There are humans who can make a lifelong career out of saying and writing things that sound correct, but aren't correct. GPT-4 and beyond at the very least gives this ability to everyone who can afford 20 dollars a month. The winners in an AI dominated world are those who are least susceptible to manipulation by AI leveraged tactics.
GPT-4
661–670 of 1001 posts
Re: GPT-4
#662Test taking will change. In the future I could see the student engaging in a conversation with an AI and the AI producing an evaluation. This conversation may be focused on a single subject, or more likely range over many fields and ideas. And may stretch out over months. Eventually teaching and scoring could also be integrated as the AI becomes a life-long tutor. Even in a future where human testing/learning is no l…
With the current progress, human learning seems to be obsolete soon, so there's little point in optimizing an AI for teaching. Unless you mean only as a hobby to pass the time. > AIs may be tutoring and raising other baby AIs, preparing them to join the community. Probably I'm not futurist enough, but I'm always amazed at how chill everyone is with supplanting humanity with AIs. Because there doesn't seem to be a pla…
I mean I guess a lot of us might be giving up and expecting an ASI within a short period of AGI that will put an end to our sorry lot pretty quickly
Now if there is just a slow race to AGI then things are going to be very politically messy and violent ( even much more so than now ) in the next decade.
Re: GPT-4
#663Are they going to limit access to this because they think its too "dangerous". That would be a tragedy if they did. We've seen how opening access up to as many people as possible has produced some of the best results and demonstrated the usefullness of these LLMs. They need to get it out to the public as soon as possible and then see what the public come up with. I really feel like a new age of innovation is upon us with these "AI" programs, its going to be a blast to see where we go from here. Its going to upend a lot of predictions people have made about the future.
Re: GPT-4
#664That lightening/VGA visual example seems like absolute black magic.
Cherry picked sure, but still feels like it is approaching complex thought
Re: GPT-4
#665Imagine 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…
Yeah looking at the responses they include without using a safety layer it’s pretty clear that the underlying unfiltered model assigns quite a bit of truth to 4chan-esque ideals and values It’s an open question how much of this makes it through the safety layer like if asked to interview job candidates would these undesired biases make it through or are they caught along the way
we need to remove empirical data and stats from the training data, to prevent the AI from noticing the wrong things.
Re: GPT-4
#666Access 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.
Can you elaborate on how this works?
Re: GPT-4
#667Seems 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]"
I work at company that uses AI to automate about ⅓ of the job of trained licensed professionals. Looking at GPT4 those licensed professionals are now completely irrelevant. It's going to take years to build the supporting software around gpt4 to completely eliminate those jobs, but today I am convinced that we are on the verge of massive unemployment. Today thousands of job types have just been made redundant. What s…
a big problem with these AI developments is that they change things so much and so fundamentally that it makes us all like boomers trying to use the internet in 1990. no idea whats going on. stumbling and fumbling along. you say that it will take years to build the supporting software. yeah, if it were just humans doing it.
its so refreshing to see all the people in this thread who are expressing their concern about the most extreme employment/economic shockwave of all time. it gets way too little attention. but the impact on employment will be the very least of our worries not too long from now. mostly because people are too stupid to either realize that something terrible is happening or do anything about it.
Re: GPT-4
#668After watching the demos I'm convinced that the new context length will have the biggest impact. The ability to dump 32k tokens into a prompt (25,000 words) seems like it will drastically expand the reasoning capability and number of use cases. A doctor can put an entire patient's medical history in the prompt, a lawyer an entire case history, etc. As a professional...why not do this? There's a non-zero chance that i…
I am surprised they allow only 32k tokens when Reformer can have context length of 1M on 16GB VRAM. It seems like they have some ways to optimize it further.
Re: GPT-4
#669[flagged]
> I heard
where?
Re: GPT-4
#670A 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…
LLMs aren’t reasoning about the puzzle. They’re predicting the most likely text to print out, based on the input and the model/training data. If the solution is logical but unlikely (i.e. unseen in the training set and not mapped to an existing puzzle), then the probability of the puzzle answer appearing is very low.
You are incorrect and it's really time for this misinformation to die out before it perpetuates misuse from misunderstanding model capabilities.
The Othello GPT research from Harvard months ago demonstrated that even a simple GPT model is capable of building world representations from which it reasons outputs. This makes intuitive sense if you understand the training, as where possible having reversed an abstraction in the NN is going to perform better than simply extrapolating predictively from the data.
Not only is GPT-4 more robust at logic puzzles its predecessor failed, I've seen it solve unique riddles outside any training data and the paper has explicit examples of critical reasoning, especially in the appendix.
It is extremely unlikely given the Harvard research and the size of the training data and NN that there isn't some degree of specialized critical reasoning which has developed in the NN.
The emerging challenge for researchers moving forward is to get better insight into the black box and where these capabilities have developed and where it's still falling into just a fancy Markov chain.
But comments like yours reflect an increasingly obsolete and yet increasingly popular misinformation online around the way they operate. So someone reading your comment might not think to do things like what the Bing team added with providing an internal monologue for reasoning, or guiding it towards extended chain of thought reasoning, because they would be engaging with the models thinking it's only frequency based context relative to the training set that matters.
If you haven't engaged with emerging research from the past year, you may want to brush up on your reading.