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.
What are spatial intelligence models?
It’s important to develop AI literacy and understand the terminology and concepts related to the physical world and 3D AI technologies:
Spatial intelligence encompasses specialized approaches such as geospatial models for mapping the physical world and building information modeling (BIM) for modeling physical structures. It also extends to generative 3D, robotics, and physical reasoning applications.
World models are a class of neural network architectures and are currently a prominent approach to building spatial intelligence.
Digital twins are live, virtual replicas of physical assets that combine 3D models with real-time sensor data. Spatial intelligence, an emerging capability of digital twins, adds natural-language prompting, generative scenario exploration, and physics-aware reasoning.
Spatial computing refers to digital content anchored in and interacting with physical space, sensed and rendered in three dimensions and delivered through AR/VR and mixed-reality systems.
“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.”
Monitoring the built environment
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.”
Avoiding collisions
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.”
Applying spatial intelligence
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.”
Where to get started
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.
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.
Water’s Split Personality
Scientists call water supercooled when it cools below its usual freezing point but remains liquid. In this state, water’s unusual traits become even more noticeable. Molecules require something like a scratch or impurity to start freezing into ice. Without these, water can stay liquid even when it is much colder than 32°F.
Many physicists believe that supercooled water is made up of two different structures: a loosely packed, highly ordered, low-density liquid (LDL) and a tightly packed, disordered, high-density liquid (HDL). The balance between these forms is thought to drive water’s unusual properties. Scientists use structural descriptors to measure how a molecule sits between these two extremes by tracking features such as the angles between neighboring atoms, distances to nearby molecules, or the formation of hydrogen bonds. However, these tools were never meant to be compared directly.
Distinguishing Hot From Cold
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.
Two Descriptors Pull Ahead
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-lengthbooks 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.”
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.
A Problem Too Complex to Map by Hand
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.
Millions of Curves
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.
A Map That Points to New Physics
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.
In a groundbreaking study poised to redefine our understanding of auditory perception across species, researchers have unveiled strikingly similar auditory perceptual maps in both humans and mice. This discovery not only bridges a crucial gap between animal models and human sensory processing but also offers predictive insight into how perceptual decisions are formed during auditory discrimination learning. The implications ripple across neuroscience, psychology, and artificial intelligence, promising new avenues for studying sensory perception and developing advanced hearing-related technologies.
Auditory perception—the way organisms interpret and make sense of sound stimuli—is a complex process that involves integrating multifaceted sound attributes such as frequency, intensity, and temporal patterns. While mouse models have long been foundational in auditory research due to their genetic malleability and experimental accessibility, skepticism has persisted about the extent to which their perceptual frameworks parallel those of humans. This novel research offers compelling evidence that these frameworks are not only comparable but also exhibit shared underlying structures.
The study employed a sophisticated experimental design in which both human participants and mice were exposed to a wide array of tonal stimuli varying systematically across multiple auditory dimensions. Using advanced computational modeling and neural decoding techniques, the researchers were able to construct perceptual maps—conceptual frameworks representing how different sounds are internally organized within the brain’s auditory space. These maps enabled a quantifiable comparison of perceptual similarity and distance between sounds for both species, revealing a remarkable congruence in their auditory representations.
What makes these findings particularly compelling is the demonstration that these perceptual maps are predictive of behavioral outcomes during discrimination tasks. In other words, the way an individual’s brain groups different sounds predicts how well that individual can distinguish between them in learning scenarios. This link between neural representation and perceptual decision-making underlines a fundamental cognitive mechanism shared across mammalian species, offering crucial insights into the principles guiding sensory learning processes.
Beyond the fundamental science, the implications for translational research are profound. Understanding shared auditory perceptual structures facilitates the development of more accurate animal models for studying hearing impairments and neurological disorders affecting auditory processing. It also guides the design of auditory prosthetics, such as cochlear implants, by informing the algorithms that mimic natural sound perception. By aligning animal and human perceptual frameworks, therapies can be tailored with greater precision and efficacy.
A key technical achievement of the study was the use of multidimensional scaling techniques combined with machine learning algorithms to decode auditory neural responses with unprecedented granularity. The researchers collected high-resolution neural data using electrophysiological recordings in mice and non-invasive neuroimaging in humans. This dual approach allowed cross-species alignment of perceptual space, overcoming previous methodological challenges that hindered direct comparisons between human and rodent sensory processing.
Moreover, the study explored dynamic aspects of perceptual learning by measuring how auditory maps evolve through repeated training sessions. The maps exhibited plasticity reflective of learning-induced changes, emphasizing the adaptive nature of auditory perception. This plasticity was mirrored across both species, suggesting an evolutionarily conserved mechanism for tuning sensory systems based on environmental demands and experience.
One of the study’s most innovative aspects lies in its predictive modeling of discrimination performance. By mapping perceptual distances within the auditory space, the researchers could forecast individual differences in learning speed and accuracy, an advancement that holds enormous promise for personalized auditory training protocols. Such protocols could optimize rehabilitation strategies for individuals with auditory processing deficits by targeting specific perceptual aspects most relevant to their unique neural patterns.
The shared structure of auditory perceptual maps also raises intriguing questions about the evolutionary trajectory of sensory systems. The findings suggest that despite divergent evolutionary paths, fundamental auditory processing architectures remain conserved, supporting efficient sound discrimination necessary for survival and communication. This conservation across species underpins the generalizability of animal research findings to human sensory neuroscience.
Furthermore, these insights pave the way for advancing artificial sensory systems, especially in the realm of auditory artificial intelligence. By modeling computational auditory perception after natural neural maps, engineers can design more sophisticated speech recognition systems and assistive devices that better emulate human-like sound processing. The study’s integrative framework thus acts as a blueprint for creating tonal perceptual algorithms with enhanced contextual understanding.
This research also contributes importantly to theoretical debates surrounding sensory representation. By empirically demonstrating a shared, structured perceptual space, the study challenges views that sensory maps are strictly idiosyncratic or species-specific. Instead, it supports a paradigm that sensory systems leverage common organizational principles, rooted perhaps in fundamental neural coding constraints and environmental pressures shaping auditory perception.
The study’s methodology and results were meticulously detailed in a recent publication, where the authors delineated the computational techniques, behavioral paradigms, and statistical analyses that underscored their conclusions. This transparency fosters reproducibility and invites further interdisciplinary collaboration between computational neuroscientists, experimentalists, and cognitive psychologists aiming to unravel the complexities of sensory perception.
Importantly, these findings hold societal relevance beyond academic circles. Hearing loss and auditory processing disorders affect millions worldwide, often hindering communication and quality of life. Technologies and therapies inspired by a unified perceptual mapping approach could revolutionize auditory rehabilitation, offering hope for more effective interventions grounded in a thorough understanding of perceptual neuroscience.
In conclusion, this pioneering work illuminates the common auditory perceptual code shared by humans and mice, demonstrating that the brain’s representation of sound is not merely a byproduct of species-specific adaptations but a fundamental, conserved neural language. This discovery not only furthers scientific knowledge but also catalyzes innovation in medicine and technology, underscoring the power of cross-species research in unraveling the mysteries of the sensory brain.
As this field develops, future investigations are likely to explore how these perceptual maps interact with other cognitive systems such as memory and attention, and how factors such as age, experience, and pathology modulate the structure and function of auditory perception. With burgeoning advances in neurotechnologies and computational methods, the confluence of cross-species neuroscience and perceptual science promises to unlock new dimensions of understanding sensory experience.
Ultimately, the revelation that humans and mice share auditory perceptual maps that predict learning efficacies serves as a testament to nature’s optimization strategies, propelling a unified framework for deciphering sensory perception. This convergence of biology, computation, and psychology heralds a new chapter in auditory neuroscience with far-reaching implications for science and society alike.
Subject of Research: Auditory perceptual maps and discrimination learning in humans and mice
Article Title: Auditory perceptual maps in humans and mice share common structures and predict perceptual decisions in discrimination learning
Article References:
Seiler, J.PH., Cazzetta, G., Ghobadi, A. et al. Auditory perceptual maps in humans and mice share common structures and predict perceptual decisions in discrimination learning. Commun Psychol 4, 98 (2026). https://doi.org/10.1038/s44271-026-00485-w
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.
Managed RAG could boost developer productivity
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.
Taking aim at RAG stacks?
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.
Alors que l'IA connaît une phase d'expansion extrêmement rapide, cette technologie est désormais capable de traiter tout type de tâches aussi efficacement, et même plus efficacement, que les humains. Dès lors, comment nous positionner face aux machines ?
À mesure que les systèmes d’intelligence artificielle gagnent en puissance et en autonomie, leur fonctionnement devient de plus en plus difficile à saisir. Des chercheurs mettent en garde contre une tendance qui pourrait compliquer notre capacité à garder le contrôle sur ces technologies.
Pendant longtemps, prédire le vainqueur d’une Coupe du monde relevait presque de la divination. Aujourd’hui, les algorithmes sont à l’œuvre. En combinant statistiques, expertise sportive et apprentissage automatique, des chercheurs simulent des milliers de scénarios pour estimer les chances...
It feels like there's no escaping AI right now, whether you’re trying to type a sentence without being interrupted by a digital “assistant” or struggling to find a new refrigerator that doesn’t require a Wi-Fi connection for some reason. You’d be forgiven for wondering if we’re in the midst of a quantum leap in tech or whether people are just hyping up a heap of slop.
So what should we make of the growing use of AI in weather and climate modeling?
The conversation didn't get off to a great start earlier this year when a National Weather Service office posted a forecast map featuring nonexistent cities in Idaho with names like “Whata Bod” and “Orangeotild.” Thankfully, that was just an AI-generated image produced for social media, not the actual forecast model. Meteorologists and climate scientists are not yet being replaced by large language model prompt engineers.
Login
