systems could make solar power more accessible, but some experts caution that there are safety concerns.  Read the full story on balcony solar’s potentially massive impact in the US. —Casey Crownhart This article is from The Spark, our weekly climate newsletter. Sign up to receive it in your inbox every Wednesday. Resistance: 10 Things That Matter in AI Right Now Resistance against AI’s proliferation is growing. People from all walks of life are speaking out against rising electricity bills from data centers, disappearing jobs, chatbots’ impact on teen mental health, the military’s use of AI, and copyright infringement—among other concerns.  People want to have a say in how the technology transforms their future. And they’re starting to create small cracks in AI labs’ vision for the future. Find out how. —Michelle Kim Resistance is on our list of the 10 Things That Matter in AI Right Now, MIT Technology Review’s guide to what’s really worth your attention in the buzzy world of AI.  The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 After years of insults, Anthropic and SpaceX have teamed upAnthropic will tap SpaceX’s GPUs to meet surging demand. (Axios)+ While SpaceX gets a marquee customer for its AI ambitions. (Wired $)+ Anthropic says the deal will double Claude Code’s rate limits. (Ars Technica)+It’s also exploring building compute capacity in space. (CNBC)+ Musk previously called Anthropic “evil” and “misanthropic.” (Gizmodo) 2 Ex-OpenAI leaders say Sam Altman sowed “chaos” and distrustFormer CTO Mira Murati said she couldn’t trust his words. (The Verge)+ He also bypassed OpenAI’s safety board before a model release. (Gizmodo)+ And pitted leaders against one another. (Forbes)+ But Elon Musk still tried to recruit Altman to lead a Tesla AI lab. (FT $)+ Here’s why Musk and Altman are in court. (MIT Technology Review) 3 China’s humanoid robots are fueling its next export boomMorgan Stanley says Beijing has taken an early lead in the sector. (Bloomberg $)+ Gig workers are training humanoids at home. (MIT Technology Review) 4 SpaceX’s IPO plans will give Elon Musk “virtually unchecked” authorityAnd erode typical shareholder protections. (Reuters $)+ Activists and pension funds are pushing back against the IPO. (Wired $)+ While SpaceX is shifting focus from Falcon 9 to Starship. (Ars Technica) 5 Google DeepMind will use the MMORPG Eve Online for AI model testingIt’s also bought a stake in the game’s maker. (Ars Technica)+ DeepMind also recently built a new video-game-playing agent. (MIT Technology Review) 6 The US risks isolating its automakers by banning a Chinese EV standardIt’s prohibiting software that’s dominating global EV markets. (Rest of World) 7 Elon Musk’s proposed Texas chip factory could cost $119 billionIt would manufacture chips for Tesla, SpaceX, and xAI. (CNBC)+ Future AI chips could be built on glass. (MIT Technology Review) 8 Why the “attention-span crisis” is misunderstoodTechnology may be exhausting attention rather than shortening it. (Atlantic $) 9 Scientists are getting closer to explaining what causes lightningNew tools are revealing unexpected physics inside thunderstorms. (Quanta) 10 Kids have found an age verification loophole: fake mustachesResourceful children are foiling blocks on adult websites. (TechCrunch) Quote of the day “My concern was about Sam saying one thing to one person and completely the opposite to another person.” —Mira Murati, the former CTO of OpenAI, testifies ‌in court that CEO Sam Altman was deceptive, Reuters reports. One More Thing ALAMY A brief, weird history of brainwashing During the Cold War, the US prepared for a psychic war with the Soviet Union and China by spending millions of dollars on research into manipulating the human brain.  The science never exactly panned out, but residual beliefs fostered by this bizarre conflict continue to play a role in ideological and scientific debates to this day. And now, new technologies are altering how we think about mind control.  This is how the race for mind control changed America forever. —Annalee Newitz 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.) + Listen to the 10 bird songs of spring in this lovely compilation of American species.+ Good Samaritans saved a 29-foot whale that had wandered too far into a river.+ Explore the intersection of human emotion and machine learning in this look at AI’s influence on art.+ Break down the walls between streaming services and manage all your digital music in one place with this app.