AI chronicles

The first step is to understand the fundamental difference between humans and AIs. We are analog, chemical beings, with emotions and feelings. Compared with machines, we think slowly—and we act too fast, failing to consider the long-term consequences of our behavior (which AI can help predict). So we should not compete with AI; we should use it. At... See more

“We’re at a moment in history when we need to rebuild our confidence in the capabilities of humans to reason wisely, to make collective decisions,” Vallor tells me. “We’re not going to deal with the climate emergency or the fracturing of the foundations of democracy unless we can reassert a confidence in human thinking and judgment. And everything ... See more

An LLM is trained by allowing it to adjust the connections in its neural network until it reliably gives good answers, a process that Hinton compared to “parenting for a supernaturally precocious child.” But because AI can “know” vastly more than we can, and “thinks” much faster, Hinton concludes that it might ultimately supplant us: “It’s quite co... See more

To understand AI algorithms, Vallor argues we should not regard them as minds. “We’ve been trained over a century by science fiction and cultural visions of AI to expect that when it arrives, it’s going to be a machine mind,” she tells me. “But what we have is something quite different in nature, structure, and function.”

Rather, we should imagine A... See more

Illuminating answer (that matches what I am hearing from others) about why AIs don’t come with release notes: no one knows well what new models are capable of doing well or badly in advance. It is why it is so important to test on your own use cases. Don’t just listen to others.