Scientists have created a programmable optical chip that can slow light on demand, giving engineers far greater control over how optical signals propagate through a circuit. The technology could provide the delays, synchronization, and buffering functions needed to make light-based computing more practical. A single chip could eventually perform several tasks that currently require separate devices, potentially reducing energy use, cost, and complexity in AI servers and data centers.
Researchers designed a programmable photonic integrated circuit based on coupled-resonator-induced transparency (CRIT) to dynamically control optical signal speed and bandwidth.
Traditional CRIT devices have fixed functionality after fabrication; the new design uses two controllable loop couplers for flexible delay and spectral control.
A new book claims AI has been built on a flawed assumption dating back to Alan Turing's famous 1950 paper. Peter J. Denning argues that the most important parts of human intelligence, including common sense, intuition, culture, and practical know-how, cannot be encoded into computers. He believes this makes true human-level AI impossible, regardless of how large language models become.
Computer scientist Peter J. Denning challenges Turing's assumptions about AI in a new book
Denning argues tacit knowledge like common sense, intuition, and culture cannot be encoded in machines
A new study from the University of Illinois Urbana-Champaign reveals that decision-making begins earlier in the brain than previously believed, challenging the traditional hierarchical model. The researchers found that even primary sensory regions like the somatosensory cortex are influenced by higher brain areas through rapid feedback loops, suggesting a more dynamic process. These insights could inspire future AI systems that are more efficient and brain-like.
Decision-related activity was observed in the primary somatosensory cortex (S1), indicating early involvement in decision-making.
The brain uses bidirectional feedback loops instead of a one-way information flow, challenging the hierarchy model.
A new AI-powered framework could transform how astronomers measure the expansion of the Universe. By analyzing images of Type Ia supernovae and modeling their environments in unprecedented detail, researchers can estimate cosmic distances with near-spectroscopic accuracy. The technique is designed for the flood of data expected from the upcoming Vera C. Rubin Observatory and may greatly improve our understanding of dark energy.
Researchers led by the University of Barcelona have developed CIGaRS, a new AI framework to extract more information from Type Ia supernovae.
The method primarily uses imaging data instead of expensive spectroscopy, modeling supernovae, host galaxies, dust, and more simultaneously.
Researchers gave top AI models a classic attention test used in psychology and found a major flaw. While the models could correctly name colors in short lists, their performance deteriorated sharply as the task became longer and more complex. Some leading systems fell from over 90% accuracy to nearly complete failure.
AI models performed well on short Stroop tests but accuracy plummeted with longer lists.
GPT-4o dropped from 91% accuracy on 5 words to 15% on 40 words; Claude 3.5 fell to 24% after 20 words.
New studies suggest consciousness can't be judged solely by behavior, whether it's a chatbot discussing philosophy or a bee searching for nectar. Researchers are increasingly focusing on the internal mechanisms of brains and computers, concluding that today's AI is likely not conscious while leaving open the possibility for both conscious insects and future machines.
Behavior alone is insufficient to determine consciousness; internal mechanisms matter.
Two new papers examine consciousness in AI and insects, focusing on structural and neural models.
NASA is testing a next-generation space computer chip that could give spacecraft the ability to operate far more independently in deep space. The radiation-hardened processor is showing performance levels hundreds of times beyond current spaceflight computers while surviving punishing tests designed to mimic the harsh conditions of space. The technology could enable AI-powered spacecraft, faster scientific discoveries, and smarter missions to the Moon and Mars.
NASA's new High Performance Spaceflight Computing processor offers up to 500 times the performance of current radiation-hardened chips.
The system-on-a-chip design integrates CPU, memory, and I/O, tailored for deep space endurance.
A quantum-inspired algorithm developed at Aalto University simulates quasicrystals with over 268 million sites in seconds, enabling design of topological qubits and lossless electronics.
Algorithm uses tensor networks to achieve exponential speedup for quasicrystal simulations.
Focus on topological quasicrystals that protect conductivity from noise.
Everyday speech patterns like pauses and filler words are linked to executive function. Using AI to analyze natural conversations can predict cognitive performance accurately, paving the way for simple speech-based tools to detect early signs of dementia well before traditional tests.
Subtle speech features (pauses, “ums”) are closely tied to executive function, reflecting brain health.
AI analysis of natural speech predicts cognitive test scores, independent of age, sex, and education.
Synthegy, a new AI system developed at EPFL, allows chemists to guide synthesis and reaction planning using natural language. It combines large language models with traditional algorithms to score and explain the best pathways, outperforming in double-blind studies with 71.2% agreement with chemists.
Synthegy uses LLMs to interpret chemical strategies written in plain language.
It improves retrosynthesis planning by scoring pathways against user instructions.
For decades, psychologists have debated whether the human mind can be explained by one unified theory or must be broken into separate parts like memory and attention. A recent AI model called Centaur seemed to offer a breakthrough, claiming it could mimic human thinking across 160 different cognitive tasks. But new research is challenging that bold claim, suggesting the model isn’t truly “thinking” at all—it’s just memorizing patterns.
Centaur model appeared to simulate cognition across 160 tasks, but new study suggests it may be overfitting.
When prompted with "Please choose option A," it still selected original correct answers, revealing lack of task understanding.
Hyper-realistic AI personas may soon flood social media, shaping opinions while posing as everyday people. If left unchecked, they could quietly tip elections and erode trust in what’s real. Researchers warn that the next election could be the true test of this technology’s power.
AI personas can mimic human behavior online, infiltrating communities and influencing opinions at scale.
Unlike traditional bots, these systems coordinate, adapt, and refine messaging in real time.
A new study from Iowa State University reveals that while anthropomorphic language in news coverage of AI is less common and more nuanced than expected, even occasional use can mislead the public about AI's true capabilities. Analyzing a 20-billion-word corpus, researchers found that mental verbs like "needs" often describe basic requirements, not human-like traits. They urge writers to consider context and impact.
News writers use anthropomorphic language for AI less frequently than expected, and often in non-humanizing contexts.
Phrases like "AI needs large amounts of data" are basic requirement descriptions, not implying thought or desire.
In crowded environments, more robots don't always mean faster results—in fact, too many can bring everything to a standstill. Harvard researchers discovered a surprising fix: adding a bit of randomness to how robots move can actually prevent gridlock and boost efficiency. By allowing robots to 'wiggle' slightly instead of marching in straight lines, they can slip past each other and keep tasks flowing smoothly.
Harvard study finds adding controlled randomness to robot movement prevents traffic jams in dense swarms.
Mathematical models and experiments show an optimal 'sweet spot' between noise and efficiency.
Researchers at Chalmers University of Technology have developed a theoretical quantum system based on 'giant superatoms,' merging giant atoms and superatoms to protect, control, and distribute quantum information, potentially enabling scalable quantum computers.
Giant superatoms combine giant atoms and superatoms into a single engineered system.
The design reduces decoherence and enhances stability of quantum information.
A team of engineers at the University of Southern California has created a breakthrough memory device that continues to operate at 700°C, far beyond the limits of current electronics. The memristor, made from tungsten, hafnium oxide, and graphene, can store data and perform computations under extreme heat, with potential applications in space exploration, geothermal energy, and AI computing.
New memristor operates at 700°C, exceeding current chip limits.
Accidental discovery reveals graphene prevents heat-induced short circuits.