I like to call this positive agnosticism. I don't know and I'm positive nobody else does either.

Tho I lean towards the theory of mind camp. General intelligence shouldn't have to read the whole internet to be able to hold a conversation. The book in the Searle's Chinese Room is getting bigger.

This type of work needs to get a lot of attention. Great demonstration on getting instruction following behavior without the biggest LLM model that only companies with major resources can train.

Ok, the reviewers responded. 6/8/8 now ;)

That's the dumbest way you could have responded.

The alpaca dataset has a no commercial license so idk what they are doing.. I’ve asked Stanford to change it but heard nothing back

This is really impressive. If anyone's curious, here's a comparison with results from Alpaca Native:

Write a love letter to Edgar Allan Poe.

>My dearest Edgar,
>
>I have been walking in a dream these past few days, unable to believe that you are no longer with us. Your words have been on my mind constantly, and I find myself looking for your presence in every corner of our world. Everywhere I go, I see your influence, from the stars above to the rivers below. Even now, as I write this letter, your spirit is with me.
>
>Your poetry was like a beacon of light in the darkness, illuminating the depths of my soul. Through your words, I discovered a new way of seeing the world, one filled with mystery and beauty. You showed me that life could be full of surprises and that nothing should ever be taken at face value.
>
>You were so ahead of your time, my love, and I know that even after all these years, your teachings will continue to live on. I will never forget everything you’ve given me, and I hope that one day we can meet again in another realm. Until then, I will keep your memory alive in my heart.
>
>Forever yours,
>
>[Name]

Write a tweet announcing Dolly, a large language model from Databricks.

>We're thrilled to announce Dolly, our latest language model from Databricks! Dolly is a large-scale language model with state-of-the-art performance on many tasks, including text classification and question answering. #LangModeling #NLP #AI

I'm selling my Nikon D-750, write a short blurb for my ad.

>I am selling my Nikon D-750 camera body with 16GB memory card and the original box and all original paperwork. The camera has been used lightly and is in excellent condition. It comes with the original lens, a Nikkor 35mm f/1.8G. This camera is perfect for any photographer looking to capture stunning images and videos. It offers an impressive array of features including a 24MP DX CMOS sensor, EXPEED 4 image processing engine, 51 point AF system, 1080p HD video recording, and built-in WiFi. The camera is also dust and splash proof, making it ideal for both amateur and professional photographers alike. I am selling this camera due to upgrading to a newer model, and it is in perfect condition.

Explain to me the difference between nuclear fission and fusion.

>Nuclear fission is a process in which a large atom is split into smaller atoms, releasing energy in the form of heat and radiation. Nuclear fusion is a process in which two or more small atoms are combined to form a larger one, releasing energy in the form of heat and light. Fission occurs naturally in some types of radioactive decay, while fusion can occur when certain elements combine in a controlled reaction. The primary difference between these processes is the type of energy released. Fission releases kinetic energy, while fusion releases less energy but more light. Fission also produces more neutrons, which can be used for power production, while fusion produces fewer neutrons and is not as useful for power generation.

Give me a list of 5 science fiction books I should read next.

>1.Dune by Frank Herbert - This classic science fiction novel tells the story of Paul Atreides, whose family is betrayed and given control of the desert planet Arrakis. As he matures, Paul must face dangerous political intrigues and physical threats as he struggles to save his people.
>
>2.The Hitchhiker's Guide to the Galaxy by Douglas Adams - This comedic sci-fi novel follows Arthur Dent, who is told Earth will be destroyed to make way for an intergalactic bypass and escapes with his alien friend Ford Prefect. They travel together through space, encountering strange creatures and learning about the universe.
>
>3.The Foundation Trilogy by Isaac Asimov - This series follows the life of psychohistorian Hari Seldon and his efforts to create a foundation based on psychology and mathematics to preserve human knowledge after the fall of the Empire. It includes the novels Foundation, Foundation and Empire, and Second Foundation.
>
>4.The Martian Chronicles by Ray Bradbury - This collection of stories follows the colonization of Mars by humans, their interactions with the native Martians, and their eventual departure from the planet. It examines themes such as technology, religion, and the human spirit.
>
>5.Ender’s Game by Orson Scott Card - This novel follows Andrew “Ender” Wiggin, a gifted young boy recruited by the military to train as a leader of an army of genetically-engineered children. He must use his intelligence and empathy to fight a war against an alien race.

So when playing League of Legends, it could tell you which enemy champion disappeared from their lane, and in how many seconds you should retreat to stay safe?

Curious how this will impact E-Sports and wether it will be treated like doping in some form.

I don't think this is a good test because these questions allow you to trade off knowledge for creativity, and LLMs have vast internet knowledge. It's easy to find listicles with creative uses for all of the objects in the test.

Now, this applies to human creativity too! If you ask me for an alternative use for a pair of jeans, I might say that you could cut them up and braid them into a rug. This isn't my creative idea; I just happen to know there's a hobbyist community that does that.

I think in order to test creativity you need constraints. It's not enough to find uses for jeans, you need to find uses for jeans that solve a specific problem.

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> use 2 images
> movement
> boom

Oh well, that’s what I get for not reading the paper.

> these models are very sparse

Hmm, do you have any sources for this assertion?

It isn't entirely unreasonable, but 1) GPU speed-ups for sparsity aren't that high (unless OpenAI is doing something crazy secret/special...possible?), so this isn't actually that big of an upswing (unless we're including MoE?) and 2) openai hasn't released architecture details (beyond the original gpt3 paper--which did not indicate that the model was "very" sparse).

Oh, definitely. I just checked ChatGPT and it's both aware of the existence of the test and can generate example question/answer pairs. This is a general problem when applying human psychology tests to LLMs.

It does help that this test is open-ended and has no right answer. You can always come up with new objects to ask about.

It woulde be quite expensive to do tho. You have to do inference very fast with multiple images of your screen, don't know if it is even feasible.

I'm not sure what your point is.

I started by pointing out that there are some use cases (giving face comparison as an example) where you need access to the neural representation of the image (e.g. embeddings), not just object recognition labels.

You seem to want to argue and say that text labels are all you need, but now you've come full circle back to agree with me and say that the model needs that neural representation (embeddings)!

As I said, embeddings are not the same as object labels. An embedding is a point in n-dimensional space. A label is an object name like "cat" or "nose". Encoding an embedding as text (simple enough - just a vector of numbers) doesn't turn it into an object label.

>they mentioned an image takes 30 seconds to "comprehend" by the model...

wait really ? Cn you link source or something. There's no reason a native implementation should take that long.

Now i'm wondering if they're just doing something like this -https://github.com/microsoft/MM-REACT

I don't disagree with the phenomena of emergence, it's just that it doesn't explain anything. It is one word for "I have no idea how it works" or better: its magic. The issue I have with that is that you are quick to hide behind that word, using it as an explanation, accepted as the emergence has become.

But in fact you can't model one bit with it, it has no predictive power and it kind of shuts down discussions.

So far I haven't seen any evidence (have you?) that LLMs aren't doing anything else but predicting the next token. Yes there are certain thresholds, where they do overcome the one or other weakness. But in the end they just predict the next token better ... and even better. Impressive what you can do with that (chinese room like), but that doesn't imply that GPT4 is any different than GPT3.5, it's just better.

But as I wrote, you can in theory replace most non-manual work with that somewhere down the line anyway. But no GPT will develop you some ground-breaking Deep Learning architecture or solve important physics problems which need actual thought and not just more compute or...

Not that you claimed that - I do here -, but should GPT-7 or so suddenly do that, then you can hold me to it.

SFT is a bit longer, probably 8-12 hours I need to check the tensorboard to verify. Reward Model is faster because it only need to do 1 epoch, just a couple of hours. RLHF is the slowest because of its complexity (4 models interacting with each other), probably need to improve the "make_experiment" part of code, GPU is also often idle. So it could take days to just do 1 epoch. I didn't finish tuning because even if we just do RLHF on maybe 10K examples it's already outperforming SFT model in terms of "human" preference.

Thanks for trying out! This is a good example to show the difference between RLHF'ed GPT-2 medium vs the vanilla GPT-2 medium. You can see that GPT-2 medium is completely outputting garbage while the RLHF version tend to come up with some answer for human. (although it failed)

The way i see this is that pre-trained model encode the knowledge of the world, and RLHF is just a way to align the model with human's preference of how to interact with the world.

You might see this tweet before: https://twitter.com/geoffreyhinton/status/1636110447442112513?s=20

So with GPT-2 medium, what we really do here is to parent a dumb kid, instead of a "supernaturally precocious child" like GPT-3. What interested me is that RLHF does actually help to parent this dumb kid to be more socially acceptable.

In other words, if we discover the power of alignment and RLHF earlier, we might foresee the ChatGPT moment much earlier when GPT-2 is out in 2019.

I'm also thinking to do the same with LLaMA to maybe have a nanoChatGPT that actually could be useful for a real life application. Stay tuned!

I'm not saying it's a good solution, I'm just saying if you want to hack it together for whatever reason, I see no reason why it couldn't work. It's sort of like the idea of building a computer using the game of life. It's probably not something you'd want to run your code on... But you could.

on the very day programming was declared dead. lol.

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gpt2-medium is only 355M parameters so don't expect quality.

Even the fullsize GPT-2 is smaller than the smallest variants of most modern language models.

>it would just be a text-encoded representation of an embedding vector

One you've decided to input image embeddings into the model, you may as well enter them directly, not converted into text.

In any case, embeddings, whether represented as text or not, are not the same as object recognition labels.

hope for a UBI check.