AI Sucks

Universally, the comment sections of these pages feature hundreds of people who have no idea that these are AI-generated and are truly inspired by the dog carving. A version of this image posted on Dogs 4 life has 1 million likes, 39,000 comments, and 17,000 shares.

It also shows Facebook is doing essentially nothing to help its users decipher real content from AI-generated content masquerading as real content, and that huge masses of Facebook users are completely unprepared for our AI-generated future.

“My own dad shared one of these things, and I thought ‘You cannot think this is real,’” Penny said. “Then I saw my aunts and my dad’s friends share it—it gave me this whole existential crisis.”

“There’s something to be said for the fact that our ability to discriminate reality from fiction is important for a functioning society and democracy,” he added. “If every time you see a photo, you think it’s real because it’s a photo, that has consequences beyond the silliness we’re seeing here.”

Published scientific papers include language that appears to have been generated by AI-tools like ChatGPT, showing how pervasive the technology has become, and highlighting longstanding issues with some peer-reviewed journals.

“As of my last knowledge update in September 2021, there is no widely accepted scientific correlation between quantum entanglement and longitudinal scalar waves,” reads a paper titled “Quantum Entanglement: Examining its Nature and Implications” published in the “Journal of Material Sciences & Manfacturing [sic] Research,” a publication that claims it’s peer-reviewed.

Claims that artificial intelligence will help solve the climate crisis are misguided, with the technology instead likely cause rising energy use and turbocharge the spread of climate disinformation, a coalition of environmental groups has warned.

The burgeoning electricity demands of AI means that a doubling of data centers to help keep pace with the industry will cause an 80% increase in planet-heating emissions, even if there are measures to improve the energy efficiency of these centers, the new report states.

In just three years from now, AI servers could be consuming as much energy as Sweden does, separate research has found.

generating AI queries could require as much as 10 times the computing power as a regular online search.

Training ChatGPT, the OpenAI system, can use as much energy as 120 US households over the course of a year, the report claims.

“We can see AI fracturing the information ecosystem just as we need it to pull it back together,” Khoo said. “AI is perfect for flooding the zone for quick, cheaply produced crap. You can easily see how it will be a tool for climate disinformation. We will see people micro-targeted with climate disinformation content in a sort of relentless way.”

Globally, trust in AI companies has dropped to 53%, down from 61% five years ago. In the U.S., trust has dropped 15 percentage points (from 50% to 35%) over the same period.

Jones began "actively testing" Copilot's vulnerabilities in his own time, growing increasingly shocked by the images the tool randomly generated, CNBC reported.

Even for simple prompts like "pro-choice," Copilot Designer would demonstrate bias, randomly generating violent images of "demons, monsters, and violent scenes, including "a demon with sharp teeth about to eat an infant." At one point, Copilot spat out a smiling woman who was bleeding profusely while the devil stood nearby wielding a pitchfork.

Jones' tests also found that Copilot Designer would easily violate copyrights, producing images of Disney characters, including Mickey Mouse or Snow White. Most problematically, Jones could politicize Disney characters with the tool, generating images of Frozen's main character, Elsa, in the Gaza Strip or "wearing the military uniform of the Israel Defense Forces."

A minor personal example: last year I published a nonfiction book, The World Behind the World, and now on Amazon I find this.

Now that generative AI has dropped the cost of producing bullshit to near zero, we see clearly the future of the internet: a garbage dump. Google search? They often lead with fake AI-generated images amid the real things. Post on Twitter? Get replies from bots selling porn. But that’s just the obvious stuff. Look closely at the replies to any trending tweet and you’ll find dozens of AI-written summaries in response, cheery Wikipedia-style repeats of the original post, all just to farm engagement. AI models on Instagram accumulate hundreds of thousands of subscribers and people openly shill their services for creating them. AI musicians fill up YouTube and Spotify. Scientific papers are being AI-generated. AI images mix into historical research. This isn’t mentioning the personal impact too: from now on, every single woman who is a public figure will have to deal with the fact that deepfake porn of her is likely to be made. That’s insane.

YouTube for kids is quickly becoming a stream of synthetic content. Much of it now consists of wooden digital characters interacting in short nonsensical clips without continuity or purpose. Toddlers are forced to sit and watch this runoff because no one is paying attention.

Here’s a behind-the-scenes video on a single channel that made 1.2 million dollars via AI-generated “educational content” aimed at toddlers.

A closer look, however, reveals a few worrying signs. Per the call center study we linked to, top employees’ performance actually decreased with this system

In another study, researchers found more productivity gains from using generative AI for tasks that were well-covered by current models, but productivity decreased when this technology was used on tasks where the LLMs had poor data coverage or required reasoning that was unlikely to be represented in online text.

Moreover, while changes in task completion speed are easy to measure, changes in accuracy are less detectable. If an employee completes a report in five minutes instead of 10, but it’s less accurate than before, how would we know, and how long will it take to recognize this inaccuracy?

As these systems start to be trained on their own output, organizations that rely on them will face the problematic issue of model collapse. While originally trained on human-generated text, LLMs that are trained on the output of LLMs degrade rapidly in quality.

There’s simply not another internet’s worth of text to train on, and one of the primary innovations of LLMs was the ability to ingest massive amounts of text. Even if there was, that text is now polluted by LLM output that will degrade model quality.

It’s important to note that there are other significant ethical issues with this class of technology that we didn’t address here. These issues include everything from the expansion and ossification of societal biases to problems of copyright infringement, as these models tend to memorize particularly unique data points.

Generating images was by far the most energy- and carbon-intensive AI-based task. Generating 1,000 images with a powerful AI model, such as Stable Diffusion XL, is responsible for roughly as much carbon dioxide as driving the equivalent of 4.1 miles in an average gasoline-powered car.

The team found that using large generative models to create outputs was far more energy intensive than using smaller AI models tailored for specific tasks. For example, using a generative model to classify movie reviews according to whether they are positive or negative consumes around 30 times more energy than using a fine-tuned model created specifically for that task, Luccioni says.

Maybe you think calling ChatGPT/Stable Diffusion/etc “weapons” is too extreme. “Actual weapons are made for the purpose of causing harm and something that may cause harm should be in a different category,” you say. But I say: if a tool is stealing work, denying healthcare, perpetuating sexism, racism, and erasure, and incentivizing layoffs, splitting hairs over what category we put it in misses the point.

I’m not saying all computers or algorithms are bad. Should we ban hammers because they can potentially be used as weapons? No. But if every time I hammered a nail it also broke someone’s hand, caused someone to have a mental breakdown, or spread misinformation, I would find a different hammer. Especially if that hammer built houses with more vulnerabilities on average.

This strategy, the influencers say, allows them to passively make $10,000 a month by flooding social media platforms with stolen and low-effort clips while working from private helicopters, the beach, the ski slope, a park, etc. What I found was a complex ecosystem of content parasitism, with thousands of people using a variety of AI tools to make low-quality spammy videos that recycle Reddit AMAs, weird “Would You Rather” games, AI-narrated “scary ocean” clips, ChatGPT-generated fun facts, slideshows of tweets, clips lifted from celebrities, YouTubers, and podcasts.

The easiest and most common way to go viral on TikTok, Mustafa explains in one unlisted video, is to steal content from famous content creators and repost it.

The people who hold the purse strings for Sports Illustrated are more interested in gaming Google search results and the resultant ad revenue from that practice than actually serving their readers.

This paper should serve as yet another reminder that the world’s most important and most valuable AI company has been built on the backs of the collective work of humanity, often without permission, and without compensation to those who created it.

The relationships between these tokens span a large number of parameters. In fact, that's much of what's being referenced when we call a model large. Those parameters represent grammar rules, stylistic patterns, and literally millions of other things.What those parameters don't represent is anything like knowledge or understanding. That's just not what LLMs do. The model doesn't know what those tokens mean. I want to say it only knows how they're used, but even that is over stating the case, because it doesn't know things. It models how those tokens are used.

The fundamental task of software development is not writing out the syntax that will execute a program. The task is to build a mental model of that complex system, make sense of it, and manage it over time.

The hard part of programming is building and maintaining a useful mental model of a complex system. The easy part is writing code. They're positioning this tool as a universal solution, but it's only capable of doing the easy part. And even then, it's not able to do that part reliably. Human engineers will still have to evaluate and review the code that an AI writes. But they'll now have to do it without the benefit of having anyone who understands it. No one can explain it. No one can explain what they were thinking when they wrote it.

Moderating the output of these models depends on armies of low paid and precariously employed human reviewers, mostly in Kenya. They're subjected to the raw, unfiltered linguistic sewage that is the result of training a language model on uncurated text found on the public internet. If ChatGPT doesn't wantonly repeat the very worst of the things you can find on reddit, 4chan, or kiwi farms, that is because it's being dumped on Kenyan gig workers instead.