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The Lone Banana Problem in AI

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Re: The Lone Banana Problem in AI

#71
post #65
post #51

Earlier quoted context omitted.

I am mystified. The page is not long, the text is not complex. The message is obvious and it is contained in the title as shown here on HN. The first pic and its caption make it plain. The next group of 4 pics and their SINGLE SENTENCE caption spell it out clearly. As for "almost the whole article" -- it is short! It took about 90sec to read the whole page, top to bottom. "AI" chat bots can't count. This is well know…

> As for "almost the whole article" -- it is short! It took about 90sec to read the whole page, top to bottom. Cool. The article has 2348 words, so that's around 26 words per second, or ~1560 words per minute. According to some speed reading pages I randomly found via Google the consensus seems to be that 1000+ words per minute is quite exceptional. https://irisreading.com/what-is-the-average-reading-speed/ puts aver…

Yeah, I am a speedreader, and I did used to be able to do circa 3000wpm at a push. Good to know that even as a myopic 55YO I can still do half that without trying.

Points of comparison: I originally read Hal Clement's classic Mission of Gravity in about 25min, and Joseph Conrad's Victory in about 3 hours.

Re: The Lone Banana Problem in AI

#72
post #70

Earlier quoted context omitted.

My point exactly. This article could have been written: "AI is biased against drawing just one banana" and I would have preferred it. And I said "almost" because I skimmed two of the paragraphs in the middle that seemed unlikely to contain information.

But that isn't the point. The point is, LLMs look smart but they are not, and an easily-verifiable data point is that they can't count.

Ah, right, and that's not obvious to some people... I guess I'm just not the target audience.

Re: The Lone Banana Problem in AI

#74

Earlier quoted context omitted.

This is the biggest communication problem most people have. Say it plain and simple. No one wants to read your train of thought.

Thought process is fine, but lead with the plain and simple version. Offer context after you've introduced the topic and described what you're talking about.

I think when you're trying to be convincing of a counter intuitive or controversial point, it helps to start with less objectionable parts of the argument first. If an argument involves several steps, A to B to C ending in D, but D is wildly counter intuitive, some audiences, hearing D first will reduce their likelihood of actually integrating the consequences of A and so on. Maybe someone who's done more research on the psychology of priming would know?

Re: The Lone Banana Problem in AI

#76
post #71
post #65

Earlier quoted context omitted.

> As for "almost the whole article" -- it is short! It took about 90sec to read the whole page, top to bottom. Cool. The article has 2348 words, so that's around 26 words per second, or ~1560 words per minute. According to some speed reading pages I randomly found via Google the consensus seems to be that 1000+ words per minute is quite exceptional. https://irisreading.com/what-is-the-average-reading-speed/ puts aver…

Yeah, I am a speedreader, and I did used to be able to do circa 3000wpm at a push. Good to know that even as a myopic 55YO I can still do half that without trying. Points of comparison: I originally read Hal Clement's classic Mission of Gravity in about 25min, and Joseph Conrad's Victory in about 3 hours.

Did you forget you’re a speed reader when asserting how long it should take to read the whole article?

Re: The Lone Banana Problem in AI

#77
> AIs, at their current level of development, don’t perceive objects in the way that we do – they understand commonly occurring patterns.

You see this claim everywhere - that AI operates on statistics and patterns and not actual understanding. But human understanding is entirely about statistics and patterns. When a human sees a collection of particles and recognizes it as, say, a car, all they are doing is recognizing the car-like patterns in how the particles are organized that have a strong statistical correlation with prior observations of things classified as a car. Am I missing something?

Re: The Lone Banana Problem in AI

#78
post #23

Again, there is a lot of words to describe the fact that machine learning is just lossy compression for a bunch of data with the possibility to interpolate between data points and get somewhat plausible results. This means data points may get lost during compression/training, and certain things will look off, whether it be a preference for banana pairs, even numbers of fingers or certain weasel words in verbiage.

And is RL not? All of these models are constrained by finite weights and then tuned. Are you suggesting we grow a neural network until certain criterion are met with regard to out of distribution test criteria? Hmm

What would that buy you? You train until you push your loss under a certain threshold, then check the external criteria and if they don't hold you train again? Your external criteria would essentially become another part of your loss function, but the whole training would become vastly more inefficient.

I'm saying that we shouldn't expect the models to come up with things we didn't train them to come up with.

Re: The Lone Banana Problem in AI

#79

> AIs, at their current level of development, don’t perceive objects in the way that we do – they understand commonly occurring patterns. You see this claim everywhere - that AI operates on statistics and patterns and not actual understanding. But human understanding is entirely about statistics and patterns. When a human sees a collection of particles and recognizes it as, say, a car, all they are doing is recognizi…

We're not (initially anyway) trained on photography and literature and reddit, our first experience of a banana is probably eating one.

Re: The Lone Banana Problem in AI

#80
post #20

Earlier quoted context omitted.

On /r/midjourney subreddit there are posts like "the most stereotypical person in [state/country]", "what midjourney thinks professors look like based on their department". You can see some interesting biases in the training data. https://www.reddit.com/r/midjourney/top/?sort=top&t=year

https://r.nf/r/midjourney/top/?sort=top&t=year Don't feed the beast.

how is that still working after the api-ocalypse?
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