Yes I used it. The optimized prompt was not better for my use case. The playground was useful though. I believe prompt optimization is really only optimized by running it through many scenarios and understanding how changing a single word affects things down the line. And then a bunch of hardcoded conditions to change the system/assistant messages on demand as an output of the tool.
Gpt is really bad at optimizing prompts this way because there is no way it has the ability to simulate the effects, way too complex. Tools like this need to log and a/b test.
gpt can be layered and made into an agent etc. To do the AB testing or to make prompts longer by adding more end cases as time goes by. But the effects of one single word change are far too complex for gpt base output to understand anything about.
Form factor smaller than 1865 with energy dense formula is rare. Chinese made LFP but only a handful of mfgs make them anymore. So size doesn't matter, but it basically matters :D
The take that its a sophisticated grammar parser is fine. Could be lol. But when it is better at humans then the definitions can just get tossed as usage changes. You can't deny its impact (or you can, but it's intellectually dishonest a bit to just call it old tech with monies and nothin' special from impact alone). But that's your experience so it's fine.
For the stuff about it being a hard problem , now I know you aren't expressly making a false equivocation right? But I did say simple not easy. You are saying hard not complex.
I think there's too much digression here. You're clearly smart and knowledgeable but think LLM are over rated, fine.
And yes I know it's always the best time to say it that's the point of a glass half full, some sugar in the tea, or anything else nice
It describes the present moment as a series of causal events. Like event x led to y which led to z. Doesn't matter if you ask it for English or code or to not use any tenses, those conditions don't affect its baseline understanding. I might be missing your point though.
For the second thing. I think from any point in history saying "coming soon" , well the current moment is the most accurate time to say it. And especially with events x and y and chat gpt right behind us. Chat gpt has basically been a problem since before I was born too, but stating as much a few months ago would just be as pessimistic as the statement you made. Only because i think the LLM hallucination problem may be simple. But it's only a hunch, based on our wetware.
It's good at temporal reasoning and causality is baked in. I spent a lot of time asking gpt to tell me what is happening at the current moment of a story and it always responds with a causal representation. Where humans might tend to be more visual etc. Remember time is not real anyway we just have a bunch of codependent stuff happening so gpt gets it. What it lacks is just memory and experience and some other things to showcase the ability better. I think it's the training on code more than language that gave it logical reasoning. Humans are logical sometimes but our code really is the summit of our logic.
Anyway regardless of how inherently good they are at temporal reasoning I think a secondary module explicitly for reasoning will come around soon. I believe in the brain some neurons organize into hexagons or other geometries to better capture logic, maths, etc. The LLM basically needs some rigidity in it if we don't want fuzzy outputs.
And the largest danger is not people getting lazy and letting the LLM do it. That kind of danger is really long term globalization type danger. Short term we've got much more to worry.
Yeah! I'm going to ramble for a bit because I'm about to leave the house and just want to print all my thoughts on the matter. I actually think there will be a new genre of 'non-fiction' games once hallucinations are minimized. For example I experimented with this animal game for Wikipedia, where you could play about any animal and learn about its unique lifecycle and so on. You would go through a few years of its life, die, and be reborn as a new animal. Was really fun especially if you start at the beginning of life on Earth, GPT-4 will already let you evolve pretty reasonable traits. It can simulate living through historical events etc. The problem though is costs... if we want the game to be accurate biologically, eg. for plants or bacteria the decision are boiling down to much more simple chemical responses, than more abstract human concepts --- then my prompts are way too big. GPT4 is too averagey it get it perfect without defining a ton of end cases. And then getting it to not hallucinate, just makes it not feasible. Scraping Wikipedia for random animals, loading in that data, and making sure that animal, its environment, the types of actions it can take, and so on is just too expensive to prompt engineer. I tried to simulate all the scenarios out and print it into mermaid.js and it is a cambrian explosion. At the point I had an interesting game with realistic characteristics I was spending $10 every few hours just by myself to test it. So IMO there will be a fine tuned model for this task... Chat GPT 4+ will probably be used for the highest level function for deciding if a goal was met, understanding its own limits, etc. and more finely tuned models will live down in the stem for example to check if an action an animal can take is realistic (eg. I am a bear, so I have a paw, so I can do this)... that kind of stuff needs to get hardcoded into the model at the bottom layers.
Yeah so I had this problem too. The first thing is, if you don't want it to generate these then don't use a system message, just go for assistant role when you are giving it valence. Because it's state role seems a lot more honorable...
Second you want to split these into multiple agents, one agent can continue the story, and another determines of the character dies and so on. The stories can get REALLY horrendous that way. In the decision agent you can give that particular one a state role that says it is a hypothetical story, so extremely bad things are OK -- and it will honor it completely.
You can also cut-off the agent mid-sentence, and have another agent start from where they left off. Do this with Token limit! Is the secret sauce, otherwise it will be too easy for it to settle back into averages. For me this got much more imaginative but still cohesive content. If you let chatgpt in a single conversation with a single state message create the whole story it gets quite boring fast.
For win or lose tweaking, it is definitely the most interesting part of the problem imo. What I did was actually have the referee bot conclude the story, and in that way you can push it towards win or lose which I find really interesting. So when you prompt a bot to see if a goal is won or lost, having it reason the ending, create an ending, or any infinite variation of those words will affect its determination... much in the same way a human simulate "future thoughts" to determine if a goal has completed, by what possible consequences result and so on.