[HN Gopher] Playing games with AIs: The limits of GPT-3 and simi...
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Playing games with AIs: The limits of GPT-3 and similar large
language models
Author : nigamanth
Score : 73 points
Date : 2023-01-07 06:19 UTC (16 hours ago)
(HTM) web link (link.springer.com)
(TXT) w3m dump (link.springer.com)
| visarga wrote:
| While I agree with the authors that large language models only
| trained on text lack the ability to distinguish "possible worlds"
| from reality, I think there is a path ahead.
|
| Large language models might be excellent candidates for
| evolutionary methods and RL. They need to learn from solving
| language problems on a massive scale. But problem solving could
| be the medicine that cures GPT-3's fuzziness, a bit of symbolic
| exactness injected into the connectionist system.
|
| For example: "Evolution through Large Models"
| https://arxiv.org/abs/2206.08896
|
| They need learning from validation to complement learning from
| imitation.
| zwaps wrote:
| This is what ChatGPT does, for instance.
| tbalsam wrote:
| In a sense, I thought it was more human-in-the-loop rather
| than an explicit RL objective (i.e. potentially somewhat
| limited reward surface, even if there's a reward model
| trained from it)
| make3 wrote:
| the step from gpt 2 to gpt3 showed us that any attempt at
| predicting the behavior of sufficiently scaled up models is
| really futile
| optimalsolver wrote:
| Would there be any benefit in modeling raw binary sequences
| rather than tokens?
|
| I think text prediction only gets you so far. But I guess you
| could use the same principles to predict the next symbol in a
| binary string. If this binary data represents something like
| videos of physical phenomena, you might get the AI to profound,
| novel insights about the Universe just with next-bit prediction.
|
| Hmmm, maybe even I could code something like that.
| dwaltrip wrote:
| I'm waiting for someone to make a GPT-style model trained for
| video and audio prediction (e.g. frame by frame, perhaps) in
| addition to the existing text prediction. Imagine using a
| significant percentage of YouTube content, for example.
|
| It would probably be insanely expensive. But I feel like it
| would be almost guaranteed to acquire a world model far richer
| and more robust than ChatGPT's.
|
| Human babies learn by watching the world around them. Video
| frame prediction feels much closer to that than text
| prediction, and given the wildly impressive results we are
| seeing with large text prediction models alone, it seems like
| an obvious next step.
| tintor wrote:
| There is VideoGPT paper. It uses small frame sizes, but that
| will improve with time.
| boredemployee wrote:
| >> Human babies learn by watching the world around them.
|
| While I understood what you meant, I'd just add that babies
| learn by a combination of multisensory triggers (so not only
| _watching_)
| nodemaker wrote:
| The reason this works for tokens is that tokens are put in a
| vector space where similar words are in a similar place. The
| same effect could not be achieved with characters or bits. If
| you think about it our brains also remember words and not
| characters
| eternalban wrote:
| > our brains also remember words
|
| Word sounds. I can _not_ read without hearing the word. (Now
| I wonder about those born deaf.) Based on that subjective
| experience which I presume is rather universal among the
| hearing, tokenized phones and phonemes seem promising.
| worik wrote:
| I am not deaf.
|
| I do not hear words as I read. (As I write I do)
| visarga wrote:
| So you are proposing a massive video model, on the likes of
| GPT-3? The architecture is simple, but making it train
| correctly and efficiently is really hard, especially for video.
| [deleted]
| marmadukester39 wrote:
| Is it? Videos are just sequences of frames
| rdedev wrote:
| Each frame of the image would have to be divided into many
| sequences. Atleast that's how transformer based image
| models work. Then you have to account for audio data too in
| the same way. It just blows up the compute required
| optimalsolver wrote:
| Not quite. I meant something that models pure binary
| sequences, not higher level tokens. That way, it could learn
| from any source that can be represented as binary data. Could
| be video, text, audio, or all three at once.
|
| It wouldn't be "video model", it would be an "anything that
| can be expressed in binary" model.
| visarga wrote:
| Maybe you are interested in this paper:
|
| > Perceiver: General Perception with Iterative Attention
|
| Biological systems perceive the world by simultaneously
| processing high dimensional inputs from modalities as
| diverse as vision, audition, touch, proprioception, etc.
| Perceiver is a deep learning model that can process
| multiple modalities, such as images, point clouds, audio,
| and video, simultaneously. It is based on the transformer
| architecture and uses an asymmetric attention mechanism to
| distill a large number of inputs into a smaller latent
| bottleneck. This allows it to scale to handle very large
| inputs and outperform specialized models on classification
| tasks across various modalities.
|
| https://arxiv.org/abs/2103.03206
| optimalsolver wrote:
| Thanks! This looks really interesting.
| decremental wrote:
| That might be too low(?) resolution. It would be learning
| encodings instead of features of the thing that is being
| encoded. Like training it on terabytes of zip files and
| expecting it to reproduce from the files contained in the
| archives.
| optimalsolver wrote:
| Imagine a single, 120 minute movie in a tar file.
|
| How much of this file's raw data would be encoding and
| metadata, vs the content of the movie?
| decremental wrote:
| The thing is even the video data outside of the tar file is
| also encoded. Most likely the compressed video data will be
| basically random. You can't train it on random data, it's
| just noise. Rather it would make more sense to train on
| sequences of RGBA pixels.
|
| It's a seductive thought to be able to just throw raw bits
| at a model, regardless of what those bits represent, and
| have it just magically attain LLM qualities in reproducing
| the data you would want it to.
|
| Something to think about: GPT3/ChatGPT tokenize at the byte
| level. If they tokenized at the bit level the model would
| learn Utf8 encoding over time. Unicode characters that
| require more than one byte to represent, such as emojis,
| are not learned directly but the model can still reproduce
| them.
| stared wrote:
| Tokenization for models like GPT or BERT can be seen as
| compression. That is, frequent words are separate tokens.
| Frequent sequences are separate tokens. On the other hand, if a
| sequence is very uncommon, then it will contain many tokens.
|
| Sure, you encode bit-by-bit. But it is a fixed-length code,
| which is even worse than character-by-character.
|
| Maybe you only get worse training and inference time. But I
| wouldn't be surprised if the encoding also serves as a Bayesian
| prior, and with a different encoding, you get worse results
| (for given data).
| tbalsam wrote:
| There's a couple of massive intuition leaps here (around tokens
| and the ease of which predicting one modality extends to
| another), but if you're interested in diving into the field at
| the place where they're asking questions like this, you could
| start by looking at the transition from BPE to the tokenizer we
| have today for the tokenization front, and PercieverIO for the
| multimodal generalization front.
| ttul wrote:
| Google Research has a character-based transformer that learns
| to tokenize text rather than relying on hand coded tokenizers.
| It demonstrates superior performance on a variety of LLM tasks.
|
| If you have the money, you can apply the transformer
| architecture to many different tasks and people are
| experimenting all the time. I think one of the big challenges
| is always to come up with methods for training such enormous
| models pragmatically without cost exploding.
|
| [1] https://huggingface.co/docs/transformers/model_doc/canine
| Der_Einzige wrote:
| Related, I wrote "language games" for playing word games with
| word vectors. I've thought about remaking this beyond my original
| weekend project and including the latest of language models in.
| https://github.com/Hellisotherpeople/Language-games
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