I would stop that kind of evolution, as it can be catastrophic, but I know that humanity is not able to stop itself from evolving further.
An 18 year old will have been training for ~160,000 hours on a volume of raw data that is probably far beyond our ability to currently store let alone train an AI with.
As far as training for a specific task, all that training on other matters kicks in to help the human learn or accomplish a “novel” task more rapidly, for example, knowing how to read and interpret the instructions for that task, knowing how to move your appendages and the expected consequences of your physical interactions with a material object. You’re certainly not taking a fetus with a blank slate and getting it to accomplish much at all.
If you are also saying AIs will be a more intelligent species, able to adapt better on Earth than humans, that requires extraordinary evidence. A human could solve complex problems no other species it machine can solve on nothing more than a handful of rice for a week. Where is the similar scale species/machine?
And even then for proper agi it would need to close the loop after inducing an hypothesis with testing.
Meatbags are going to be useful for a while still.
It can translate code from one language to another. Code that it has never seen before. Or between natural language and programming language.
It's certainly not perfect but it is reasoning all right
Its funny how many people will immediately poke holes in it for software development, but two years ago I could not imagine an AI could write code like chatgpt is doing now.
As shown by this post, we're still discovering what's possible with the tools we have now.
Also, human programmers aren't amazing at writing defect free code.
On that thought, does anyone even still use Copilot?
The AI can output a lot of text but can you input a 100000 line code base into it? No you can't. You can't even input 50 lines of code that is already in the data set!
And by failure I mean somethin akin to a blue screen and not that the output was wrong, there was no output!
This is obviously true, and yet we've invented so many things. From the wheel, to control of fire, to farming and animal husbandry, to mathematics, to metallurgy, to physics, to semiconductors, etc.
The interesting question is, was the invention of all those things simply the re-synthesis of "stuff we already know?" If the answer is yes, doesn't that mean we're now on the cusp of a something akin to a singularity? We can now synthesize nearly-unlimited streams of coherent human thought. If we had a way of differentiating the wheat from the chaff, we could analyze what would have been millennia of human output in the proverbial blink of an eye. If human knowledge is just "stuff we already know", then we better buckle up. It's about to be a wild ride.
This is the key to AGI. We need verification systems, they can be a code execution environment, a database of facts, a math symbolic engine, a physical simulation, a game, or real world lab experiments. These verifiers will produce signal that can be used by the language models to improve. The cheaper and faster verification is, the faster we can iterate. Generating ideas is cheap, proof matters.
Just remember AlphaZero a bit - it started from scratch, playing against itself, in a few hours it surpassed human level. Go simulation and verification is trivial. The board is just a matrix. So learning from massive search and verification is a proven path to super-human level.
Here is a related paper:
> Evolution through Large Models
And the space is combinatorial. Most of it is unexplored. It is creativity, but also a form of search.
If you think about it the most creative is random noise. then you apply rules it it to constrain the noise or limit the predictions. And add more rules in layers. so getting the system to produce more creativity is just relaxing the rules at different layers.
I think what's interesting is that many types of creativity may really just be re-synthesizing "stuff we already know."
So a lot of the negative comments along the lines of, "it can't be creative because it never thinks of anything beyond its training data" don't click with me. I think synthesizing two existing concepts into some third thing is actually a form of creativity.
These nets may not learn the same way we do exactly, and they may not possess the same creative abilities as us — but there's definitely something interesting going on. I for one am taking a Beginner's Mind view of it all. It's pretty fascinating.