Examples

All of these examples basically start by giving example of question/answer pairs.

• English to Command Line arguments (video)
  • what I find most interesting about this example, and others, is the way in which it is able to adapt to the requests that you make of it. there’s nothing particularly difficult about regurgitating what the command line code for print working directory is. what’s surprising is that it understands what you mean when you try to correct it, or update it
• Dialogues (gwern)
  • this is more in the realms of the Turing Test, but it’s pretty shocking how it is able to maintain a pretty high-level/meta conversation about the nature of intelligence.
• Gary Marcus Issues (gwern):
  • ML dissident Marcus has an article arguing that GPT-2, and language models in general, are not capable of any form of intelligence. and he proposes some questions to ask the language model, which gwern finds GPT-3 is able to answer in a satisfactory manner.

Fairness

This is a research direction on their waitlist:

Fairness and Representation: How should performance criteria be established for fairness and representation? How can responsive systems be established to effectively support the goals of fairness and representation in specific, deployed contexts?

So far we’ve seen people explore the biases inherent in word embeddings and language models; GPT-3 will inevitably fall into the purview of such scrutiny. Is there something manifestly different now, with GPT-3, that makes it a much more interesting question, since it feels as if GPT-3 is able to perform some low-level kinds of reasoning.

In the more basic models, you could easily attribute all the inherent biases to a reflection of the state of the world learnable through text.

My feeling is that this is slowly encroaching into the problem of how to equip artificial intelligence with notions of morality (I wonder if anyone has asked ethical questions—probably), though more importantly, might require you to actively force it to take a stand on certain kinds of topics.

Ideas

• it seems to me that the missing link is the #reinforcement_learning type self-learning aspect. like, right now, the weights of GPT-3 are basically set. but it would be 1000x more worrying if it was talking to itself, or learning from all the interactions we are doing with it. that feedback mechanism feels like it would be the missing picture.

Resources

• https://www.gwern.net/GPT-3
• https://www.gwern.net/newsletter/2020/05#gpt-3
• https://www.lesswrong.com/posts/6Hee7w2paEzHsD6mn/collection-of-gpt-3-results
• https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/to-what-extent-is-gpt-3-capable-of-reasoning

Backlinks

• [[linguistic-neuroscience]]
  • As somewhat of an interdisciplinary nut55 like a gun-nut, except, with interdisciplinary research, instead of guns, duh!, it comes as no surprise that I think the advent of computational linguistics has really helped us better understand language. Something that I try to emphasize in class is the uniqueness of languages and words as a data form. In some (highly reductionist) sense, it’s just categorical data. For one thing, that’s definitely not how we would think about it at the outset, but the moment you start thinking statistically, you realize it’s a very natural formulation. Secondly, there’s all this interesting structure and rules (i.e. grammer), meaning, relationships, logic, intelligence. In fact, as we’ve sort of discovered from [[gpt3]], it is quite the proxy for general intelligence. And in between all this are the distributed semantic representation lessons from #embeddings. But I digress.
• [[a-universal-law-of-robustness-via-isoperimetry]]
  • thus, in the context of scale (à la [[gpt3]]), this result says that scale is necessary to achieve robustness (though not sufficient).

Deep Learning 1.0

Limitations:

• training time/labeled data: this is a common refrain about DL/RL, in that it takes an insurmountable amount of data. Contrast this with the [[bitter-lesson]], which claims that we should be utilizing computational power.

• adversarial attacks/fragility/robustness: due to the nature of DL (functional estimation), there’s always going to be fault-lines.
  • selection bias in training data
  • rare events/models are not able to generalize, out of distribution

• i.i.d assumption on DGP
  • temporal changes/feedback mechanisms/causal structure?

Deep Learning 2.0

Self-supervised learning—learning by predicting input

• Supervised learning : this is like when a kid points to a tree and says “dog!”, and you’re like, “no”.
• Unsupervised learning : when there isn’t an answer, and you’re just trying to understand the data better.
  • classically: clustering/pattern recognition.
  • auto-encoders: pass through a bottleneck to reconstruct the input (the main question is how to develop the architecture)
    • i.e. learning efficient representations
• Semi-supervised learning : no longer just about reconstructing the input, but self-learning
  • e.g. shuffle-and-learn: shuffle video frames to figure out if it’s in the correct temporal order
  • more about choosing clever opjectives to optimize (to learn something intrinsic about the data)

The brain has about 10^4 synapses and we only live for about 10^9 seconds. So we have a lot more parameters than data. This motivates the idea that we must do a lot of unsupervised learning since the perceptual input (including proprioception) is the only place we can get 10^5 dimensions of constraint per second.

Hinton via /r/MachineLearning.11 I’m not sure I follow the logic here. My guess is that you have a dichotomy between straight up perceptual input and actually learning by interacting with the world. But that latter feedback mechanism also contains the perceptual input, so it should be at the right scale.

• much more data than supervised or #reinforcement_learning
• if coded properly (like the shuffle-and-learn paradigm), then it can learn temporal/causal notions
• utilization of the agent/learner to direct things, via #attention, for instance.

Figure 1: Newborns learn about gravity at 9m. Pre: experiment push car off and keep it suspended in air (with string) won’t surprise. Post: surprise.

• case study: BERT. self-supervised learning, predicting the next word, or missing word in sentence (reconstruction).
  • not multi-sensory/no explicit agents. this prevents it from picking up physical notions.
    • sure, we need multi-sensory for general-purpose intelligence, but it’s still surprising how far you can go with just statistics
• less successful in image/video (i.e. something special about words/language).22 Could it be the fact that we deal with already highly representative objects that are already classifications of a sort, whereas everything else is at the bit/data level, and we know that’s not really how we process such things. This suggests that we should find an equivalent type of language/vocabulary for images.
  • we operate at the pixel-level, which is suboptimal
• prediction/learning in (raw) input space vs representation space
  • high-level learning should beat out raw-inputs33 My feeling though is that you eventually have to go down to the raw-input space, because that is where the comparisons are made/the training is done. In other words, you can transform/encode everything into some better representation, but you’ll need to decode it at some point.

Remark. Pattern recognition \(\iff\) self-supervised learning?