A couple years ago, I wrote several posts about AI art. But AI is a moving target, and there’s no sense to committing to one single view about it. So let’s reconsider.
1. AI art as theft
The argument against AI images that has had the most staying power, is the idea that training an image generator on art is stealing from the artists. I’ve become somewhat more sympathetic to this argument over time.
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Marcus reminded me of the common claim: “AI is just remixing existing works”. Or the more colorful version, “AI just regurgitates existing art”. This is in reference to creative uses of AI image generators or LLMs.
While there may be a grain of truth to the claim, I have difficulty making sense of what it’s even saying. It’s basically an unverifiable statement. I think both pro- and anti-AI folks would be better served by a more technical understanding. So, instead of being stuck at an impasse, we might be able to actually find answers.
After writing about LLM error rates, I wanted to talk about a specific kind of error: the hallucination. I am aware that there is a lot of research into this subject, so I decided to read a scholarly review:
“Survey of Hallucination in Natural Language Generation” by Ziwei Ji et al (2023), publicly accessible on arxiv.
I’m not aiming to summarize the entire subject, but rather to answer a specific question: Are hallucinations are an effectively solvable problem, or are they here to stay?
What is a hallucination?
“Hallucination” is a term used in the technical literature on AI, but it’s also entered popular usage. I’ve noticed some differences, and I’d like to put the two definitions in dialogue with each other.
I have had some professional experience in marketing. It’s a job, you know? Targeted advertising is a very common data science application. Specifically, I’ve built models that use credit data to decide who to send snail-mail. Was this a positive contribution to society? Eh, probably not.
In the title I ask, “good or evil?”, but obviously most people think the answer is “evil”. I’m not here to convince you that targeted advertising is good actually. But I have a bunch of questions, ultimately trying to figure out: why do we put up with targeted ads?
For the sake of scope, I’m thinking mainly about targeted ads as they appear on social media platforms. And I’m just thinking of ads that try to sell you a commercial product, as opposed to political ads or public service announcements. These ads may be accused of the following problems:
I worked on LLMs, and now I got opinions. Today, let’s talk about when LLMs make mistakes.
On AI Slop
You’ve already heard of LLM mistakes, because you’ve seen them in the news. For instance, some lawyers submitted bogus legal briefs–no, I mean those other lawyers–no the other ones. Scholarly articles have been spotted with clear chatGPT conversation markers. And Google recommended putting glue on Pizza. People have started calling this “AI Slop”, although maybe the term refers more to image generation rather than text? This blog post is focused exclusively on text generation, and mostly for non-creative uses.
A reader asked: what is the environmental impact of large language models (LLMs)? So I read some articles on the subject, and made comparisons to other technologies, such as video games and video streaming. My conclusion is that the environmental footprint is large enough that we shouldn’t ignore it, but I think people are overreacting.
Pricing
I’m not an expert in assessing environmental impact, but I’ve had a bit of experience assessing computational prices for LLMs. Pricing might be a good proxy for carbon footprint, because it doesn’t just represent energy costs, but also the costs of building and operating a data center. My guess is that across many different kinds of computation tasks, the carbon footprint per dollar spent is roughly similar. And in my experience, LLMs are far from the most significant computational cost in a typical tech company.
Generative AI sure is the talk of the town these days. Controversial, is it not? It’s with some trepidation that I disclose that I spent the last 6 months becoming an expert in large language models (LLMs). Earlier this year when I moseyed through the foundational LLM paper, that was where it began.
I’d like to start talking about this more, because I’ve been frustrated with the public conversation. Among both anti-AI folks as well as AI enthusiasts, people have weird, impossible expectations from LLMs, while being ignorant of other capabilities. I’d like to provide a reality check, so that readers can be more informed as they argue about it.