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) Read Post »
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 Read Post »
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 Read Post »
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 Read Post »
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 Read Post »
arXiv:2609.34717v2 Announce Type: replace Abstract: Open-weight large language models (LLMs) can generate function-level programs from natural-language prompts, but plausible candidates still fail on hidden semantics and repeat mistakes across repair attempts. We present ReMCTS, an execution-grounded, memory-augmented, LLM-guided MCTS-style search framework. It organizes program candidates as tree states, retains branch-local debugging context, retrieves failure experience across branches, and distinguishes failed checks from unavailable evidence. On HumanEval and MBPP-Sanitized, visible-test ReMCTS improves over direct generation in 8 of 10 model-dataset pairs under held-out evaluation, whereas proxy-only search is less stable. Controlled tree-search, sampling, repair, and memory ablations characterize the source and limits of these gains. A 30-task HumanEval-X C++ pilot further demonstrates compatibility with compiler-backed execution, but does not constitute a broad multilingual evaluation.
ReMCTS: Reflection-Enhanced Monte Carlo Tree Search for Code Generation Read Post »
In this article, you will learn how to compare two strategies for adapting a large language model to a specific domain: fine-tuning with LoRA versus…
Fine-Tuning vs. Frozen Embeddings: When to Adapt the LLM vs. the Sklearn Head Read Post »
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. AI labs are buying data from dead startups to train their models When Keelan Miskell began winding down his crypto startup, he didn’t think its data was worth much. But he’s since turned the company’s digital archives into tens of thousands of dollars, selling its codebase and workspace data. He’s part of a growing market in which companies that help shut down startups sell their data to AI labs. Demand has grown as training labs hit the limits of public and synthetic datasets, with buyers seeking real-world records to help train AI agents. But the sales raise legal and ethical questions. Read the full story on the AI labs buying up the remains of failed startups. —Matthew King Are we there yet? Interspecies communication Humans are far from the only species with a gift for gab, and our furry, feathered, and finned friends could have a lot to tell us. Decoding how animals communicate with one another could reveal the inner lives of other species—and give humans a window onto how they see the world. A growing arsenal of devices and advances in AI have made it easier to study their howls, squeaks, and clicks, but making sense of it all remains complex. Find out how close we are to interspecies communication. —Cassandra Willyard 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. MIT Technology Review Narrated: what it would take to build a city on the moon The US and China are racing to build bases on the moon, with NASA hoping to establish a small lunar city by the end of the century. But the moon is a tremendously hostile place to set up camp. A sprawling lunar base must be safe for its inhabitants, protected from falling asteroids, endless cosmic radiation, razor-sharp lunar dust, and torturous temperature swings. It also must be somewhat self-sustaining. That will require autonomous robots, nuclear power, and a way to use the moon’s resources to keep the base operating. But the chief obstacle may be one of political will. This is our latest story to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Hackers have disrupted the US Federal Election Commission’s websiteThe FEC filing system went offline a week before the midterms. (Reuters $)+ The attack is hiding spending data during the costliest midterms ever. (Gizmodo)+ Social media platforms are struggling to contain AI election lies. (Guardian)+ Software flaws in US voting machines threaten election security. (Axios)+ A blueprint for using AI to strengthen democracy. (MIT Technology Review) 2 Starlink restrictions have driven US allies to rethink their dependenceRestrictions in Ukraine have spooked officials across Europe. (Bloomberg $)+ And given European firms a new pitch for their rival services. (Axios)+ An Israeli spy firm allegedly targeted Starlink users. (Reuters $)+ Stratospheric internet could take off this year. (MIT Technology Review) 3 Greenhouse gas levels keep reaching new records worldwideCarbon dioxide, methane, and nitrous oxide are all on the up. (New Scientist $)+ Trump is making it easier for fossil fuel companies to leak methane. (Verge)+ The IPCC is fighting for survival. (Nature)+ How to study a swiftly melting climate disaster. (MIT Technology Review) 4 Monkeys’ brains have revealed new clues about consciousnessTheir neural activity encodes images that escape their awareness. (Nature)+ Bees’ feelings are also changing views of consciousness. (New Scientist $)+ Debates over AI consciousness are a trap. (MIT Technology Review) 5 Chinese scientists have developed a catalyst to make Mars rocket fuelIt converts Martian atmospheric carbon dioxide into liquid methanol. (SCMP) 6 Tech billionaires have teamed up against California’s wealth taxSergey Brin, Eric Schmidt, and others have funded a major campaign. (NYT $) 7 Delaware plans to let AI agents run entire businessesThe proposal could let owners evade responsibility for harm. (Ars Technica)+ A better web for AI agents could hurt humans. (MIT Technology Review) 8 Palmer Luckey has unveiled plans for American-made military laptopsAnduril will build rugged PCs in Ohio using Qualcomm chips. (WSJ $) 9 Gen Z is turning away from the social media it grew up withYoung people are fueling a boom in digital detox tools. (Guardian) 10 Trump now wants to ban the term “artificial intelligence”He’s demanding we all start using “Super Intelligence.” (Gizmodo) Quote of the day “For the next two years, we’re going to be living in a state where, on average, every week there is a critical, internet-wide vulnerability.” —Matthew Prince, Cloudflare’s chief executive, tells the New York Times that AI is currently finding software vulnerabilities quicker than they can be patched. One more thing Inside the controversial tree farms powering Apple’s carbon-neutral goal In 2020, Apple set a goal to become net zero by the end of the decade. To hit that target, the company is offsetting its emissions by planting millions of eucalyptus trees in Brazil. Apple is betting that the strategy will lead to a greener future. But critics warn that the industrial tree farms will do more harm than good. Here’s why the plans have sparked a backlash. —Gregory Barber 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.) + Ugly shoes have become beautifully mainstream.+ Scientists have built a stunning new simulation of the early universe.+ Guitarist Kent Nishimura has stripped Thriller back to
The Download: dead startups fueling AI and interspecies communication Read Post »
Across a business, any number of AI platforms, systems, and tools may be at work simultaneously—for example, AI-powered customer platforms, conversational assistants, and workflow and process automation tools. Each one is sophisticated, advanced, and quick to respond. But, when set to work, the outcomes and actions that emerge may be wrong, confused, or lacking a proper understanding of the business. Many organizations find themselves in this situation—and it could be one of key reasons they struggle to extract returns on their AI investments. DOWNLOAD THE REPORT An MIT Technology Review Insights poll finds that while every organization surveyed is using AI today to some degree, fewer than one in five (16%) are extracting transformative value from their deployments. The largest percentage of respondents (40%) say they are seeing moderate gains in specific tasks or teams, while 21% report meaningful gains, leaving 23% that are seeing minimal returns or none at all. Companies are mostly still piloting AI use cases (according to 38% of respondents) or using it to assist employees’ work with tools like co-pilots or chat assistants (35%). Just 12% of enterprises have AI agents in production and 3% say they are using AI to do most of the work in key processes. “There’s a big gap between what the models are capable of and how the models are actually being useful inside organizations,” says Prukalpa Sankar, co-founder of Atlan. “For every real-life intent and purpose, artificial general intelligence is already here. It’s just not really useful in most companies today.” The missing layer, Sankar says, is context, or how well AI understands a business. A context layer is the architectural infrastructure that sits between an organization’s business systems (for example, systems of record and data, and its AI platforms). It translates enterprise intelligence into governed, machine-usable context for AI agents or models and helps knowledge compound. “Without a context layer for AI, it’s almost like you’re hiring the smartest intern in the world, blindfolding them, and throwing them into your organization,” Sankar says. 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.
Creating AI value with enterprise context Read Post »
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