As ever the truth is in the middle. - LLM's provide functionality that was very difficult to implement until 2 years ago. - We can decode natural language sta

As ever the truth is in the middle. - LLM's provide functionality that was very difficult to implement until 2 years ago. - We can decode natural language statements relatively well and relatively easily. - We have an approximate common sense knowledge base. - We can encode statements into human readable text flexibly. (this was never so much of a problem as the first two - but it's still useful). But, these are not magic boxes that can tell our fortunes. So we can do good things if we engineer things well, and there is a lot of synergy with other AI tech that's been evolving in the last ten years. STT and object recognition are both very useful, end to end differentiable reasoners are coming in now as well. ML was becoming important in 2019, 2023 created an inflection and some hysteria, but there's substantial value to be had.