3 ms·
It has no long term memory. Everything what happens within one session is forgotten. With limited 'window' it keeps forgetting even within the session. There ar
by two_in_one 3y ago
It has no long term memory. Everything what happens within one session is forgotten. With limited 'window' it keeps forgetting even within the session. There are no interconnections between sessions. The result: it cannot execute long plans or have permanent 'life'. At least for now. This will be fixed in more complex AI systems, I believe 'soon'. There is strong demand from military here and 'there'. Plus there are many other uses for embodied AI. Like space travel with speed of light.
- lucubratory 3y agoPersonally I think multi-year scale memory is possible with currently available research, if we just put it all together. What happens if we combine very long context lengths, dedicated summarising LLMs, RAG, MemGPT, sparse MoE, and a perennial Constitutional AI overseer? I don't see any reason why these systems couldn't work together. It just hasn't really been that much time, particularly considering that large training runs can take months, and if you want the best performance you often need to train with your specific architecture in mind. As well as time, it's not like all of these advances are coming from the same group of people, they're from AI researchers spread across the globe. Get them all in a room with a CERN-level budget for compute resources and I think they could do it.
- c7b 3y agoIt could fine-tune / train new LLMs to incorporate new knowledge and then, given that it has CLI acces, launch new agent instances using those models. And we're talking about sessions without a defined end here, that's the premise of the paperclip thought experiment.