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Category: robotics/AI

Ultrafast switching device unlocks low-power optical-to-electrical conversion for AI hardware

Modern energy demands are soaring as technologies like AI and IoT become more common, and researchers have been working hard to develop hardware that can keep up. Now, a team of researchers from the University of Tokyo has developed an ultrafast and energy-efficient nonvolatile switching device, described in an article published in the journal Science, that may soon be able to significantly reduce power consumption for high-energy demand technologies.

Currently, most nonvolatile switching devices for data processing architectures have operating speeds in the nanosecond range. However, faster speeds are required for modern central processing units (CPUs), which operate in the gigahertz range.

At 5 GHz, a single cycle lasts only 200 picoseconds. If a switching device takes a nanosecond (1,000 picoseconds) to turn on or off, it misses multiple clock cycles, creating a major bottleneck that prevents the processor from operating continuously at full capacity. Optical interconnects are being explored to overcome electronic bottlenecks, but more efficient optical-to-electrical (O/E) conversion is still needed.

Written in the eye: How the retina’s biological age could help predict osteoporosis risk

Eyes, the high-resolution biological devices that help us visualize the outside world, are now being used as a portal to assess our internal health. Scientists have found that a closer evaluation of how one’s retina is aging can provide crucial hints about bone health, especially in conditions such as osteoporosis, which makes bones weaker and more prone to fractures.

A recent study conducted in Singapore and the UK collected over 45,000 retinal images and used an artificial intelligence (AI) tool called RetiAGE to estimate a person’s retinal biological age. When researchers compared retinal age with bone mineral density, they found an inverse relationship between the two.

Among participants in Singapore, people with older-looking retinas tended to have lower bone mineral density and higher fracture risk scores. Meanwhile, the UK-based cohort, where participants were studied for over a decade, revealed that a higher retinal biological age at the start of the study was a predictor for a greater chance of developing osteoporosis by the end of it.

Bioengineers condense protein engineering and testing to a single day

Proteins are critical to life—and to industry. There are countless proteins that could be engineered to treat and even cure serious diseases and cellular dysfunctions. Industrial applications are similarly promising, with proteins increasingly used as enzymes in food manufacturing and in consumer detergents.

While AI can help suggest improvements, each novel protein must still be created in the real world and tested for performance. It is a labor-intensive process that involves constructing the DNA instructions for each protein in yeast or bacteria and growing individual clones for protein production and testing. This can take many days for a single protein of interest and even longer if the protein needs to be tested in mammalian cells, a process that requires retrieving DNA from microbes for transfer to the mammalian cells.

In a new paper, Michael Z. Lin, a professor of neurobiology and of bioengineering in the schools of Engineering and Medicine, and graduate students, Yan Wu in bioengineering and Pengli Wang in chemical engineering, say they have condensed the time-intensive protein building and testing process to just 24 hours.

Reconfigurable Ge-Si photodetector achieves ultrahigh-speed data transmission using low-loss packaging

The rapid growth of large language models is placing increasing demands on data centers, where large volumes of data must be transferred efficiently between servers. Optical interconnects are essential for enabling this communication, but as data rates continue to rise, these systems must deliver higher bandwidth while maintaining low latency and energy efficiency. However, integrating electronic and photonic components remains challenging, as conventional approaches often introduce signal loss, limit interconnect density, and restrict scalability.

As reported in Advanced Photonics Nexus, Dr. Wei Chu and colleagues have developed a reconfigurable germanium–silicon photodetector using a low-loss integration strategy based on fan-out wafer-level packaging (FOWLP). This approach enables seamless integration of electronic integrated circuits and photonic integrated circuits on a single platform without the need for traditional wire bonding, reducing parasitic loss and improving signal integrity.

The system uses a dense network of fine metal interconnects, known as a redistribution layer (RDL), to connect components with high precision. This structure supports high interconnect density—exceeding 102 connections per square millimeter—while maintaining a low insertion loss of less than 0.3 dB/mm at 100 GHz. In addition, the use of benzocyclobutene as a low-dielectric insulating material reduces transmission loss and improves thermal stability for reliable high-frequency operation.

AI shapes the design of the electron-ion collider

Artificial intelligence and machine learning are shaping major design and research decisions for the planned Electron-Ion Collider (EIC), a next-generation nuclear physics research facility that will collide electrons with protons or nuclei to probe matter’s structure.

The EIC—being built at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory in partnership with DOE’s Thomas Jefferson National Accelerator Facility (Jefferson Lab)—will reveal the inner structure of matter in unprecedented detail. It is the world’s first collider designed with AI and machine learning integrated into both its accelerator and detector systems.

“EIC is a new facility that can take advantage of AI and machine learning from the start,” said Tanja Horn, a professor of physics at The Catholic University of America, and co-chair of AI4EIC, a working group devoted to developing AI for the EIC. “A wide array of AI tools is now available—perfectly timed for the EIC.”

Generalization Dynamics of LM Pre-training

An AI has a limited amount of “capacity” (brainpower). Early in training, it develops quick, shallow circuits to memorize data because that’s the easiest way to get the right answer. Later, it develops complex circuits for actual reasoning. Because space is limited, these two internal systems are constantly competing for control. Whichever type of data the AI happens to be reading in a specific moment determines which circuit wins the battle.

People typically assume that LMs stably mature from pattern-matching parrots to generalizable intelligence during pre-training. We build a toy eval suite and show this mental model is wrong: throughout pre-training, LMs frequently and suddenly hop between parrot-like and intelligence-like modes, i.e. distinct algorithms implemented by distinct circuits. We call this mode-hopping. Across our suite, LMs can suddenly latch onto memorized or in-context patterns instead of in-context learning, use System 1 instead of System 2 thinking, pick up what sounds true instead of what is true, fail at multi-hop persona QA, out-of-context reasoning, and emergent misalignment — then just as suddenly revert and generalize. Mode-hopping is not explained by standard optimization dynamics: it is locally stable and can not be fixed by checkpoint averaging. We instead think of it as a capacity allocation problem: in a capacity-bounded model, generalizable circuits must compete with the shallow ones learned early in training, and the data in each pre-training window decides which circuits win. Our suite provides a cheap set of pre-training monitors and a new lens on generalization. Building upon our insights, we demonstrate three applications: (i) select intermediate pre-training checkpoints that strongly generalize reasoning and alignment, better than the final pre-or mid-training checkpoints, (ii) select pre-training data that controls and stabilizes generalization dynamics, and (iii) test prior generalization predictors, falsifying the monolithic belief that “simpler solutions generalize better”

Building general AI without generalization is doable but meh. We want an intelligence that learns deep, transferable structure, not a parrot that matches shallow patterns. Real generalization would unblock many today’s key open problems: data-efficient (online) learning, shortcut learning, transfer capabilities from verifiable domains (math, coding) to broader non-verifiable yet economically valuable domains, and maintain a coherent character that truly aligns with human values.

The distinction between parrots and intelligence is computational. Parrots repeat in-context patterns; intelligence infers in-context functions. Parrots encode a persona as bags of disconnected facts and traits; intelligence learns a shared persona representation that connects all. Parrots memorize reasoning steps; intelligence forms general reasoning circuits for entity tracking, backtracking, or even for highly abstract concepts like truth.

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