151–160 of 683 posts
Gemini demo looks like ChatGPT with a video feed, except it doesn't exist, like ChatGPT. I have ChatGPT on my phone right now, and it works (and it can process images, audio, and audio feed in). This means Google has shown nothing of substance. In my world, it's a classic stock price manipulation move.
Gemini Pro is available on Bard now. Ultra is not yet available.
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This seems to be a common view among some folks. Personally, I'm impartial. Search or even asking other expert human beings are prone to provide incorrect results. I'm unsure where this expectation of 100% absolute correctness comes from. I'm sure there are use cases, but I assume it's the vast minority and most can tolerate larger than expected inaccuracies.
I know exactly where the expectation comes from. The whole world has demanded absolute precision from computers for decades. Of course, I agree that if we want computers to “think on their own“ or otherwise “be more human“ (whatever that means) we should expect a downgrade in correctness, because humans are wrong all the time.
Computer engineers maybe. I think the general population is quite tolerant of mistakes as long as the general value is high.
People generally assign very high value to things computers do. To test this hypothesis all you have to do is ask folks to go a few days without their computer or phone.
That's not the only thing wrong. Gemini makes a false statement in the video, serving as a great demonstration of how these models still outright lie so frequently, so casually, and so convincingly that you won't notice, even if you have a whole team of researchers and video editors reviewing the output. It's the single biggest problem with LLMs and Gemini isn't solving it. You simply can't rely on them when correctn…
This seems to be a common view among some folks. Personally, I'm impartial. Search or even asking other expert human beings are prone to provide incorrect results. I'm unsure where this expectation of 100% absolute correctness comes from. I'm sure there are use cases, but I assume it's the vast minority and most can tolerate larger than expected inaccuracies.
First, we know they are imperfect. People seem to put more faith into machines, though I do sometimes see people being too trusting of other people.
Second, we have methods for measuring their imperfection. Many people develop ways to tell when someone is answering with false or unjustified confidence, at least in fields they spend significant time in. Talk to a scientist about cutting edge science and you'll get a lot of 'the data shows', 'this indicates', or 'current theories suggest'.
Third, we have methods to handle false information that causes harm. Not always perfect methods, but there are systems of remedies available when experts get things wrong, and these even include some level of judging reasonable errors from unreasonable errors. When a machine gets it wrong, who do we blame?
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Do you believe everything verbatim that companies tell you in advertising?
If they show a car driving I believe it's capable of self-propulsion and not just rolling downhill.
So what?
That's not the only thing wrong. Gemini makes a false statement in the video, serving as a great demonstration of how these models still outright lie so frequently, so casually, and so convincingly that you won't notice, even if you have a whole team of researchers and video editors reviewing the output. It's the single biggest problem with LLMs and Gemini isn't solving it. You simply can't rely on them when correctn…
LLMs right now are most practical for generating templated text and images, which when paired with an experienced worker, can make them orders of magnitude more productive.
Oh, DALL-E created graphic images with a person with 6 fingers? How long would it have taken a pro graphic artist to come up with all the same detail but with perfect fingers? Nothing there they couldn't fix in a few minutes and then SHIP.
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This seems to be a common view among some folks. Personally, I'm impartial. Search or even asking other expert human beings are prone to provide incorrect results. I'm unsure where this expectation of 100% absolute correctness comes from. I'm sure there are use cases, but I assume it's the vast minority and most can tolerate larger than expected inaccuracies.
I'm a software engineer, and I more or less stopped asking ChatGPT for stuff that isn't mainstream. It just hallucinates answers and invents config file options or language constructs. Google will maybe not find it, or give you an occasional outdated result, but it rarely happens that it just finds stuff that's flat out wrong (in technology at least). For mainstream stuff on the other hand ChatGPT is great. And I'm s…
If I ever worried about being quoted then I’ll verify the information
otherwise I’m conversational, have taken an abstract idea into a concrete one and can build on top of it
But I’m quickly migrating over to mistral and if that starts going off the rails I get an answer from chatgpt4 instead
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Lying implies an intent to deceive despite, or giving a response despite having better knowledge, which I'd argue LLMs can't do, at least not yet. It just requires a more robust theory of mind than I'd consider them to realistically be capable of. They might have been trained/prompted with misinformation, but then it's the people doing the training/prompting who are lying, still not the LLM.
Not to say this example was lying but they can lie just fine - https://arxiv.org/abs/2311.07590
I think this is a different usage of the word, and we're pretty used to making the distinction, but it gets confusing with LLMs.
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Is it possible for humans to be wrong about something, without lying?
I don't agree with the argument that "if a human can fail in this way, we should overlook this failing in our tooling as well." Because of course that's what LLMs are, tools, like any other piece of software. If a tool is broken, you seek to fix it. You don't just say "ah yeah it's a broken tool, but it's better than nothing!" All these LLM releases are amazing pieces of technology and the progress lately is incredib…
Overpowered LLMs like GPT-4 are both broken (according to how you are defining it) and useful -- they're just not the idealized version of the tool.