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Category: robotics/AI

This Photonic AI Chip is the FUTURE of Computer Vision

This AI chip doesn’t use electricity to compute — it uses light.

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Engineers at the University of Pennsylvania have built a photonic neural network capable of classifying nearly 2 billion images per second, operating at speeds millions of times faster than today’s electronic computer vision systems.

In this video, we explore how photonic neural networks work, why traditional image recognition is so computationally expensive, and how light-based hardware could overcome fundamental limits of GPUs and silicon. We go over how convolution layers, weighted sums, and activation functions are implemented directly on a photonic chip — without memory, clock cycles, or digital logic.

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First monolithic 3D chip built in U.S. foundry delivers major AI speed gains

A collaborative team has achieved the first monolithic 3D chip built in a U.S. foundry, delivering the densest 3D chip wiring and order-of-magnitude speed gains.

Engineers at Stanford University, Carnegie Mellon University, University of Pennsylvania, and the Massachusetts Institute of Technology have collaborated with SkyWater Technology, the largest exclusively U.S.-based pure-play semiconductor foundry, to develop a novel multilayer computer chip whose architecture could help usher in a new era of AI hardware and domestic semiconductor innovation.

Unlike today’s largely flat, 2D chips, the new prototype’s key ultra-thin components rise like stories in a tall building, with vertical wiring acting like numerous high-speed elevators that enable fast, massive data movement. Its record-setting density of vertical connections and carefully interwoven mix of memory and computing units help the chip bypass the bottlenecks that have long slowed improvement in flat designs. In hardware tests and simulations, the new 3D chip outperforms 2D chips by roughly an order of magnitude.

AI tool can detect missed Alzheimer’s diagnoses while reducing disparities

Researchers at UCLA have developed an artificial intelligence tool that can use electronic health records to identify patients with undiagnosed Alzheimer’s disease, addressing a critical gap in Alzheimer’s care: significant underdiagnosis, particularly among underrepresented communities.

The study appears in the journal npj Digital Medicine.

Squashing ‘fantastic bugs’ hidden in AI benchmarks

After reviewing thousands of benchmarks used in AI development, a Stanford team found that 5% could have serious flaws with far-reaching ramifications.

Each time an AI researcher trains a new model to understand language, recognize images, or solve a medical riddle, one big question remains: Is this model better than what went before? To answer that question, AI researchers rely on batteries of benchmarks, or tests to measure and assess a new model’s capabilities. Benchmark scores can make or break a model.

But there are tens of thousands of benchmarks spread across several datasets. Which one should developers use, and are all of equal worth?

Destructured Drug Discovery: How Sequence-Based AI Speeds and Expands the Search for New Therapeutics

Predictive computational methods for drug discovery have typically relied on models that incorporate three-dimensional information about protein structure. But these modeling methods face limitations due to high computational costs, expensive training data, and inability to fully capture protein dynamics.

Ainnocence develops predictive AI models based on target protein sequence. By bypassing 3D structural information entirely, sequence-based AI models can screen billions of drug candidates in hours or days. Ainnocence uses amino acid sequence data from target proteins and wet lab data to predict drug binding and other biological effects. They have demonstrated success in discovering COVID-19 antibodies and their platform can be used to discover other biomolecules, small molecules, cell therapies, and mRNA vaccines.

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