Side track: There is so much going on this space. I wish there was a chronological flow of a machine learning scenario/story with all the terms being introduced as we meet them (data, pre-training, training, inference, mixture of experts, RAG). Like someone walking me through a factory explaining what happens at each stage (like Mr Rogers used to do). Most of the time I do not know where the terms fit in the big picture. When I first came across pre-training I thought it was something done to the data before training happened but it was actually another training.
> Most of the time I do not know where the terms fit in the big picture.
Nor do the majority of "AI" experts and consultants that I see on LinkedIn, Twitter or in podcasts.
The S/N ratio is very low in this field. Just pick some documentation from "industry leaders" like Langchain and see that not only is it already and always outdated, it sometimes simply contradicts itself.
In the "blockchain hype" this was similar, so I guess it's a trait of the hype train.
Totally agree with the above, although I’m not sure that documentation on tools like Langchain is a reflection of the hype in the way social media is. I think in that case it’s just a reflection of the pace things are moving at.
I mean yes, this is what a rapidly expanding field looks like that's probing the boundaries of its problem space. Kind of like following physics in the early to mid 1900s. Different classes of problems have barely been tested against each other, much less fully explored themselves.
In some ways it reminds me of the earlier days of the internet when progress was still very rapid.
It can be hard to know where to start with some of these concepts, especially so given that a lot of recent developments (e.g. RAG) are developing so rapidly that there’s unlikely to be a reference book you could refer to anytime soon that would be current.
That said, I do find that documentation is getting better depending on where you look. The documentation for higher level tools like LlamaIndex is a good starting point for understanding the concepts (not so much in terms of explaining the concepts, but showing where they fit into the overall picture, then you can deep-dive elsewhere on the different parts).
YouTube has always been a mixed bag of very little solid information in a sea of non-experts trying to attract clicks for the latest trends, so it’s not a great starting point IMHO.
As an outsider but avid reader of this stuff linked from HN, I would recommend the channel 3blue1brown. He's got several NN and AI related videos, and the couple I've seen were pretty good.
Yeah but the other side of the coin is that they only explain the very basic concepts that are already settled for several years, not any of these "latest trends"
Anything that is not settled for several years, like papers published last year or so. Like RingAttention, quantization/pruning, rotary embedding, distillation, RLHF, L2 regularization, multimodal, MoE etc.
Llamaindex docs are absolutely terrible IMO. I have gone through it so many times but still do not understand the terms and organization. Router for querying router query engine?
People waste too much time building out stuff that is really bad in AI right now.
Of course, everything is, but instead of taking on the task of patching that up, the better approach would be to pretend there will be something that is a lot better than GPT-4 in the near future (because there will be) and design a differentiated product under that premise.
Can I assume AI are continuing their training as they interact with people when deployed? Are ChatGPT, Claude, learning from my interactions with them? I do, BTW, correct them when they unknowingly (I assume) steer me wrong.
One wonders, if that's the case, how quickly an AI might improve if it has something close to Google's search site throughput. I mean fielding several billion queries a day, for a year — that would be some pretty stellar training right there I would think.
They don't train as they go. Training is incredibly expensive.
They do take your feedback and presumably do something with it. Your actual queries are only indirectly useful since they might have private info in them.
> Can I assume AI are continuing their training as they interact with people when deployed?
Yes, you can. Some of the big providers are fairly clear on where in their products this happens, and all offer a way out (mostly when paying for api access)
> One wonders, if that's the case, how quickly an AI might improve if it has something close to Google's search site throughput
Indeed. Another possibility is that user input will turn out to be increasingly less important for upcoming state of the art models.
I think a general way to answer this is by considering for any domain you know: What would you pay a human to do right now, that LLMs frustratingly can't, but should in theory, if only they were a bit better and more consistent?
This could mean: Instead of diving into langchain and trying to program your way out of a bad model, or trying to do weird prompts, just write a super clear set of instructions and wait for a model that is capable of understanding clear instructions, because that is an obvious goal of everyone working on models right now and they are going to solve this better than your custom workaround can.
This is not a rigid rule, just a matter of proportions. For example, you should probably be willing to try a few weird intermediary prompt hacks, if you want to get going with AI dev right now. But if most of what most people do will probably be solved by a somewhat better model, that's probably a cause for pause.
I suppose with an eye on open-source, an interesting 'rule' would be to set a cut-off point for models that can run locally, and/or are considered to be feasible locally soon.
I may be misunderstanding your meaning, but I'm not convinced that "prompts as a service" is short term. I think we'll see a number of apps pop up that will be essentially that, i.e. powered by a generative AI, but with a great UX. Not everyone is good at prompting, and although it is a skill many will develop, packaging up great prompts in niche problem areas still looks like an area of opportunity to me. I'm not talking necessarily about chat experiences, but apps that can, as an example, maintain task lists for you after consuming your incoming communications.
I don't understand your comment. I was talking about apps built on LLMs where the prompts aren't given by the users, but the LLM is still an important part of the functionality.
Strongly recommend watching Andrej Karpathy’s “Lets build GPT-2” videos on YouTube which dives into an actual PyTorch implementation, then download the code and study it carefully. Then study “Spreadsheets is all you need” to see what the internal data structures look like.
