This type of thinking really needs to look beyond what the hype train is on with OpenAI right now and look at some of the more groundbreaking work going on. De

This type of thinking really needs to look beyond what the hype train is on with OpenAI right now and look at some of the more groundbreaking work going on. Deepmind has quite a bit of work outside the language area that has a lot of potential. Peter Velockovic, Matt Botvinick, Chelsea Finn, Taco Cohen, Micheal Bronstein, et al are all working completely outside the arena of NLP, but they have much more groundbreaking work than what everyone seems laser focused on right now. There are tons of examples really, but for instance: their agents that have emergent properties of teamwork, as well as their work on developing entirely new mathematics in some of the hardest fields (knot theory and representation theory). Creating new mathematics and proving theorems is probably the most creative form of human expression there is, so the fact that AI can achieve this is quite a big leap. Combining this with the deficits in LLMs can construct something that has much fewer flaws. While some of the hype around language models is fading now, we will probably see another resurgence of interest by the media when Deepmind releases a system with these capabilities underneath a LM. These LMs have been around for many years, and are just now getting mass attention. So it will likely take some more time of these much more sophisticated systems being solely in the academic arena before people become aware of what is really going on in AI (company products typically lag very far behind what academia is capable of). However, soon there will be products that have attached a linguistic interface to these types of systems, and will likely yield some really impressive capabilities.