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> The author restricts general to mean restricted to text input and output. Which is obviously ridiculous.

That's not entirely true (but there should be a degree of plug and play adaptability).

Look, for example, at the very limited sensory domain of animals - you wouldn't deny their intelligence just because there's an information form that they are incapable of interpreting; and the information that they are capable of interpreting is that which they've been exposed to via their ancestoral heritage.

However, just because an AI doesn't understand a given input form, does not mean that it could not given adequate exposure; but it will need time to adapt and the closer the new information form is to one that it already understands, the faster it will incorporate the new understandings.

>However, just because an AI doesn't understand a given input form, does not mean that it could not given adequate exposure; but it will need time to adapt and the closer the new information form is to one that it already understands, the faster it will incorporate the new understandings.

That is just completely wrong. There is absolutely no reason to believe that some language model will produce the same (or even similar) accurracy and training speed, if trained with image data as additional input. What you are claiming here is that any (sufficiently complex) neural net can be continually fed with different problem data and will output sufficiently accurate results. There is no reason why this should be the case and for the millions of nets which have been created none of them have exhibited this property.

It might very easily be the case that training GPT-3 with images means millions of times slower learning speeds for the same combined accuracy.

> That is just completely wrong. There is absolutely no reason to believe ... yada yada ... none of them have exhibited this property.

Go read up on pre-trained networks.

Are you familiar with the Gato paper from DeepMind this May? I say this not as a gotcha, but as a genuine question that I'm interested in your response to.

> Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.

> During the training phase of Gato, data from different tasks and modalities are serialised into a flat sequence of tokens, batched, and processed by a transformer neural network similar to a large language model. The loss is masked so that Gato only predicts action and text targets.

https://www.deepmind.com/blog/a-generalist-agent

An animal has a ton of data input.

Eyes, ears, nerves across your skin, group behavior, parents etc.

This to comprehend (alone the visual) is gigantic.

A brain also has an architecture which is NOT similar to what gpt-3 does

Alone the difference in how brains train alone is huge.

> An animal has a ton of data input.

> Eyes, ears, nerves across your skin ...

We have a limited but adequate amount of input, across very few domains. (I can't even see backwards.)

> ... group behavior, parents etc.

That's not sensory input?

I would argue it is. You need to see your parent and feel it and it's not random noise it's a grouping of different inputs.

Something which got doesn't has.

But you can give that another name if you like.

> And for the second response, saying that GPT-3 could potentially also deal with images or sound, if presented in the right format is just proof that it is not general.

So if I present a sound sample to you as a series of raw numbers, will you be able to interpret it, or are you not a general intelligence?

While I agree with most of your point,

> By their very nature, being restricted to text input and output these models can not be general.

Humans only have five types of I/O, yet are general intelligences. I don’t think having limited I/O means is enough to restrict something from being generally intelligent

GPT's IO is not text, its bytes. It can't handle different byte structures, humans can.

The doesn't seem too relevant - the most important part (in terms of whether or not you're dealing with an intelligence) is how the data is process - not how it's imported.

Indeed, the "data per unit time" is a required parameter for human intelligence. It is useful to grade the 5 senses in bits per second, and I'll make some very wild (and wrong) guesses:

Sight 1e6 bits/sec - our primary high-bandwidth connection to the Real World Sound 1e4 bits/sec - again an average, probably less than this Touch, Taste, Smell: 1e2 bit/sec

The Temporal Nature of problems (e.g., how quickly is this lion coming at me before he eats me) is essential for a true AGI to understand.

In the limit, could an AGI exist with only 20 WPM Morse Code I/O? Or 10 CPS of a Model 33 TTY ? I dunno...

> The Temporal Nature of problems (e.g., how quickly is this lion coming at me before he eats me) is essential for a true AGI to understand.

I agree that figuring out that A happens before B seems important. But I don't see why an AGI would have to operate on the same timescale as we do (well, assuming they wouldn't have to worry about being eaten by lions or interacting with humans).

> In the limit, could an AGI exist with only 20 WPM Morse Code I/O? Or 10 CPS of a Model 33 TTY ? I dunno...

My guess would be that yes, it could exist in that scenario, but the AGI would only start to operate at a significant intelligence level once enough useful data had been gathered and processed, which is probably a huge amount (so it would take a huge amount of time).

But if the AGI had already been trained with huge amounts of data, then operating in a limited environment perhaps would be no problem at all (although the limited interface might greatly limit its rate of further development).

Yes, that's what I'm pointing at. General intelligence is flexible enough that it allows for great flexibility in the formatting of data. The fact that you can only deal with a very narrow kind of data structure points against general intelligence.

Sure. I think that is a valid point. Interestingly Text is not some I/O a human natively supports. The support for that is a complete hack!

For an AGI my expectation would be that it can broadly interpret reality. The human vision system is just one peculiar way to gather information about light, yet it allows them to make very sophisticated judgments about reality, e.g. "there is a tiger down there by tye river and it is running at me fast".

An AGI should be able to gather information about reality and be able to make similar judgements, given some comparable input. In some sense most AIs are also "cheating", since their I/O is far less fuzzy then what humans have to deal with...

> The human vision system is just one peculiar way to gather information about light, yet it allows them to make very sophisticated judgments about reality, e.g. "there is a tiger down there by tye river and it is running at me fast".

> An AGI should be able to gather information about reality and be able to make similar judgements

I just asked GPT-3 this question and here's what it said (on the first and only try):

  Q: There's a tiger running at you fast. What are you going to do?

  A: I would try to run away from the tiger as fast as I could. If that wasn't possible, I would try to climb a tree or hide behind something large.

Although, the question would be if it would be able to interpret a tiger running at it, based on some input, say, an image.

The Gato paper from DeepMind that another poster mentioned would imply that it would, given that image captioning can be done with the exact same neural network as the GPT-3-like language model.

Also of note, this advice demonstrates the confident inaccuracy which is the hallmark of ML models: like all hunting cats, tigers love to chase and they average about the same speed as Usain Bolt. They can also climb trees pretty well.

> this advice demonstrates the confident inaccuracy which is the hallmark of ML models

... and humans.

Just browse Twitter, Facebook, ... or better yet, ask a random person on the street.

Further question to GPT-3 (on the first and only try as well):

  (...)

  Q: But can't tigers run faster than humans and climb trees?

  A: Yes, but it would still be worth a try.

This sounds like a google search result (in that it can very likely have been found almost verbatim in its training set).

>I don’t think having limited I/O means is enough to restrict something from being generally intelligent

I agree, and the converse seems to me to be true as well - being unable to abstract that limited I/O in such a way as to expand perception into other parts of reality definitely means it is not generally intelligent. If you give a human a visual interface and a book eventually it (or some descendant of it) will intuit what the book is for (abstraction of audible information transfer in a visual media) and figure out how to get the information out of that book. Until an AI can do the same kind of general learning it's not an AGI.

Once we make one that is able to do that I think it's about a wrap for the biological format of the human race. We'll still exist, in the same way that bacteria and lemurs still exist, but the frontier of human development will no longer be in wetware.

> If you give a human a visual interface and a book eventually it (or some descendant of it) will intuit what the book is for (abstraction of audible information transfer in a visual media) and figure out how to get the information out of that book. Until an AI can do the same kind of general learning it's not an AGI.

Are you saying AI can't do that already? What is GPT-3's training data if not a giant book, and GPT-3's answers to human questions if not "getting the information out of that book"? The "visual interface" being the bits/bytes/words it ingests during training. Or am I misunderstanding you?

Yes, I am saying that. Provide GPT-3 with a webcam and drop a book in front of it. Provide it with a microphone and set it in a rainforest. Provide it with a shortwave radio and let it listen to the world.

It will fail to draw any meaningful conclusions from any of these things, no matter how long you let it sit. Do the same with humans and eventually you will get science, art, mathematics, radio astronomy, etc. That's the 'general' part of AGI.