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Wednesday, March 08, 2023

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Nade

Most of the key ML/AI papers come from Google Brain and DeepMind. The 2017 paper on transformers "Attention is all you need" came from them too. OpenAI based their GPT models on that (GPT = Generative Pre-trained Transformer).

Since OpenAI couldn't have an edge on research, what could they do to be competitive? Release earlier. That's pretty easy as Google and DeepMind are super cautious.
Microsoft then joined and invested on OpenAI, of course they want to compete against Google and now you have Bing powered by a language model. I've got the early access to it since a while and it looks more capable than ChatGPT.

However, as you could see, there have been issues immediately. Mostly because of Reddit users trolling the model, then journalists went there and took the sensationalist route. Then Microsoft had to take action by regulating and restricting the usage of the model and now it's harder to investigate its capabilities.

As for the NY journalist, he did the same move as Lemonine: prompt leading. The model then gives you what you request (or what you hint, even subconsciously: someone isn't aware of doing that) and the model follows your prompt.

ChatGPT, by default, essentially roleplays as a robotic assistant, so people use it as a sort of oracle or they ask calculations to... a language model, apt to, guess what, language tasks. You can ask it to summarize, to generate text of any kind in any style, etc.
Character.ai, developed by previous developers of Google's LaMDA, can pretend to be any character, from game characters to famous people, imitating their speech and personality.
And you can trick these models by making them generate anything else.
However, prompt engineering could also be malicious. OpenAI (and Microsoft with Bing Chat) tries to prevent that with some filter and pre-prompting (there is a hidden prompt before your input), but that works only so much.

At any rate, it's true that these models work with patterns. Neural networks (your brain too) are pattern recognition systems. This is also how you try to predict things, from the next word to finding patterns in stock market graphs. Often wrongly and that's also how stereotypes happen, but pattern recognition has its advantages too.
It's also how you learn, by training, repetition, until you "get it", as the mentioned song and the sentences you heard so many times; sadly propaganda, especially on social media, does the same by repeating misinformation, aided by bots, until people begin to parrot it; so, ironically, they are trained by propaganda bots.

The human brain not only predicts the next word and the next sentence, though, but also tries to predict what the interlocutor may reply. Having empathy and a developed theory of mind helps.
Since that also helps in predicting the next word (or better, the next token), there are papers investigating possible emergent abilities in language models:
https://arxiv.org/abs/2206.07682 "Emergent Abilities of Large Language Models"
https://arxiv.org/abs/2302.02083 "Theory of Mind May Have Spontaneously Emerged in Large Language Models"
(there are many more)

Hallucination is also another feature of neural networks, that fills the missing spots, beginning with the blind spot in the retina up to your dreams. So it's not something we are going to get rid. Instead, without hallucinations you couldn't have Stable Diffusion, nor in-painting techniques. Someone even thinks the human brain lives in a controlled hallucination and even the sense of self and consciousness may be illusory.
Anyway, that's true also for your "faulty", lossy fuzzy memories (that however can archive a huge amount of info), so that you have to check your notes and photos, because that red tulip you remember maybe it's your brain making it up for some missing information and it was a pink petunia instead.

Since these LLMs are good at making stuff up and they are language models, one of the best use cases is indeed chat-based roleplay gaming. We had some examples of that several years ago already with e.g. AI Dungeon. GPT-2 based AI Dungeon was hilarious with all the nonsense generated and it was quickly incoherent. GPT-3 based AI Dungeon was somewhat better, but still derailing, roleplaying for you, etc. But have you tried paragraph-roleplaying with ChatGPT? It's way more coherent. Essentially it's still GPT-3, but much improved (it's GPT-3.5) and it takes advantage of a larger context window, InstructGPT and several other things.

Betty Tureaud

If you want to try out chatGTP in Second Lifet go to BOO CLUB and chat withe "Betty Bot" Blais and I made a chatbot for you.
Taxi: https://maps.secondlife.com/secondlife/Danish%20Visions/188/233/250

John

Not only is content 'mediocre', but it gets *more* mediocre and fuzzy over time: https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web

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