Self-Taught AI May Have a Lot in Common With the Human Brain...

Self-Taught AI May Have a Lot in Common With the Human Brain

RelatedHighlights

What the neural network “learns” is emergent rather than deduced. For example, it may notice a pattern that if it’s cloudy, then people are more likely to carry an umbrella. But it would not be able to explain that this is because cloudy implies rain and rain implies umbrella. Instead it effectively identifies a “rainy” vibe through correlations of... See more

Nameless Feeling

Elena added

AI is learning how to create itself

Will Douglas Heaven technologyreview.com

Kasper Jordaens added

So how does deep learning do this? Fundamentally, these algorithms use massive amounts of data from a specific domain to make a decision that optimizes for a desired outcome. It does this by training itself to recognize deeply buried patterns and correlations connecting the many data points to the desired outcome. This pattern-finding process is ea

Kai-Fu Lee • AI Superpowers: China, Silicon Valley, and the New World Order

If AI starts to generate intelligence by itself, there’s no guarantee that it will be human-like. Rather than humans teaching machines to think like humans, machines might teach humans new ways of thinking.

Will Douglas Heaven • AI is learning how to create itself

Kasper Jordaens and added

about systems that learn from data without being explicitly programmed, and about how exactly—and what exactly—we are trying to teach them.

Brian Christian • The Alignment Problem

The neural networks coded by human scientists are often the result of trial and error. There is little theory to what does and doesn’t work, and no guarantee that the best designs have been found.

Will Douglas Heaven • AI is learning how to create itself

Kasper Jordaens added

Rich Sutton's "The Bitter Lesson" reveals a fascinating pattern in AI research:

For decades, AI researchers have tried to encode human knowledge and expertise into their systems. They believed this would lead to better results. They were wrong. Time and time again, the approaches that won out were simple, general methods that leveraged raw computat

alex added

Deep learning systems are black boxes; we can look at their inputs, and their outputs, but we have a lot of trouble peering inside. We don’t know exactly why they make the decisions they do, and often don’t know what to do about them (except to gather more data) if they come up with the wrong answers...Hybrids that allow us to connect the learning

Gary Marcus • Deep Learning Is Hitting a Wall

Prashanth Narayan added