5,044 karma · joined February 22, 2007
If your main concern is question answering or summarization or code completion there are plenty of ways to do that now. If you really require the advanced emergent properties of LLMs, you'll have to work with a company that can afford to train a transformer on the Entire Internet.
Of course the utility will depend on the training data you collect. For example to go from corporate speak -> english you would need thousands of pairs of "translated" sentences. Or you could use an LLM to translate for you, paying as you go
I'd imagine that you'd prioritize saving a human child from a fire over saving a machine or a meat based robot. Why?
There are concerns about every form of technology. Nuclear energy. Plastics. Online advertising. Vaccines. But we generally don't take those things seriously until there is proven harm. This could mean waiting until it's too late and a catastrophe has already happened. But that's the risk we take with thousands of things every day.
If YOU take the issue seriously, I'll listen. but there's only so much to be gained from debating the threat posed by something that is not yet part of reality.
LLMs are no more valuable to a large spam operation than they are to any other business. This is like a 5%-10% profit increase, not 50%-100%. The key to that game is finding suckers. Language models help, but only so much.
Would love to see the reasoning behind this. Nothing I've seen about LLMs makes me think they will produce any kind of bounty for anyone.
It sounds like you're weaving science fiction ideas about AGI into your comment. There's no safety issue here unless you think that ChatGPT will use api access to pursue its own goals and intentions.
I've had many stories rejected by Clarkesworld. but if I submitted something so bad they assumed GPT wrote it, I might die on the spot.
It stands to reason that the education system should also change to reflect the world that we live in. Screens, AI, all of it. Children will learn how to become members of the society that they were born into, not the one that we were born into.
Also I'd guess that it still generally sucks at programming. Code has a lot of very similar sequences and logical patterns that can be broken, which makes it prone to hallucinating. I'd imagine that more parameters will help with this.
As a professional...why not do this? There's a non-zero chance that it'll find something fairly basic that you missed and the cost is several cents. Even if it just phrases something obvious in a way that makes you think, it's well worth the effort for a multimillion dollar client.
If they further increase the context window, this thing becomes a Second Opinion machine. For pretty much any high level job. If you can put in ALL of the information relevant to a problem and it can algorithmically do reasoning, it's essentially a consultant that works for pennies per hour. And some tasks that professionals do could be replaced altogether. Out of all the use cases for LLMs that I've seen so far, this seems to me to have the biggest potential impact on daily life.
edit (addition): What % of people can hold 25,000 words worth of information in their heads, while effectively reasoning with and manipulating it? I'm guessing maybe 10% at most, probably fewer. And they're probably the best in their fields. Now a computer has that ability. And anyone that has $20 for the OpenAI api can access it. This could get wild.
This is very valuable if you have a vague idea that machine learning will be helpful for your problem, but you don't want to go to the trouble of preparing data / hiring a data science team. Or if your ML model would benefit from having access to all of the data and collective wisdom of the internet. Or if you don't understand what machine learning is but still want to make basic predictions with available data.
It can apply what I've been thinking of as "common sense by api". For example, a brand advertiser can ask "Is it appropriate to show this ad to this customer?" to avoid showing ie, a pregnancy ad to a teenager. There has never been anything like this before. You had to hire a human to make decisions like that or program every edge case into the code.
I've been experimenting with this for weeks. From everything I've seen its a powerful general purpose machine learning model that is valuable in a small number of specific situations. I still don't have a full grasp of the boundaries and limitations, but its really an amazing thing. And its only going to get better with GPT4. I think most of the discussion around the technology is ignoring what its really providing to the world.
I know that a lot of people won't do this, and will just accept whatever an LLM says at face value. And again, that's true for any tool. But if you invest the time to understand the parameters and limitations of the model, it can be incredibly valuable.
[0] in my experience, GPT davinci is much more likely to hallucinate in (non-programming) situations that would be difficult for a human to explain. using the above example, it can easily handle a standard credit application. But it will be more likely to hallucinate reasoning for a rare case like someone with a very high income but a low credit score. YMMV, just sharing what I've seen
This is overstated and easily disproved. ChatGPT produces accurate facts as a matter of course. test it right now: "How tall is the Statue of Liberty?", "In what state was Abraham Lincoln born?", etc. There are an infinite list of factual questions for which it produces accurate and correct answers.
It loves to hallucinate api methods that don't exist. It can struggle with indivudual logic problems or questions. But these limitations have clear explanations. It's doing language completion so it will infer probable things that don't actually exist. It wasn't designed to do logic problems so it will struggle with classes of them.
Dismissing it as completely unreliable is unnecessary hyperbole. It's a software tool that has strengths, weaknesses and limitations. Like with any other software, it's up to us to learn what those are and how it can be used to make useful products.
Also, "We host with AWS" isn't really a response to "Will my data be secure?"
If the "actual machine learning practitioners" can break down behavior to the neuron level, then I'd say they understand it to some degree.
The reason that many of these problems still exist is because chasing down individual errors is a seductive waste of time. If you have to tell the network "here's how you handle this one pattern and here's how you handle this other one", then you're building an expert system. Yes it's tempting to correct errors as you see them pop up. But it's better to construct a network that can learn from data to handle any situation that you didn't think of specifically.
Building a robust network requires lots of time and data. There will always be edge cases that cannot be fixed individually. The fact that Tesla or anyone else hasn't built a system for X with no embarrassing edge cases yet does not mean that we should go back to coding individual instructions and conditionals line by line.
I am curious about whether it hallucinates api methods that don't exist, like ChatGPT does. I haven't seen that yet but the underlying concept of an LLM is the same.
What matters is the percentage accuracy. A black box with a 10% failure rate is better than a fully explainable system that fails 20% of the time. Explanations make us feel better and they can be very important. But for most cheap, repeated processes they aren't necessary. Not to mention that neural networks can be tested and interrogated in ways that other systems cannot.
I've used gpt enough that I can recognize its linguistic patterns. I treat gpt written communication like I do any other auto-generated email.
Don't take my word for it. Ask it: "How many people are wearing tennis shoes in the United States right now?"
The response I got: "I'm sorry, I don't have the current data on the number of people in the United States who are wearing tennis shoes right now. It's not possible to determine this number as it would require constantly monitoring the footwear of every person in the country, which is not feasible. The number would also vary greatly from moment to moment and would be influenced by a number of factors such as time of day, weather, and location."
No hallucinations, no bullshitting. Just "I don't know" and an explanation of what it would take it find out. No idea why you think it has trouble expressing a lack of confidence in its answers.
I've seen gpt hallucinate all kinds of things. But very rarely for something like explaining a process or system.
Also, other comments have suggested that this was trained on 120MM of the audio previews instead of the full songs. That might explain why the recommendations seem a little off for some people.