jah242's comments

Interested to hear people's thoughts on why even though AI developments seem so revolutionary it may actually still not be the explosion of growth and societal change that often seems accepted in AI circles.

This is text + image -> text but pretty cool and still might be of interest to you:

https://llava-vl.github.io

This is text + image -> text but pretty cool and still might be of interest to you:

https://llava-vl.github.io

This might be of interest to you (Google are getting there :))- https://palm-e.github.io

Google actually recently went some steps further and combined the PaLM LLM (bigger than GPT-3.5) with a 22 billion parameter Vision Transformer to do this -

https://palm-e.github.io

Whilst maybe GPT-4 will change this, I think it is important to remember that these general ChatBots are not the way we have generally trained LLMs to write the best code. In fact, coding is one of the few areas where training specifically just using source code and maybe some stack overflow (not all natural language on the internet) leads to better results on the previous iteration of LLMs (GPT-3 wave). So the real test will be whether the GPT-4 wave of specific coding LLMs i.e GPT-4-Codex can 'actually write code' see:

AlphaCode Codex CodeGen

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

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).

I m sorry but in stating the goal posts haven't moved, you've literally just moved the goal posts.

'everything a human can do' is not the same as 'anything any human can do as well as the best humans at that thing (because those are the ones we pay)' - most humans cannot do any of the things you state you are waiting for an AI to do to be 'general'.

Therefore, the first part of your statement is the initial goal post and the second part of your statement implies a very different goal post. The new goal post you propose would imply that most humans are not generally intelligent - which you could argue... but would definitely be a new goal post.

So I appreciate the jist of your point but the way these models work is rather more complicated that copying and pasting snippets and so it certainly is not 'literally' true. The models are trained to predict sub-word level tokens from the internet training dataset, so the level of re-formation and regurgitation in a generated sentence can be vast, to the point of final sentence being novel it's own right.

> but all it does is regurgitate existing works

I am genuinely intrigued by this point of view and so would love to hear people who hold it's reasoning.

Over the last few days I've seen hundreds of poems and stories from ones about climate change in the style of a sonnet to peanut butter sandwiches getting stuck in toasters in the style of the bible. I even asked it to make a text adventure game for me to play where I could put in any instruction, leading to a unique series of events and narrative.

Is the claim that these were all simply copy and pastes of something on the internet in their entirety? And that as such the internet already seems to contain essentially every permutation of everything I could ask ChatGPT, as to me this sounds highly implausible.

If the claim is that whilst these are not direct copy pastes, it is essentially a remix of lots of different things people have said before on the internet repurposed to a different end, is that not literally just what language is? Humans use common sayings, idioms, slang and phrases all the time, never mind the 'tropes' and story lines that are reused constantly. Coders use common patterns and styles and copy from stackoverflow. In fact language literally only works because we all share it and share the meaning of it.

If we are saying that all ChatGPT does is remix existing language and phrases to a new purpose... to me we are saying ChatGPT does the same thing as humans.

Any thoughts would be appreciated.

Thanks! A less impressive but fun variant of this I am using is for open ended text adventure:

> I want you to act as a text based adventure game. I will type commands and you will reply with what the adventure game should show. I want you to only reply with the adventure game output inside one text block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. My first command is go left

For context, looking at UK data:

1. Yes incidence of cancer in 25-49 year olds has increased 22% from 1993 to 2018 - but that is 22% on a very low number which means it is still a very low number. When you account for increased screening, greater awareness, and better testing, the increase is likely even smaller.

https://www.cancerresearchuk.org/health-professional/cancer-...

2. Better treatment (and more effective screening) means mortality rates per 100k from all cancers in 25-49 has dropped c.40% over the same period (despite higher incidence).

https://www.cancerresearchuk.org/health-professional/cancer-...

So whilst this is obviously important to study. At least the UK data doesn't seem too terrifying but I m no expert.

> Secondly, the tropical hole wouldn’t have qualified as a hole under the old definition of a hole, which was defined as having ozone levels below a certain threshold. Lu’s study redefines a hole as an area with ozone loss greater than 25%.

Unless I'm missing something isn't this 'we discovered a new hole because we redefined what a hole is'.... I think I could make a lot of important discoveries if I was allowed to do this.

Still interesting though.

> correlated surge in the 45-64 bucket, the last two charts in the link I posted.

Correlated surge is an absurd way to describe something that basically fluctuates around 0 over the course of a year, which makes me think this definitely isn't good faith.

> What you linked takes me to some generic description of the dataset.

I recommend clicking the buttons on the side, it's a dashboard.

Just in case you are asking in good faith.

If you look at the same data release, the excess deaths are almost entirely in cancer and dementia in old people (with the exception of a spike in diabetes at the same time as the spike in COVID infections likely due to it being a significant comorbidity).

So whilst I m sure the higher excess deaths in Australia is difficult to explain (as there is probably a multitude of factors). The theory that somehow the vaccines give people dementia or cancer (which have not been associated with COVID or the vaccine) AND these usually relatively long term diseases go on to kill them within a 2-4 month period AND this only happens to old people for some reason AND other countries have not observed a similar pattern despite the vast number of vaccines given, seems highly unlikely.

In addition, assuming the author is referring to the UK ONS, our public health body does release excess mortality estimates on a weekly basis - and unsurprisingly they correlate with the waves of COVID infections, not the vaccination program.

https://app.powerbi.com/view?r=eyJrIjoiYmUwNmFhMjYtNGZhYS00N...

I second this and can't recommend Keynes and his writing highly enough.

I would also add that if you can get hold of Volume 12 of The Collected Writings of John Maynard Keynes it goes through his time as an investor and all related correspondence in great detail. Fascinating from a historical and practical perspective.

Contrarian hypothesis for you :)

The more I work on AI/ML the more I think the fears around AGI have been misplaced. General intelligence almost by definition involves general motivations (rewards). Very general rewards inevitably make the AI / ML model hard to control, unpredictable and difficult communicate with (just like a human). If you find managing humans to be like herding sheep, try herding sheep, then try herding a non-biological general intelligence that perceives the world in a totally different way.

Therefore, what humans want AGI to be is largely a contradiction. We want something so clever and general it has the complexity and nuance of a human but so reliable and compliant as to be like a machine in a factory i.e very not human. These two things just don't go together.

Take the classic AGI that goes all out making paper clips and destroys everything. Are we seriously suggesting we have made something so nuanced and clever as to understand building regulations, supply chains, the art of the deal, the entire manufacturing process, human incentives, business, finance, HR, taxes and everything else that goes with exclusively running a paper clip manufacturing operation. Yet it is also so utterly single minded and blinkered that can't conceive of anything beyond more paper clips, even the obvious inevitable consequences of such a pursuit. In reality it really seems wants and skills are very much too sides of the same coin, you just don't learn about things you don't want and so the only way to learn about lots of things is to want lots of things, not just paper clips.

So I think maybe we could make AGI relatively soon and even now could have a good stab at lower level intelligence. The reality I see though is that we just actually don't want to because it wouldn't actually be very useful. What we want, is what are doing, extremely capable but ultimately intellectually dumb factory machines / cars that do as we wish on repeat.

This would be one example from Deepmind using raw pixel input to stack objects. This has a relatively detailed reward function (but is also a very complicated task) - https://arxiv.org/abs/2110.06192

There are other examples from OpenAI a while back using even just sparse rewards (i.e binary 1, 0 for success or failure over the whole task) - but these weren't pixel input if I remember correctly - https://openai.com/blog/ingredients-for-robotics-research/

I m afraid if you think providing any reward function is cheating then we have fundamentally different views of what AI/ML even means/involves. It appears humans and likely all animals have largely pre-programmed reward functions developed over billions of years of evolution (pain is bad, food is good, etc.). These reward functions are ultimately what underpin what we are trying to do, what outcomes are good/bad, to what degree we 'want' to explore vs exploit. The idea that human and animal 'intelligence' is born as a blank slate with nothing to guide it and no reward function to maximise doesn't seem to bear any resemblance to reality.

The only difference between a reward function that tells a robot 'you need to stack these objects but I m not going to tell you where in 3D space the objects are or where they need to go to stacked or the shapes/forces involved' and an animal that is born with a reward function that says 'you need to find food and shelter but I m not going to tell you how to collect the food or where to find shelter' is the level of abstraction. Fundamentally they appear the same.

You are pulling a sleight of hand when you suggest 'in the manner of animals who respond to the world without already having all the information about it' - there is a vast difference between an abstract reward function (which humans and animals also have) and 'having all the information about [the world]'.

There might be some truth in what you say for very large image and language models that use supervised learning.

It is really hard to see how this 'it's just a lot of good data' view applies to deep reinforcement learning where the model learns multi step policies from raw input data (e.g a camera on a robot) with only a rough high level reward function to guide it.

If therefore (as seems to be the case) you can abstract the information humans need to provide to the model/learning system to ever high levels of reward function (and thereby vastly reduce the information provided by humans) then it seems very hard to argue that the model (and the training process) isn't doing to some degree what you describe as:

'incredible amounts of experimental work to carve-the-world along its joints, ie., to have the right concepts; and incredible amounts of work to measure along its joints, ie., to have the right units. And then to eliminate all the coincidences and irrelevances.'

For example, imagine a robot learning from scratch to pick objects up based on raw pixel data with only a scalar reward function - where in this process is the human preparing the data so the model only has to average?

Sounds like we are in very similar positions and have a very similar question :). My only real plan so far is to try and beat or match SOTA on a recent benchmark from a large corporate / research lab, give them an email and hope they are willing to talk to you.