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. By putting AI into everything, Google wants to make it invisible If you want to know where AI is headed, this year’s Google I/O has you covered. The company’s annual showcase of next-gen products, which kicked off yesterday, has all of the pomp and pizzazz, the sizzle reels and celebrity walk-ons, that you’d expect from a multimillion dollar marketing event. But it also shows us just how fast this still-experimental technology is being subsumed into a line-up designed to sell phones and subscription tiers. Never before have I seen this thing we call artificial intelligence appear so normal. Read the full story.—Will Douglas Heaven AI could keep us dependent on natural gas for decades to come Last December, Meta announced plans to build a massive $10 billion data center for training its artificial intelligence models in rural northeast Louisiana. Stretching for more than a mile, it will be Meta’s largest in the world, and it will have an enormous appetite for electricity. To power the data center, a Meta contractor called Entergy will build three large natural-gas power plants with a total capacity of 2.3 gigawatts. It’ll also upgrade the grid to accommodate the huge jump in anticipated demand.The choice of natural gas as the go-to solution to meet the growing demand for power from AI is not unique to Louisiana. The fossil fuel is already the country’s chief source of electricity generation, and large natural-gas plants are being built around the country to feed electricity to new and planned AI data centers. That’s all but wiping out any prospect that the US will wean itself off natural gas anytime soon. Read the full story.—David Rotman This story is part of Power Hungry: AI and our energy future—our new series shining a light on AI’s energy usage. Check out the rest of the package here. Take a new look at AI’s energy use Big Tech's appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does a single AI query use? And what does it mean for the climate?  Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O'Donnell at 1.30pm ET today for a subscriber-only Roundtables conversation digging into our new package of stories about AI's energy demands now and in the future. Register here.The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Democrats are on the hunt for a digital thought leader They’re (finally) realizing how far they’re lagging behind their opponents’ online efforts these days. (NYT $) + AI’s impact on elections is being overblown. (MIT Technology Review)2 At least two newspapers printed an AI-generated summer reading list 📰 The only problem is, some of the books don’t actually exist. (404 Media)+ It’s a useful reminder to never take anything chatbots produce as fact. (Axios)+ Even regional newspapers aren’t safe from AI slop. (The Atlantic $)+ Why AI hallucinates, and why we can’t stop it. (MIT Technology Review)3 The Earth may already be too hot to maintain polar ice sheetsEven if it stays at current temperature levels. (WP $)+ Why climate researchers are taking the temperature of mountain snow. (MIT Technology Review)4 How New York City’s child abuse algorithm flags families for investigation Critics believe it’s open to racial bias. (The Markup) 5 Here’s what it’s like to interview for a job at DOGE   The hiring process is remarkably fast, for a government entity. (Wired $)+ The department reportedly tried to enter the US government’s publishing operation. (Politico)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review)6 Fortnite has finally returned to Apple’s App StoreAfter five years and a lengthy legal battle. (NYT $) + The recent ruling has major implications for the iOS economy. (Reuters)7 Most chatbots can be tricked into dispensing dangerous informationFrom hacking advice, to describing how to make drugs. (The Guardian) + Anthropic has a new way to protect large language models against jailbreaks. (MIT Technology Review)8 Young Indonesians are being trafficked to scam farmsFraudulent job ads on Telegram and Facebook lure them into a life of crime. (Rest of World) + Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review) 9  Inside the building in China where stolen western iPhones are stripped and sold You’ll find a buyer for every single component inside the Feiyang Times. (FT $)10 Amazon has started randomly refunding customers for old purchasesSome orders were placed as far back as 2018. (Bloomberg $) Quote of the day “Anybody who’s a computer scientist should not be retired right now. They should be working on AI.” —Google cofounder Sergey Brin says people with the right technical skills should copy him and quit being retired, TechCrunch reports. One more thing This fuel plant will use agricultural waste to combat climate change A startup called Mote plans to build a new type of fuel-producing plant in California’s fertile Central Valley that would, if it works as hoped, continually capture and bury carbon dioxide.It’s among a growing number of efforts to commercialize a concept first proposed two decades ago as a means of combating climate change, known as bioenergy with carbon capture and sequestration, or BECCS.It’s an ambitious plan. However, there are serious challenges to doing BECCS affordably and in ways that reliably suck down significant levels of carbon dioxide. Read the full story.—James Temple 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.) + These creepy little Labubu toys are everywhere. But why?+ Happy 25th birthday to one of London’s finest institutions, the Tate Modern gallery.+ Why the Mission Impossible film franchise just won’t die.+ Hummingbirds can fly backwards!? Wow.

An artist’s impression of Earth’s quasi-satellite Kamo`oalewa, the first destination of the Tianwen-2 missionAddy Graham/University of Arizona Final preparations are under way for China to launch an uncrewed craft to visit both an asteroid and a comet, in the hope of learning more about the space rocks in our solar system. The Tianwen-2 mission by the China National Space Administration (CNSA) will collect a 100-gram sample from the asteroid Kamoʻoalewa and return it to Earth. After dropping off the sample, the probe will use our planet’s gravity as a slingshot Advertisement The mission is due to launch from the Xichang Satellite Launch Center in Sichuan province on 29 May. It won’t be the first to return samples of asteroids to Earth, as both NASA’s OSIRIS-REx and JAXA’s Hayabusa missions have already done that. But it will be China’s first mission to an asteroid involving the return of a rock sample, and it is likely to be the first mission to a unique type of body called a quasi-satellite. Quasi-satellites like Kamoʻoalewa don’t strictly orbit Earth, but travel in a similar orbit to us around the sun, swinging elliptically around our planet as they do so. This unusual situation has led scientists to suspect that this particular one is a chunk of the moon ejected millions of years ago by an asteroid impact. On the other hand, 311P/PanSTARRS has an asteroid-like orbit – spinning around our sun in the asteroid belt between Mars and Jupiter – but with an appearance more like a comet because it has tails. These are suspected of being bits of dust and rubble flung out from its spinning body. Voyage across the galaxy and beyond with our space newsletter every month. Sign up to newsletter The CNSA has previously said that 311P/PanSTARRS is a “living fossil”, making it useful for studying the early material composition, formation process and evolutionary history of the solar system. And Tianwen-2 will provide scientists with a better understanding of both Kamoʻoalewa and 311P/PanSTARRS. However, the results won’t come quickly: the craft is due to reach 311P/PanSTARRS in 2034, and even the Kamoʻoalewa sample is expected to return to Earth only in late 2027. Exactly how much the CNSA will share about the discoveries is also unclear. Leah-Nani Alconcel at the University of Birmingham, UK, says the mission’s outline is known, and one likely goal is to study the differences between the asteroid and the comet to gain a deeper understanding of the range of bodies in our solar system, but precise details haven’t been forthcoming. Alconcel’s previous experience working with the CNSA on the Double Star satellite leads her to suspect that the agency will hold on to the resulting scientific data tightly. “It was extremely difficult to negotiate [with the CNSA],” says Alconcel.
”Once they kind of had some information from us, they were not very keen to reciprocate. There will not be a public repository of this data, I don’t think.” She says that the mission is daring, as Kamoʻoalewa is spinning, which will make landing harder. Navigation algorithms are likely to demand such powerful computers that images and sensor readings will be sent back to Earth for computation. “If we were to always pick lovely, cooperative objects, we wouldn’t learn a lot,” she says. “There’s a lot that could potentially go wrong.” The CNSA didn’t respond to New Scientist‘s request for interview. Topics:

Ming - stock.adobe.com Opinion UK Fraud Bill targets benefit claimants for mass surveillance The UK government’s proposed Fraud Bill will disproportionately place millions of benefit claimants under constant surveillance, creating a two-tier system where people are automatically suspected of wrongdoing for seeking welfare By Anna Dent Published: 21 May 2025 Earlier this month the House of Lords had its first debate on the Public Authorities (Fraud, Error and Recovery) Bill.  The Bill aims to reduce government losses to benefit fraud and error, and would give the Department for Work and Pensions (DWP) unprecedented powers of investigation to routinely and covertly check benefit claimants' bank accounts, the right to enter private homes, and to seize drivers' licenses or money from bank accounts. Expert organisations including Justice, the Public Law Project and Big Brother Watch warn that although the aim of the Bill is hugely important, the proposed powers are disproportionate and represent a breach of human rights. As Baroness Finn remarked in the Lords, "support for the goal must not mean silence about the means." The Bill promises an expanded regime of digital surveillance for people in receipt of Universal Credit, Employment and Support Allowance, and State Pension Credit. This includes the introduction of an 'Eligibility Verification Measure' (EVM) which would enable DWP to direct banks to check millions of bank accounts for as yet unspecified indicators of benefit fraud and error.  DWP already has similar powers but crucially can only use them where it has 'reasonable grounds' to suspect fraud is taking place, which is the standard threshold for many comparable state powers. This Bill would completely remove that threshold, and enable intrusive surveillance without any justification. Details on exactly how the EVM will be used are sparse, but it could theoretically result in every single claimant’s bank account being checked, with no suspicion or indication of any fraud, error or over-payment needed.  Accounts flagged through these checks would then be passed to a member of DWP staff for further investigation, but what this will involve is still unclear. A code of practice to accompany the Bill is still unpublished, so we don't actually know what sequence of events would play out. Will individuals be informed when they are being investigated? Will their benefits be suspended during an investigation? Will their claim be assessed by other fraud algorithms? In some circumstances, the Department could automatically take money from the bank accounts of people no longer on benefits if they are deemed to have committed fraud or been accidentally overpaid. Banks will be prevented from informing their customers that this recovery process is happening, so the first that a customer might know is when money disappears from their account.  The finance industry has expressed concern about tensions between the obligations the Bill would create and financial institutions' consumer obligations, the risks of financial harm to vulnerable customers, and the lack of robust safeguards for the transfer of banking data. Others are concerned about the potential for miscarriages of justice which many will be unable to effectively challenge: the benefits covered by the Bill are only available to people on a low income, who are unlikely to have the means to engage in legal action. These powers would apply to both fraud and error, including over-payments caused by the Department's own mistakes. Overpayments can be caused by myriad mistakes on the part of DWP or claimant, or both, and the complexity of making claims is well known. But under the Bill, fraud and error will be treated the same, with the same digital surveillance deployed and the same powers to seize money and other assets. Universal Credit already appears to be particularly prone to mistakes and over-payments. Rather than punishing people after the fact, it would be better for DWP to work out why it is particularly vulnerable, do more to help people avoid mistakes, and reduce the rate at which their own mistakes cause over-payments. Treating all claimants like intentional fraudsters stigmatises people on benefits, and risks people who need financial support disengaging from the benefit system. Rather than focus on professional criminals who are actively defrauding the Department, the bill scoops everyone up and places them all under suspicion. It creates a two-tier system in which benefit claimants would be subject to an invasion of privacy not applied to the rest of the population: millions of people who have done nothing wrong will be laid open to mass surveillance. DWP officers would be given police-like powers of entry to private premises and seizure of private property. This is an extraordinary expansion of very serious powers targeted at one section of society. It fundamentally undermines basic rights to privacy and to be presumed innocent until proven otherwise.  Whether the ends justify these heavy-handed means is doubtful. The Department's own impact assessment estimates that just 2% of social security fraud and error over-payments will be clawed back through the use of these powers over 10 years: a disproportionate invasion of privacy for little benefit. The finance industry also warns that the organised gangs who carry out large-scale benefit fraud using sophisticated methods to avoid detection will easily find ways to work around the new powers.  Also on its way through the legislative process is the Data Use and Access Bill, which proposes to reduce the requirement for human oversight of automated decisions within government. If both Bills pass into law as currently drafted, there is a potential future in which the human-in-the-loop safeguards relating to digital bank surveillance are no longer legally required. This leaves the door open to hugely consequential decisions being made entirely by automated systems. We know that existing DWP fraud algorithms generate bias; the introduction of more automation and digital surveillance and the potential for less human oversight should trouble us all.  Increasing the number of people that are flagged for investigation via these new powers could put thousands more through the difficult process of fraud investigation. DWP’s own figures show that three-quarters of people whose benefit claims are flagged as suspicious actually have no fraud or error related to their claim at all. Ten million people receive the benefits that will be covered by the new powers: if even 1% of claimants are wrongly investigated many thousands of people will be affected.  As well as the immediate impact of these disproportionate powers, politicians need to be aware of the precedents they are setting, and how the legislation could be used by other administrations in future. Eliminating the need for the government to justify wholesale digital surveillance removes a basic protection against state over-reach. Only the state pension is explicitly excluded from the powers in the Bill: a government with an even more single-minded obsession with efficiency and fraud could roll out the bank surveillance to many, many other people. What will affect a minority now could become the norm for the majority. Anna Dent is an independent researcher and policy consultant, working on the digital welfare state and human-centred technology Read more about digital surveillance UK government outlines plan to surveil migrants with eVisa data: Electronic visa data and biometric technologies will be used by the UK’s immigration enforcement authorities to surveil migrants living in the country and to ‘tighten control of the border’, attracting strong criticism from migrant support groups. AI surveillance towers place migrants in ‘even greater jeopardy’: The use of autonomous surveillance towers throughout the English coast forces migrants into increasingly dangerous routes and contributes to their criminalisation. Invasive tracking ‘endemic’ on sensitive support websites: Websites set up by police, charities and universities to help people get support for sensitive issues like addiction and sexual harassment are deploying tracking technologies that harvest information without proper consent. In The Current Issue: UK critical systems at risk from ‘digital divide’ created by AI threats UK at risk of Russian cyber and physical attacks as Ukraine seeks peace deal Standard Chartered grounds AI ambitions in data governance Download Current Issue Microsoft entices developers to build more Windows AI apps – Cliff Saran's Enterprise blog Red Hat launches llm-d community & project – Open Source Insider View All Blogs

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. By putting AI into everything, Google wants to make it invisible If you want to know where AI is headed, this year’s Google I/O has you covered. The company’s annual showcase of next-gen products, which kicked off yesterday, has all of the pomp and pizzazz, the sizzle reels and celebrity walk-ons, that you’d expect from a multimillion dollar marketing event. But it also shows us just how fast this still-experimental technology is being subsumed into a line-up designed to sell phones and subscription tiers. Never before have I seen this thing we call artificial intelligence appear so normal. Read the full story.—Will Douglas Heaven AI could keep us dependent on natural gas for decades to come Last December, Meta announced plans to build a massive $10 billion data center for training its artificial intelligence models in rural northeast Louisiana. Stretching for more than a mile, it will be Meta’s largest in the world, and it will have an enormous appetite for electricity. To power the data center, a Meta contractor called Entergy will build three large natural-gas power plants with a total capacity of 2.3 gigawatts. It’ll also upgrade the grid to accommodate the huge jump in anticipated demand.The choice of natural gas as the go-to solution to meet the growing demand for power from AI is not unique to Louisiana. The fossil fuel is already the country’s chief source of electricity generation, and large natural-gas plants are being built around the country to feed electricity to new and planned AI data centers. That’s all but wiping out any prospect that the US will wean itself off natural gas anytime soon. Read the full story.—David Rotman This story is part of Power Hungry: AI and our energy future—our new series shining a light on AI’s energy usage. Check out the rest of the package here. Take a new look at AI’s energy use Big Tech's appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does a single AI query use? And what does it mean for the climate?  Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O'Donnell at 1.30pm ET today for a subscriber-only Roundtables conversation digging into our new package of stories about AI's energy demands now and in the future. Register here.The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Democrats are on the hunt for a digital thought leader They’re (finally) realizing how far they’re lagging behind their opponents’ online efforts these days. (NYT $) + AI’s impact on elections is being overblown. (MIT Technology Review)2 At least two newspapers printed an AI-generated summer reading list 📰 The only problem is, some of the books don’t actually exist. (404 Media)+ It’s a useful reminder to never take anything chatbots produce as fact. (Axios)+ Even regional newspapers aren’t safe from AI slop. (The Atlantic $)+ Why AI hallucinates, and why we can’t stop it. (MIT Technology Review)3 The Earth may already be too hot to maintain polar ice sheetsEven if it stays at current temperature levels. (WP $)+ Why climate researchers are taking the temperature of mountain snow. (MIT Technology Review)4 How New York City’s child abuse algorithm flags families for investigation Critics believe it’s open to racial bias. (The Markup) 5 Here’s what it’s like to interview for a job at DOGE   The hiring process is remarkably fast, for a government entity. (Wired $)+ The department reportedly tried to enter the US government’s publishing operation. (Politico)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review)6 Fortnite has finally returned to Apple’s App StoreAfter five years and a lengthy legal battle. (NYT $) + The recent ruling has major implications for the iOS economy. (Reuters)7 Most chatbots can be tricked into dispensing dangerous informationFrom hacking advice, to describing how to make drugs. (The Guardian) + Anthropic has a new way to protect large language models against jailbreaks. (MIT Technology Review)8 Young Indonesians are being trafficked to scam farmsFraudulent job ads on Telegram and Facebook lure them into a life of crime. (Rest of World) + Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review) 9  Inside the building in China where stolen western iPhones are stripped and sold You’ll find a buyer for every single component inside the Feiyang Times. (FT $)10 Amazon has started randomly refunding customers for old purchasesSome orders were placed as far back as 2018. (Bloomberg $) Quote of the day “Anybody who’s a computer scientist should not be retired right now. They should be working on AI.” —Google cofounder Sergey Brin says people with the right technical skills should copy him and quit being retired, TechCrunch reports. One more thing This fuel plant will use agricultural waste to combat climate change A startup called Mote plans to build a new type of fuel-producing plant in California’s fertile Central Valley that would, if it works as hoped, continually capture and bury carbon dioxide.It’s among a growing number of efforts to commercialize a concept first proposed two decades ago as a means of combating climate change, known as bioenergy with carbon capture and sequestration, or BECCS.It’s an ambitious plan. However, there are serious challenges to doing BECCS affordably and in ways that reliably suck down significant levels of carbon dioxide. Read the full story.—James Temple 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.) + These creepy little Labubu toys are everywhere. But why?+ Happy 25th birthday to one of London’s finest institutions, the Tate Modern gallery.+ Why the Mission Impossible film franchise just won’t die.+ Hummingbirds can fly backwards!? Wow.

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. By putting AI into everything, Google wants to make it invisible If you want to know where AI is headed, this year’s Google I/O has you covered. The company’s annual showcase of next-gen products, which kicked off yesterday, has all of the pomp and pizzazz, the sizzle reels and celebrity walk-ons, that you’d expect from a multimillion dollar marketing event.But it also shows us just how fast this still-experimental technology is being subsumed into a line-up designed to sell phones and subscription tiers. Never before have I seen this thing we call artificial intelligence appear so normal. Read the full story. —Will Douglas Heaven AI could keep us dependent on natural gas for decades to come Last December, Meta announced plans to build a massive $10 billion data center for training its artificial intelligence models in rural northeast Louisiana. Stretching for more than a mile, it will be Meta’s largest in the world, and it will have an enormous appetite for electricity.To power the data center, a Meta contractor called Entergy will build three large natural-gas power plants with a total capacity of 2.3 gigawatts. It’ll also upgrade the grid to accommodate the huge jump in anticipated demand.The choice of natural gas as the go-to solution to meet the growing demand for power from AI is not unique to Louisiana. The fossil fuel is already the country’s chief source of electricity generation, and large natural-gas plants are being built around the country to feed electricity to new and planned AI data centers. That’s all but wiping out any prospect that the US will wean itself off natural gas anytime soon. Read the full story. —David Rotman This story is part of Power Hungry: AI and our energy future—our new series shining a light on AI’s energy usage. Check out the rest of the package here. Take a new look at AI’s energy use Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does a single AI query use? And what does it mean for the climate? Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell at 1.30pm ET today for a subscriber-only Roundtables conversation digging into our new package of stories about AI’s energy demands now and in the future. Register here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Democrats are on the hunt for a digital thought leaderThey’re (finally) realizing how far they’re lagging behind their opponents’ online efforts these days. (NYT $) + AI’s impact on elections is being overblown. (MIT Technology Review) 2 At least two newspapers printed an AI-generated summer reading list The only problem is, some of the books don’t actually exist. (404 Media)+ It’s a useful reminder to never take anything chatbots produce as fact. (Axios)+ Even regional newspapers aren’t safe from AI slop. (The Atlantic $)+ Why AI hallucinates, and why we can’t stop it. (MIT Technology Review)3 The Earth may already be too hot to maintain polar ice sheetsEven if it stays at current temperature levels. (WP $)+ Why climate researchers are taking the temperature of mountain snow. (MIT Technology Review) 4 How New York City’s child abuse algorithm flags families for investigationCritics believe it’s open to racial bias. (The Markup) 5 Here’s what it’s like to interview for a job at DOGE  The hiring process is remarkably fast, for a government entity. (Wired $)+ The department reportedly tried to enter the US government’s publishing operation. (Politico)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review) 6 Fortnite has finally returned to Apple’s App StoreAfter five years and a lengthy legal battle. (NYT $)+ The recent ruling has major implications for the iOS economy. (Reuters) 7 Most chatbots can be tricked into dispensing dangerous informationFrom hacking advice, to describing how to make drugs. (The Guardian)+ Anthropic has a new way to protect large language models against jailbreaks. (MIT Technology Review) 8 Young Indonesians are being trafficked to scam farmsFraudulent job ads on Telegram and Facebook lure them into a life of crime. (Rest of World)+ Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review) 9  Inside the building in China where stolen western iPhones are stripped and soldYou’ll find a buyer for every single component inside the Feiyang Times. (FT $) 10 Amazon has started randomly refunding customers for old purchasesSome orders were placed as far back as 2018. (Bloomberg $) Quote of the day “Anybody who’s a computer scientist should not be retired right now. They should be working on AI.” —Google cofounder Sergey Brin says people with the right technical skills should copy him and quit being retired, TechCrunch reports. One more thing This fuel plant will use agricultural waste to combat climate changeA startup called Mote plans to build a new type of fuel-producing plant in California’s fertile Central Valley that would, if it works as hoped, continually capture and bury carbon dioxide.It’s among a growing number of efforts to commercialize a concept first proposed two decades ago as a means of combating climate change, known as bioenergy with carbon capture and sequestration, or BECCS.It’s an ambitious plan. However, there are serious challenges to doing BECCS affordably and in ways that reliably suck down significant levels of carbon dioxide. Read the full story. —James Temple 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.) + These creepy little Labubu toys are everywhere. But why?+ Happy 25th birthday to one of London’s finest institutions, the Tate Modern gallery.+ Why the Mission Impossible film franchise just won’t die.+ Hummingbirds can fly backwards!? Wow.

Data Scarcity in Generative Modeling Generative models traditionally rely on large, high-quality datasets to produce samples that replicate the underlying data distribution. However, in fields like molecular modeling or physics-based inference, acquiring such data can be computationally infeasible or even impossible. Instead of labeled data, only a scalar reward—typically derived from a complex energy function—is available to judge the quality of generated samples. This presents a significant challenge: how can one train generative models effectively without direct supervision from data? Meta AI tackles this challenge with Adjoint Sampling, a novel learning algorithm designed for training generative models using only scalar reward signals. Built on the theoretical framework of stochastic optimal control (SOC), Adjoint Sampling reframes the training process as an optimization task over a controlled diffusion process. Unlike standard generative models, it does not require explicit data. Instead, it learns to generate high-quality samples by iteratively refining them using a reward function—often derived from physical or chemical energy models. Adjoint Sampling excels in scenarios where only an unnormalized energy function is accessible. It produces samples that align with the target distribution defined by this energy, bypassing the need for corrective methods like importance sampling or MCMC, which are computationally intensive. Source: https://arxiv.org/abs/2504.11713 Technical Details The foundation of Adjoint Sampling is a stochastic differential equation (SDE) that models how sample trajectories evolve. The algorithm learns a control drift u(x,t)u(x, t)u(x,t) such that the final state of these trajectories approximates a desired distribution (e.g., Boltzmann). A key innovation is its use of Reciprocal Adjoint Matching (RAM)—a loss function that enables gradient-based updates using only the initial and final states of sample trajectories. This sidesteps the need to backpropagate through the entire diffusion path, greatly improving computational efficiency. By sampling from a known base process and conditioning on terminal states, Adjoint Sampling constructs a replay buffer of samples and gradients, allowing multiple optimization steps per sample. This on-policy training method provides scalability unmatched by previous approaches, making it suitable for high-dimensional problems like molecular conformer generation. Moreover, Adjoint Sampling supports geometric symmetries and periodic boundary conditions, enabling models to respect molecular invariances like rotation, translation, and torsion. These features are crucial for physically meaningful generative tasks in chemistry and physics. Performance Insights and Benchmark Results Adjoint Sampling achieves state-of-the-art results in both synthetic and real-world tasks. On synthetic benchmarks such as the Double-Well (DW-4), Lennard-Jones (LJ-13 and LJ-55) potentials, it significantly outperforms baselines like DDS and PIS, especially in energy efficiency. For example, where DDS and PIS require 1000 evaluations per gradient update, Adjoint Sampling only uses three, with similar or better performance in Wasserstein distance and effective sample size (ESS). In a practical setting, the algorithm was evaluated on large-scale molecular conformer generation using the eSEN energy model trained on the SPICE-MACE-OFF dataset. Adjoint Sampling, especially its Cartesian variant with pretraining, achieved up to 96.4% recall and 0.60 Å mean RMSD, surpassing RDKit ETKDG—a widely used chemistry-based baseline—across all metrics. The method generalizes well to the GEOM-DRUGS dataset, showing substantial improvements in recall while maintaining competitive precision. The algorithm’s ability to explore the configuration space broadly, aided by its stochastic initialization and reward-based learning, results in greater conformer diversity—critical for drug discovery and molecular design. Conclusion: A Scalable Path Forward for Reward-Driven Generative Models Adjoint Sampling represents a major step forward in generative modeling without data. By leveraging scalar reward signals and an efficient on-policy training method grounded in stochastic control, it enables scalable training of diffusion-based samplers with minimal energy evaluations. Its integration of geometric symmetries and its ability to generalize across diverse molecular structures position it as a foundational tool in computational chemistry and beyond. Check out the Paper, Model on Hugging Face and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Google AI Releases MedGemma: An Open Suite of Models Trained for Performance on Medical Text and Image ComprehensionAsif Razzaqhttps://www.marktechpost.com/author/6flvq/NVIDIA Releases Cosmos-Reason1: A Suite of AI Models Advancing Physical Common Sense and Embodied Reasoning in Real-World EnvironmentsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA OptimizationAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Chain-of-Thought May Not Be a Window into AI’s Reasoning: Anthropic’s New Study Reveals Hidden Gaps 🚨 Build GenAI you can trust. ⭐️ Parlant is your open-source engine for controlled, compliant, and purposeful AI conversations — Star Parlant on GitHub! (Promoted)

Our latest and most advanced technologies — from AI to Industrial IoT, advanced robotics, and self-driving cars — share serious problems: massive energy consumption, limited on-edge capabilities, system hallucinations, and serious accuracy gaps.  One possible solution is emerging in the Netherlands. The country is developing a promising ecosystem for neuromorphic computing, which draws on neuroscience to boost IT efficiencies and performance. Billions of euros are being invested in this new form of computing worldwide. The Netherlands aims to become a leader in the market by bringing together startups, established companies, government organisations, and academics in a neuromorphic computing ecosystem. A Dutch mission to the UK In March, a Dutch delegation landed in the UK to host an “Innovation Mission” with local tech and government representatives. Top Sector ICT, a Dutch government–supported organisation, led the mission, which sought to strengthen and discuss the future of neuromorphic computing in Europe and the Netherlands.  We contacted Top Sector ICT, who connected us with one of their collaborators: Dr Johan H. Mentink, an expert in computational physics at Radboud University in the Netherlands. Dr Mentink spoke about how neuromorphic computing can solve the energy, accuracy, and efficiency challenges of our current computing architectures.  Grab that deal “Current digital computers use power-hungry processes to handle data,” Dr Mentink said.  “The result is that some modern data centres use so much energy that they even need their own power plant.”  Computing today stores data in one place (memory) and processes it in another place (processors). This means that a lot of energy is spent on transporting data, Dr Mentink explained.  In contrast, neuromorphic computing architectures are different at the hardware and software levels. For example, instead of using processors and memories, neuromorphic systems leverage new hardware components such as memristors. These act as both memory and processors.  By processing and saving data on the same hardware component, neuromorphic computing removes the energy-intensive and error-prone task of transporting data. Additionally, because data is stored on these components, it can be processed more immediately, resulting in faster decision-making, reduced hallucinations, improved accuracy, and better performance. This concept is being applied to edge computing, Industrial IoT, and robotics to drive faster real-time decision-making.  “Just like our brains process and store information in the same place, we can make computers that would combine data storage and processing in one place,” Dr Mentink explained.   Early use cases for neuromorphic computing Neuromorphic computing is far from just experimental. A great number of new and established technology companies are heavily invested in developing new hardware, edge devices, software, and neuromorphic computing applications. Big tech brands such as IBM, NVIDIA, and Intel, with its Loihi chips, are all involved in neuromorphic computing, while companies in the Netherlands, aligned with a 2024 national white paper, are taking a leading regional role.  For example, the Dutch company Innatera — a leader in ultra-low power neuromorphic processors — recently secured €15 million in Series-A funding from Invest-NL Deep Tech Fund, the EIC Fund, MIG Capital, Matterwave Ventures, and Delft Enterprises.  Innatera is just the tip of the iceberg, as the Netherlands continues to support the new industry through funds, grants, and other incentives. Immediate use cases for neuromorphic computing include event-based sensing technologies integrated into smart sensors such as cameras or audio. These neuromorphic devices only process change, which can dramatically reduce power and data load, said Sylvester Kaczmarek, the CEO of OrbiSky Systems, a company providing AI integration for space technology.   Neuromorphic hardware and software have the potential to transfer AI running on the edge, especially for low-power devices such as mobile, wearables, or IoT.  Pattern recognition, keyword spotting, and simple diagnostics — such as real-time signal processing of complex sensor data streams for biomedical uses, robotics, or industrial monitoring — are some of the leading use cases, Dr Kaczmarek explained.  When applied to pattern recognition and classification or anomaly detection, neuromorphic computing can make decisions very quickly and efficiently,  Professor Dr Hans Hilgenkamp, Scientific Director of the MESA+ Institute at the University of Twente, agreed that pattern recognition is one of the fields where neuromorphic computing excels.  “One may also think about [for example] failure prediction in industrial or automotive applications,” he said.    The gaps creating neuromorphic opportunities Despite the recent progress, the road to establishing robust neuromorphic computing ecosystems in the Netherlands is challenging. Globalised tech supply chains and the standardisation of new technologies leave little room for hardware-level innovation.  For example, optical networks and optical chips have proven to outperform traditional systems in use today, but the tech has not been deployed globally. Deploying new hardware involves strategic coordination between the public and private sectors. The global rollout of 5G technology provides a good example of the challenges. It required telcos and governments around the world to deploy not only new antennas, but also smartphones, laptops, and a lot of hardware that could support the new standard.  On the software side, meanwhile, 5G systems had a pressing need for global standards to ensure integration, interoperability, and smooth deployment. Additionally, established telcos had to move from pure competition to strategic collaboration— an unfamiliar shift for an industry long built on siloed operations. Neuromorphic computing ecosystems face similar obstacles. The Netherlands recognises that the entire industry’s success depends on innovation in materials, devices, circuit designs, hardware architecture, algorithms, and applications.  These challenges and gaps are driving new opportunities for tech companies, startups, vendors, and partners.  Dr Kaczmarek told us that neuromorphic computing requires full-stack integration. This involves expertise that can connect novel materials and devices through circuit design and architectures to algorithms and applications. “Bringing these layers together is crucial but challenging,” he said.  On the algorithms and software side of things, developing new paradigms of programming, learning rules (beyond standard deep learning backpropagation), and software tools native to neuromorphic hardware are also priorities.  “It is crucial to make the hardware usable and efficient — co-designing hardware and algorithms because they are intimately coupled in neuromorphic systems,” said Dr Kaczmarek.  Other industries which have developed or are considering research on neuromorphic computing include healthcare (brain-computer interfaces and prosthetics), agri-food, and sustainable energy.  Neuromorphic computing modules or components can also be integrated with conventional CMOS, photonics, AI, and even quantum technologies.  Long-term opportunities in the Netherlands We asked Dr Hilgenkamp what expertise or innovations are most needed and offer the greatest opportunities for contribution and growth within this emerging ecosystem. “The long-term developments involve new materials and a lot of research, which is already taking place on an academic level,” Dr Hilgenkamp said.  He added that the idea of “materials that can learn” brings up completely new concepts in materials science that are exciting for researchers.  On the other hand, Dr Mentink pointed to the opportunity to transform our economies, which rely on processing massive amounts of data.  “Even replacing a small part of that with neuromorphic computing will lead to massive energy savings,” he said.  “Moreover, with neuromorphic computing, much more processing can be done close to where the data is produced. This is good news for situations in which data contains privacy-sensitive information.”  Concrete examples, according to Dr Mentink, also include fraud detection for credit card transactions, image analysis by robots and drones, anomaly detection of heartbeats, and processing of telecom data. “The most promising use cases are those involving huge data flows, strong demands for very fast response times, and small energy budgets,” said Dr Mentink.  As the use cases for neuromorphic computing increase, Dr Mentink expects the development of software toolchains that enable quick adoption of new neuromorphic platforms to see growth. This new sector would include services to streamline deployment. “Longer-term sustainable growth requires a concerted interdisciplinary effort across the whole computing stack to enable seamless integration of foundational discoveries to applications in new neuromorphic computing systems,” Dr Mentink said.  The bottom line The potential of neuromorphic computing has translated into billions of dollars in investment in the Netherlands and Europe, as well as in Asia and the rest of the world.  Businesses that can innovate, develop, and integrate hardware and software-level neuromorphic technologies stand to gain the most.   The potential of neuromorphic computing for greater energy efficiency and performance could ripple across industries. Energy, healthcare, robotics, AI, industrial IoT, and quantum tech all stand to benefit if they integrate the technology. And if the Dutch ecosystem takes off, the Netherlands will be in a position to lead the way. Supporting Dutch tech is a key mission of TNW Conference, which takes place on June 19-20 in Amsterdam. Tickets are now on sale — use the code TNWXMEDIA2025 at the checkout to get 30% off. Story by Ray Fernandez Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work ha (show all) Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work has been published in Bloomberg, TechRepublic, The Sunday Mail, eSecurityPlanet, and many others. He is a contributing writer for Espacio Media Incubator, which has reporters across the US, Europe, Asia, and Latin America. Get the TNW newsletter Get the most important tech news in your inbox each week. Also tagged with

Our latest and most advanced technologies — from AI to Industrial IoT, advanced robotics, and self-driving cars — share serious problems: massive energy consumption, limited on-edge capabilities, system hallucinations, and serious accuracy gaps.  One possible solution is emerging in the Netherlands. The country is developing a promising ecosystem for neuromorphic computing, which draws on neuroscience to boost IT efficiencies and performance. Billions of euros are being invested in this new form of computing worldwide. The Netherlands aims to become a leader in the market by bringing together startups, established companies, government organisations, and academics in a neuromorphic computing ecosystem. A Dutch mission to the UK In March, a Dutch delegation landed in the UK to host an “Innovation Mission” with local tech and government representatives. Top Sector ICT, a Dutch government–supported organisation, led the mission, which sought to strengthen and discuss the future of neuromorphic computing in Europe and the Netherlands.  We contacted Top Sector ICT, who connected us with one of their collaborators: Dr Johan H. Mentink, an expert in computational physics at Radboud University in the Netherlands. Dr Mentink spoke about how neuromorphic computing can solve the energy, accuracy, and efficiency challenges of our current computing architectures.  Grab that deal “Current digital computers use power-hungry processes to handle data,” Dr Mentink said.  “The result is that some modern data centres use so much energy that they even need their own power plant.”  Computing today stores data in one place (memory) and processes it in another place (processors). This means that a lot of energy is spent on transporting data, Dr Mentink explained.  In contrast, neuromorphic computing architectures are different at the hardware and software levels. For example, instead of using processors and memories, neuromorphic systems leverage new hardware components such as memristors. These act as both memory and processors.  By processing and saving data on the same hardware component, neuromorphic computing removes the energy-intensive and error-prone task of transporting data. Additionally, because data is stored on these components, it can be processed more immediately, resulting in faster decision-making, reduced hallucinations, improved accuracy, and better performance. This concept is being applied to edge computing, Industrial IoT, and robotics to drive faster real-time decision-making.  “Just like our brains process and store information in the same place, we can make computers that would combine data storage and processing in one place,” Dr Mentink explained.   Early use cases for neuromorphic computing Neuromorphic computing is far from just experimental. A great number of new and established technology companies are heavily invested in developing new hardware, edge devices, software, and neuromorphic computing applications. Big tech brands such as IBM, NVIDIA, and Intel, with its Loihi chips, are all involved in neuromorphic computing, while companies in the Netherlands, aligned with a 2024 national white paper, are taking a leading regional role.  For example, the Dutch company Innatera — a leader in ultra-low power neuromorphic processors — recently secured €15 million in Series-A funding from Invest-NL Deep Tech Fund, the EIC Fund, MIG Capital, Matterwave Ventures, and Delft Enterprises.  Innatera is just the tip of the iceberg, as the Netherlands continues to support the new industry through funds, grants, and other incentives. Immediate use cases for neuromorphic computing include event-based sensing technologies integrated into smart sensors such as cameras or audio. These neuromorphic devices only process change, which can dramatically reduce power and data load, said Sylvester Kaczmarek, the CEO of OrbiSky Systems, a company providing AI integration for space technology.   Neuromorphic hardware and software have the potential to transfer AI running on the edge, especially for low-power devices such as mobile, wearables, or IoT.  Pattern recognition, keyword spotting, and simple diagnostics — such as real-time signal processing of complex sensor data streams for biomedical uses, robotics, or industrial monitoring — are some of the leading use cases, Dr Kaczmarek explained.  When applied to pattern recognition and classification or anomaly detection, neuromorphic computing can make decisions very quickly and efficiently,  Professor Dr Hans Hilgenkamp, Scientific Director of the MESA+ Institute at the University of Twente, agreed that pattern recognition is one of the fields where neuromorphic computing excels.  “One may also think about [for example] failure prediction in industrial or automotive applications,” he said.    The gaps creating neuromorphic opportunities Despite the recent progress, the road to establishing robust neuromorphic computing ecosystems in the Netherlands is challenging. Globalised tech supply chains and the standardisation of new technologies leave little room for hardware-level innovation.  For example, optical networks and optical chips have proven to outperform traditional systems in use today, but the tech has not been deployed globally. Deploying new hardware involves strategic coordination between the public and private sectors. The global rollout of 5G technology provides a good example of the challenges. It required telcos and governments around the world to deploy not only new antennas, but also smartphones, laptops, and a lot of hardware that could support the new standard.  On the software side, meanwhile, 5G systems had a pressing need for global standards to ensure integration, interoperability, and smooth deployment. Additionally, established telcos had to move from pure competition to strategic collaboration— an unfamiliar shift for an industry long built on siloed operations. Neuromorphic computing ecosystems face similar obstacles. The Netherlands recognises that the entire industry’s success depends on innovation in materials, devices, circuit designs, hardware architecture, algorithms, and applications.  These challenges and gaps are driving new opportunities for tech companies, startups, vendors, and partners.  Dr Kaczmarek told us that neuromorphic computing requires full-stack integration. This involves expertise that can connect novel materials and devices through circuit design and architectures to algorithms and applications. “Bringing these layers together is crucial but challenging,” he said.  On the algorithms and software side of things, developing new paradigms of programming, learning rules (beyond standard deep learning backpropagation), and software tools native to neuromorphic hardware are also priorities.  “It is crucial to make the hardware usable and efficient — co-designing hardware and algorithms because they are intimately coupled in neuromorphic systems,” said Dr Kaczmarek.  Other industries which have developed or are considering research on neuromorphic computing include healthcare (brain-computer interfaces and prosthetics), agri-food, and sustainable energy.  Neuromorphic computing modules or components can also be integrated with conventional CMOS, photonics, AI, and even quantum technologies.  Long-term opportunities in the Netherlands We asked Dr Hilgenkamp what expertise or innovations are most needed and offer the greatest opportunities for contribution and growth within this emerging ecosystem. “The long-term developments involve new materials and a lot of research, which is already taking place on an academic level,” Dr Hilgenkamp said.  He added that the idea of “materials that can learn” brings up completely new concepts in materials science that are exciting for researchers.  On the other hand, Dr Mentink pointed to the opportunity to transform our economies, which rely on processing massive amounts of data.  “Even replacing a small part of that with neuromorphic computing will lead to massive energy savings,” he said.  “Moreover, with neuromorphic computing, much more processing can be done close to where the data is produced. This is good news for situations in which data contains privacy-sensitive information.”  Concrete examples, according to Dr Mentink, also include fraud detection for credit card transactions, image analysis by robots and drones, anomaly detection of heartbeats, and processing of telecom data. “The most promising use cases are those involving huge data flows, strong demands for very fast response times, and small energy budgets,” said Dr Mentink.  As the use cases for neuromorphic computing increase, Dr Mentink expects the development of software toolchains that enable quick adoption of new neuromorphic platforms to see growth. This new sector would include services to streamline deployment. “Longer-term sustainable growth requires a concerted interdisciplinary effort across the whole computing stack to enable seamless integration of foundational discoveries to applications in new neuromorphic computing systems,” Dr Mentink said.  The bottom line The potential of neuromorphic computing has translated into billions of dollars in investment in the Netherlands and Europe, as well as in Asia and the rest of the world.  Businesses that can innovate, develop, and integrate hardware and software-level neuromorphic technologies stand to gain the most.   The potential of neuromorphic computing for greater energy efficiency and performance could ripple across industries. Energy, healthcare, robotics, AI, industrial IoT, and quantum tech all stand to benefit if they integrate the technology. And if the Dutch ecosystem takes off, the Netherlands will be in a position to lead the way. Supporting Dutch tech is a key mission of TNW Conference, which takes place on June 19-20 in Amsterdam. Tickets are now on sale — use the code TNWXMEDIA2025 at the checkout to get 30% off. Story by Ray Fernandez Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work ha (show all) Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work has been published in Bloomberg, TechRepublic, The Sunday Mail, eSecurityPlanet, and many others. He is a contributing writer for Espacio Media Incubator, which has reporters across the US, Europe, Asia, and Latin America. Get the TNW newsletter Get the most important tech news in your inbox each week. Also tagged with

Purdubik's Cube record of 0.103 seconds is faster than a human brain's reaction time. Credit: Purdue University / Elmore Family School of ECE Get the Popular Science daily newsletter💡 Breakthroughs, discoveries, and DIY tips sent every weekday. Mitsubishi’s bragging rights for designing the world’s fastest Rubik’s cube-solving robot have officially been stolen by a team of undergrads in Indiana. Earlier this month, Purdue University announced four collaborators in its Elmore Family School of Electrical and Computer Engineering (ECE) successfully designed and built a bot that not only set the new Guinness World Record—it absolutely demolished the multinational company’s previous time. Meet Purdubik’s Cube: a machine capable of completing a randomly shuffled Rubik’s cube in just 0.103 seconds. At 1-2 times faster than the blink of a human eye, the feat is difficult to see, much less comprehend. Mitsubishi turned heads (and blocks) in May 2024, after its TOKUFASTbot solved a Rubik’s cube puzzle in just 0.305 seconds. Short for TOKUI Fast Accurate Synchronized Motion Testing Robot TOKUFASTbot shaved 0.075 seconds from the previous all-time record through a combination of compact, high-power, servomotors, a specially designed color-recognition AI algorithm, and sophisticated motion-control technology typically used in the company’s manufacturing plants. It was an impressive accomplishment from a company with vast resources at its disposal. But Matthew Patrohay, Junpei Ota, Aden Hurd, and Alex Berta at Purdue University in West Lafayette, Indiana soon began wondering if there was any room left for improvement. “They said, ‘You know what? I think we can do better,’” ECE head Milind Kulkarni said in the university’s accompanying video. Patrohay and his teammates then managed to conceive their own bot design in a matter of months before building the final Purdubik’s Cube model over a single semester. Their results are not only faster than blinking an eye, says Patrohay. “Human reaction time is 100-200 [milliseconds], as well. So we’re faster than that,” he said. “Basically, before you even realize it’s solved, we’ve solved it. Before you even realize it’s moving, we’ve solved it.” As with TOKUFASTbot, the limitations at this point were less about the robot itself and more about the physical limitations of the puzzle toy. “The cubes themselves just kind of disintegrate. The pieces just snap in half and fall apart,” said Patrohay. To sidestep these problems, the team needed to optimize the mechanical interior of their test Rubik’s cubes. This required a custom built interior replacement core to better hold all the pieces together, as well as adjustments to the individual color blocks. Patrohay expressed hope ahead of Purdubik’s Cube Guinness World Record trial on April 21 that their machine could trim off a few more milliseconds to firmly set their record under a tenth of a second. However, it appears that 0.103 is currently the time to beat moving forward. It’s hard to imagine a robot solving a Rubik’s cube much faster than that, but then again, that’s what most people were saying this time last year.