First off, LLMs, ChatGPT, DALL-E, etc... these are all just neat party tricks. Generative AI is nothing special and - along with the other comments - going to b

First off, LLMs, ChatGPT, DALL-E, etc... these are all just neat party tricks. Generative AI is nothing special and - along with the other comments - going to be the butt of many jokes in the years to come (and already is: see South Park). Everyone clamoring to get on that bandwagon either doesn't understand it or is attempting to capitalize on (and sell it) to people who don't understand it. That said, what _powers_ those models: encoders, attention, and transformers, has been a massive leap in AI model generation. This cannot be overstated! I don't care about AI generating a token or pixel. What I care about is being able to throw very complex data at a model and have it "learn" relationships between them in ways I couldn't see before and then being able to perturb the data to see what how that changes the "meaning" of it. For example, in biotech (my particular field), training models on the gene expression profiles of healthy vs. diseased tissues and then perturbing expression levels (in silico) to determine if doing so encodes the gene profile as "healthy" is ground-breaking. As data profiles of are added it has the potential to also reveal potential toxicities of gene inhibition (or expression) without ever needing to test it in the lab saving $100s of millions. The party tricks are neat, and they are what bring in the $$. But it's the building-block use-case getting very little attention where all the benefits are going to be had in the coming years. And they are coming fast! P.S. My personal prediction is that the next massive leap in AI is going to be a paradigm shift away from how we train and simulate networks. The current framework of more and bigger GPUs to process larger and larger models is unsustainable. Someone, somewhere in the next 5-10 years will revolutionize how this is done and THEN we'll have our true, AI "revolution".