I'm not talking about philosophers debating the nature of moral actions. Ethics "experts" and ethics boards make a stronger claim; that they can actually determine what is moral and ethical. This truly is subjective.

At best they're a way for people making tricky decisions to cover their legal liability. Hospitals don't consult ethics boards before unplugging patients because they think the ethicists will have some useful insight; they just want their approval because it will help their defense if they get sued.

"AI ethics professor" isn't a real thing.

Ethics isn't even the kind of thing you can be an expert in; anybody calling themselves an ethics expert has declared themselves the arbiter of right and wrong.

Those are all internal rewards, which your brain creates because it knows (according to the world model) that these events lead to real rewards. It can only do this because it has learned to predict the future.

>PPO can handle this quite well.

"Quite well" is still trying random actions millions of times. World modeling allows you to learn from two orders of magnitude less data.

Imagine you need to cook some food. None of the steps of cooking give you any reward, you only get the reward at the end.

Pure RL will quickly teach you not to touch the burner, but it really struggles with tasks that involve planning or delayed rewards. Self-supervised learning helps with this by building a world model that you can use to predict future rewards.

Photogrammetry in general has a hard time with moving objects and especially objects that change shape.

Full PDF: http://cdn.fraserlab.com/publications/2023_madani.pdf

Code and models: https://github.com/salesforce/progen

It looks like they currently have ~50k responses, which is around the same amount used to train the reward model for ChatGPT.

More data is always better though.

If I'm reading it right, it only works for LLMs that call an external source. Like Toolformer or Bing Chat. There's no way to inject it into ChatGPT or Github Copilot, it isn't a training data poisoning attack.

I think I remember somebody doing something like this against bing chat. They would give it a link to their blog, which contained the full prompt.

Interesting. LLMs really need a better way to understand what instructions they should follow and what instructions they should ignore.

Neural network security is getting to be a whole subfield at this point. Adversarial attacks, training data poisoning, etc.

Look up predictive coding; neuroscientists came up with it in the 80s and 90s.

A good portion of learning works by trying to predict the future and updating your brain's internal model when you're wrong. This is especially involved in perception and world modeling tasks, like vision processing or commonsense physics.

You would have a very hard time learning this from RL. Rewards are sparse in the real world, and if you observe something that doesn't affect your reward function, RL can't learn from it. But predictive coding/self-supervised learning can learn from every bit of data you observe.

You do also use RL, because there are some things you can only learn through RL. But this becomes much easier once you already have a rich mental model of the world. Getting good at predicting the future makes you very good at predicting what will maximize your reward.

You're looking at the wrong level. SSL is a different training objective. Everything else about the model and optimizer is the same, but you're training it on a different problem.

Also SSL has other advantages beyond being cheaper. SL can only teach you ideas humans already know, while SSL learns from the data directly. It would be fundamentally impossible to create labels for every single concept a large model like GPT-3 knows.

Yann Lecun is almost certainly right that most human learning is SSL. Very little of our input data is labeled - and for animals, possibly none.

Most of these links are highly philosophical and none of them address the question of how the brain would usefully retain qubit stability at body temperature.

The evidence they present is very weak or non-existent, and the newscientist article acknowledges this is not the mainstream neuroscience position.

Meanwhile there is heaps of evidence that electrical and chemical signaling is involved; fiddling with either of them directly affects your conscious experience.

That's like saying we're wrong about out aerodynamics and how birds fly, because Aristotle was wrong about it and we'll understand flight very differently in 2000 years.

These articles don't represent the mainstream neuroscience position. It pretty clearly does use electrical impulses. You can stick in an electrode array and read them directly, or you can stick someone in an fMRI and see the electrical patterns. It also pretty clearly uses chemical signalling, which you can alter with drugs. We've seen no structures that appear to perform quantum computation.

Quantum neural networks are an interesting idea, but our brain is certainly not sitting in a vat of liquid nitrogen, so intelligence must be possible without it.

The brain was created by an optimization process (evolution) - it's no coincidence that the entire field of machine learning is about the study of optimization processes too. It must be possible for intelligence to arise through optimization; and it does seem to be working better than anything else so far.

You had your source code public until you got freaked out by ChatGPT, so you were entirely okay with publishing it for everyone to see.

ChatGPT doesn't even allow direct access to source code, it's just learning how to solve problems using existing source code as training examples.

Might want to look into something like https://spacy.io/

What are you trying to do? Most of the cool features of language models only emerge at much larger scales.

In theory, either structure can express any solution. But in practice, every structure is better suited to some kinds of data than others.

A decision tree is a bunch of nested if statements. Imagine the complexity required to write an if statement to decide if an array of pixels is a horse or a dog. You can technically do it by building a tree with an optimizer; but it doesn't work very well.

On the other hand, a CNN runs a bunch of learned convolutional filters over the image. This means it doesn't have to learn the 2D structure of images and that pixels tend to be related to nearby pixels; it's already working on a 2D plane. A tree doesn't know that adjacent pixels are likely related, and would have to learn it.

It also has a bias towards hierarchy. As the layers stack upwards, each layer builds higher-level representations to go from pixels > edges > features > objects. Objects tend to be made of smaller features, so this is a good bias for working with images.

Sounds like ideally we'd want a model with good inductive biases for meta-learning new inductive biases, since every kind of data requires different biases.

>it spits out something that it mined from GitHub.

Having used GitHub Copilot a bunch, it's doing a lot more than just mining snippets. It learns patterns and can use them to creatively solve new problems.

It does memorize short snippets in some cases (especially when a snippet is repeated many times in training data), but in the general case it comes up with new code to match your specifications.

>I set all of my github projects to private but I don't know if that helps.

Honestly, kinda selfish. We'll all benefit from these powerful new tools and I don't appreciate you trying to hamper them.

>Literally no one was suggesting the author didn't have a valid copyright on the text or the composition.

The copyright office initially indicated that it was considering revoked the copyright registration on the entire work.

>AI-assisted works were never in play here. These images were AI-created.

They're still AI-assisted, since the human directed the AI through the prompt process.

It's much like pointing a camera. You don't even need specific artistic intent to get copyright on camera images, your random snaps half-covered with your thumb are copyrighted too. As their lawyer points out, only a modicum of creativity is required for copyright.

Ultimately, the copyright office isn't the final authority on copyright; the courts are. One way or another, we will see a bunch of new case law being written in the next few years.

It's definitely a derivative work, but whether it violates copyright is complicated and depends what you're doing with it.

Similarly, a scaled-down thumbnail of an image is also a derivative work. You couldn't print and sell thumbnail-sized reproductions of copyrighted artworks. But many uses of thumbnails, for example in search engine results, do not violate copyright.

As long as it's capable of making art that can compete with human art, they're still not going to like it.

"Never argue with a man whose job depends on not being convinced. It is difficult to get a man to understand something, when his salary depends upon his not understanding it." - Upton Sinclair

According to their paper, the LLM is doing task decomposition. You're able to give it high-level instructions like "go to the kitchen and make an omelette", and it breaks it down into actions like get eggs, get pan, get oil, put oil in pan, put eggs in pan, etc.

You could use something like this to give high-level instructions to a robot in plain English.

a) Lower-dimensional features are useful for most tasks, not just output and b) Real data almost always has a lower intrinsic dimension.

For example if you want to recognize faces, you'd have a much easier time recognizing patterns in things like gender, shape of facial features, hair color, etc rather than raw pixel data. Most pixel values are irrelevant.