In an experiment last year at the Massachusetts Institute of Technology, more than fifty students from universities around Boston were split into three groups and asked to write SAT-style essays in response to broad prompts such as "Must our achievements benefit others in order to make us truly happy?" One group was asked to rely on only their own brains to write the essays. A second was given access to Google Search to look up relevant information. The third was allowed to use ChatGPT, the artificial-intelligence large language model (L.L.M.) that can generate full passages or essays in response to user queries. As students from all three groups completed the tasks, they wore a headset embedded with electrodes in order to measure their brain activity.

That complexity is a problem when AI models need to work in real time in a pair of headphones with limited computing power and battery life. To meet such constraints, the neural networks needed to be small and energy efficient. So the team used an AI compression technique called knowledge distillation. This meant taking a huge AI model that had been trained on millions of voices (the "teacher") and having it train a much smaller model (the "student") to imitate its behavior and performance to the same standard. The student was then taught to extract the vocal patterns of specific voices from the surrounding noise captured by microphones attached to a pair of commercially available noise-canceling headphones.

Frank Rosenblatt, associate pro fessor of neurobiology at Cor nell University, died here yes terday in a boating accident. It was his 43d birthday. He lived in Brooktondale, N. Y., an Ithaca suburb. An originator of perception theory, he had developed an experimental machine that could be trained to identify automatically objects or pat terns such as letters of the al phabet. The instrument was an electromechanical device con sisting of a sensory unit of photo cells that viewed the pat tern shown to the machine, as sociation units that contained the machine's memory and re sponse units that displayed vis ually its pattern‐recognition re sponse.

In order to find patterns, it first needs to learn what the pattern is. Cornell's library of birdsong recordings provides the training that the AI needs to learn which sounds are bird songs and which ones are you watering the garden. Cornell has been tweaking its neural net for some time. If you'd like to experience this without investing in a Haikubox, you can grab Cornell's Merlin Bird ID app, which relies on a small subset of the data and an AI processor similar to what the Haikubox uses. Haikubox creator David Mann told WIRED that the Haikubox uses a modified version of BirdNet, which is called BirdNet for Haikubox.

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron, aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. An "earable" that uses sonar to read facial expressions was among the projects that caught our eyes over these past few weeks. So did ProcTHOR, a framework from the Allen Institute for AI (AI2) that procedurally generates environments that can be used to train real-world robots. Among the other highlights, Meta created an AI system that can predict a protein's structure given a single amino acid sequence.

For the MIT Schwarzman College of Computing Dean Dan Huttenlocher, bringing disciplines together is the best way to address challenges and opportunities posed by rapid advancements in computing. What does it mean to be human in an age where artificial intelligence agents make decisions that shape human actions? That's a deep question with no easy answers, and it's been on the mind of Dan Huttenlocher SM '84, PhD '88, dean of the MIT Schwarzman College of Computing, for the past few years. "Advances in AI are going to happen, but the destination that we get to with those advances is up to us, and it is far from certain," says Huttenlocher, who is also the Henry Ellis Warren Professor in the Department of Electrical Engineering and Computer Science. Along with former Google CEO Eric Schmidt and elder statesman Henry Kissinger, Huttenlocher recently explored some of the quandaries posed by the rise of AI, in the book, "The Age of AI: And Our Human Future."

What does it mean to be human in an age where artificial intelligence agents make decisions that shape human actions? That's a deep question with no easy answers, and it's been on the mind of Dan Huttenlocher SM '84, PhD '88, dean of the MIT Schwarzman College of Computing, for the past few years. "Advances in AI are going to happen, but the destination that we get to with those advances is up to us, and it is far from certain," says Huttenlocher, who is also the Henry Ellis Warren Professor in the Department of Electrical Engineering and Computer Science. Along with former Google CEO Eric Schmidt and elder statesman Henry Kissinger, Huttenlocher recently explored some of the quandaries posed by the rise of AI, in the book, "The Age of AI: And Our Human Future." For Huttenlocher and his co-authors, "Our belief is that, to get there, we need much more informed dialogue and much more multilateral dialogue. Our hope is that the book will get people interested in doing that from a broad range of places," he says.

Did you know the perceptron, the first neural network stimulating the thought processes of the human brain, was actually created at a university? A major factor deciding the leading universities today is their research inclination. Universities supporting research not only strengthen the students with the academic knowledge but also enable them to apply their learnings to innovate. In AI, such universities are cutting edge because they encourage deep analysis, scholarly investigation and research to develop the industry. These help shape the leaders of tomorrow.

Last Fashion Week in Milan, Lior Cole headed to the National Museum of Science and Technology of Milan on her one day off from walking runways. A science buff studying information science at Cornell, she uses her downtime to explore artificial intelligence and how it merges with spirituality and religion. "It works very well with modeling. In between jobs you have downtime, and with computer stuff you can do it whenever you want," says Cole over Zoom. "I did a photo shoot for a magazine the other day, and I brought my computer, and I was coding."

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