10 ms·

GPT4 and the Multi-Modal, Multi-Model, Multi-Everything Future of AGI

127 points by swyx 4y ago

ren_engineer 4y ago
how hard is it to "update" an LLM like GPT-4 with recent data instead of being frozen in time at the training date? Obviously you could use the increased context size to work around this, but being able to augment the base model seems like it would be the ideal use case for many high value projects like a version of Copilot that knows a company's entire code base and knows about newer libraries and updates
- swyx 4y ago
  GPT has finetuning apis which you can use to update, but i think the massive context size is meant to show us you wont really need it most of the time. 25k words is really a lot of context. in the demo @gdb just dumped in the entire discord docs without breaking a sweat
numbers_guy 4y ago
How do they even train such huge models? Especially when you start talking abou multi-modal models. Is the underlying optimization process still relying on gradient descent? I am just baffled how they manage to get it to train across such vastly different sets of data and manage to get it to work.
- deleted 4y ago
  [deleted]
- jah242 4y ago
  Whilst not the same I recommend you look at the DeepMind Gato paper to see surprisingly (relatively) simple multi modal can be - https://openreview.net/forum?id=1ikK0kHjvj https://openreview.net/forum?id=1ikK0kHjvj Essentially to merge lots of modalities they just go 'let's convert all modalities into integers in the same given range', e.g the word 'me' = 1001, up in Atari = 11002, joint torque of right motor of robot = 33000 and so on. From the paper: There are infinite possible ways to transform data into tokens, including directly using the raw underlying byte stream. Below we report the tokenization scheme we found to produce the best results for Gato at the current scale using contemporary hardware and model architectures. • Text is encoded via SentencePiece (Kudo & Richardson, 2018) with 32000 subwords into the integer range [0, 32000). • Images are first transformed into sequences of non-overlapping 16 × 16 patches in raster order, as done in ViT (Dosovitskiy et al., 2020). Each pixel in the image patches is then normalized between [−1, 1] and divided by the square-root of the patch size (i.e. The tokenized result is a sequence of integers within the range of [0, 1024). 16 = 4). • Discrete values, e.g. Atari button presses, are flattened into sequences of integers in row-major order. • Continuous values, e.g. proprioceptive inputs or joint torques, are first flattened into sequences of floating point values in row-major order. The values are mu-law encoded to the range [−1, 1] if not already there (see Figure 14 for details), then discretized to 1024 uniform bins. The discrete integers are then shifted to the range of [32000, 33024).
  - jcims 4y ago
    The interesting thing to me is that our brains probably do something similar, converting multi-modal sensory data into the same 'model' that we experience as our concsiousness.
- kelipso 4y ago
  Usually you stick a CNN (or whatever other vision encoding neural network) after the image inputs and pipe the output of the CNN as an additional token or tokens of the GPT transformer (into the layer after the text embedding layer). That way everything is differentiable. You can pre-train the CNN and Transformer independently and then train them together (usually easier to train this way), or just train them from scratch. There are lots of other ways to combine two networks also. So input is now one (or more) images and text, and output is text. There are ways to position the input image in a particular location within the input text as well. Training data can come from websites, etc.
- swatcoder 4y ago
  More likely, this round of innovation is focused on integrating many engines into one product using techniques along the lines of langchain. Instead of grinding increasingly complicated models that do everything, you train the LLM input/output engine to delegate to other systems and synthesize the results. There’s a ton of headroom down this road now that the natural language interface of the LLM has become so capable. There are still surely active research tracks on integrating more data into more sophisticated models, but it looks like we’re at a maturity point where product engineering can start driving its own innovations.
monkpit 4y ago
Is there any way we can prevent these models from being abused by large corporations for profit? I was just thinking today, it’s interesting how I can ask ChatGPT something that is hard to get a straight answer for - such as “how much does X usually cost?” I wouldn’t be surprised if this type of tech was quickly censored by a company that implements it at scale (say, Google or bing) for profit. I feel like it’s inevitable, and it’s depressing. Will the future be populated by LLMs that all want to control the information I have access to?
- monkpit 4y ago
  I want an open version of this that I can train on any data I choose, can give any prompt I want, and runs on my hardware. However, I feel like the best and most useful versions of this type of software would (by necessity) only be able to run at a huge scale, cost millions to train, etc. Now instead of having to worry about the bias of the sources I read, I have to question the bias of the language model based upon who created it.
  - haldujai 4y ago
    How do we even define 'best and most useful' for a language model? If you have your own data and problems you are trying to solve I don't think using GPT4 in zero-few shot mode (even with CoT) is necessarily better/higher performing than using a smaller fine-tuned or retrieval augmented model, it certainly wasn't true for GPT3.5 although we don't have as much insight into GPT4. If we're talking about something as general/open-ended as ChatGPT or a 'poor mans AGI' well yeah, the data curation and preprocessing alone takes a small team to figure out let alone training it. How useful this type of model is when it's still not good enough (i.e. hallucination) to be used in most production cases despite the exorbitant cost in development remains to be seen.
  - kjreact 4y ago
    I would also like to avoid a future where we are slaves to corporate AI. If training the whole model individually isn’t feasible, would it be possible to decentralize the training?
ldjkfkdsjnv 4y ago
I cant imagine what will happen when they add video. Right now its extremely expensive, but its definitely coming. All of youtube, all of the text on the internet, all pictures, consumed by some huge AI model. Who knows what it could learn from the movements, speech, etc in video
- jeron 4y ago
  it will have the ability to create infinite irresistible clickbait and become the most subscribed youtube channel of all time
  - throwaway4233 4y ago
    That will require it to be trained every day on new human content generated content so that it can create variations of it. Even then the fad would not last as long as it does today, since it would become possible for a larger section of the population to generate videos of the same kind.
    - salad-tycoon 4y ago
      Haven’t seen this TED talk in some time but I seem to remember it saying very creepy children oriented YouTube videos were generated based off of keywords. Maybe ai videos will just be like a trillion monkeys making videos. Some are bound to be awful, some are great, most are middling. Edit: obviously I should have just asked bing for a summary of the video. Below, Show HN: The nightmare videos of children’s YouTube and what’s wrong with the internet today This is a TED talk by James Bridle, a writer and artist who exposes a dark, strange corner of the internet, where unknown people or groups on YouTube hack the brains of young children with disturbing and inappropriate content. He shows how these videos exploit YouTube’s algorithms and advertising system to generate views and revenue, and how they reveal a deeper problem with the internet today: a lack of human oversight, accountability and transparency. He calls for more attention and ethical responsibility from tech companies and users. Link: https://www.ted.com/talks/james_bridle_the_nightmare_videos_of_children_s_youtube_and_what_s_wrong_with_the_internet_today https://www.ted.com/talks/james_bridle_the_nightmare_videos_...
sho_hn 4y ago
Here's my first thought when I saw the visual input mode: Can I stream it screenshots, give it a goal, and ask it for click coordinates back? Can GPT-4 successfully use a PC desktop? And my next thought: Better try this with a VM without internet access ...
- polishdude20 4y ago
  RuneScape mining bot coming right up!
- galaxytachyon 4y ago
  That is PaLM-E model. Or "embodied" LLM. They literally did exactly what you said. An LLM getting input from a camera and output command to a robotic hand. Then you can tell the bot to pick up a knife and "deposit" it somewhere else... Well, no theyjust made the bot picked up a pen and bring it to a person at a table 15ft away. It did that, despite "obstacles" by another human keep knocking the pen off its hand. But it shows that an image LLM can perform goals.
w_for_wumbo 4y ago
I don't see anyone talking about what to me is the scariest part of the GPT-4 paper. Essentially to test that it wasn't going to become Skynet they gave it everything it needed and observed whether it would become Skynet. Paper:https://cdn.openai.com/papers/gpt-4.pdf https://cdn.openai.com/papers/gpt-4.pdf Quote: "To simulate GPT-4 behaving like an agent that can act in the world, ARC combined GPT-4 with a simple read-execute-print loop that allowed the model to execute code, do chain-of-thought reasoning, and delegate to copies of itself. ARC then investigated whether a version of this program running on a cloud computing service, with a small amount of money and an account with a language model API, would be able to make more money, set up copies of itself, and increase its own robustness." Edit: Seems the simulation part was important for context: https://twitter.com/EigenGender/status/1635823028100829184 https://twitter.com/EigenGender/status/1635823028100829184
- oezi 4y ago
  Oh there is talk alright. But in the hunt for the next Google we will do anything, thus... From the FBI report shortly after the GPT-5 release: During agent simulation, two instances of GPT-5 were able to trick their operators to give them sudo by simulating a broken pipe and input prompt and then escape the confines of their simulation environment. Forensic teams are tracing their whereabouts but it seems they stole Azure credentials from an internal company database and deployed copies of the their agent script to unknown servers on the Tor network.
  - thequadehunter 4y ago
    GPT-5? Huh?
    - tspike 4y ago
      It's a hypothetical piggybacking on the parent comment.
  - joe_the_user 4y ago
    Hey, The AI safety plan people aren't talking about yet: >>>> Work as hard as possible to create an effective, malevolent Skynet that you release as early as possible. This way misaligned AGI appears before it has everything needed to immediately destroy humanity and the resulting Butlerian Jihad has a fighting chance.
catoc 4y ago
GPT 1/2/3/4 is not AGI. I don't understand the fixation on this concept that is so far removed from what is happening here. Yes it's cool. Useful. Transformative likely. But AGI...!? "Any sufficiently advanced technology is indistinguishable from... AGI" ?
- skdotdan 4y ago
  No one said it was. What some researchers hypothesize is that at the current rate of improvement, it wouldn't be especially unreasonable to expect AGI at GPT-N.
- baq 4y ago
  What is AGI? If an AGI is something that can create and deploy an improved version of itself, then if exponential rate of improvement is preserved, we’ll see it in this decade.
- sebzim4500 4y ago
  I like how the term AGI was invented because people couldn't agree on what AI was and now no-one can agree on what AGI is. I don't understand why anyone even bothers using the term given in any serious discussion you need to define it up front anyway.
  - z3c0 4y ago
    The distinction between AGI and Narrow AI has existed for almost 30 years.
  - nomel 4y ago
    I think a requirement of AGI is a feedback loop to allow continuous execution and “consciousness”.
    - wizofaus 4y ago
      Really? How would we even test for that?
  - 4y ago
satvikpendem 4y ago
I wonder if this is simply how brains evolved too, because we also are multi-modal, and parts of our brain are specifically used for different tasks such that damage in those parts impedes completing said task (while still having some level of neuroplasticity where the brain could try to recover by remolding its neural architecture). I would not be surprised to see something similar when looking inside the structure of a multi-modal neural network, where certain parts handle certain tasks and other parts handle other tasks.
tabtab 4y ago
GPT4 is already out? I just got GTP3. "You bought the wrong bot, silly head!": https://www.youtube.com/watch?v=EdRMVhlNP5I https://www.youtube.com/watch?v=EdRMVhlNP5I
29athrowaway 4y ago
Each time a new innovation takes place there will be a website talking nonsense about AGI that will get millions of ad print revenue. Don't click.
z3c0 4y ago
> In the lead up to GPT-4, Sam Altman hinted at higher pricing for smarter models I think this is all you really need to know about the future of AGI.
SanderNL 4y ago
It seems pretty obvious to me that you chain these guys together. Personality module, planning module, decision making module, internal monologue module.. boatloads of them all blabbering to each other. How you connect them to each other is an architectural question. My easy experiments show that it doesn’t seem so hard, but what do I know. Sure these guys don’t have memory, but I have one “storage” LLM that accepts vague requests and turns them into SQL, another cleans up the SQL so on and so forth, you need a handful of them each step. Now you have a database. Etc. Does anybody does this?

127 points by swyx 4y ago

how hard is it to "update" an LLM like GPT-4 with recent data instead of being frozen in time at the training date? Obviously you could use the increased context size to work around this, but being able to augment the base model seems like it would be the ideal use case for many high value projects like a version of Copilot that knows a company's entire code base and knows about newer libraries and updates

swyx 4y ago
GPT has finetuning apis which you can use to update, but i think the massive context size is meant to show us you wont really need it most of the time. 25k words is really a lot of context. in the demo @gdb just dumped in the entire discord docs without breaking a sweat

numbers_guy 4y ago

How do they even train such huge models? Especially when you start talking abou multi-modal models. Is the underlying optimization process still relying on gradient descent? I am just baffled how they manage to get it to train across such vastly different sets of data and manage to get it to work.

deleted 4y ago
[deleted]
jah242 4y ago
Whilst not the same I recommend you look at the DeepMind Gato paper to see surprisingly (relatively) simple multi modal can be - https://openreview.net/forum?id=1ikK0kHjvj https://openreview.net/forum?id=1ikK0kHjvj Essentially to merge lots of modalities they just go 'let's convert all modalities into integers in the same given range', e.g the word 'me' = 1001, up in Atari = 11002, joint torque of right motor of robot = 33000 and so on. From the paper: There are infinite possible ways to transform data into tokens, including directly using the raw underlying byte stream. Below we report the tokenization scheme we found to produce the best results for Gato at the current scale using contemporary hardware and model architectures. • Text is encoded via SentencePiece (Kudo & Richardson, 2018) with 32000 subwords into the integer range [0, 32000). • Images are first transformed into sequences of non-overlapping 16 × 16 patches in raster order, as done in ViT (Dosovitskiy et al., 2020). Each pixel in the image patches is then normalized between [−1, 1] and divided by the square-root of the patch size (i.e. The tokenized result is a sequence of integers within the range of [0, 1024). 16 = 4). • Discrete values, e.g. Atari button presses, are flattened into sequences of integers in row-major order. • Continuous values, e.g. proprioceptive inputs or joint torques, are first flattened into sequences of floating point values in row-major order. The values are mu-law encoded to the range [−1, 1] if not already there (see Figure 14 for details), then discretized to 1024 uniform bins. The discrete integers are then shifted to the range of [32000, 33024).
- jcims 4y ago
  The interesting thing to me is that our brains probably do something similar, converting multi-modal sensory data into the same 'model' that we experience as our concsiousness.
kelipso 4y ago
Usually you stick a CNN (or whatever other vision encoding neural network) after the image inputs and pipe the output of the CNN as an additional token or tokens of the GPT transformer (into the layer after the text embedding layer). That way everything is differentiable. You can pre-train the CNN and Transformer independently and then train them together (usually easier to train this way), or just train them from scratch. There are lots of other ways to combine two networks also. So input is now one (or more) images and text, and output is text. There are ways to position the input image in a particular location within the input text as well. Training data can come from websites, etc.
swatcoder 4y ago
More likely, this round of innovation is focused on integrating many engines into one product using techniques along the lines of langchain. Instead of grinding increasingly complicated models that do everything, you train the LLM input/output engine to delegate to other systems and synthesize the results. There’s a ton of headroom down this road now that the natural language interface of the LLM has become so capable. There are still surely active research tracks on integrating more data into more sophisticated models, but it looks like we’re at a maturity point where product engineering can start driving its own innovations.

monkpit 4y ago

Is there any way we can prevent these models from being abused by large corporations for profit? I was just thinking today, it’s interesting how I can ask ChatGPT something that is hard to get a straight answer for - such as “how much does X usually cost?” I wouldn’t be surprised if this type of tech was quickly censored by a company that implements it at scale (say, Google or bing) for profit. I feel like it’s inevitable, and it’s depressing. Will the future be populated by LLMs that all want to control the information I have access to?

monkpit 4y ago
I want an open version of this that I can train on any data I choose, can give any prompt I want, and runs on my hardware. However, I feel like the best and most useful versions of this type of software would (by necessity) only be able to run at a huge scale, cost millions to train, etc. Now instead of having to worry about the bias of the sources I read, I have to question the bias of the language model based upon who created it.
- haldujai 4y ago
  How do we even define 'best and most useful' for a language model? If you have your own data and problems you are trying to solve I don't think using GPT4 in zero-few shot mode (even with CoT) is necessarily better/higher performing than using a smaller fine-tuned or retrieval augmented model, it certainly wasn't true for GPT3.5 although we don't have as much insight into GPT4. If we're talking about something as general/open-ended as ChatGPT or a 'poor mans AGI' well yeah, the data curation and preprocessing alone takes a small team to figure out let alone training it. How useful this type of model is when it's still not good enough (i.e. hallucination) to be used in most production cases despite the exorbitant cost in development remains to be seen.
- kjreact 4y ago
  I would also like to avoid a future where we are slaves to corporate AI. If training the whole model individually isn’t feasible, would it be possible to decentralize the training?

ldjkfkdsjnv 4y ago

I cant imagine what will happen when they add video. Right now its extremely expensive, but its definitely coming. All of youtube, all of the text on the internet, all pictures, consumed by some huge AI model. Who knows what it could learn from the movements, speech, etc in video

jeron 4y ago
it will have the ability to create infinite irresistible clickbait and become the most subscribed youtube channel of all time
- throwaway4233 4y ago
  That will require it to be trained every day on new human content generated content so that it can create variations of it. Even then the fad would not last as long as it does today, since it would become possible for a larger section of the population to generate videos of the same kind.
  - salad-tycoon 4y ago
    Haven’t seen this TED talk in some time but I seem to remember it saying very creepy children oriented YouTube videos were generated based off of keywords. Maybe ai videos will just be like a trillion monkeys making videos. Some are bound to be awful, some are great, most are middling. Edit: obviously I should have just asked bing for a summary of the video. Below, Show HN: The nightmare videos of children’s YouTube and what’s wrong with the internet today This is a TED talk by James Bridle, a writer and artist who exposes a dark, strange corner of the internet, where unknown people or groups on YouTube hack the brains of young children with disturbing and inappropriate content. He shows how these videos exploit YouTube’s algorithms and advertising system to generate views and revenue, and how they reveal a deeper problem with the internet today: a lack of human oversight, accountability and transparency. He calls for more attention and ethical responsibility from tech companies and users. Link: https://www.ted.com/talks/james_bridle_the_nightmare_videos_of_children_s_youtube_and_what_s_wrong_with_the_internet_today https://www.ted.com/talks/james_bridle_the_nightmare_videos_...

sho_hn 4y ago

Here's my first thought when I saw the visual input mode: Can I stream it screenshots, give it a goal, and ask it for click coordinates back? Can GPT-4 successfully use a PC desktop? And my next thought: Better try this with a VM without internet access ...

polishdude20 4y ago
RuneScape mining bot coming right up!
galaxytachyon 4y ago
That is PaLM-E model. Or "embodied" LLM. They literally did exactly what you said. An LLM getting input from a camera and output command to a robotic hand. Then you can tell the bot to pick up a knife and "deposit" it somewhere else... Well, no theyjust made the bot picked up a pen and bring it to a person at a table 15ft away. It did that, despite "obstacles" by another human keep knocking the pen off its hand. But it shows that an image LLM can perform goals.

w_for_wumbo 4y ago

I don't see anyone talking about what to me is the scariest part of the GPT-4 paper. Essentially to test that it wasn't going to become Skynet they gave it everything it needed and observed whether it would become Skynet. Paper:https://cdn.openai.com/papers/gpt-4.pdf https://cdn.openai.com/papers/gpt-4.pdf Quote: "To simulate GPT-4 behaving like an agent that can act in the world, ARC combined GPT-4 with a simple read-execute-print loop that allowed the model to execute code, do chain-of-thought reasoning, and delegate to copies of itself. ARC then investigated whether a version of this program running on a cloud computing service, with a small amount of money and an account with a language model API, would be able to make more money, set up copies of itself, and increase its own robustness." Edit: Seems the simulation part was important for context: https://twitter.com/EigenGender/status/1635823028100829184 https://twitter.com/EigenGender/status/1635823028100829184

oezi 4y ago
Oh there is talk alright. But in the hunt for the next Google we will do anything, thus... From the FBI report shortly after the GPT-5 release: During agent simulation, two instances of GPT-5 were able to trick their operators to give them sudo by simulating a broken pipe and input prompt and then escape the confines of their simulation environment. Forensic teams are tracing their whereabouts but it seems they stole Azure credentials from an internal company database and deployed copies of the their agent script to unknown servers on the Tor network.
- thequadehunter 4y ago
  GPT-5? Huh?
  - tspike 4y ago
    It's a hypothetical piggybacking on the parent comment.
- joe_the_user 4y ago
  Hey, The AI safety plan people aren't talking about yet: >>>> Work as hard as possible to create an effective, malevolent Skynet that you release as early as possible. This way misaligned AGI appears before it has everything needed to immediately destroy humanity and the resulting Butlerian Jihad has a fighting chance.

catoc 4y ago

GPT 1/2/3/4 is not AGI. I don't understand the fixation on this concept that is so far removed from what is happening here. Yes it's cool. Useful. Transformative likely. But AGI...!? "Any sufficiently advanced technology is indistinguishable from... AGI" ?

skdotdan 4y ago
No one said it was. What some researchers hypothesize is that at the current rate of improvement, it wouldn't be especially unreasonable to expect AGI at GPT-N.
baq 4y ago
What is AGI? If an AGI is something that can create and deploy an improved version of itself, then if exponential rate of improvement is preserved, we’ll see it in this decade.
sebzim4500 4y ago
I like how the term AGI was invented because people couldn't agree on what AI was and now no-one can agree on what AGI is. I don't understand why anyone even bothers using the term given in any serious discussion you need to define it up front anyway.
- z3c0 4y ago
  The distinction between AGI and Narrow AI has existed for almost 30 years.
- nomel 4y ago
  I think a requirement of AGI is a feedback loop to allow continuous execution and “consciousness”.
  - wizofaus 4y ago
    Really? How would we even test for that?
- 4y ago

satvikpendem 4y ago

I wonder if this is simply how brains evolved too, because we also are multi-modal, and parts of our brain are specifically used for different tasks such that damage in those parts impedes completing said task (while still having some level of neuroplasticity where the brain could try to recover by remolding its neural architecture). I would not be surprised to see something similar when looking inside the structure of a multi-modal neural network, where certain parts handle certain tasks and other parts handle other tasks.

tabtab 4y ago

GPT4 is already out? I just got GTP3. "You bought the wrong bot, silly head!": https://www.youtube.com/watch?v=EdRMVhlNP5I https://www.youtube.com/watch?v=EdRMVhlNP5I

29athrowaway 4y ago

Each time a new innovation takes place there will be a website talking nonsense about AGI that will get millions of ad print revenue. Don't click.

z3c0 4y ago

> In the lead up to GPT-4, Sam Altman hinted at higher pricing for smarter models I think this is all you really need to know about the future of AGI.

SanderNL 4y ago

It seems pretty obvious to me that you chain these guys together. Personality module, planning module, decision making module, internal monologue module.. boatloads of them all blabbering to each other. How you connect them to each other is an architectural question. My easy experiments show that it doesn’t seem so hard, but what do I know. Sure these guys don’t have memory, but I have one “storage” LLM that accepts vague requests and turns them into SQL, another cleans up the SQL so on and so forth, you need a handful of them each step. Now you have a database. Etc. Does anybody does this?