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Unlike static network pruning, channel gating optimizes CNN inference atrun-time byexploiting input-specific characteristics, which allows substantially reducing the compute cost with almost no accuracyloss.

Components of machine learning systems are not (yet) perceived as security hotspots. Secure coding practices, such as ensuring that no execution paths depend on confidential inputs, have not yet been adopted by ML developers. We initiate the study of code security of ML systems by investigating how nucleus sampling--a popular approach forgeneratingtext,used forapplications such as auto-completion--unwittingly leakstextstypedbyusers.

Prior works havepresented several means to combat this phenomenon in IL.

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.