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Category: robotics/AI

It took 40 years for technology to catch up to this zipper design

In 1985, the Innovative Design Fund placed an ad in Scientific American offering up to $10,000 to support clever prototypes for clothing, home decor, and textiles. William Freeman PhD ’92, then an electrical engineer at Polaroid and now an MIT professor, saw it and submitted a novel idea: a three-sided zipper. Instead of fastening pants, it’d be like a switch that seamlessly flips chairs, tents, and purses between soft and rigid states, making them easier to pack and put together.

Freeman’s blueprint was much like a regular zipper, except triangular. On each side, he nailed a belt to connect narrow wooden ‘teeth’ together. A slider wrapping around the device could be moved up to fasten the three strips into place, straightening them into a triangular tube. His proposal was rejected, but Freeman patented his prototype and stored it in his garage in the hopes it might come in handy one day.

Nearly 40 years later, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers wanted to revive the project to create items with ‘tunable stiffness.’ Prior attempts to adjust that weren’t easily reversible or required manual assembly, so CSAIL built an automated design tool and adaptable fastener called the ‘Y-zipper.’ The scientists’ software program helps users customize three-sided zippers, which it then builds on its own in a 3D printer using plastics. These devices can be attached or embedded into camping equipment, medical gear, robots, and art installations for more convenient assembly.

A new system developed at MIT CSAIL helps users design three-sided fasteners called “Y-zippers,” then 3D prints them. The devices can be attached or embedded to camping equipment, medical gear, robots, and art installations, seamlessly switching each item between soft and rigid.

A global screen for magnetically induced neuronal activity in the pigeon brain

What if every scientific paper you read was just the “highlight reel” of a much longer, messier, and more complicated movie? You see the breakthrough, but you never see the hundreds of hours of footage showing what didn’t work.

Ultimately, the ARA marks a shift toward a future where “The Last Human-Written Paper” isn’t the end of science, but the beginning of a much deeper, machine-readable conversation.

However, this shift toward radical transparency comes with its own set of hurdles. While ARAs make AI agents more efficient, the study found a “prior-run box” effect where seeing a human’s past failures actually limited an AI’s ability to think outside the box and find creative new solutions. There is also a significant cultural and technical gap to bridge: the system relies on researchers being willing to expose their “messy” unfinished work, and even with better data, the jump in actual experiment reproduction was relatively modest. Furthermore, the reliance on “compilers” to translate old papers into this new format risks baking in errors or “hallucinations” if the original source was vague, proving that while machine-readable data is powerful, it isn’t a magic fix for the inherent complexities of scientific discovery.

How animals detect Earth’s magnetic field remains a mystery in sensory biology. Despite extensive behavioral evidence, the neural circuitry and molecular mechanisms responsible for magnetic sensing remain elusive. Adopting an unbiased approach, we used whole-brain activity mapping, tissue clearing, and light sheet microscopy to identify neuronal populations activated by magnetic stimuli in the pigeon (Columba livia). We demonstrate robust, light-independent bilateral neuronal activation in the medial vestibular nuclei and the caudal mesopallium. Single-cell RNA sequencing of the semicircular canal cristae revealed specialized type II hair cells that express the molecular machinery necessary for the detection of magnetic stimuli by electromagnetic induction.

Performance of a large language model on the reasoning tasks of a physician

What if every scientific paper you read was just the “highlight reel” of a much longer, messier, and more complicated movie? You see the breakthrough, but you never see the hundreds of hours of footage showing what didn’t work.

Ultimately, the ARA marks a shift toward a future where “The Last Human-Written Paper” isn’t the end of science, but the beginning of a much deeper, machine-readable conversation.

However, this shift toward radical transparency comes with its own set of hurdles. While ARAs make AI agents more efficient, the study found a “prior-run box” effect where seeing a human’s past failures actually limited an AI’s ability to think outside the box and find creative new solutions. There is also a significant cultural and technical gap to bridge: the system relies on researchers being willing to expose their “messy” unfinished work, and even with better data, the jump in actual experiment reproduction was relatively modest. Furthermore, the reliance on “compilers” to translate old papers into this new format risks baking in errors or “hallucinations” if the original source was vague, proving that while machine-readable data is powerful, it isn’t a magic fix for the inherent complexities of scientific discovery.

We systematically evaluated the medical reasoning abilities of an LLM across six diverse experiments, comparing the model with hundreds of expert physicians. Overall, the model outperformed physicians across experiments, including in cases utilizing real and unstructured clinical data taken directly from the health record in an emergency department. These diagnostic touchpoints mirror the high-stakes decisions taken in emergency medicine departments, where nurses and clinicians make time-sensitive choices with limited information. Our results showed that humans, GPT-4o, and o1 all improved their diagnostic abilities as more information was available; o1 outperformed humans at multiple touchpoints, with the widest gap at initial ER triage, where there is the least information available.

The rapid pace of improvement in LLMs has substantial implications for the science and practice of clinical medicine. Although applying AI to assist with clinical decision support is sometimes viewed as a high-risk endeavor (22, 23), greater use of these tools might serve to mitigate the human and financial costs of diagnostic error, delay, and lack of access (24, 25). Our findings suggest the urgent need for prospective trials to evaluate these technologies in real-world patient care settings and for health care systems to prepare for investments for computing infrastructure and design for clinician-AI interaction that can facilitate the safe integration of AI tools into patient-care workflows. This includes the development of robust monitoring frameworks to oversee the broader implementation of AI clinical decision support systems (22), monitoring not just final diagnostic accuracy but other metrics crucial for successful deployment, including safety, efficiency, and cost.

We emphasize that our study addresses only text-based performance for both humans and machines; clinical medicine is multifaceted and awash with nontext inputs, including auditory (such as the patient’s level of distress) and visual information (for example, interpretation of medical imaging studies) that clinicians routinely use. Existing studies suggest that current foundation models are more limited in reasoning over nontext inputs (26, 27); future work is needed to assess how humans and machines may effectively collaborate (28) in use of nontext signals. This requires new benchmarks, trials, and technological solutions to more faithfully measure clinical encounters. Existing investment in increasingly pervasive ambient AI scribes and other passive monitoring technologies holds promise to serve as the basis for such investigations.

Continue reading “Performance of a large language model on the reasoning tasks of a physician” | >

Hiring a Futurist? The Red Flag Most Leadership Teams Miss

When a leadership team hires a futurist to think about the future for them, the hire itself is the failure.

I say this as someone who gets booked to think about the future.

Executives increasingly hire futurists, consultants, and now AI tools to handle their thinking about what comes next. It feels like rigor. It looks like preparation. It is, in fact, an abdication.

Nobody can tell you what is coming with the precision your strategy deck assumes. Not a futurist. Not a consultant. Not an AI system trained on every word ever written. Plenty of people sell certainty about the future. Nobody can actually deliver it.

So how do you tell whether you are using a futurist well, or whether the booking itself is the warning sign?

I wrote a short piece arguing that the answer comes down to one question every executive should ask before signing the contract.

Full piece: [ https://www.singularityweblog.com/hiring-a-futurist/](https://www.singularityweblog.com/hiring-a-futurist/)

Continue reading “Hiring a Futurist? The Red Flag Most Leadership Teams Miss” | >

Metastatic cancer detection and management with artificial intelligence and augmented reality (Review)

Metastatic cancer remains a significant global health challenge, contributing to the majority of cancer-related mortality due to late detection, therapeutic resistance and the complexity of disseminated disease. Recent advances in artificial intelligence (AI) and augmented reality (AR) are transforming the landscape of metastatic cancer detection and management. AI-driven tools, including radiomics, deep learning models, and predictive analytics, enhance early identification of metastatic lesions, improve diagnostic accuracy, and support personalized treatment strategies by integrating multimodal clinical, imaging and molecular data. At the same time, AR technologies are increasingly applied in image-guided surgery, real-time tumor visualization and patient education, enabling more precise interventions and improved clinical decision-making.

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.

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