How many parameters does it have? Are there different versions like LLaMa?
GPT-4
541–550 of 1001 posts
Re: GPT-4
#542Imagine ingesting the contents of the internet as though it's a perfect reflection of humanity, and then building that into a general purpose recommendation system. That's what this is Is the content on the internet what we should be basing our systematic thinking around? No, I think this is the lazy way to do it - by using commoncrawl you've enshrined the biases and values of the people who are commenting and provid…
I think specialized models will be built with high quality curated content and will receive the equivalent of the Good Housekeeping seal of approval. Building a model from 10 years of upvoted Hacker News or Metafilter content looks far different than a model trained on the cesspool of 8chan.
Re: GPT-4
#543I think it's interesting that they've benchmarked it against an array of standardized tests. Seems like LLMs would be particularly well suited to this kind of test by virtue of it being simple prompt:response, but I have to say...those results are terrifying. Especially when considering the rate of improvement. bottom 10% to top 10% of LSAT in What are the implications for society when general thinking, reading, and…
Here's what's really terrifying about these tests: they are exploring a fundamental misunderstanding of what these models are in the first place. They evaluate the personification of GPT, then use that evaluation to set expectations for GPT itself.
Tests like this are designed to evaluate subjective and logical understanding. That isn't what GPT does in the first place!
GPT models the content of its training corpus, then uses that model to generate more content.
GPT does not do logic. GPT does not recognize or categorize subjects.
Instead, GPT relies on all of those behaviors (logic, subjective answers to questions, etc.) as being already present in the language examples of its training corpus. It exhibits the implicit behavior of language itself by spitting out the (semantically) closest examples it has.
In the text corpus - that people have written, and that GPT has modeled - the semantically closest thing to a question is most likely a coherent and subjectively correct answer. That fact is the one singular tool that GPT's performance on these tests is founded upon. GPT will "succeed" to answer a question only when it happens to find the "correct answer" in the model it has built from its training corpus, in response to the specific phrasing of the question that is written in the test.
Effectively, these tests are evaluating the subjective correctness of training corpus itself, in the context of answering the tests' questions.
If the training is "done well", then GPT's continuations of a test will include subjectively correct answers. But that means that "done well" is a metric for how "correct" the resulting "answer" is.
It is not a measure for how well GPT has modeled the language features present in its training corpus, or how well it navigates that model to generate a preferable continuation: yet these are the behaviors that should be measured, because they are everything GPT itself is and does.
What we learn from these tests is so subjectively constrained, we can't honestly extrapolate that data to any meaningful expectations. GPT as a tool is not expected to be used strictly on these tests alone: it is expected to present a diverse variety of coherent language continuations. Evaluating the subjective answers to these tests does practically nothing to evaluate the behavior GPT is truly intended to exhibit.
Re: GPT-4
#544I'll be finishing my interventional radiology fellowship this year. I remember in 2016 when Geoffrey Hinton said, "We should stop training radiologists now," the radiology community was aghast and in-denial. My undergrad and masters were in computer science, and I felt, "yes, that's about right." If you were starting a diagnostic radiology residency, including intern year and fellowship, you'd just be finishing now.…
If you are in the US. It is more important to have the legal paperwork, than to be factually correct. The medical cartels always will get their cut.
I'm curious who the medical cartels in this context. Can you elaborate?
Re: GPT-4
#545https://cdn.openai.com/papers/gpt-4.pdf >Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar. At that point, why bother putting out a paper?
It's not a paper, though. It's a technical report. I do concede there isn't much technical detail lol.
Re: GPT-4
#546Access is invite only for the API, and rate limited for paid GPT+. > gpt-4 has a context length of 8,192 tokens. We are also providing limited access to our 32,768–context (about 50 pages of text) version, gpt-4-32k, which will also be updated automatically over time (current version gpt-4-32k-0314, also supported until June 14). Pricing is $0.06 per 1K prompt tokens and $0.12 per 1k completion tokens. The context le…
One way to get around context length is to perform embedding and retrieval of your entire corpus. Langchain ( https://langchain.readthedocs.io/en/latest/ ) and Milvus ( https://milvus.io ) is one of the stacks you can use.
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Re: GPT-4
#547Seems like OpenAI is forecasting massive changes to the job market. I highly recommend reading page 18 of the research paper. "GPT-4 or subsequent models may lead to the automation of certain jobs.[81] This could result in workforce displacement.[82] Over time, we expect GPT-4 to impact even jobs that have historically required years of experience and education, such as legal services.[83]"
Artists in shambles
Re: GPT-4
#548All these folks in the comment section working in the knowledge economy--we're gonna be out of jobs in the next few years aren't we?
Re: GPT-4
#549A class of problem that GPT-4 appears to still really struggle with is variants of common puzzles. For example: >Suppose I have a cabbage, a goat and a lion, and I need to get them across a river. I have a boat that can only carry myself and a single other item. I am not allowed to leave the cabbage and lion alone together, and I am not allowed to leave the lion and goat alone together. How can I safely get all three…
Have you seen it play chess[0]? It's pretty funny.
It doesn't really "get" the rules of chess, but it has seen lots of matches and can do some "linguistic" predictions on the next move. It gets hilariously lost pretty fast, tho.
[0] https://www.reddit.com/r/AnarchyChess/comments/10ydnbb/i_pla...
Re: GPT-4
#550I'll be finishing my interventional radiology fellowship this year. I remember in 2016 when Geoffrey Hinton said, "We should stop training radiologists now," the radiology community was aghast and in-denial. My undergrad and masters were in computer science, and I felt, "yes, that's about right." If you were starting a diagnostic radiology residency, including intern year and fellowship, you'd just be finishing now.…
It all comes down to labelled data. There are millions images of VGA connectors and lightning cables on the internet with description, where CLIP model and similar could learn to recognize them relatively reliably. On the other hand, I'm not sure such amount of data are available for AI training. Especially if the diagnostic is blinded, it will be even harder for the AI model to reliably differentiate between them, m…
As someone who has worked at a Radiology PACS with petabytes of medical images under management, this is 100% accurate.
You might have images, but not the diagnoses to train the AI with.
In addition, there are compliance reasons, just because you manage that data doesn't mean that you can train an AI on it and sell it, unless of course you get explicit permission from every individual patient (good luck).
I do believe that with enough effort we could create AI specialist doctors, and allow the generalist family doctor to make a comeback, augmented with the ability to tap into specialist knowledge.
Technology in the medical industry is extremely far behind modern progress though, CT images are still largely 512 by 512 pixels. It's too easy to get bogged down with legacy support to make significant advancements and stay on the cutting edge.