[kosh2]

I'm constantly amazed that most discussions on technical forums center around what ChatGPT can't do and why it can't replace X and how often it produced nonsense.

Yes, it's true. But then again, if it didn't make mistakes anymore, we would have created a general porpuse solution machine working with all of human knowledge.

"We've created a plane that can fly 10 km!"

"Meh, 10 km is not that useful. Also, it's still expensive"

20 year ago, even current ChatGPT would be straight up science fiction. We are getting to a point where we develop tools that are unlike any other in their power to solve problems for us. And development will likely only get more intense on that front. These systems made quite a splash recently so there will be even more money going into it. Custom hardware for AI systems is being advanced all the time and every large software company wants AI developers.

I'm amazed that we don't think about how we are going to handle this. There are a lot of areas where the next gen (or the one after etc.) ChatGPT might have dramatic consequences both good and bad.

[mrshadowgoose]

It's just another instance of the same broken thinking one sees in other ML fields. For whatever reason, people 1) hold ML systems to a standard of success far in excess of that demonstrated by humans 2) endlessly quibble about whether the ML system internally has "true understanding", despite it not mattering for the system's ability to affect the external world.

Thermodynamically, general intelligence is on the order of 10 watts, as is evidenced by most human brains. This leads me to the belief that we likely already have the computational capability for AGI, and simply have not figured out the correct architecture and weightings. As we've seen with the flurry of increasingly SOTA image generation models this year, innovations in the ML space tend to arrive with little warning, and have rapid and real effects on the world. Within the context of AGI, this pattern causes me a lot of existential dread.

[mycall]

When comparing how the human brain works, the transformer model is not the same thing. Until convergence of the mechanics occur, there will be limitations in the efficiency of AGI. Stil, I am eager to see what a 100 trillion or 1 quadtrillion parameter GPT5 with Adaptive Computation Time will do.

[maria2]

The human brain may only be 10 watts, but the “weights” in the brains NN were computed over billions of years with countless amounts of data.

[ZephyrBlu]

The probabilistic and unverifiable aspects of GPT are what make me doubt it.

It's more like, "we've created a plane that can fly 10km 50% of the time and crash 50% of the time", which is worse than useless.