Autistic children showed earlier and less age-related refinement in the brain signals involved in processing faces. A child looks at a face, and within milliseconds the brain begins sorting its features, expression, and identity. Researchers have now traced that process across the entire scalp in one of the most comprehensive studies of face perception in [...]
An AI-assisted counterexample disproves the Jacobian conjecture above two dimensions while leaving its original two-dimensional form open. A formula short enough to fit inside a single post on X has sent a surge of excitement through the mathematical community, just as millions of people were settling down after the FIFA World Cup Final. Levent Alpöge, [...]
By now, you’d be hard pressed to find anyone who isn’t broadly familiar with the AI heavyweights and their flagship models. OpenAI has ChatGPT, Anthropic has Claude, Google has Gemini. But what does Amazon have?
Not a whole lot, it turns out. Unlike its competitors in the tech megacorp scene, Amazon’s standout AI model — organized under the name “Nova” — is about the farthest thing from a household name. And that’s not likely to change anytime soon, because the ecommerce giant is now gutting its in-house AI labs, Bloomberg reports.
Per the outlet, Amazon is scaling back the ambitions of most of its Nova AI models and reorganizing its AI development teams as part of an effort to narrow its focus on “highest priority” goals, after its power-hungry large language models turned out to be a bust.
Accordingly, the company has put most of its AI models on ice, Amazon insiders told Bloomberg. This includes its text-based Nova models, as well as video- and image-generating models, all of which have been placed in a state of “keep the lights on,” meaning they’re technically still supported, but only receive the bare minimum resources needed to sustain them.
Instead, labor and computing power are being diverted toward a singular “frontier-model effort” led by Pieter Abbeel, director of the Berkeley Robot Learning Lab whose robotics company Covariant was hoovered up by Amazon in 2024. At the time Abbeel joined the tech giant, Covariant’s founders were working on AI models for robots, which lines up pretty well with Amazon’s push to automate everything it possibly can.
News of the shift in Amazon’s strategic focus comes a week after it closed one of its key AI offices in San Francisco, an 80-person site specializing in research on artificial general intelligence, the supposed next-level of AI development where the tech obtains human-level intelligence and reasoning skills.
With urgent questions swirling over whether LLMs can ever usher in a return on investment — let alone AGI — it may be that Amazon is jumping out of the AI hype cycle while it still can.
More on Amazon: Amazon’s AI-Generated Animated Series Canceled After Relentless Derision
The post Amazon Is Gutting Its AI Division After Sustained Failure appeared first on Futurism.
© ID 392097269 | Agentic Ai © Tongsupatman | Dreamstime.com
Last week, ChatGPT maker OpenAI issued what seems like a frightening memo about an “unprecedented cyber incident” involving cutting-edge AI models breaking loose and hacking another company.
As the AI lab tells it, a group of next-generation AI models — including one that hasn’t even been disclosed to the public yet — had busted out of their secure testing environment and into a production database owned by the machine learning startup Hugging Face.
“It’s quite mind-blowing that all of this happened autonomously!” Hugging Face CEO Clement Delangue tweeted, seemingly backing the story. “The investigation is ongoing, and we’ll share more learnings from what might be the first incident of its kind!”
Mainstream press took the industry’s story and ran with it, issuing a flurry of anxious dispatches fretting about dangerous AI. One CBS headline called it “very alarming,” while NBC fretted that it was “just the start” of an ominous trend.
Look beyond the alarmist headlines, however, and you’ll find a familiar pattern: a massive tech company using scare tactics to manipulate public opinion.
As assistant professor of computer science at Cornell University John Thickstun observes for the Guardian, this isn’t OpenAI’s first time peddling the “scary AI” narrative. Back in February of 2019 — way before the public release of ChatGPT — the company announced GPT-2, then a cutting-edge large language model. But GPT-2, the company insisted, was too dangerous to release to the dimwitted public, a pronouncement which fed the AI hypebeast and went on to secure OpenAI a cool $1 billion investment from Microsoft.
“This was an early example of a pattern in OpenAI’s communications,” Thickstun explains: “loudly proclaim how dangerous AI is, and investors will hear how powerful it is.”
Whether the tale of OpenAI’s rogue agent stands up to scrutiny remains to be seen, but there are reasons to be skeptical.
If OpenAI’s models really were actually gnawing on the wires of other American companies, it would present a substantial threat to economic activity that any Justice Department worth its salt would want to shut down (maybe not this iteration of the Justice Department, but you get the idea.)
And as AI labs go, OpenAI is notoriously secure both from prying eyes outside and internal threats within its halls. Sure, it’s possible OpenAI has overstated its own internal safeguards as a kind of security theater. But on the flipside, OpenAI is now a major AI contractor for the US Pentagon, a relationship which demands cutting edge security practice.
It likewise follows the broader AI industry’s playbook. Anthropic, the company behind Claude, has deployed similar rhetoric, warning of a similar breakout in which a rogue AI agent escaped its cage all on its lonesome. In one strange instance, the company’s billionaire co-founder Chris Olah even warned the Pope that the Vatican needs to help stop rogue AI from “dominating humanity.”
More on OpenAI: Man Sues OpenAI, Saying ChatGPT Almost Killed Him With Horrendously Dangerous Medical Advice
The post Suspicion Grows About OpenAI’s Tale About Its Rogue Hacker AI appeared first on Futurism.
Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.
One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.
Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.
To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.
The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes.
They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios.
After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR).
They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model.
They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.
Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions.
The post Predicting microbial antibiotic resistance with AI appeared first on Sciworthy.
© William Wong
To date, AI industry spending has topped $1.6 trillion, and shows no sign of slowing anytime soon.
So what do we actually have to show for it? Historically, it’s been a whole lot of nothing: as numerous studies have shown us, tools like AI chatbots and autonomous agents have been ineffective at completing real world tasks in a competent way.
The tech industry insists that’s all about to change within the next few years, as AI’s capabilities grow by leaps and bounds, enabling economic growth the likes of which the world has never seen. But is it really?
Not necessarily. A new study out of the University of California Berkeley’s Center for Responsible, Decentralized Intelligence — flagged by the College Fix — shows that frontier AI tools of all makes and models are still incapable of completing the vast majority of workplace tasks at an acceptable level, throwing a major wrench in the tech industry’s assertions that the AI revolution is imminent.
To come to that conclusion, the UC researchers designed a rigorous assessment they call the “Agents’ Last Exam,” developed to test “job-readiness” across numerous state-of-the-art AI models. Basically, the ALE — an impish riff on “Humanity’s Last Exam” — is designed to put an AI system through its paces, covering “more than 1,500 expert-sourced tasks spanning 55 occupations,” the researchers wrote in apress release.
Those test spans the typical line-up of AI-exposed jobs like software engineering and graphic design, but also a substantial number of jobs whose fates remain less certain, such as maritime engineering, agriculture, audio production, and public health operations.
Using the ALE benchmark, researchers took a hard look at advanced “closed” models — proprietary AI systems developed by private companies — like Anthropic’s Fable 5, OpenAI’s GPT-5.5, Cursor’s Composer 2.5, and Google’s Gemini 3.1 Pro. (For good measure, they also looked at two open-source models by Chinese developers.)
As cutting-edge as these AI models are, the research found that they’re far from ready for the complex needs of the modern workplace. Out of all of the models put through the gauntlet, each of them failed spectacularly. OpenAI’s GPT-5.5 came in with the highest score: a passing rate of just 24 percent overall.
“Today’s agents can solve a meaningful fraction of professional tasks,” the researchers wrote. “However, when we look at the hardest tasks that require sustained reasoning, deep domain expertise, and reliable execution over long horizons, they are still far from human-level performance.”
And as tasks became more complicated, even those meager aggregate scores fell off fast.
“On ALE’s hardest tier, every frontier agent we tested, including Fable 5, achieved a 0 percent success rate,” the presser explains.
The researchers also break down some cost considerations. The cutting edge Fable 5, they note, delivers “similar performance” to models like GPT-5.5 and Composer 2.5, “while costing roughly 4-12× more per completed task.”
Despite the horrible test results, researchers caution that the technology could still upend the market for more AI-exposed roles — as plenty of corporate executives have shown us, the tech doesn’t need to work particularly well to keep workers on their back heels.
“Even if current pass rates remain relatively low, occupations dominated by routine and well-defined procedures are likely to experience disruption first, while decision-intensive roles will remain more resilient for longer,” Berkeley computer science researcher and study co-author Dawn Song told College Fix.
“The key factor,” Song added, “is not the industry itself, but the nature of the work.”
More on AI: OpenAI Appears to Be Missing Its Sales Goals by a Vast Margin
The post Frontier AI Is Faceplanting at Real-World Workplace Tasks appeared first on Futurism.
A Adobe anunciou a adição de novos recursos temporários impulsionados por inteligência artificial (IA) ao Project Indigo, seu app de câmera exclusivo para iOS, com a ferramenta AI Playground.
Os quatro principais recursos de IA do Project Indigo incluem a manipulação de objetos (Object Editing, ou “edição de objeto”, em tradução direta), a estilização de uma foto em uma determinada técnica artística (Styles, ou “estilos”), o feedback por IA das suas fotos (Photo Guidance, ou “orientação fotográfica”) e a edição a partir de comandos de linguagem natural (Custom Edit, ou “edição customizável”).
De forma opcional, a Adobe afirmou ainda que o acesso ao AI Playground estará restrito a uma parcela dos usuários do Project Indigo, sem menção aos critérios exatos de disponibilização. O teste ocorrerá durante algumas semanas e a ideia é compreender o uso de IA no app, embora ela reitere seus pilares de privacidade:
Quem aceitar participar precisará concordar em compartilhar apenas as interações com os botões do recurso. A Adobe afirma que não acessará os comandos digitados, nem as imagens, não fará upload delas para seus servidores e não as utilizará para treinar modelos de IA. Como não há login, todos os dados coletados são anônimos. Quem preferir não participar pode recusar e continuar usando o aplicativo normalmente.
Recursos como a estilização de fotos ou a remoção de objetos farão uso do modelo Nano Banana, do Google, ou dos próprios modelos Firefly da Adobe, mas ela reitera que o leque de modelos poderá se expandir no futuro.
Por fim, a Adobe explicou ainda que os modelos atuais de geração de imagens produzem apenas resultados com alcance dinâmico padrão (SDR 1Standard dynamic range, ou faixa dinâmica padrão.) e, para preservar a aparência das fotos capturadas com HDR 2High dynamic range, ou ampla faixa dinâmica. pelo Project Indigo, a empresa desenvolveu um modelo próprio de ML 3Machine learning, ou aprendizado de máquina. que converte automaticamente o resultado gerado pela IA para HDR, utilizando a foto original como referência, sem empregá-la para treinar modelos de IA.
via TechCrunch
A reverse prediction test suggests that supposedly brainlike AI models may rely on visual strategies the primate brain does not use. Artificial intelligence can sometimes predict how the brain responds when people recognize objects. But that resemblance may hide an important weakness: the internal workings of today’s vision models do not necessarily match the processes [...]
While Wall Street was fast asleep, a Chinese-made large language model quietly leapfrogged 16 other models to become number one on the AI charts.
The model is called Kimi-K3, developed by Beijing-based firm Moonshot AI. On Thursday, the AI benchmark platform Arena.ai announced that Kimi had gone from number 17 in the “Frontend Code Arena” — a measure of an LLM’s ability to perform multi-step web development tasks — to number one, surpassing the buzzy Claude Fable 5 and GPT-5.6 Sol by a mile.
In the “Text Arena,” a measure of an LLM’s ability in text-to-text tasks like creative writing, Kimi-K3 earned the number nine spot, a significant improvement from Moonshot AI’s previous model, Kimi-K2.6, which held number 38.
The news comes as investors are facing a major reckoning, with US semiconductor stocks plummeting on Friday morning and the tech-heavy Nasdaq composite sliding by 1.4 percent. Those losses are extending a horrible week for tech stocks, which had been driven by concerns over an all-American AI bubble.
The moniker, “Moonshot,” might be an understatement. The major catch here is that not only did a Chinese AI model surpass every US-designed model in front-end coding in a benchmark, it did so using a dramatically different approach.
Just like DeepSeek, a similar Chinese AI model that rankled the US stock market last year, Kimi is an open-weight model, meaning its inner workings are viewable to the public. Compared to proprietary models like GPT-5.6 that are kept under lock and key, open models cost users on average six times less, though their performance has historically been ever-so-slightly worse than their closed counterparts.
Responding to the news, Xiaoyin Qu, former Meta senior product manager turned AI entrepreneur, posed an important question: “When the best open weight model exceeds the best closed-source model, how does [Anthropic] justify its Fable pricing? Why would anyone pay for that?”
Even before Kimi-K3 dropped, the proposition of paying up to six times more for the slight performance boost offered by closed models was already pushing US companies toward Chinese AI. Now that the performance gap is closing fast, there’s even less reason for companies or individuals to pay exorbitant prices associated with Silicon Valley’s frontier models.
That simple math is bad news for the US tech industry, which has spent years insisting that it will take trillions of dollars to make AI work.
As Qu observed, “Kimi’s most recent funding round values the company at $20 Billion as of two months ago. Anthropic is worth almost 1 trillion, 50x. Why?”
More on AI: Chinese Court Rules That a Worker Cannot Be Replaced by AI
The post A Chinese AI Model Just Shot to Number One on the Charts, Sending Shockwaves Through the American Tech Industry appeared first on Futurism.
PrismML AI compression could give Apple a way to place far more capable artificial intelligence directly inside the iPhone. The Caltech spinout says it reduced a 27-billion-parameter model from approximately 54GB to less than 4GB, small enough to run locally on an iPhone 17 Pro.
That represents a reduction of about 93%, but the more useful number may be memory efficiency. PrismML says its technology can use up to 15 times less memory than conventional versions of the same model. Apple has reportedly held early discussions with the startup about possible uses for the technology.
Neither company has announced a partnership, acquisition or product plan. PrismML’s claims also need to be judged through independent testing, sustained performance and the compromises introduced by heavy compression. Even so, the demonstration points directly at one of Apple’s largest AI challenges: fitting stronger models inside devices without destroying battery life, consuming too much memory or sending every complex request to the cloud.
Large AI models are difficult to run on phones because their parameters require substantial storage and working memory. A standard model may perform well in a data center equipped with powerful accelerators, large memory pools and constant electricity, then become impractical inside a device designed to last all day on one battery.
PrismML approaches that problem by representing model weights with far fewer bits. The startup has developed what it calls Bonsai models, including 1-bit versions designed to preserve much of the original model’s capability while sharply reducing size and energy use.
The reported iPhone demonstration used a compressed version of Alibaba’s Qwen model with 27 billion parameters. That is far larger than the compact language models normally associated with smartphones. Running it locally suggests that model size may no longer be controlled only by the number of parameters. How efficiently those parameters are represented becomes just as relevant.
Compression is not new. Developers already use quantization, pruning and other methods to make models smaller. PrismML’s claim is that its approach pushes that process much further while retaining useful reasoning and language performance.
That claim is exactly why Apple would be interested.
Apple’s AI strategy depends on dividing work between the device and the cloud. Simpler Apple Intelligence requests can run locally, while more demanding tasks may move to Private Cloud Compute. That hybrid structure allows Apple to offer stronger capabilities without forcing the iPhone to carry every model it may ever need.
The cloud layer, however, introduces cost, latency and availability requirements. A request must leave the device, reach a server, be processed and return. Apple has designed Private Cloud Compute around strong privacy protections, but local processing remains faster and easier to explain.
A larger on-device model could improve Siri AI’s language understanding, reasoning and ability to complete multi-step tasks without a network connection. It could also reduce the number of requests sent to Apple’s servers.
That does not mean cloud processing would disappear. Server models can remain larger and support especially demanding requests. PrismML AI compression could instead move the dividing line. Tasks that currently need Private Cloud Compute might become possible directly on a future iPhone.
The benefits could include quicker responses, stronger offline operation and less server demand as Apple Intelligence reaches more users.
On-device processing has always been central to Apple’s AI positioning. Personal information can remain on the iPhone rather than moving through a conventional cloud service. The device can interpret a request, use local context and produce an answer without exposing the underlying data beyond the hardware.
A model capable of handling more work locally would reinforce that approach. Siri AI could potentially understand messages, notes, schedules or onscreen content while reducing the need to transfer information elsewhere.
Apple already uses Private Cloud Compute when local processing is insufficient, and the company has built significant safeguards around that system. Yet every task completed on the iPhone removes one additional processing step.
Privacy is not the only advantage. Local models can continue operating when connectivity is poor or unavailable. That could help Siri AI during flights, rural travel, crowded events or other situations where cloud access becomes unreliable.
A capable offline assistant would also feel more like part of the operating system and less like a remote service accessed through an interface.
Shrinking a model to less than 4GB does not automatically make it ideal for everyday iPhone use. The model must also run quickly enough, avoid excessive heat and leave enough memory for iOS and active apps.
A technical demonstration may complete tasks successfully while consuming more energy or time than users would accept from Siri. Apple would need to optimize the model for its Neural Engine, memory architecture and software frameworks rather than simply loading PrismML’s existing build onto every supported device.
Storage is another consideration. A model occupying several gigabytes would compete with apps, photos, offline media and system files. Apple could reserve more storage for intelligence features, deliver models selectively or use several specialized models instead of one large general system.
Battery use may create the hardest constraint. An AI model can fit in memory and still require too much sustained computation for frequent mobile use. The ideal system needs to be small, fast and efficient enough to disappear into normal iPhone behavior.
Apple’s chip design gives it an advantage here. The company controls the processor, Neural Engine, operating system and AI software stack. PrismML could provide the compression technique while Apple handles the deeper hardware integration.
The strongest use of PrismML’s technology may not be putting one enormous model on every iPhone. Apple could use compression across a family of models designed for different devices and tasks.
A smaller model could handle routine language requests. A more capable local model could be downloaded only to recent Pro devices with sufficient memory. Specialized models could support writing, image generation, app actions or Visual Intelligence. Private Cloud Compute could remain available when the request exceeds local capability.
That would give Apple more flexibility than an all-local or all-cloud design. Each device could carry the intelligence appropriate for its chip, memory and battery capacity.
The approach could also help Macs, iPads and future home products. A compressed model may run more comfortably on a MacBook Air, HomePod or smart home hub without requiring constant server access. Apple could scale the same intelligence foundation across products while adjusting model size to the hardware.
This is especially relevant as Siri AI moves beyond answering questions. App actions, personal context and ambient smart home control require intelligence that remains available throughout the day. Lower memory and energy requirements could make those experiences more practical.
A 93% reduction is a striking headline, but compression can affect output quality. A smaller representation may lose precision, weaken reasoning or struggle with uncommon information. The real test is not whether the model runs. It is whether it performs well enough across the tasks Apple expects Siri AI to handle.
PrismML says its technology preserves strong performance compared with full-precision models. Apple would still need extensive evaluation across languages, safety categories, tool use and personal-assistant scenarios.
Reliability is especially sensitive for an operating-system assistant. A model used for casual brainstorming can tolerate occasional weakness. A model handling messages, appointments, directions or app actions needs a higher level of consistency.
Apple may therefore use aggressive compression selectively. Some tasks can accept a smaller model, while others may remain on a higher-quality server model. The user would not need to know which model is active as long as the response remains fast and dependable.
Reports indicate Apple has spoken with PrismML, but early conversations can lead in several directions. Apple could license the startup’s technology, work on a technical integration, hire members of the team or explore an acquisition.
The company has a long history of purchasing smaller firms for specialized technology and engineering talent. Model compression would fit that pattern because it supports Apple’s existing AI strategy rather than creating a separate consumer product.
PrismML could also remain independent and provide its tools to multiple hardware companies. Its technology has potential uses across phones, computers, robotics, vehicles and industrial equipment where cloud access is expensive or unreliable.
Apple’s interest alone gives the startup attention. The larger significance is that efficient models are becoming as valuable as larger ones. The AI industry spent years treating scale as the primary route to improvement. Mobile computing forces a different question: how much intelligence can fit inside the power and memory limits of one personal device?
Apple does not need to place the biggest available model on an iPhone. It needs a model capable enough to improve the interactions users repeat every day.
PrismML AI compression could help Siri understand more context, complete more requests locally and respond without waiting for a server. It could strengthen Apple’s privacy argument while reducing the infrastructure load created by millions of AI-enabled devices.
The technology remains early, and a startup demonstration is not the same as a shipping Apple feature. The 93% figure will mean little if quality, battery life or speed suffer under daily use.
But the direction fits Apple unusually well. The company has spent years designing smaller, more efficient hardware systems that perform beyond their apparent limits. PrismML is proposing a similar strategy for AI models.
If the startup’s claims hold up, the next major Siri improvement may not come from sending a larger model into the cloud. It may come from fitting one into the iPhone without making the iPhone feel the weight.
The post PrismML AI 93% Compression Could Give Siri a Major Boost appeared first on AppleMagazine.
It’s been several years since generative AI and large language models (LLMs) took the world by storm. LLMs surpassed earlier natural-language systems at generating text, while diffusion models enabled generating images, music, and videos.
These generative AI models work well in the digital world, but on their own, they have limited capabilities to comprehend the three-dimensional physical world and other spaces. This includes the objects occupying an area, how they relate to each other, tracking movement, and answering complex questions requiring an understanding of dimensions, distances, motion, and collisions.
Spatial intelligence is an AI capability that allows models to reason about three-dimensional space. These models can generate 3D scenes of the world and other spaces. This content can then be displayed through traditional renderers, game engines, or AR/VR systems that use spatial computing techniques. But it’s the spatial intelligence model’s ability to connect natural language with 3D models that has the most applications in robotics, manufacturing, construction, and other physical environments.
Dr. Fei-Fei Li, often called the godmother of AI, published a manifesto on how spatial intelligence is AI’s next frontier, contrasting it with LLMs. “While current state-of-the-art AI can excel at reading, writing, research, and pattern recognition in data, these same models bear fundamental limitations when representing or interacting with the physical world,” wrote Dr. Li. “Our view of the world is holistic—not just what we’re looking at, but how everything relates spatially, what it means, and why it matters. Understanding this through imagination, reasoning, creation, and interaction—not just descriptions—is the power of spatial intelligence.”
The concept of spatial intelligence isn’t new and was described in Howard Gardner’s book, Frames of Mind, in 1983. Recent breakthroughs, including the launch of World Labs’ Marble and its $1 billion funding round, and competing approaches from Google’s Genie 3 and Nvidia Cosmos, should put spatial intelligence and world models on more R&D road maps.
It’s important to develop AI literacy and understand the terminology and concepts related to the physical world and 3D AI technologies:
“Spatial intelligence models go beyond pixels to understand the 3D structure of the world—how objects are positioned, how they move, and how they interact,” says David Fattal, founder and CTO at Leia. “This enables applications like more realistic video generation, spatial computing interfaces, and AI systems that can reason about physical environments. As real-world 3D data becomes more available, these models will become foundational to the next generation of visual AI.”
To better understand spatial intelligence, let’s consider physical infrastructure such as bridges and buildings. The American Society of Civil Engineers estimates a $9.1 trillion investment is needed from 2024 through 2033 to achieve a state of good repair. When maintenance and monitoring lag, it can lead to major failures such as the 2022 collapse of the Fern Hollow Bridge in Pittsburgh.
Spatial intelligence and the development of digital twins may help identify issues earlier and prioritize where investments are needed. “Spatial intelligence models serve as the 4D digital blueprints for our built environment, allowing us to visualize and predict the complex interactions between aging assets and the shifting ground beneath them,” says Patrick Cozzi, chief platform officer at Bentley Systems. “By synthesizing disparate geospatial data into a living digital twin, these models provide the foresight necessary to mitigate the hidden risks of structural fatigue and subsurface instability.”
There’s a significant challenge in bridge health monitoring and transitioning from manual, infrequent structural inspections to leveraging sensors, digital twins, and spatial intelligence. Cozzi adds, “This integration of continuous field data moves beyond static documentation, empowering agencies to evolve from reactive repairs to proactive, resilient asset management that safeguards the long-term integrity of our most critical public systems.”
Bridges are largely static, but the real world is increasingly being occupied by autonomous systems such as self-driving cars, robots, and drones. And where there are moving systems, there is a risk of collisions.
“Spatial intelligence models are AI systems that reason about the physical world by combining vision, sensor data, and contextual cues to understand space, motion, and object relationships,” says Sudeep George, CTO at iMerit. “The value of spatial intelligence models lies not just in perceiving an environment, but in enabling machines to act within it safely and in real time. That is especially important in robotics and autonomous systems, where decisions must be made in complex, multimodal, fast-changing settings.”
To see one example, this tutorial for simulating robotic environments combines Nvidia Isaac Sim, an open source robotics reference framework, with spatial intelligence in Marble from World Labs.
Today’s collision detection systems, such as those used in autonomous vehicles, typically rely on modules for sensing, perception, planning, and control. Spatial intelligence models may offer improvements by assessing the collision risks of unidentified objects or by tracking objects that move out of sensor view. For example, Waymo’s World Model, built on Genie 3, is a simulator that generates complex weather conditions and other critical safety events.
For an out-of-this-world example, James Urquhart, field CTO and technology evangelist at Kamiwaza, has delivered several examples of spatial intelligence applications, including one for satellite collision detection and conflict analysis. Urquhart says, “Models that specialize in these types of data sets, as well as the physics and geography of the real world, enable faster and more accurate decision-making for tasks that depend on them.”
Recent spatial intelligence announcements include creating 3D worlds from image or text prompts with Marble and simulating water physics, lighting, weather, and animal behavior with Genie 3. But difficulties remain in bringing spatial intelligence to physical-world use cases.
“Spatial intelligence and world models are laying the groundwork for future AI agents that will be able to interact in and with our physical world,” says Jason Corso, cofounder and chief scientist at Voxel51. “These models are significantly more challenging to develop and test, largely because the data underlying their development is complex, and it’s hard to handle all of the combinatorics involved in the physical world.”
In addition to learning the models and prototyping with them, development and data leaders need to review the data assets that will feed spatial intelligence models. “Spatial intelligence models translate location signals into a structured understanding of the real world, but they’re only as reliable as the data beneath them,” says Dan Adams, executive vice president and general manager of Enrich at Precisely. “The real unlock isn’t the model—it’s the reference layer with persistent identifiers, confidence metadata, and source lineage that lets AI reason about places, not just match strings.”
Even once applications are developed, there will be infrastructure challenges in deploying them at the edge. Ali Kayyam, principal research scientist at BrainChip, says, “The key to unlocking spatial intelligence at scale is having the low-power, event-driven hardware that can run it at the sensor in real time where it matters most.”
My suggestions for developers looking to get hands-on with spatial intelligence and world models:
The properties of water are unusual among liquids—it expands when it freezes, reaches its highest density just above freezing, and does not behave like the simple liquids chemists often use to understand matter.
Now, a new study in Communications Chemistry uses artificial intelligence to compare competing methods for measuring water’s hidden molecular order and determine which are truly effective.
Researchers at the University of Osaka developed a neural network to test 16 different ‘structural descriptors,’ which are mathematical tools for measuring how ordered or disordered water molecules are at the microscopic scale. Over the past 30 years, scientists have independently developed these descriptors, each with its own approach and scale, all aimed at explaining the same underlying puzzle.
Until now, researchers lacked a standard method for comparing them directly—a gap this study fills.
To establish a common standard, the Osaka team trained a neural network using molecular dynamics simulations of water at temperatures from 200 to 300 Kelvin. Using only the values from a single structural descriptor, it had to predict the temperature at which each molecular configuration was generated. If a descriptor captured meaningful structural information, the network could easily distinguish hot from cold.
“Past studies have shown that using machine learning to classify and understand structural data is effective,” says corresponding author Kang Kim. “We specifically wanted to incorporate a neural network model into this study to evaluate how accurate the descriptors were at capturing key structural information, in a way that is like human cognition.”
The network assessed each descriptor using a standard classification measure. To deepen their analysis, the team used an explainable AI technique called LIME. This method looks at how the model makes its decisions and checks whether its reasoning matches established physical principles, instead of relying only on statistical patterns.
Out of the 16 descriptors tested, two stood out. The Local Structure Index (LSI) and another, referred to as zeta (ζ), both of which measure the distance between the first and second layers of neighboring molecules, achieved nearly perfect accuracy across the tested temperatures. Another group, based on how hydrogen bonds connect, also scored well but was not as accurate as the others. Older descriptors that rely on tetrahedral angles or interaction energies became less accurate at higher temperatures, where molecules move more, and the differences blur.
“The network used what it had learned to compare how 16 descriptors differentiated between LDL and HDL structures at different temperatures,” says senior author Nobuyuki Matubayasi. “In this way, we determined the most efficient descriptors.”
The LIME analysis backed up the network’s results with evidence consistent with scientists’ expectations. Molecules with high Local Structure Index and zeta values led the model to predict lower temperatures, which is consistent with the idea that more ordered, LDL-like structures appear as water cools. This suggests that the network captured more than just statistical correlations.
With a common framework for comparing descriptors in place, researchers could attempt to develop descriptors that directly identify HDL and LDL environments rather than infer them indirectly. Whether these new tools will finally solve one of water’s biggest mysteries, the point where its two proposed liquid forms fully separate, is something scientists are just starting to investigate.
Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.
A lot of people still seem convinced that AI will replace every job on Earth, despite the fact that the tech still struggle with simple math. As the AI hype wears increasingly thin, that gap between hype and reality is becoming impossible to ignore.
The large language models making up the world’s favorite chatbots might be powerful compared to the predictive text algorithms that came before them, but they’re far from the epoch-defining super intelligence many have assumed is right around the corner.
In one since-deleted thread posted on the Reddit forum r/singularity, an AI aficionado posed what they clearly thought was a brilliant question: “why hasn’t AI text generation massively disrupted books yet, when it’s technically capable?”
“Language and writing are the strongest abilities of LLMs, since they’re LLMs,” the user continued. “And yet, people are still reading human made books. Why is that?”
“Just ask the LLM to write you the sequel to your favorite [H]arry [P]otter novel, and it will,” they enthused.
The answer, as some pointed out in the comments, has to do with LLMs’ lack of an ability to concentrate. As the length of an AI chatbot’s response grows, so too does its inability to keep everything cohesive, a debilitating limitation known as “context rot.” Context rot is why you never see individual AI-generated video clips longer than a few seconds, or full-length books that humans actually want to read.
Books aren’t the only thing tech optimists expected AI to devour. In a separate post on X-formerly-Twitter, OpenAI staffer Ryan Brewer expressed frustration that AI “didn’t create an educational renaissance.”
“Shouldn’t I be able to learn a language in a month?” Brewer asked in a post that quickly earned over 2.1 million views. “What did we get wrong?”
The reality, of course, is that attempts to integrate AI into education have been consistently disastrous. As it turns out, a technology that spits out a confident mixture of fact and fiction without challenging the user to track down or digest information on their own isn’t conducive to particularly effective schooling.
“It’s so f**king depressing that so many people think this is how learning works,” one user quipped.
Another simply shared a screenshot of an AI chatbot’s textbox containing the prompt: “learn for me.”
More on AI: Workplaces Have Gotten So Bizarre That People Are Just Sending AI Slop Back and Forth at Each Other
The post Tech Bros Puzzled by Why AI Hasn’t “Massively Disrupted” Books Yet appeared first on Futurism.
A single machine learning model just turned one of condensed matter physics’ most persistent problems into a navigable map.
In a study published in National Science Review, researchers led by Nanjing University used AI to sort millions of magnetoresistance curves into 13 clean categories, then charted exactly how tuning a material’s electronic properties pushes it from one behavior to another.
The work targets the anomalous Hall effect, a decades-old phenomenon that governs how electrons move through certain magnetic materials, and it could hand physicists a shortcut to some of the most sought-after states in modern electronics.
The anomalous Hall effect occurs when electrons moving through a magnetic material exhibit behavior beyond the predictions of the conventional Hall effect. Standard Drude theory predicts that resistance should shift as electrons move through a magnetic field. Anomalous Hall systems break that rule, producing resistance curves with double peaks, plateaus, and non-saturating shapes that shift with the material’s band structure and the way its electrons scatter.
The challenge becomes even greater when multiple electron energy bands are active near the Fermi level. Each additional parameter increases the difficulty of interpreting the resulting resistance curves, and until now, constructing a comprehensive map linking electronic properties to resistance behaviors has not been possible.
The Nanjing team set out to create that map. They generated more than 2.27 million magnetoresistance curves from a two-band model spanning five electronic parameters, then set an unsupervised algorithm loose on the dataset with no instructions beyond finding the pattern.
Every curve in the dataset collapsed into just 13 distinct types, a verdict so blunt that even the researchers seemed surprised. A trained neural network took over from there, sorting new curves into those same 13 categories with 99% accuracy and turning what had looked like an unruly set of data into something closer to a field guide.
Next, the team created phase diagrams and topological networks to visualize how changing just one parameter could shift a material from one type of resistance curve to another. Instead of a fixed chart, the result is more like an interactive roadmap; adjust a setting, and you can see the transition happen in real time.
To check whether any of this held up outside a computer model, the researchers tested their framework against real measurements from gated Fe5GeTe2 nanoflakes, a magnetic material whose properties can be tuned electrically. The experimental data closely matched the AI-generated phase diagrams, providing the model with a real-world anchor rather than a purely theoretical one.
“This is just the beginning. We believe this framework can be extended to a wide range of intricate models and will help uncover many more complex problems in condensed matter physics,” said Hongtao Yuan, professor at Nanjing University and an author of the study.
The significance of this work extends beyond classification. The phase diagrams indicate which parameter combinations are likely to produce large magnetoresistance, including giant magnetoresistance, a phenomenon already used in hard drive technology. These maps also identify regions where the quantum anomalous Hall effect may occur, a state in which an electric current flows along the edges of a material without energy loss due to resistance.
That kind of lossless conduction has long been seen as a target for low-power electronics, but identifying the narrow parameter windows that produce it has historically relied on trial-and-error. A phase diagram that predicts those windows in advance changes the search from guesswork into targeting.
“Our model provides a framework for comprehensively addressing the complex magnetoresistance behavior in anomalous Hall systems, and serves as a platform for predicting parameter regions that may host intriguing quantum phenomena such as giant magnetoresistance,” said author of the study Ganyu Chen.
The team frames the two-band model as a starting point rather than a finished tool. The same machine-learning approach, they note, should extend to more complex systems beyond ferromagnets, including topological insulators and superconducting junctions, areas of physics that have their own tangled, high-dimensional parameter spaces still waiting for a map. Whether AI-built phase models can keep pace with that added complexity, or whether some of these systems will require entirely new modeling strategies, remains an open question the field is only beginning to test.
Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.
For many developers, the hard part of building an AI application isn’t the model anymore. It’s keeping the application’s knowledge current.
Retrieval-augmented generation (RAG) has become a popular technique for grounding AI applications in enterprise data, but it also introduces a steady stream of operational work, including tasks such as updating embeddings and indexes, synchronizing data sources, and tuning retrieval performance.
AWS is seeking to remove much of that burden with Bedrock Managed Knowledge Base, a new managed service that automates the retrieval layer behind enterprise AI applications.
“By default, the service automatically selects and manages a default embeddings model, re-ranker model, and foundational model on your behalf, so you can get up to speed quickly without needing to pick or maintain one yourself,” Daniel Abib, senior solutions architect at AWS, wrote in a blog post.
In order to help maintain data pipelines without building and managing custom integrations, the service also comes with six native connectors for enterprise data sources, including Amazon S3, SharePoint, Confluence, Google Drive, OneDrive, and web content, Abib wrote.
For developer teams, the ability to automatically manage infrastructure could provide an immediate boost in productivity, according to Pareekh Jain, principal analyst at Pareekh Consulting.
“Enterprises spend significant time building data connectors, managing document ingestion and indexing, tuning retrieval quality, enforcing access controls, and maintaining vector databases, often making the RAG infrastructure more complex than the AI application itself. With this, developers can now focus on building the application,” Jain said.
“That should accelerate deployment timelines and reduce maintenance costs while enabling teams to focus on business outcomes,” Jain added.
Beyond reducing infrastructure management overhead, Managed Knowledge Base also targets retrieval accuracy. The service, according to Abib, also comes with features, such as Smart Parsing and Agentic Retriever, which are aimed at helping improve accuracy across different content types and sources, which is often an issue with RAG pipelines and queries spanning multiple repositories.
Improved retrieval quality could prove particularly important for organizations looking to move AI projects from experimentation to production, according to Jain.
“This is a common challenge across enterprises because business data is scattered across multiple systems. As organizations move from AI pilots to production, retrieval quality becomes critical for user trust, making RAG infrastructure a major bottleneck that often delays deployments,” Jain said.
AWS is also positioning Managed Knowledge Base as a building block for agentic applications, which, Jain said, can place even greater demands on enterprise knowledge and retrieval systems.
The service, according to the hyperscaler, integrates with Bedrock AgentCore, reducing the amount of code and configuration required to connect enterprise knowledge sources to AI agents while providing built-in monitoring, evaluation, and access management capabilities.
That integrated approach could also have implications for the broader RAG tooling ecosystem, Jain said.
“Managed services such as Bedrock Managed Knowledge Base could reduce demand for standalone RAG orchestration and retrieval frameworks, including tools such as LangChain and LlamaIndex, as well as some custom combinations of vector databases, ingestion pipelines, and retrieval services,” Jain noted.
However, Jain cautioned that the convenience of an integrated approach comes with tradeoffs, potentially increasing customer dependence on a single cloud provider and limiting flexibility in how AI infrastructure is assembled and managed.
Amazon Bedrock Managed Knowledge Base is currently available across North Virginia, Oregon, Sydney, Tokyo, Dublin, Frankfurt, London, and AWS GovCloud (US-West) Regions.
The service follows a usage-based pricing model, with charges tied to the volume of indexed data stored and retrieval requests processed.
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