Chat GPT AI

Lambourne said:

User got the GitHub AI to list the rules it's not supposed to list.


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The same for Bing Chat

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Are these the actual rules or are they just "rules" that the chatbot came up with?

Palum said:

The models aren't the hardest part, it's the data tagging, labeling, classification, validation. Especially with ingestion of new data if you hope to keep yourself up to date. Wikipedia is perhaps something of an analogue here and it's chock full of misinformation. Open source is going to be equally biased by activists and other interest groups, just in different ways.

Since alternative tech platforms are often destroyed, it seems unlikely a legitimate rival product with actual investment that has controversial outputs would be allowed to continue operations.

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I wonder if corps will publish datasets. Or if new standards will emerge that tag text data in useful ways to common AI models. Image datasets are super common ( 20+ Best Image Datasets for Computer Vision [2023] ). Sometimes companies will publish datasets as a way to crowdsource solutions, ex: Open Dataset – Waymo

I don't know what ChatGPT datasets look like, but I could imagine a straightforward method to annotate a book, paper, website text etc. This type of data will become public over time, along with tools to ingest it and the models that are trained for it, and the training itself.

To your point though, ideological bias is going to be pervasive in open-source exactly like you said. There's not going to be a dominant tech platform purity-testing data to feed a chatbot so it produces "impartial" output. However, I could definitely see a group sitting on top of the open-source community and just exposing a chatbot that has fewer filters.

Aldarion said:

Solutions in (pre-AI) teaching material are often incorrect.

Like Qhue said, this stuff was the academic grunt labor. It wasnt farmed out to the best and brightest.

Don't get me wrong, LLMs produce good looking bullshit a lot of the time. The thing is, that describes most of academia too. And pretty much 100% of online ""content"". Lets just simplify this a bit. Most human produced content is good looking bullshit.

I think people overestimate the value of the criticism that LLMs produce good looking bullshit. If its as good looking as the other bullshit available, and it takes much less time and money to produce, it wins.

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Good or bad looking bullshit in mathematics is still bullshit. And AI produces a lot of bullshit. This isn't an undergraduate biology book that is being discussed but one where mathematically correct solutions are required. If one produces a set of hundreds of mathematical solutions using AI, how does one know these solutions are correct? One certainly can't assume they are correct because AI constantly produces incorrect answers to basic math problems.

As an aside, the online content for hard sciences at undergraduate level is excellent. How would you know that it is bad since you do not consume any?

Aldarion said:

When I said "online content" I was not referring to "online content for hard sciences at undergraduate level", I was being much more general.

Look, it all comes down to error rate. I'm just saying there were plenty of errors in existing teaching materials. The question should not be "how can AI content be useful since it makes errors", the question is "does it make more errors than human produced teaching materials?"

I'm just saying perfection isnt required, the bar is lower than that. For most content.

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For most content you are right. But not for physics and math.

Here is an example potato physics:

Q: "A diver 60 m deep in 10 degrees Celsius fresh water exhales a 1.3-cm-diameter bubble. What is the bubble's diameter just as it reaches the surface of the lake, where the water temperature is 20 degrees Celsius ? Assume that the air bubble is always in thermal equilibrium with the surrounding water."

A: "To solve this problem, we can use Boyle's law and the ideal gas law. Boyle's law states that at constant temperature, the pressure and volume of a gas are inversely proportional. The ideal gas law combines Boyle's law with the relationship between pressure, volume, and temperature.

Let's start by calculating the initial volume of the bubble at the diver's depth of 60 m. We know the initial diameter of the bubble is 1.3 cm, which means the initial radius is 0.65 cm (or 0.0065 m). We can calculate the initial volume using the formula for the volume of a sphere:

V₁ = (4/3) * π * r₁³

where V₁ is the initial volume and r₁ is the initial radius.

V₁ = (4/3) * π * (0.0065 m)³ V₁ = 0.00000008724 m³

Next, we need to calculate the final volume of the bubble at the surface of the lake. Since the air bubble is in thermal equilibrium with the surrounding water, the temperature of the bubble will also be 20 degrees Celsius. We'll use the ideal gas law to calculate the final volume.

PV = nRT

where P is the pressure, V is the volume, n is the number of moles of gas, R is the ideal gas constant, and T is the temperature.

At the diver's depth of 60 m, the pressure can be calculated using the hydrostatic pressure formula:

P₁ = ρ * g * h

where P₁ is the initial pressure, ρ is the density of water, g is the acceleration due to gravity, and h is the depth.

The density of fresh water is approximately 1000 kg/m³, and the acceleration due to gravity is approximately 9.8 m/s². Plugging in these values, we get:

P₁ = 1000 kg/m³ * 9.8 m/s² * 60 m P₁ = 588,000 Pa

Since the bubble is in thermal equilibrium with the surrounding water, the final pressure will also be 588,000 Pa.

Now, we can rearrange the ideal gas law equation to solve for the final volume:

V₂ = (P₁ * V₁ * T₂) / (P₂ * T₁)

where V₂ is the final volume, P₂ is the final pressure, T₁ is the initial temperature, and T₂ is the final temperature.

Plugging in the known values, we have:

V₂ = (588,000 Pa * 0.00000008724 m³ * (20 + 273.15) K) / (588,000 Pa * (10 + 273.15) K) V₂ ≈ 0.00000009019 m³

Finally, we can calculate the final radius of the bubble using the formula for the volume of a sphere:

V₂ = (4/3) * π * r₂³

0.00000009019 m³ = (4/3) * π * r₂³

Solving for r₂:

r₂ = (0.00000009019 m³ / ((4/3) * π))^(1/3) r₂ ≈ 0.00669 m

Therefore, the diameter of the bubble just as it reaches the surface of the lake is approximately 0.01338 m or 1.338 cm."


Now the approach it took is correct but the answer is wrong. Why is it wrong? Because P2 is not the same as P1. P1 is pressure under water and P2 is pressure at the surface. But the AI has no concept of what is what and just spews out, with 100% self certainty, an answer that is incorrect. I would not trust this thing with any sort of math, even basic shit like above.

Pasteton said:

It’s literally a generative language model , exactly what it’s called. It doesn’t understand anything it’s putting out, it is just putting out the words that based on its massive scraping of data make the most sense in that order. What’s insane to me is just how that simple principle can be used to put out so many (correct) things (but also many incorrect too). That’s also why the problem of confabulation and inaccuracy is going to be a very difficult one to solve. As far as I’m aware, it has no innate error correcting ability , from even a fundamental level. If you ask it to error correct it will just generate more bullshit based on the request

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I think it depends on the topic/prompt. Like mentioned before it generates text and is shit at any kind of computation.

This recent Lex Fridman podcast w/ Steven Wolfram touches on a lot of stuff from last set of posts here. pretty interesting IMO.