Silicon Valley startup Cerebras Systems on Monday unveiled the world's largest chip, designed especially for AI. Cerebras CEO and co-founder Andrew Feldman talks with ZDNet about some of the implications for training of deep learning networks.
What is output by a neural network, such as an image classifier, is only a small portion of what the network in a sense "knows" about the data. And the prospect that the network is retaining sensitive user data such as social security numbers could open a new area of concern about AI.
In a nondescript headquarters building on a quiet side street in Helsinki, with a modest amount of startup capital, Finland’s Varjo has put together a headset that offers a major advance in augmented, or “mixed” reality. ZDNet traveled to Finland and gave it a try.
IBM’s latest foray into making A.I. more amenable to the world is a toolkit of algorithms that can be used to explain the decisions of machine learning programs. It raises a deep question: Just what is an explanation, and how can we find ones that we will accept?
What’s the point of teaching a computer how to knit? MIT researchers found it goes beyond just some nice gloves and scarves, to a window into deep learning forms of artificial intelligence.
Nature magazine's cover story is about a Chinese chip that can run traditional deep learning code and also perform "neuromorophic" operations in the same circuitry. The work’s value seems obscured by a lot of hype about "artificial general intelligence" that has no real justification.
The DeepMind unit of Google is finding ways to detect deterioration in patients in hospitals, but it's not ready for primetime; instead, the software that's actually making a difference is a simple mobile alerts app.
The bar is set pretty low for chatbots, which exhibit fairly tedious and even idiotic streams of thought when engaging in chit-chat with people. A raft of new papers from Facebook and academic partners provide promising new directions, though the goal of human dialogue still seems fairly far away.
U Mass professor Hava Siegelmann has for decades articulated how current computing limits the prospects for artificial intelligence. Research into "neuromorphic" computing at her lab at U Mass., and multiple projects at DARPA, hold promise for moving AI to a new level.
With a technique called cell painting, Recursion Pharmaceuticals is creating a really big picture of morphology of cells in the body. But that's just the beginning; the really hard part is knowing how to ask questions of the images with machine learning, and how to manage petabytes of data.