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.

PrismML AI Compression Changes the Size Problem

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.

Why Siri AI Needs Smaller Models

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.

Privacy Fits Apple’s Preferred Direction

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.

The iPhone Still Has Physical Limits

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.

Better AI Without Replacing the Cloud

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.

Compression Always Includes Trade-Offs

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.

Apple Wants the Technology, Not Necessarily the Company

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?

A Smaller Model Could Make Siri Feel Larger

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.

Some of BrainChip’s latest chips and systems integrate SNNs to boost AI model efficiency.

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.

Water’s Split Personality

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-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.

Cyth Systems used LabVIEW to let AI play Guitar Hero.

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.

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.

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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

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s44271-026-00485-w

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Single cells used past nutrient shocks to change how they grew.

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.

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Internal documents show how tech giants grabbed children’s attention throughout the day, a strategy that schools say has undermined education.

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