First of all, the take that LLM are just Parrots without being able to think for themself is dumb. They do in a limited way! And they are an impressive step compared to what we had before them.
Secondly, the take that LLMs are dumb and make mistakes that takes more work to correct compared to do the work yourself from the start. That is something I often hear from programmers. That might be true for now!
But the important question is how will they develop! And now my take, that I have not seen anywhere besides it is quite obvious imo.
For me, the most impressive thing about LLMs is not how smart they are. The impressive thing is, how much knowledge they have and how they can access and work with this knowledge. And they can do this with a neuronal network with only a few billion parameters. The major flaws at the moment is their inability to know what they don’t know and what they can’t answer. They hallucinate instead of answering a question with “I don’t know.” or “I am not sure about this.” The other flaw is how they learn. It takes a shit ton of data, a lot of time and computing power for them to learn. And more importantly they don’t learn from interactions. They learn from static data. This similar to what the Company DeepMind did with their chess and go engine (also neuronal networks). They trained these engines with a shit tone of games that were played by humans. And they became really good with that. But then the second generation of their NN game engines did not look at any games played before. They only knew the rules of chess/go and then started to learn by playing against themself. It took only a few days and they could beat their predecessors that needed a lot of human games to learn from.
So that is my take! When LLMs start to learn while interacting with humans but more importantly with themself. Teach them the rules (that is the language) and then let them talk or more precise let them play a game of asking and answering. It is more complicated than it sounds. How evaluate the winner in this game for example. But it can be done.
And this is where the AGI will come from in the future. It is only a question how big do these NN need to be to become really smart and how much time they need to train. But this is also when AI can gets dangerous. When they interact with themself and learn from that without outside control.
The main problem right now is they are slow as you can see when you talk to them. And they need a lot of data, or in this case a lot of interactions to learn. But they will surely get better at both in the near future.
What do you think? Would love to hear some feedback. Thanks for reading!
What’s the point of talking to yourself? Can you get better output by running it by yourself?
Well, me as a human, yes! We all constantly have an inner dialog that helps us to solve problems. And LLMs could do this as well. It is in principle not so much different from playing chess against yourself. As far as I know, these chess NN are playing against older versions of themself to learn. So it doesn’t have to play against the exact copy of itself.
Some of the training of image generators is done by two different AIs. AI-1 learns to differentiate between generated and real images and AI-2 tries to trick AI-1 by generate images that AI-1 can’t differentiate from real images. They both train each other! And the result is that AI-2 can create images that are very close to real images. All without any human interaction. But they do need real images as training data.
There are two steps:
Play chess against the best known version, with both sides being the stronger version. You have a new version baking that’s learning from this.
Test the new version when it’s got enough games learned against the known best and see if it’s winning more matches to become the new best.
But how does it learn from watching? It has a predictive NN that tries to predict the best next move simply by looking at the board. The next move is generated by thinking a long time about a bunch of positions, so if you can reliably get the next move by just doing one board position, it would be great. It also has the ability to guess who’s winning and by how much (either percentage or material)
It increases this ability by comparing its output to the positions/win rates read out by the strongest version. You either improved or you didn’t, there’s a metric you can check and you can also do some test matches once you stop improving so quickly.
It’s not clear what metric you want to optimize