The Winograd Schema is a test of commonsense reasoning. It's hard because it requires not just knowledge of english, but also knowledge of the real world.

But as you found, it's pretty much solved now. As of 2019 LLMs could complete it with better than 90% accuracy, which means it was actually already solved when Tom Scott made his video.

You're right, I misread it. I thought they held out 4 patients for tests. But upon rereading, their dataset only had 4 patients total and they held out the set of images that were seen by all of them.

>NSD provides data acquired from a 7-Tesla fMRI scanner over 30–40 sessions during which each subject viewed three repetitions of 10,000 images. We analyzed data for four of the eight subjects who completed all imaging sessions (subj01, subj02, subj05, and subj07).

...

>We used 27,750 trials from NSD for each subject (2,250 trials out of the total 30,000 trials were not publicly released by NSD). For a subset of those trials (N=2,770 trials), 982 images were viewed by all four subjects. Those trials were used as the test dataset, while the remaining trials (N=24,980) were used as the training dataset.

4 patients is small by ML standards, but with medical data you gotta make do with what you can get.

I think my second question is still valid though. How much of the image comes from the brain data vs from the StableDiffusion pretraining? Pretraining isn't inherently bad - and if your dataset is 4 patients, you're gonna need it - but it makes the results hard to interpret.

It's a joke. OpenAI was supposed to be a nonprofit too, now they look more like a Microsoft subsidiary.

I'm a wee bit cautious.

Their test set is a set of patients, not images, so their MRI->latent space model has seen every one of the 10,000 images in the dataset. Couldn't it simply have learned to classify them? Previous work has very successfully classified objects based on brain activity.

How much information are they actually getting out of the brain? They're using StableDiffusion to create the images, which has a lot of world knowledge about images pretrained into it. I wish there was a way to measure how of the output image is coming from the MRI scan vs from StableDiffusion's world knowledge.

I'm all for prompt engineering in general, it's like programming but in english.

But selling a prompt? Lol.

No thanks.

Lol, people are trying to sell ChatGPT prompts?

Congrats! Can't wait until you get your first $10-billion investment from a major tech company.

>"Sentience is the capacity to experience feelings and sensations". Scientists use this to study sentience in animals for example (not in rocks, because THEY HAVE NONE).

How do you know whether or not something experiences feelings and sensations? These are internal experiences. I can build a neural network that reacts to damage as if it is in pain, and with today's technology it could be extremely convincing. Or a locked-in human might experience sensations, even though we wouldn't be able to tell from the outside.

Your metastudy backs me up. Nobody's actually studying animal sentience (because it is impossible to study); all the studies are about proxies like pain response or intelligence and they simply assume these are indicators of sentience.

>What we found surprised us; very little is actually being explored. A lot of these traits and emotions are in fact already being accepted and utilised in the scientific literature. Indeed, 99.34% of the studies we recorded assumed these sentience related keywords in a number of species.

Here's some reading for you:

https://en.wikipedia.org/wiki/Hard_problem_of_consciousness
https://en.wikipedia.org/wiki/Mind%E2%80%93body_problem

People much much smarter than either of us have been flinging themselves at this problem for a very long time with no progress, or even no ideas of how progress might be made.

Sure it's idiotic. But you can't disprove it. That's the point; everything about internal experience is shrouded in unfalsifiability.

>it's very easy to understand what each neuron does,

That's like saying you understand the brain because you know how atoms work. The world is full of emergent behavior and many things are more than the sum of their parts.

>And then again, we do have a definition for sentience

And it is?

>, and there have been studies that have proven for example in multiple animal species that they are in fact sentient

No, there have been studies to prove that animals are intelligent. Things like the mirror test do not tell you that the animal has an internal experience. A very simple computer program could recognize itself in the mirror.

If you know of any study that directly measures sentience or consciousness, please link it.

Hah! Not even close, they're almost black boxes.

But even if we did, that wouldn't help us tell whether or not they're sentient, because we'd still need understand to sentience. For all we know everything down to dumb rocks could be sentient. Or maybe I'm the only conscious entity in the universe - there's just no data.

How could we even tell if it was? You can't even prove to me that you're sentient.

We don't have tools to study consciousness, or an understanding of the principles it operates on.

Who says intelligence has to work exactly like our brain?

A Boeing 747 is very different from a bird, even though they fly on the same principles.

DistillBERT worked though?

It's not dead, but gradient-based optimization is more popular right now because it works so well for neural networks.

But you can't always use gradient descent. Backprop requires access to the inner workings of the function, and requires that it be smoothly differentiable. Even if you can use it, it may not find a good solution if your loss landscape has a lot of bad local minima.

Evolution is widely used in combinatorial optimization problems, where you're trying to determine the best order of a fixed number of elements.

That's always a balance you'll have to make. You can only run what fits on your available hardware.

> It says they've cut their costs by 90%

Honestly this seems very possible. The original GPT-3 made very inefficient use of its parameters, and since then people have come up with a lot of ways to optimize LLMs.

Problem is we don't actually know how big ChatGPT is.

I strongly doubt they're running the full 175B model, you can prune/distill a lot without affecting performance.

Yup. But in neural networks, bigger is better!

Basically all of the text-to-image generators available today are diffusion models based around convolutional U-Nets. Google has an (unreleased) one that uses vision transformers.

There is more variety in the text encoder, which turns out to be more important than the diffuser. CLIP is very popular, but large language models like T5 show better performance and are probably the future.

I can't afford to rent 10x A100s on cloud platforms for very long either,

Pretrained models are pretty great though. Most of my use cases are not particularly unique; models are the software libraries of the future.

Can I? I don't have a cluster of 10x A100s.

All the interesting stuff in ML seems to require expensive hardware. I guess it'll be cool in 5-10 years when consumer hardware catches up.

Can't the reward model be discarded at inference time? I thought it was only used for fine-tuning.

Definitely in the realm of running on your computer. Almost in the realm of running on high-end smartphones with TPUs.

T5 and Flan-T5 have weights available.