There's plenty of retrieval-based models that do cite sources. They just didn't want to deal with it for this release.[1] I'm sure it's already on the roadmap.
[1] In fact, some snooping suggests they specifically disabled that feature, but do have it in test environments. See the "browsing disabled" flag they have in the hidden prompt. That could easily be used for citations. Source: (https://twitter.com/goodside/status/1598253337400717313)
No, it would take approximately 3 minutes where you either write a paragraph in the desired style yourself or paste one from the Internet and then ask it to continue in that style. Even if you decided to go the more onerous fine-tuning route, it would require 1-2mb of text, cost very little, and you'd be done in a few hours. It's easy.
Athanasius is greek for "immortal" and is a common name of saints, church fathers etc. Lubensk (Лубенськ) is an obscure city near Luhansk. Saint Athanasius of Lubensk refers to Athanasius III of Constantinople, saint of the Orthodox church known as an "enlightener", who died near Lubensk. According to the Wikipedia article[1] he sometimes appears to people in dream visions, so your experience is not entirely without precedent.
Your feelings are understandable, but I'd say you will be more effective in actually helping people if you manage to develop compassion even towards people who seem hate-worthy. I wish you continued success in your practice.
"Monks, do not wage wordy warfare, saying: 'You don't understand this Dhamma and discipline, I understand this Dhamma and discipline'; 'How could you understand it? You have fallen into wrong practices: I have the right practice'; 'You have said afterwards what you should have said first, and you have said first what you should have said afterwards'; 'What I say is consistent, what you say isn't'; 'What you have thought out for so long is entirely reversed'; 'Your statement is refuted'; 'You are talking rubbish!'; 'You are in the wrong'; 'Get out of that if you can!'
"Why should you not do this? Such talk, monks, is not related to the goal, it is not fundamental to the holy life, does not conduce to disenchantment, dispassion, cessation, tranquillity, higher knowledge, enlightenment or to Nibbana. When you have discussions, monks, you should discuss Suffering, the Arising of Suffering, its Cessation, and the Path that leads to its Cessation. Why is that? Because such talk is related to the goal... it conduces to disenchantment... to Nibbana. This is the task you must accomplish."
Sorry that my answer comes so late, but I'll put this here for posterity. I will only address the latter part. My point was that a brain is an N x N -matrix in the same sense as an ANN. An ANN is no more an N x N -matrix "in reality" than a biological brain; in reality it is some collection of analog electric potentials and configurations of matter which can sometimes be represented as a digital N x N -matrix for the convenience of the programmer. Thus the situation is exactly identical to a biological brain, which is also not "in reality" an N x N -matrix but can be represented as one. If we had sufficiently advanced (nano-)technology, we could manipulate human brains through their abstract representation as a matrix just as we can ANNs. Any distinction is purely pragmatic.
In any case I was not saying that being representable as an N x N -matrix is sufficient for consciousness (which I do not believe), simply that it is clearly compatible with consciousness. I agree that a self-aware ANN would probably require a body (possibly simulated) and some notion of agency.
It depends on what you'd consider fundamental. It's true that most of our advances since the mid-80s have been about improving the robustness, data- and compute-efficiency of the training process through optimized architectures and learning algorithms, and in principle in the limit of infinite data and compute you could have taken a model from 1986 and scaled it up to do everything that our current models do. In that sense there have been no "fundamental" advances.
On the other hand, in the limit of infinite size and complexity most mathematical functions can be represented by hash maps, yet to say that there have been no fundamental advances in programming since the invention of hash maps in the fifties would seem like an odd claim to make.
There's a good reason for this -- the version of GPT-3 used for these tests was fine-tuned on CYOA adventures, where this kind of sudden death is very common.
If you know what a Markov chain is then you must also know that modern language models are nothing like Markov chains. Just as an example, a Markov chain can't do causal reasoning or correctly solve unseen programming puzzles, the way GPT-3 can.
As for self-awareness, your brain is an N x N -matrix in the same sense as an ANN, so surely it must be possible for one to be self-aware? Not claiming that GPT-3 is, of course.
Ending up "net ahead" does not necessarily mean maximizing the fraction of successful projects. I don't think pg is saying that implausible-sounding projects usually turn out good. He is saying that when they turn out good, they have outsized impact, precisely because they initially sounded implausible and therefore produce lots of new information if true.
Wouldn't the empirical success of GPT-3 in simple programming tasks itself be evidence against this interpretation?
Furthermore, GPT-3 is only a language model because it is trained on textual data. Transformer architectures simply map sequences to other sequences. It doesn't particularly matter what those sequences represent. GPT-2 has been used to complete images, for example: https://openai.com/blog/image-gpt/
Context-free grammars were originally invented by linguists for analyzing natural language. You can see examples of phrase structure and dependency diagrams e.g. here: https://en.wikipedia.org/wiki/Dependency_grammar
For what it's worth, I agree with OP, and have a very "language-oriented" thinking style. I certainly don't visualize anything while programming or doing math (except for geometry and the like). My thinking feels like it's more based on constraint solving and seeing analogies between domains.
I don't think so. Uncurated GPT-3 output is generally very noticeable because it is unable to hold coherence for an article of this length, and will often make the same points over and over in slight variations and sometimes even contradict itself. I see none of that in this article. I do agree that the general "feel" of the text is similar to reading GPT-3 output, but this seems to be just because the author has a somewhat rambling argumentation style. (I can relate; my own tendency is to ramble on loosely related topics when writing down my thoughts.)
This could of course be curated GPT-3 content, but sufficiently curated GPT-3 output is indistinguishable from human writing, so there's little reason to criticize the article on those grounds.
edit: Well, inspecting the site some more, I agree that there's a moderate chance that this is a test run for a process or a program that uses some kind of novel technique to get GPT-3 or some other LM to produce more coherent essays. I'm still about 80% confident there's a human in the loop, though.
Hmm, this still doesn't quite answer my question at the level of concreteness I was looking for. But thank you for clarifying.
The thing is, you can only define predictive accuracy relative to some experimental design. Otherwise you can always claim that there is some unknown, unperformed experiment where the predictions of the model and your actual behaviour would diverge to a greater degree than is permissible by your accuracy threshold, no matter how many successful experiments have already been done in constrained conditions.
Imagine a task where you have to classify images as being of dogs or non-dogs. We can already train a model that can almost perfectly predict the choices you would make during the runs of such an experiment. But we obviously wouldn't call such a model a "model of your brain"!
My question is this: what would be a sufficient experimental design or empirical criterion to decide that some program is a model of you? The loosest criterion I could imagine would be something like "can successfully deceive your loved ones into believing they are you in a single text chat of unbounded duration with some extremely high success rate." Recent advances in NLP lead me to believe that we'll be able to reach at least this level of fidelity quite soon.
[1] In fact, some snooping suggests they specifically disabled that feature, but do have it in test environments. See the "browsing disabled" flag they have in the hidden prompt. That could easily be used for citations. Source: (https://twitter.com/goodside/status/1598253337400717313)