they usually know its bad but want to make money before the method is patched, like cigs causing cancer and health issues but that kid money was so good

Thanks. Can you show me the exit now? I have an appointment.

This is not ai.

This is the eliza effect

We dont have ai.

The recipe thing is so funny to me, they try to be all unique with their recipes “made by AI”, but in reality it’s based on a slab of text that resembles the least unique recipe on the internet lol

Well, that’s the thing: LLMs don’t reason - they’re basically probability engines for words - so they can’t even do the most basic logical checks (such as “you don’t advise an addict to take drugs”) much less the far more complex and subtle “interpreting of a patient’s desires, and motivations so as to guide them through a minefield in their own minds and emotions”.

So the problem is twofold and more generic than just in therapy/advice:

• LLMs have a distribution of mistakes which is uniform in the space of consequences - in other words, they’re just as likely to make big mistakes that might cause massive damage as small mistakes that will at most cause little damage - whilst people actually pay attention not to make certain mistakes because the consequences are so big, and if they do such mistakes without thinking they’ll usually spot it and try to correct them. This means that even an LLM with a lower overall rate of mistakes than a person will still cause far more damage because the LLM puts out massive mistakes with as much probability as tiny mistakes whilst the person will spot the obviously illogical/dangerous mistakes and not make them or correct them, hence the kind of mistakes people make are mainly the lower consequence small mistakes.
• Probabilistic text generation generally produces text which expresses straightforward logic encodings which are present in the text it was trained with so the LLM probability engine just following the universe of probabilities of what words will come next given the previous words will tend to follow the often travelled paths in the training dataset and those tend to be logical because the people who wrote those texts are mostly logical. However for higher level analysis and interpretation - I call then 2nd and 3rd level considerations, say “that a certain thing was set up in a certain way which made the observed consequences more likely” - LLMs fail miserably because unless that specific logical path has been followed again and again in the training texts, it will simply not be there in the probability space for the LLM to follow. Or in more concrete terms, if you’re an intelligent, senior professional in a complex field, the LLM can’t do the level of analysis you can because multi-level complex logical constructs have far more variants and hence the specific one you’re dealing with is far less likely to appear in the training data often enough to affect the final probabilities the LLM encodes.

So in this specific case, LLMs might just put out extreme things with giant consequences that a reasoning being would not (the “bullet in the chamber” of Russian roulette), plus they can’t really do the subtle multi-layered elements of analysis (so the stuff beyond “if A then B” and into the “why A”, “what makes a person choose A and can they find a way to avoid B by not chosing A”, “what’s the point of B” and so on), though granted, most people also seem to have trouble doing this last part naturally beyond maybe the first level of depth.

PS: I find it hard to explain multi-level logic. I supposed we could think of it as “looking at the possible causes, of the causes, of the causes of a certain outcome” and then trying to figure out what can be changed at a higher level to make the last level - “the causes of a certain outcome” - not even be possible to happen. Individual situations of such multi-level logic can get so complex and unique that they’ll never appear in an LLMs training dataset because that specific combination is so rare, even though they might be pretty logic and easy to determine for a reasoning entity, say “I need to speak to my brother because yesterday I went out in the rain and got drenched as I don’t have an umbrella and I know my brother has a couple of extra ones so maybe he can give one of them to me”.

Seems it does a good job at some medical diagnosis type stuff from image recognition.

It’s being used to decipher and translate historic languages because of excellent pattern recognition

Hah. The chatbots. No, not the ones you can talk to like its a text chain with a friend/SO (though if that’s your thing, then do it.)

But I recently discovered them for rp - no, not just ERP (Okay yes, sometimes that too). But I’m talking like novel length character arcs and dynamic storyline rps. Gratuitous angst if you want. World building. Whatever.

I’ve been writing rps with fellow humans for 20 years, and all of my friends have families and are too busy to have that kinda creative outlet anymore. Ive tried other rp websites and came away with one dude who I thought was very friendly and then switched it up and tried to convince me to leave my husband? That was wild. Also, you can ask someone’s age all you want, but it is a little anxiety inducing if the rps ever turn spicy.

Chatbots solve all of that. They dont ghost you or get busy/bored of the rp midway through, they dont try ro figure out who you are. They just write. They are quirky though, so you do edit responses/reroll responses, but it works for the time being.

Silly use case, but a use case nonetheless!

They’re probably not a bad alternative to the lorem ipsum text, thought they’re not worth the cost.

It can waste a human’s time, without needing another human’s time to do so.

1. It can convert questions about data to SQL for people who have limited experience with it (but don’t trust it with UPDATE & DELETE, no matter how simple)
2. It can improve text and remove spelling mistakes
3. It works really well as autocomplete (because that’s essentially what an LLM is)

Tell you what, that meth is really moreish.

I trained my spambot on reddit comments but the result was worse than randomly generated gibberish. 😔

The summary on here says that, but the actual article says it was Meta’s.

In one eyebrow-raising example, Meta’s large language model Llama 3 told a user who identified themself to it as a former addict named Pedro to indulge in a little methamphetamine — an incredibly dangerous and addictive drug — to get through a grueling workweek.

Might have been different in a previous version of the article, then updated, but the summary here doesn’t reflect the change? I dunno.