lamuswawir

If no more optimisations could be done in OTP 22, does that mean 22 is completed software.

Berkshire still owns many boring companies. They are mostly wholly owned and so are not listed under 'stocks'.

10

It is pretty much free. 10 daily downloads is enough for books and the papers are unlimited.

Do you even read 10 books per day anyway?

I have seen this also, but attention is far better for gtp-4, it seems to follow a system prompt for say, outputing json, uniformly compared to gpt-3.5. I have also found that gpt-3.5 follows a system prompt to do the same for only 2 successive outputs. You have to give it that prompt with every single time. So I think increasing context windows may not make it follow the system prompt uniformly.

Builder here. I am building an application that explains STEM concepts, gives exercises and learns how the user best learns.

I couldn't agree more, these tools are very useful, but interaction is almost just like that with humans, you use a word, they understand it as another. So you have to correct them, to explicitly tell them what you want. They are very good tools when you they and you are talking about the same thing. Indeed just like human conversation.

The instant feedback is better than with humans. Like humans, these models can take one thing to mean another, but because we are still using them for tasks that require instant feedback, we can catch the errors early and correct. It becomes worse if the are to be used on longer lasting tasks. The entire project could be derailed by one misunderstood concept. Just like a human project.

Natural language is a fair interface. But with lots of ambiguity. When I say 'clear', do you mean understandable, easily recognisable or clear like water. I faced that dilemma when I used that word as a design prompt because it can mean things which are opposites of each other. Some languages are so rich they avoid such ambiguities.

A common language can be good. It should not be designed by a committee, it should evolve with these tools naturally.

IoT sensors powered by ML may not provide good use cases for consumers, mostly because all things they can do can be done by a large model in the cloud, plus a phone. It will get interesting when we ask what use cases can't be solved by phone+cloud combo.

Such things as air quality management are good use cases. You can't use your phone to do that.

Thought of the same thing with voice. Can there be any limits on voice reproduction by AI?

Good work. Love the simplicity.

It feels like flow: described a century earlier.