@_deepfates went in sermon mode this week and I love it: https://t.co/p7SQdV9YnB
@_deepfates went in sermon mode this week and I love it: https://t.co/p7SQdV9YnB
And in the end, OpenAI doesn’t matter . They are making the same mistakes we are in their posture relative to open source, and their ability to maintain an edge is necessarily in question. Open source alternatives can and will eventually eclipse them unless they change their stance. In this respect, at least, we can make the first move.
Simon Willison • Leaked Google document: “We Have No Moat, And Neither Does OpenAI”
Of course, there are major ethical issues to work out—leaps forward in technology often walk a fine line between deeply-impactful and dystopian. Among the questions we need to figure out:
- Who is responsible for AI’s mistakes?
- Who is the creator of an AI work? Is it the AI? The developers? The person who wrote the prompt? The people whose work was use
Rex Woodbury • AI in 2023: The Application Layer Has Arrived
The tendency to think of A.I. as a magical problem solver is indicative of a desire to avoid the hard work that building a better world requires. That hard work will involve things like addressing wealth inequality and taming capitalism. For technologists, the hardest work of all—the task that they most want to avoid—will be questioning the assumpt
... See moreTed Chiang • Will A.I. Become the New McKinsey?
The facts that (a) an AI somewhere could in principle do this task better, and (b) this task is no longer an economically rewarded element of a global economy, don’t seem to me to matter very much.
Dario Amodei • Machines of Loving Grace
In three words: deep learning worked.
In 15 words: deep learning worked, got predictably better with scale, and we dedicated increasing resources to it.
That’s really it; humanity discovered an algorithm that could really, truly learn any distribution of data (or really, the underlying “rules” that produce any distribution of data). To a shocking deg... See more
In 15 words: deep learning worked, got predictably better with scale, and we dedicated increasing resources to it.
That’s really it; humanity discovered an algorithm that could really, truly learn any distribution of data (or really, the underlying “rules” that produce any distribution of data). To a shocking deg... See more