Google researchers developed a way to peer inside the minds of deep-learning systems, and the results are delightfully weird.
What they did: The team built a tool that combines several techniques to provide people with a clearer idea of how neural networks make decisions. Applied to image classification, it lets a person visualize how the network develops its understanding of what is, for instance, a kitten or a Labrador. The visualizations, above, are ... strange.
Why it matters: Deep learning is powerful—but opaque. That’s a problem if you want it to, say, drive a car for you. So being able to visualize decisions behind image recognition could help reveal why an autonomous vehicle has made a serious error. Plus, humans tend to want to know why a decision was made, even if it was correct.
But: Not everyone thinks machines needs to explain themselves. In a recent debate, Yann Lecunn, who leads Facebook’s AI research, argued that we should simply focus on their behavior. After all, we can’t always explain the decisions humans make either.
This artist is dominating AI-generated art. And he’s not happy about it.
Greg Rutkowski is a more popular prompt than Picasso.
What does GPT-3 “know” about me?
Large language models are trained on troves of personal data hoovered from the internet. So I wanted to know: What does it have on me?
DeepMind has predicted the structure of almost every protein known to science
And it’s giving the data away for free, which could spur new scientific discoveries.
An AI that can design new proteins could help unlock new cures and materials
The machine-learning tool could help researchers discover entirely new proteins not yet known to science.
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