I have very mixed feelings about #ChatGPT and related large language models (LLMs). They are certainly a cool tool, but I also currently don't believe that these "stochastic parrots" will easily replace search, article/paper writing, coding, and similar tasks. That is, they can be used to augment, but not replace those human activities - at least in the near future.

One more specific example is #Github #Copilot: I am impressed by the code suggested with small "query blocks", either in the form of method signatures or even just comments. While I wouldn't ever just Tab-complete that code into a production codebase (and I hope others also shy away from it), using it as a smarter auto-complete can be a total time-saver. While still learning #Rust, I find it a great tool to provide me with correct syntax while I try to express the semantic concepts in my head. That is, the #VSCode + #Copilot + #rustanalyzer based workflow is a magnitude faster than what I can achieve, at my current level of competence with #Rust, with any other IDE.
However, on the conceptual level, its suggestions range from spot-on (supposedly for parts that have been used in the same form in many Github projects already) to "terrible, awful, don't even think about it". Don't trust the concepts generated by #Copilot, but for getting a concept into correct syntax, it can be a great help.

I find the same to be true of #ChatGPT: it produces correct (human language) syntax, but its concepts on a semantic level are more random than anything else. It doesn't have a reasonable world model, and without such a world model, even internal consistency of statements (let alone factual validity in alignment with what we consider the real world) is darn hard (impossible?) to achieve. It's currently a toy, and should only be used for toy purposes.
On that note, the best use I could personally find for #ChatGPT is to reply to spammers, who operate on a comparable level of factual and consistent concepts (i.e., #BS): https://github.com/rmayr/chatgpt-email

Maybe #ReinforcementLearning will at some point allow LLMs to be factually more accurate, including the ability to cite sources. I don't know, as I'm not an expert in this field. But the current #UnsupervisedLearning approaches with just scraping massive amounts of (syntax, but not semantic) web content seem fundamentally flawed to me.

On a sidenote, if the #OpenAI mission is really to "ensure that artificial general intelligence benefits all of humanity", shouldn't the trained models be, erm, #open (source)? After all, the training data is publicly generated, so it seems more beneficial to humanity if the derivations are also #open for building upon them.

What I really appreciate about @danielmiessler and the #UnsupervisedLearning #podcast is the level of effort he has put into content curation and delivery over the years. Every facet of the episode has been scrutinized from the length of the sound bites, to the audio gear, to the types content selected. His discipline of distilling the message to just the core essence and punctuating each statement with a very brief bit of commentary allows him to cover a lot of ground - in terms of topics - in a very short weekly show.