[mdemare]

More AGI Final Frontiers:

"Reimplement Sid Meier's Alpha Centauri", but with modern graphics, smart AIs that role-play their personalities, all bugs fixed, a much better endgame, AI-generated unexpected events, and a dev console where you can mod the game via natural language instructions."

"Reimplement all linux command line utilities in Rust, make their names, arguments and options consistent, and fork all software and scripts on the internet to use the new versions."

[SequoiaHope]

That’s still just code! How about “design a metal 3D printing machine which can be built for $2000 and can make titanium, steel, aluminum, and copper parts with 100 micron precision, then design a simple factory for that machine. Write the manufacturing programs for all of the CNC machines, and work instructions for every step of the process. Order the material and hire qualified individuals to operate the machines. Identify funding opportunities and raise funds.”

I could go on. One of the challenges here is that many things like this cannot be designed by simply thinking, unless you have extremely super human performance, because complex subassemblies have to be built and prototypes and debugged. And right now there’s no good datasets for machine design, PCB design, machine tool programming, hiring, VC fund raising, negotiating building leases, etc.

We will never have real AGI unless it can learn how to improve without extensive datasets.

[glimshe]

Let's say we had a ChatGPT-2000 capable of all of this. How would digital life look like? What people would do with their computers?

[Lerc]

Even if we were not past a hard takeoff point where AIs could decide for themselves what to work on, the things that would be created in all areas would be incredible.

Consider every time you played a game and thought it would be better if it had x,y, or z. Or you wished an application had this one simple nrw feature.

All those things would be possible to make. A lot of people will discover why their idea was a bad idea. Some will discover their idea was great, some will erroneously think their bad idea is great.

We will be inundated with the creation of those good and bad ideas. Some people will have ideas on how to manage that flood of new creations, and create tools to help out, some of those tools will be good and some of them will be bad, there will be a period of churn where finding the good and ignoring the bad is difficult, a badly made curator might make bad ideas linger.

That's just in the domain of games and applications. If AI could manage that level of complexity, you can ask it to develop and test just about any software idea you have.

I barely go a day without thinking of something that I could spend months of development time on.

Some idle thoughts that such a model could develop and test.

Can you make a transformer that instead of linear space V modifiers it instead used geodesics? Is it better? Would it better support scalable V values?

Can you train a model to identify which layer is the likely next layer purely based upon the input given to that layer? If it only occasionally gets it wrong does the model perform better if you give the input to the layer that the predictor thought was the next layer. Can you induce looping/skipping layers this way?

If you train a model with the layers in a round robin ordering on every input, do the layers regress to a mean generic layer form, or do they develop into a general information improver that works purely by the context of the input.

What if you did every layer on a round robin twice, so that every layer was guaranteed to be followed by any of the other layers at least once?

Given you can quadruple the parameters of a model without changing it's behavour using the Wn + Randomn, Wn - Randomn trick, can you distill a model To .25 size then quadruple to make a model to retain the size but takes further learning better, broadening parameter use.

Can any of these ideas be combined with the ones above?

Imagine instead of having these idle ideas, you could direct an AI to implement them and report back to you the results.

Even if 99.99% of the ideas are failures, there could be massive advances from the fraction that remains.

[raspasov]

"Reimplement Linux in Rust" would be a good one!