Microsoft, OTOH, does now offer a HIPAA BAA for its Azure OpenAI service, which includes ChatGPT (which means either they have a bespoke BAA with OpenAI that OpenAI doesn’t publicly offer, or they just are hosting their own ChatGPT instance, a privilege granted based on them being OpenAI’s main sponsor.)

So not the greatest analogy. But still I think most doctors, lawyers etc should be okay with their own cluster running in the cloud.

If it wasn't for patents you'd never get a moat from technology. Google, Facebook, Apple and all have a moat because of two sided markets: advertisers go where the audience is, app makers go where the users are.

(There's another kind of "tech" company that is wrongly lumped in with the others, this is an overcapitalized company that looks like it has a moat because it is overcapitalized and able to lose money to win market share. This includes Amazon, Uber and Netflix.)

Once you know that OpenAI gets a certain set of results with roughly technology X, it's much easier to recreate that work than to do it in the first place.

This is true of most technology. Inventing the telephone is something, but if you told a competent engineer the basic idea, they'd be able to do it 50 years earlier no problem.

Same with flight. There are some really tricky problems with counter-intuitive answers (like how stalls work and how turning should work; which still mess up new pilots today). The space of possible answers is huge, and even the questions themselves are very unclear. It took the Wright brothers years of experiments to understand that they were stalling their wing. But once you have the basic questions and their rough answers, any amateur can build a plane today in their shed.

Most likely this.

Right now the magical demo is being paraded around, exploiting the same "worse is better" that toppled previous ivory towers of computing. It's helpful while the real product development happens elsewhere, since it keeps investors hyped about something.

The new verticals seem smaller than all of AI/ML. One company dominating ML is about as likely as a single source owning the living room or the smartphones or the web. That's a platitude for companies to woo their shareholders and for regulators to point at while doing their job. ML dominating the living room or smartphones or the web or education or professional work is equally unrealistic.

But the counter for the high moat would be the atomic bomb -- the soviets were able to build it for a fraction of what it cost the US because the hard parts were leaked to them.

GPT-3 afik is an easier picking because they used a bigger model than necessary, but afterwards there appeared guidelines about model size vs. training data, so GPT-4 probably won't be as easily trimmed down.

The sauce is special, but the recipe is already known. Most of the stuff things like LLMs are based on comes from published research, so in principle coming up with the architecture that can do something very close, is doable to everyone with the skills to understand the research material.

The problems start with a) taking the architecture to a finished and fine tuned model and b) running that model. Because now we are talking about non-trivial amounts of compute, storage and bandwidth, so quite simple resources suddenly become a very real problem.

What exists are voltage levels that cause different stuff to happen. And we can't say much more about what humans do when humans think. You can surely assign abstractions to that too. Interpret neural spiking patters as exotic biological ways to approximate numbers, or whatever.

As it happens I do think our difference from computers matter. But it's not due to our implementation details.

Are you sure? Our senses have gaps that are being constantly filled all day long, it just gets more noticeable when our brain is exhausted and makes errors.

For example, when sleep deprived, people will see things that aren't there but in my own experience they are highly more likely to be things that could be there and make sense in context. I was walking around tired last night and saw a cockroach because I was thinking about cockroaches having killed one earlier but on closer inspection it was a shadow. This has happened for other things in the past like jackets on a chair, people when driving, etc. It seems to me at least when my brain is struggling it fills in the gaps with things it has seen before in similar situations. That sounds a lot like probabilistic extrapolation from possibilities. I could see this capacity extend to novel thought with a few tweaks.

> Given that GPT-4 is a simply large collection of numbers that combine with their inputs via arithmetic manipulation, resulting in a sequence of numbers, I find it hard to understand how they're "thinking".

Reduce a human to atoms and identify which ones cause consciousness or thought. That is the fundamental paradox here and why people think it's a consequence of the system, which could also apply to technology.

I worked as a lawyer for six years; there are extremely strict ethical and legal restrictions around sharing privileged information.