Is this like when the Google self driving car director Chris Urmson said in 2015 his 11 year old son would never need to take a drivers exam? I have a hunch when they decide who to hire for upper management, they select for whoever promises the moon. The person making the promises may not even believe it.
"Is this like when the Google self driving car director Chris Urmson said in 2015 his 11 year old son would never need to take a drivers exam?"
He probably meant that he has enough money so he can hire a driver for his son.
+1. What's new "since the 80s" are faster computation, larger datasets, and a handful of mathematical breakthroughs that enable once-intractable algorithms. It's obvious that human general intelligence operates in frontiers that are plainly outside the scope of computation.
What makes that obvious to you? What seems uncomputable about human intelligence?
Despite our best efforts, we are deeply irrational. Our thinking is based on instinct, not on core principles; it's a top-down approach driven by feelings.
“person with vested business interest says their sector/product/company is going to revolutionize the world” wow no way you don’t say
I wouldn't be surprised if after this interview he got a few millions $$ more in his stock worth. It's damage management. They, and he personally, didn't see LLM potential.
As someone who has worked in the field of AI/ML for quite awhile now, the problem with current AGI predictions is ML hasn't done anything new since the 80s (or arguably earlier). At the end of the day all ML is using gradient descent to do some sort of non-linear projection of the data on to a latent space, then doing some relatively simple math in this latent space to perform some task. Personally I think the limits…
As someone who has worked in the field of AI/ML for quite awhile now, the problem with current AGI predictions is ML hasn't done anything new since the 80s (or arguably earlier). At the end of the day all ML is using gradient descent to do some sort of non-linear projection of the data on to a latent space, then doing some relatively simple math in this latent space to perform some task. Personally I think the limits…
+1. What's new "since the 80s" are faster computation, larger datasets, and a handful of mathematical breakthroughs that enable once-intractable algorithms. It's obvious that human general intelligence operates in frontiers that are plainly outside the scope of computation.
As someone who has worked in the field of AI/ML for quite awhile now, the problem with current AGI predictions is ML hasn't done anything new since the 80s (or arguably earlier). At the end of the day all ML is using gradient descent to do some sort of non-linear projection of the data on to a latent space, then doing some relatively simple math in this latent space to perform some task. Personally I think the limits…
+1. What's new "since the 80s" are faster computation, larger datasets, and a handful of mathematical breakthroughs that enable once-intractable algorithms. It's obvious that human general intelligence operates in frontiers that are plainly outside the scope of computation.
As someone who has worked in the field of AI/ML for quite awhile now, the problem with current AGI predictions is ML hasn't done anything new since the 80s (or arguably earlier). At the end of the day all ML is using gradient descent to do some sort of non-linear projection of the data on to a latent space, then doing some relatively simple math in this latent space to perform some task. Personally I think the limits…
+1. What's new "since the 80s" are faster computation, larger datasets, and a handful of mathematical breakthroughs that enable once-intractable algorithms. It's obvious that human general intelligence operates in frontiers that are plainly outside the scope of computation.
Given that a sufficiently resourced computer ought to be able to run a subatomic-level simulation of an entire human brain, and while acknowledging the usual counterpoint vis a vis C. elegans/OpenWorm but deeming it irrelevant on longer timescales, your take seems quite arrogant. “Outside the scope of computation” is an awfully broad claim.
+1. What's new "since the 80s" are faster computation, larger datasets, and a handful of mathematical breakthroughs that enable once-intractable algorithms. It's obvious that human general intelligence operates in frontiers that are plainly outside the scope of computation.
Given that a sufficiently resourced computer ought to be able to run a subatomic-level simulation of an entire human brain, and while acknowledging the usual counterpoint vis a vis C. elegans/OpenWorm but deeming it irrelevant on longer timescales, your take seems quite arrogant. “Outside the scope of computation” is an awfully broad claim.
Prove such a simulation can be created with actual hardware, taking relativity into account.