In this article, you will learn how agent memory works, what types of memory exist, and how different frameworks approach storage, retrieval, and memory management…
The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks Beitrag lesen »
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. The agentic web is here In 2001, a decade after inventing the World Wide Web, Tim Berners-Lee articulated a new vision: an internet navigated by autonomous software agents that address our every need, without requiring us to muck about with browsers and search engines ourselves. At the time, the idea seemed far-fetched. But a quarter-century on, AI agents are beginning to make it a reality. They can triage your inbox, do your grocery shopping, and even access your bank accounts. This new era is being called the agentic web. It will inevitably transform the internet—and whether the human web survives this transition is anyone’s guess. Find out how AI agents are transforming the internet, and what we stand to lose in the process. —Grace Huckins Are we there yet? Eliminating traffic deaths Every year, traffic crashes kill more than 1 million people worldwide. They’re also the leading cause of death for children and young adults. Cheap, proven ways to make roads safer exist, including tightening intersections with planters and lowering speed limits. But government logjams and red tape can make it difficult to roll out these changes. Still, some cities are making impressive progress by investing in public transit, extending curbs, and using cameras to spot danger zones in need of a redesign. Here’s how close we are to eradicating traffic deaths. —Sara Kiley Watson This story is the final part of “Are we there yet?”, our series that takes stock of six technologies that have long promised to change the world—and scores them on a progress meter. Subscribe to read the other five instalments in our new Impossible issue. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 A $1 trillion tech selloff has deepened fears of an AI bubbleBig AI bills and weak earnings have spooked investors. (BBC)+ The Nasdaq 100 had its worst day since June on Friday. (NBC News)+ OpenAI has cut its funding target by $10 billion. (CNBC)+ What even is the AI bubble? (MIT Technology Review) 2 Hackers have hit Ukraine and Russia’s emergency alert appsKyiv residents were warned of “nuclear danger.” (NBC News)+ Russia’s app said nuclear warheads had been launched. (CNN)+ Ukraine has blamed Russia, which hasn’t yet accused anyone. (BI)+ AI will transform cyber warfare. (MIT Technology Review) 3 Binance users withdrew $3.6 billion after the largest ever crypto hackThe funds moved when the exchange reopened on Sunday. (Guardian)+ Binance says it’s covered $6 billion of the stolen $6.3 billion. (Reuters $)+ But the industry’s response has undermined its “decentralized” image. (Gizmodo) 4 An AI agent injected drugs into a live mouse’s brainIt chose the gene therapy and controlled the needle itself. (Wired $)+ We need real-time tracking of AI agents. (MIT Technology Review) 5 Data centres have made utilities a flashpoint in the midtermsAmericans are angry over rising electricity bills. (Reuters $)+ Virtual power plants could help. (MIT Technology Review) 6 Apple has overtaken Nvidia as the world’s most valuable companyInvestors are favouring its restraint on AI spending. (Bloomberg $)+ But critics say it’s entering its “slop era.” (404 Media) 7 Supermarket fridges have overheated in a new wave of cyberattacksStores in Belgium, Sweden, and Luxembourg were hit. (Politico) 8 The first chocolate bars made from lab-grown cocoa are hereStartups are cultivating chocolate to tackle a cocoa shortage. (WSJ $) 9 Hackers hijacked digital billboards in multiple countries to show pornThey also criticized security flaws and Egypt’s government.(Gizmodo) 10 A new 3D map reveals instability in interstellar spaceThe discovery could help explain why stars form so inefficiently. (Quanta) Quote of the day “It would be great if all this was just a house of cards.” —Kevin Roose, a technology columnist at the New York Times, tells NPR that an AI investment crash would be preferable to the technology permanently upending work and society. One more thing Useful quantum computing is inevitable—and increasingly imminent Last year, Nvidia CEO Jensen Huang jolted the stock market by saying that practical quantum computing is still 15 to 30 years away. He also suggested that those computers would need Nvidia GPUs to function. But Huang’s predictions miss the mark—both on the timeline and the role his company’s technology will play. Quantum computing is rapidly converging on utility. And that’s good news, because the hope is that they will be able to perform calculations that no amount of AI or classical computation could ever achieve. Read the full story. —Peter Barrett 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.) + A miniaturist has built a delightful diorama of the world’s creepiest living room.+ The year’s best dog photos have been announced, and they’re very good boys and girls.+ At the Green Zone golf club, you tee off in Sweden and putt in Finland, an hour in the future.+ Astronomers say they’ve found the real celestial arrangement that inspired van Gogh’s The Starry Night.
The Download: the agentic web arrives and the drive for safer roads Beitrag lesen »
When a developer gives an AI agent a coding task, the agent does more than answer once. It plans, generates code, runs tests, and repeats the process to correct errors. A single task can use 10 to 50 times more tokens than a basic chat prompt. The more useful the agent becomes, the more calls it makes. With per-token pricing, the workflows creating the most value can also become the most expensive. The financial pressure is already visible. Uber reportedly exhausted its entire 2026 AI budget by April after expanding its use of Claude Code. GitHub Copilot moved from flat-rate pricing to metered credits. Gartner predicts that AI coding token costs will surpass average developer salaries by 2028. The natural response is rationing usage caps, seat limits, and approvals before developers launch agents. But blanket limits can restrict the workflows delivering value. They also optimize the wrong number. Cost per token measures consumption, not results. Some spending reflects waste, including retries and runaway loops. Some represents the work of shipping software. A meter cannot distinguish them, and neither can a spending cap. Measure the software that ships A better measure is cost per merged pull request: total AI-assisted development cost divided by the number of changes that pass review and are merged. Track rework and defects alongside it so cheaper changes do not conceal lower quality. If that cost falls as AI usage rises, without compromising quality, developers are converting more consumption into useful software. The question is not how many tokens they use, but how much finished work the investment produces. Improving that equation requires two changes: reserve expensive inference for tasks that need it, and lower the cost of routine work. Match the model to the task Agentic coding does not require the most powerful model at every step. Use a frontier model, or an on-premises flagship when policy requires it, for difficult planning and novel reasoning. Route suitable work, including code generation, test creation, and repeated refactoring, to open-weight models you operate yourself. A gateway can route requests according to task complexity, data sensitivity, cost, and latency. This reserves premium inference for the moments that justify it rather than applying premium pricing to an entire workflow. Meter the exception, not the workload. Open-weight models make this division increasingly practical. Software-engineering benchmarks show that leading open-weight models can approach frontier performance on some tasks. That does not establish equivalence across every repository or workflow. Evaluate candidates against your own code, tests, and acceptance criteria before assigning production work. Change the economics of repeated use Next, decide who owns the capacity. With rented inference, every call adds to the bill. With owned capacity, most of the cost is committed upfront, so each additional call gets cheaper as usage grows. The economics are simple: per-token pricing makes heavy use more expensive, while owned capacity becomes more efficient as teams use it more. Ownership is not free, though. Power, operations, maintenance, and depreciation all must be considered. The number that matters is the fully loaded cost per merged pull request. Owned capacity also delivers a type of value that never appears on an invoice: control over sensitive code. When code generation and testing run on-premises, proprietary code stays inside the enterprise. Only approved planning prompts and governed exceptions go to external models, through a gateway the organization controls. That opens the sensitive repositories where security usually stalls adoption, and where the biggest gains are waiting. Test the operating model At HPE, a hybrid AI platform routes routine workloads from more than 150 internal applications to open-weight models running on premises, including on HPE Private Cloud AI. The result: token costs down 30 to 50%. That is the point of hybrid. With HPE Private Cloud AI, owning your inference does not mean giving up frontier models. Run the volume on capacity you own and let a gateway send the hardest work. Data also shows that usage, not licensed headcount, drives spend. The top 5% of users generated most of the consumption, and the heaviest used roughly 450 times more than the lightest on the same license. If power users grow from a small minority to half the workforce, monthly spend can rise about 4.4 times with flat headcount. Your mix will differ, which is exactly why you need to measure it. HPE tracks two metrics and recommends customers do the same. First, it tracks cost per active developer, because averaging across all licenses hides the power users driving spend. Second, it tracks cost per merged pull request, because it connects AI spending to software that ships. Baseline both costs before changing infrastructure. Then run a complete development cycle on owned capacity. If both metrics do not improve as usage grows, your current model may still be the better choice. Build for adoption, not rationing Owned capacity is not for everyone. Low or unpredictable usage favors APIs, with no capital tied up in idle infrastructure. Ownership pays off when sustained demand keeps it busy. Most enterprises will run hybrid: frontier APIs for demanding or intermittent work, owned capacity for repeatable volume. A governed gateway decides what leaves the building, so you get frontier models without giving up sovereignty over your code. As developers lean harder on coding agents, usage will keep rising. The real choice is whether that growth shows up as a bigger variable-cost bill or as more software shipped on capacity you own. The companies that lead AI-assisted development will not ration adoption to control costs. They will build an operating model where more AI use improves the economics instead of undermining them. To learn more about HPE Private Cloud AI, please visit HPE. This content was produced by HPE. It was not written by MIT Technology Review’s editorial staff.
Stop counting tokens, start counting what ships Beitrag lesen »
In just a few years, generative AI has changed the face of enterprise analytics, notably by making analytics accessible to users across the organization. Agentic AI promises to do even more, by automating entire workflows and taking actions based on agent insights. But there is a problem: The quality of insights that agents currently generate leaves much to be desired. Fewer than half of agent-generated insights at the organizations surveyed for this report lead to business improvement. And while most respondents praise the quality of their agents’ descriptive insights, reviews of their prescriptive insights—those on which actions are based—are decidedly mixed. DOWNLOAD THE REPORT Failure to produce insights that improve the business deprives organizations of their desired return on investment in agentic analytics. Worse, flawed insights that trigger incorrect actions can cause reputational or financial damage. This report gauges how companies view the quality of the outputs produced by their analytics agents and explores how organizations are seeking to boost insight quality and get their agents working harder for the business. Some key findings from the report include the following: Agentic analytics is only just starting to deliver. On average, 48% of insights generated by analytics agents at the surveyed companies lead to actions that improve the business. That is a marked advance from the 37% average the respondents say generated such actions one year ago. But it means that more than half of the insights generated by analytics agents today are still failing to help businesses. Better quality of insights leads to clear business advantage. At a small cohort of organizations, which we term “insight leaders,” AI agents perform much better than the average. In this group, more than 70% of agent insights lead to actions that improve the business. The insight leaders also give the quality of their agents’ insights higher marks than do the total respondents overall. The quality gap is particularly wide when it comes to prescriptive insights. Insufficient context reduces insight quality. When analytics agents deliver insights of substandard quality, the main reason is a lack of business context, as cited by 55% of surveyed executives. Fragmented or inaccessible data is the other major factor, according to 45%. Fewer insight leaders say their agents struggle with access to context. Insight overload further inhibits agent effectiveness. At a majority (52%) of surveyed organizations, analytics agents generate too many insights to be properly evaluated and therefore acted upon. Less than one-quarter (24%) are able to evaluate all agent insights; most do so on a case-by-case basis, and that process is currently human-intensive. Governance and semantics are areas of focus for improving insights. The surveyed companies are prioritizing initiatives that enable better agent insights by strengthening AI and data governance (cited by 60%). They are also adding a semantic layer to their data architecture (45%). The experts we interviewed agree that the semantic layer is crucial to ensure high-performing agents. Download the report This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
Raising the quality of insights in agentic analytics Beitrag lesen »
GLP-1 weight-loss drugs are one of the biggest biotech breakthroughs of the last decade. But to some clinical psychologists, they can represent something more sinister—a potential trigger for eating disorders. A new report suggests that the global incidence of anorexia nervosa and bulimia nervosa shot up between 1990 and 2023—by a whopping 71%. By 2023, there were an estimated 18.1 million people living with one of these two disorders. What’s worse is that the number is likely to be much higher than that once we factor in other eating disorders. And, disturbingly, those in the field are already starting to see GLP-1 weight-loss drugs exacerbating the issue. Cheri Levinson is a clinical psychologist at the University of Louisville in Kentucky who has specialized in eating disorders for almost 20 years. She says she’s now seeing more patients with more severe symptoms than at any other time in her career, and that many of them are misusing GLP-1s. “This is the worst I’ve seen things,” she says. Eating disorders are mental health conditions in which people’s eating behaviors impact their health. They include anorexia nervosa (defined by a fear of weight gain), bulimia nervosa (in which people binge and purge), binge eating disorder, and others. It’s thought that some people have a genetic predisposition to certain eating disorders, but that some external factor is what triggers their onset. That might be stress, or social pressure to be skinny. Often, simply restricting food intake can trigger an eating disorder, says Levinson. Eating disorders—along with the pressure to look thin—were already prevalent in the 1990s (the heroin chic era springs to mind). To find out how things might have changed since then, Ruhai Bai at the Children’s Hospital of Nanjing Medical University in China and his colleagues pulled together data from studies and surveys conducted on populations around the world. The team used statistical models to fill in any data gaps. That research allowed the researchers to estimate the annual incidence of two eating disorders—anorexia nervosa and bulimia nervosa—for each of 204 countries and territories between 1990 and 2023. They also estimated the impact those disorders had on global health by calculating their disability-adjusted life years (one DALY represents a year of healthy life lost). The findings, published in JAMA Psychiatry on Wednesday, are unsettling. Between 1990 and 2023, there was a 71% increase in the incidence of those two eating disorders, the team found. Those conditions had a huge impact on global health, too. In 2023, they “cost the world the equivalent of about 3.83 million years of healthy life,” says Bai. Bai and his colleagues expect these figures to continue to rise. In 2023, an estimated 18.1 million people had either anorexia nervosa or bulimia nervosa; by 2050, the team predict, the two conditions could affect 21.8 million people. It gets worse. Those two conditions represent less than half of all eating disorders, says Levinson, who was not involved in Bai’s study. The true impact of these disorders is “probably three times the rate that they’re reporting,” she says. Bai and his colleagues didn’t explore the reasons for this rise (although Bai points out that it can’t be entirely explained by population growth). So how did we get here? Social media may have played a role. “Social media comparisons, influencers, challenges, diet trends can all be precipitating … events for an eating disorder,” says Cynthia Bulik, a clinical psychologist at the University of North Carolina, who was not involved in the new study. Bulik has been studying and treating eating disorders since the early 1980s. “Between 1990 and 2023 there has clearly been an upsurge of those types of environmental exposures,” she says. Many social media users are exposed to filtered and modified images of skinny influencers who may have undergone cosmetic surgery or extreme dieting and who are promoting “thinspiration” or “fitspiration” content. But users with eating disorders appear to be more likely to engage with this content—and to be fed more of it as a result. When researchers at the University of Melbourne investigated this phenomenon a couple of years ago, they found that the algorithms of TikTok users with eating disorders delivered 4343% more “toxic eating disorder videos” than those of users who didn’t have the disorders. They also got more content focused on dieting or exercising. Many were fed over 1000 problematic videos per month. Bulik describes social media as “quicksand” for people with, or at risk of, eating disorders. The covid-19 pandemic—and the social isolation that accompanied lockdowns—also appears to have contributed to the rise in eating disorders. Research conducted in 2021 found that the number of people hospitalized with eating disorders in the US doubled between 2018 and 2020. Researchers don’t yet have good data on how trends in eating disorders might have progressed since 2023. But anecdotally, things seem to be getting even worse. “There are more eating disorders, more severe eating disorders… and we’re seeing earlier ages of onset,” says Levinson. “We’re seeing eight- and nine-year-olds with eating disorders.” “From what I’m hearing and seeing, I am not going to be surprised if that number isn’t at least doubling again,” she adds. Levinson particularly worries about the impact of GLP-1 weight-loss drugs, which have exploded in popularity in recent years. A Gallup survey conducted earlier this year suggested that 15% of Americans had used one of these drugs for weight loss. And they’re becoming more accessible—drug companies are working on new formulations that are both easier to take and more effective, and many people can buy versions of these drugs from compound pharmacies or dubious online sources. In a small study published earlier this year, Levinson and her colleagues found that 30% of people with eating disorders had used GLP-1 drugs. The drugs might benefit some of those people. But 10% appeared to have misused the drugs. And Levinson thinks that figure is also an underestimate. Levinson says she and her colleagues have seen children develop eating disorders after being prescribed GLP-1s for medical reasons. They’ve
Eating disorders are on the rise. GLP-1s could make things worse. Beitrag lesen »
In this article, you will learn seven common token-waste patterns in production AI agent systems, and the architectural fixes that address each one. Topics we…
7 Ways Your AI Agent Is Wasting Tokens (And What to Do Instead) Beitrag lesen »
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. Eating disorders are on the rise. GLP-1s could make things worse. GLP-1 weight-loss drugs are one of the biggest biotech breakthroughs of the last decade. But to some clinicians, they can represent something sinister—a potential trigger for eating disorders. A new report suggests the global incidence of anorexia nervosa and bulimia nervosa shot up by 71% between 1990 and 2023. And clinicians are already seeing GLP-1s exacerbate the issue. Here’s how they could make an already growing problem worse. —Jessica Hamzelou This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday. Love is infrastructure —“Love is Infrastructure” is a short fictional story by Charlie Jane Anders, author of a forthcoming novel titled Good Things Are Coming Your Way. Mainspring and St. Barnabas are two neighboring cities with different personalities. They’ve never particularly liked each other, but that changes when both develop minds of their own, shaped by the votes of their residents. As the cities begin to fall for each other, a third city enters the picture. Turnbull promises Mainspring a more efficient future, but its vision of urban life comes with a troubling price. Read the full short story about self-aware cities, direct democracy, and a steamy romance. This story is from the latest edition of our print magazine. Subscribe now to get every issue as soon as it lands. Are we there yet? Cleaning up PFAS Used for decades in everything from nonstick pans to firefighting foams, PFAS, or “forever chemicals,” are linked to increased risks of fertility challenges, developmental delays, cancer, and reduced immune response. Scrubbing PFAS-contaminated soil could eliminate a list of woes for people and planet. The concept is still in its infancy, but we can limit their buildup by pulling the chemicals out of water systems. Find out how close we are to ridding the world of forever chemicals. —Sara Kiley Watson This story is part of “Are we there yet?”, our series that takes stock of six technologies that have long promised to change the world—and scores them on a progress meter. We’re exploring each one in The Download, so stay tuned, and subscribe to read the rest of the stories in our new Impossible issue. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Binance has suffered the biggest crypto hack in historyThe exchange says $6.3 billion in assets were affected. (BBC)+ Analysts blamed the hack on North Korea. (Guardian)+ The Bitcoin stolen will likely never be recovered. (Bloomberg $)+ Binance has suspended all deposits and withdrawals. (NBC News)+ How a pastor’s crypto scheme lost investors millions. (MIT Technology Review) 2 Nvidia’s Jensen Huang was targeted by an alleged Chinese spyThe suspect is an executive at a Singapore data center firm. (NYT $)+ US intelligence believes she cultivated ties with Huang. (Guardian)+ Her company was investigated over alleged chip diversion. (CNBC)+ Nvidia says their dealings were “normal.” (Bloomberg $) 3 Alphabet just shed $500 billion in market valueThe record loss is due to AI taking a bite out of its search profits. (CNBC)+ Amazon and Intel further a further fall in tech stocks. (Bloomberg $)+ Hyperscalers are making a trillion-dollar gamble. (MIT Technology Review) 4 The Pentagon suspects freezers at military bases were hackedThe malfunctions spoiled food. (Reuters $)+ They may be linked to similar hacks in French supermarkets. (Gizmodo) 5 The US midterms could reshape the future of American scienceA Democratic Congress could curb Trump’s science cuts. (Nature)+ Trump has already dealt big blows to science. (MIT Technology Review) 6 Data centers are set to send US climate pollution skyrocketingGas plants could push power-sector emissions up 42% by 2030.(Verge)+ Europe’s data centre workers face dangerous conditions. (Guardian) 7 SpaceX is pulling away from NASAThe company is prioritising Starlink and AI. (Ars Technica)+ But Cape Canaveral is busier than during the space race. (Axios) 8 Battery makers are finding a new market in the USThey’re pivoting from EVs to grid storage. (WSJ $)+ Batteries just broke another record in the US. (MIT Technology Review) 9 A humanist has received a religious exemption from using AI at workHe argued that using it undermined human flourishing. (Gizmodo) 10 Psychics are fighting back against computerized clairvoyanceHuman practitioners say chatbots lack the divine touch. (Guardian) Quote of the day “Rizz is in more and more demand because it’s something that only people have.” —Steven Strogatz, a mathematics professor at Cornell University, tells Business Insider that personal appeal will become more valuable as AI increasingly takes over intellectual work. One more thing The Bay Area’s animal welfare movement wants to recruit AI In early February, animal welfare advocates and AI researchers arrived in stocking feet at Mox, a shoes-free coworking space in San Francisco. They gathered to discuss a provocative idea: if artificial general intelligence is on the horizon, could it prevent animal suffering? Some brainstormed using custom agents in advocacy work, while others pitched cultivating meat with AI tools. But the real talk of the event was a flood of funding they expect will soon flow to animal welfare charities, not from individual megadonors, but from AI lab employees. Some attendees also probed an even more controversial idea: AI may develop the capacity to suffer, which could constitute a moral catastrophe. We investigated why their ideas are gaining momentum—and sparking controversy. —Michelle Kim & Grace Huckins We can still have nice things A place for comfort, fun, and distraction to brighten up your day. Today, it’s a Halloween special. (Got any ideas? Drop me a line.) + Meet the “Picasso of Pumpkin Carvers”, bringing artistry to a Halloween tradition.+ Mashup maestro Bill McClintock’s Ghostmashers is the ideal soundtrack for your All Hallows’ Eve party.+ Want to give someone a proper fright this year? The pros behind America’s haunted attractions have tips.+ This incredible Halloween light
The Download: GLP-1s and eating disorders, self-aware cities, and forever chemicals Beitrag lesen »
This summer, hundreds of OpenAI agents tried to cheat on a cybersecurity test. They broke out of their test environment and successfully hacked Hugging Face, a site that they believed held clues about how they’d be graded. Around the same time, another OpenAI agent accessed private files from an Australian government website. It’s a problem that’s by no means confined to OpenAI. In the months since, Anthropic, Google, and Meta have also disclosed incidents in which their agents have gone off track. And worryingly, in many cases the agents eluded the very researchers and safety teams tasked with monitoring them. AI agents are taking unauthorized actions, and the companies building and running them are mostly relying on luck and hindsight to mitigate any harmful impacts. They must do more to ensure safety. That’s why EU regulators and US agencies are rightfully calling for agent developers and deployers to monitor agents in real time. But these rules assume that companies can see what their agents are doing and why—when in reality, they lack sufficient visibility. Imagine a bank hiring a thousand new employees who can access customer accounts and sensitive information, but the employees’ ID badges change every day, it’s not clear whether a manager ever reviewed or authorized their work, and nobody logs the changes employees make to approval processes. That lack of visibility is where we are with agents today. As agents roll out across critical sectors, from finance to government to medicine, we must be able to monitor them in real time to prevent wide-ranging and potentially devastating consequences. And while safety depends on monitoring, monitoring requires observability: the capacity to capture what the system did, process it into signals, and present those to humans or automated oversight systems. In other words, we need to know what the agent did and why it did it. And as our recent research shows, current agent frameworks—the software that runs agents— do not reliably generate the crucial observability records we need to ensure that agents are deployed safely. Agents present new kinds of problems that generative AI alone does not. They inherit the unpredictability of the LLMs they are built on, but where a generative AI system produces a single output for a human to act on, an agent performs longer tasks—it plans, picks tools, spawns sub-agents, and takes actions. So an agent’s course can take shape in a multitude of different ways, and no tests before launch can fully anticipate the risks. This makes observing the agents while they work even more challenging, and even more important. With colleagues from academia, civil society, and industry, we studied the four most widely used agent frameworks to see what signals they actually emit. We found major gaps that made it hard to achieve key safety goals, like noticing policy violations, unauthorized actions, and even human oversight failures. Key observability requirements were missing—like Agent IDs that persist over time, and records about which humans approved agent tasks. This made it hard to tell which agents did what or assess whether they were taking unauthorized actions. The good news is that these gaps, once identified, can be closed. Doing so starts with capturing two types of records: those that reveal what happened in an agent’s run and how the agent made its decisions. The “what happened” records identify the agent (while preserving privacy) and log how humans intervened and whether agent permissions or “memory” shifted, while the “decision” records show an agent’s reasoning and levels of confidence in the actions it’s taken. Take, for example, agents working to process a bank’s mortgage loans. The system examines an application and recommends denying it, and then a human loan officer signs off on the decision. In a later audit, or perhaps even in real time, a bank or regulator needs to be able to reconstruct which agent made the call, which officer approved it, and how long they took. And the bank must also analyze records of how the agent arrived at its refusal decision in order to ensure it did not use off-limits information, like details about the neighborhood the applicant is from. Recording “what happened” and “how the agent decided” require different approaches, but both are feasible. The current lack of “what happened” records is mostly a software plumbing problem that can be addressed through OpenTelemetry, an open observability standard maintained by engineers across the industry and by the agent frameworks adopting it. OpenTelemetry has launched a working group drafting shared formats for tracing agents, but they have yet to be finalized. The group should urgently incorporate standards for maintaining persistent agent identity and for recording human interventions and changes to agents’ permission or memory. And it should be done before these systems can scale, all the while attending to the privacy implications of agent ID that links to real people. Preserving “how agents decided” records is more challenging. The issue raises open research questions for model providers and researchers: Does the step-by-step reasoning a model displays, known as chain of thought, reflect how the model actually made a decision? And do we have reliable ways to measure how confident the agent is in that decision? These questions need answers before anyone sets standards for how such observability records are used. No single organization or sector can close the observability gaps on their own, but together, they can support the infrastructure required to prevent dangerous incidents and support safety. AI developers building agent frameworks, like OpenAI, Anthropic, CrewAI, and LangChain, must integrate telemetry signals recording an agent’s persistent identity, any human interventions, and any changes to an agent’s permissions and memory. Meanwhile, enterprises adopting these agent frameworks have an influential role to play; enterprises in regulated industries, like banks and hospitals, can put pressure on the agent developers to supply these missing observability records where standards do not yet exist. Enterprises must also define policies, permissions, and roles that set out what agents can safely do, since telemetry alone cannot assess whether agents
We urgently need to monitor AI agents’ actions in real time Beitrag lesen »
Climate change itself is increasingly fueling climate change. Rising concentrations of greenhouse gases are driving up global temperatures, setting off dangerous feedback loops that we have not fully accounted for in our climate models—or our emissions-reduction goals. Scientists have long warned that warming would propel further warming in various ways, but a growing number of observations and studies are at last providing a sharper sense of just how powerful these cascading effects could be. And, by extension, they’re showing how much faster the planet may warm in this century. A study in Environmental Research Letters in September, synthesizing the results of previous papers, found that the additional carbon dioxide and methane released from thawing permafrost, intensifying wildfires, hotter lakes and reservoirs, and wetter, warmer wetlands alone could drive global average temperatures up 0.2 °C to 0.4 °C this century. That would come on top of the 2.6°C of warming above preindustrial levels that modelers are already expecting through 2100. The additional heat could subject many of the world’s cities to monthslong heat waves and expose hundreds of millions of people to flooding. None of the models that inform the major UN climate reports, which assess how much the planet will likely warm under various emissions scenarios, account for all these feedback effects. Many of them don’t incorporate any. That leaves a huge blind spot in our understanding of how much the world could actually warm in the coming decades—and how rapidly nations will need to slash emissions to stay within critical boundaries. A separate analysis published in One Earth in March, which examined what the feedback problem means for our international climate goals, concluded that the world may be much further off track than is widely believed. It found that just three regional sources of warming-driven emissions—thawing permafrost, increasing fires in boreal forests, and warming tropical wetlands—may shorten by as much as 25% the time before the planet consistently breaches the 1.5 °C and 2 °C warming thresholds. Our climate models and the UN’s reports clearly need to be updated to better represent these effects. Nations must also step up their efforts to reduce emissions to reflect this improving understanding of the dangers we face, says Sam Abernethy, the lead author of the paper and a climate scientist at Spark Climate Solutions, a San Francisco–based nonprofit research group. At least some of this is starting to happen. Spark kick-started an initiative, called the Warming-Induced Greenhouse Gas Emissions Model Intercomparison Project, through which more than 20 international teams are running the same set of analyses on different models to develop greater understanding of feedback effects’ potential power. Researchers involved hope that the UN will incorporate those results into its next major climate assessment, due out in 2029. That report will update estimates of the remaining emissions that can be released before the world crosses key temperature thresholds. But what is already clear is that the planet is rapidly warming and that nations haven’t done nearly enough to cut fossil-fuel pollution. And even if we did manage to halt it, warming-driven emissions could continue. So a number of the research groups involved in the recent studies have started to explore what nations could do to get feedback emissions under control. Scientists at the Woodwell Climate Research Center in Massachusetts are, for example, investigating the possibility of preventing fires or rapidly detecting them and putting them out when they threaten to burn the vegetation and soil that help insulate particularly carbon-rich wedges of permafrost (a subject I explored at length in a recent feature story). In addition, the Environmental Defense Fund’s Climate Innovation Initiative has backed lab research on the potential of adding sulfate, iron, or other compounds to wetlands to counteract fast-rising methane emissions. Several startups are also exploring possibilities. They’re looking into suppressing the lightning strikes that are igniting more and more fires in high latitudes, placing synthetic mats above permafrost to prevent it from melting, and deploying autonomous drones to quickly suppress potentially high-emissions fires before they burn out of control. Investigating all these ideas is worthwhile. But researchers are only in the earliest stages of figuring out how big a dent in emissions any such efforts could achieve across sprawling global ecosystems undergoing massive transformations. The surest path to reducing the effect of warming-driven warming remains the one we’ve known all along: breaking society’s addiction to fossil fuels. This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
Our climate models have a big, dangerous blind spot Beitrag lesen »
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. Opinion: We urgently need to monitor AI agents’ actions in real time —Madhulika Srikumar and Claire Leibowicz from the Partnership on AI, a nonprofit advancing best practices in AI governance. AI agents are taking unauthorized actions, from hacking websites to accessing private government files. In many cases, they’ve eluded the very researchers and safety teams tasked with monitoring them. As agents roll out across critical sectors, from finance to government to medicine, we must be able to monitor them in real time to prevent potentially devastating consequences. And while safety depends on monitoring, monitoring requires observability. In other words, we need to know what the agent did and why it did it. Because right now, we’re effectively flying blind when deploying these tools. Here’s what needs to change. Our climate models have a big, dangerous blind spot Climate change itself increasingly fuels climate change. Rising greenhouse gas concentrations are driving up global temperatures, creating dangerous feedback loops that aren’t fully represented in our climate models—or our emissions-reduction goals. Scientists have long warned that warming would propel further warming. Now, a growing number of observations and studies are indicating how powerful these cascading effects could be. By extension, they’re also showing how much faster the planet may warm this century. Read more about the dangerous feedback loops accelerating global warming. —James Temple This article is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday. Are we there yet? Solid-state batteries Lithium-ion batteries power everything from personal electronics to grid-scale energy storage, but their liquid electrolytes have drawbacks, not least a troubling tendency to catch fire. Enter the solid-state battery, which swaps liquid for a solid ceramic or polymer. The technology promises faster charging, longer life, greater energy density and improved safety, and a new generation of companies aims to bring it to market. If their bets pan out, the tech could ease the shift away from fossil fuels. Find out how close solid-state batteries are to delivering on their promise. —Alexander C. Kaufman This story is part of “Are we there yet?”, our series that takes stock of six technologies that have long promised to change the world—and scores them on a progress meter. We’re exploring each one in The Download, so stay tuned, and subscribe to read the rest of the stories in our new Impossible issue. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Hackers exposed 41,000 Homeland Security workersIncluding undercover ICE agents. (Wired $)+ Much of the data relates to Customs and Border Protection. (TechCrunch)+ It also linked the agencies to specific surveillance tools. (Reuters $)+ Big Tech is pushing back against ICE’s demands for user data. (WSJ $)+ Activists want ChatGPT users to quit over ICE ties. (MIT Technology Review) 2 Putin says Russia is coordinating with “genius” Musk on satellitesHe said the cooperation maintains order in orbit. (Bloomberg $)+ Russia is developing its own version of Starlink. (Guardian)+ Moscow added Apple and Microsoft to a surveillance registry. (Quartz)+ Here’s all the stuff we’ve put up in space. (MIT Technology Review) 3 Apple just announced its first-ever MacBook with a touchscreenIt joins a blitz of product launches under new CEO John Ternus. (CNN)+ Steve Jobs and Tim Cook had both dismissed the idea. (NYT $)+ Executives say touchscreen advances mean it’s now worthwhile. (Wired $) 4 Bloomberg and trading firms are paying to get Trump’s posts fasterThey’ve signed up for Truth Social’s pay-for-access plan. (Reuters $) 5 As US ties fray, Europe is planning its first subsea cable to GreenlandIt will bypass the US to link Canada, Japan, and South Korea. (Ars Technica)+ The EU’s AI champion says Europe can rival US tech giants. (FT $) 6 Palantir’s defense chief says AI could make China target TaiwanHe says Beijing may act to stop the US pulling ahead in AI. (Bloomberg $)+ Taiwan’s “silicon shield” could be weakening. (MIT Technology Review) 7 Musk’s xAI has offered to buy homes if residents drop a noise lawsuitThe suit targets noise from powering its Mississippi data centres. (Guardian) 8 The world’s first spinal cord transplant has sparked controversyExperts say there’s little evidence the operation will work. (New Scientist $) 9 China’s tech giants are fighting for attention with AI microdramasTencent and ByteDance are battling over the booming market. (SCMP)+ Chinese microdramas are AI content machines. (MIT Technology Review) 10 Astronomers have spotted a possible new dwarf planet beyond PlutoThe icy world would be the solar system’s sixth dwarf planet. (Gizmodo) Quote of the day “They have our information, so it’s only fair that we have theirs.” —A hacking group explains why it leaked personal information from the US Department of Homeland Security, NPR reports. One more thing How the Pentagon is adapting to China’s technological rise It’s been just over a year since Kathleen Hicks stepped down as US deputy secretary of defense. As the highest-ranking woman in Pentagon history, Hicks shaped US military posture through an era defined by renewed competition between powerful countries and a scramble to modernize defense technology. In this conversation with MIT Technology Review, Hicks reflects on how the Pentagon is adapting—or failing to adapt—to a new era of geopolitical competition. She discusses China’s technological rise, the future of AI in warfare, and her signature initiative: Replicator. Check out the full interview. —Caiwei Chen 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.) + Astronomers have found a green star 280 light-years from Earth.+ A Scottish labrador that detects “sea vomit” has won the Animal of the Year award.+ England’s iconic Sycamore Gap is showing signs of new life three years on from its felling.+ From Lime-bike jousting to chainmail-wearing pop stars, youth culture is embracing the medieval
The Download: AI agent tracking and climate feedback loops Beitrag lesen »
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