adampk

Do you mean that there is no way to value OpenAI for a purchase so a sale is impossible?

At the least OpenAI is worth what the market is willing to pay for it.

So strange, does the author think companies never try to understand their customers?

I don't get why a normal corporate internal resource system its being framed as ridiculous. Does the writer not know about "personas"? Weird tone.

I worked through this for a tax company. They had a huge pile of artifacts from tax questions worked up for clients. What we did is we "reverse engineered" the process of the questions that would lead to that tax memo and the research steps to find the sources and conclusions. It worked well and we were able to replicate the process which the SME's created these memos.

For a given tax question, could you come up with the same memo quoting the same sources and same conclusion?

You would think this would be obvious to everyone. Clearly Apple is prepping for a digital overlay on the real world. Also less UI interaction, more voice/AI interaction.

How easy is the set up, does this need to be deeply integrated in each step of the life-cycle?

It does mention that, it calls that out specifically.

As you grow, it’s tempting to fix every issue using the ‘cowboy’ method. It’s fast. It’s efficient. It leads to good results. But the number of things that need a cowboy fix grow exponentially, and cowboy fixes only ever fix that one thing, while system fixes fix future issues too. As you adapt from cowboy to drone, it’s easy to skew too much to one side or the other. No matter how good your systems are, sometimes stuff just needs to get done pronto. But sometimes you need to take a step back and trust that the system you built will do its job, and trying to jump in to speed things up will only make everything worse.

You introduce a point I have not seen discussed before which is that these type of content distribution platforms go through a process to find their global minima.

Twitter at the beginning you didn't know what it was going to be or what worked. Same with facebook and instagram. As time goes on these sites small features bring out their emergent properties of what 'works' there.

And once it has been 'figured out', it is not as fun. You know what you can expect there and people go there but it is no longer a dynamic feeling. Like watching the NBA today, it has been 'figured out'.

I think that may be what is the factor in the longevity of these platforms, once it is 'figured out', if what it is, appeals to enough of a large base.

Tik tok may have gone further because it never really was 'figured out' in that larger way. The algorithm really could give you wildly different content and different 'trends' would show up so it never reached that static boring point.

For these 'on the decline' sites you can almost predict exactly what you will see there and exactly what the discussions are. It is not longer an exciting TV show.

'you put more bits into the prompt, you get more bits out.'

I think your line there highlights the difference in what I mean by 'insight'. If I provided in a context window every manufacturing technique that exists, all of base experimental results on all chemical reactions, every known emergent property that is known, etc, I do not agree that it would then be able to produce novel insights.

This is not an ego issue where I do not want it be able to do insightful thinking because I am a 'profound power'. You can put in all the context needed where you have an insight, and it will not be able to generate it. I would very much like it to be able to do that. It would be very helpful.

Do you see how '“superintelligence” is nothing more than a(n extremely) clever arrangement of millions of gpt-3 prompts working together in harmony' is circular? extremely clever == superintelligence

I don't disagree that there is never a need for 'new data' to make progress. But there is plenty of novel engineering that can be done with 'new data'. Just needing insights and connections.

But realizing that you can use certain commodity devices or known processing techniques in different problem spaces does not require new data, just 'insight'.

Agreed! Don't get me wrong, the statistical distribution modeling for human language is still SUPER helpful. And for things like legal/tax/coding, which has a lot to do with applying language patterns, this is a very big deal. But the ability to find the true 'sub structure' of content that it is trained on is not something they can do. It is like there is some lower substrate that it is 'missing'. That is a lot to ask for, but once we get there it will be the 'magic' that is promised, rather than amazing, super helpful, parlor tricks.

Interesting, could you give me an example? LLMs definitely can "understand" what I am asking at times when a human couldn't. They have more data to 'find similarity' to what I might mean. But I do not think you are saying they answer questions a human couldn't?