Saved by Prashanth Narayan and
Deep Learning Is Hitting a Wall
The second challenge we call the illusory progress gap: mistaking progress in AI on easy problems for progress on hard problems. That is what happened with IBM’s overpromising about Watson, when progress on Jeopardy! was taken to be a bigger step toward understanding language than it really was.
Ernest Davis • Rebooting AI: Building Artificial Intelligence We Can Trust
Huge “foundation models” are turbo-charging AI progress
economist.com
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
amazon.com
Finally, because of the scale at which current AI can operate, there are many ways in which AI could (even in its still-primitive form) be used deliberately to cause serious public harm.
Ernest Davis • Rebooting AI: Building Artificial Intelligence We Can Trust
As we successfully apply simpler, narrow versions of intelligence that benefit from faster computers and lots of data, we are not making incremental progress, but rather picking low-hanging fruit. The jump to general “common sense” is completely different, and there’s no known path from the one to the other.
Erik J. Larson • The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do
We’re far from replacing humans for many tasks now being delegated to large language models. That doesn’t mean LLMs can’t be helpful; it just means they’re being used wrong. The key is applying them to tasks they’re well-suited to, such as analyzing, synthesizing, and manipulating data, and not for making important decisions on behalf of people.
An ... See more
An ... See more