AI Sucks

Nurses know that AI technology and algorithms are owned by corporations that are driven by profit — not a desire to improve patient care conditions or advance the nursing profession

This technology forces RNs to respond to excessive, if not faulty, alerts – which sometimes mistakenly flag that a patient’s safety is in jeopardy –  rather than using their knowledge and skills of observation to assess how to meet the needs of all patients. Conversely, patients who are at risk for deterioration are often missed by AI technology, which would have otherwise been caught by a thorough hands-on assessment from highly-trained medical professionals

AI technology also automatically completes note-taking that can miss important details and nuances about the patient.

Patient care requires — and will always require — nurses. Life-and-death decisions relating to patient acuity, treatment decisions, and staffing levels cannot be made without the assessment skills and critical thinking of registered nurses. For example, tell-tale signs of a patient’s condition, such as the smell of a patient’s breath and their skin tone, affect, or demeanor, are often not detected by AI and algorithms.

AI technologies enable mass surveillance of nurses and other health care workers at facilities, with disturbing opportunities for employers to violate individual privacy and union organizing rights.

For AI systems to get better, they will need more training — a stage that involves bombarding the software with data — and that’s going to run up against the limits of energy capacity, he said.

But there is a yawning gap between "AI tools can be handy for some things" and the kinds of stories AI companies are telling (and the media is uncritically reprinting). And when it comes to the massively harmful ways in which large language models (LLMs) are being developed and trained, the feeble argument that "well, they can sometimes be handy..." doesn't offer much of a justification

When I boil it down, I find my feelings about AI are actually pretty similar to my feelings about blockchains: they do a poor job of much of what people try to do with them, they can't do the things their creators claim they one day might, and many of the things they are well suited to do may not be altogether that beneficial. And while I do think that AI tools are more broadly useful than blockchains, they also come with similarly monstrous costs.

But what these companies and clients fail to recognize is that ChatGPT does not write, it generates text, and anyone who's spotted obviously LLM-generated content in the wild immediately knows the difference.

And the tendency for people to put too much trust into these toolsh is among their most serious problems: no amount of warning labels and disclaimers seem to be sufficient to stop people from trying to use them to provide legal advice or sell AI "therapy" services.

And the idea that we all should be striving to "replace artists" — or any kind of labor — is deeply concerning, and I think incredibly illustrative of the true desires of these companies: to increase corporate profits at any cost.

But I find one common thread among the things AI tools are particularly suited to doing: do we even want to be doing these things? If all you want out of a meeting is the AI-generated summary, maybe that meeting could've been an email. If you're using AI to write your emails, and your recipient is using AI to read them, could you maybe cut out the whole thing entirely? If mediocre, auto-generated reports are passing muster, is anyone actually reading them? Or is it just middle-management busywork?

There's a huge human cost as well. Artificial intelligence relies heavily upon "ghost labor": work that appears to be performed by a computer, but is actually delegated to often terribly underpaid contractors, working in horrible conditions, with few labor protections and no benefits.

Generative AI is being used to harass and sexually abuse. Other AI models are enabling increased surveillance in the workplace and for "security" purposes — where their well-known biases are worsening discrimination by police who are wooed by promises of "predictive policing". The list goes on.

I'm glad that I took the time to experiment with AI tools, both because I understand them better and because I have found them to be useful in my day-to-day life. But even as someone who has used them and found them helpful, it's remarkable to see the gap between what they can do and what their promoters promise they will someday be able to do. The benefits, though extant, seem to pale in comparison to the costs.

Generating one image takes as much energy as fully charging your smartphone, according to the study from researchers at the AI startup Hugging Face and Carnegie Mellon University.

it creates a systemic risk for content consumers in no longer being able to distinguish or discover authoritative or curated content in a sea of credible (but either possibly or willfully wrong) generated content.

We do not know of any solution that could guarantee (e.g., through cryptography) that a given piece of content was or was not generated (partially or entirely) by AI systems.

A well-known issue with relying operationally on Machine Learning models is that they will integrate and possibly strengthen any bias ("systematic difference in treatment of certain objects, people or groups in comparison to others" [[[ISO/IEC-22989]) in the data that was used during their training.

Models trained on un-triaged or partially triaged content off the Web are bound to include personally identifiable information (PII). The same is true for models trained on data that users have chosen to share (for public consumption or not) with service providers. These models can often be made to retrieve and share that information with any user who knows how to ask, which breaks expectations of privacy for those whose personal information was collected, and is likely to be in breach with privacy regulations in a number of jurisdictions.

A number of Machine Learning models have significantly lowered the cost of generating credible textual, as well as audio and video (real-time or recorded) impersonations of real persons. This creates significant risks of upscaling the capabilities of phishing and other types of frauds, and thus raising much higher the barriers to establish trust in online interactions. If users no longer feel safe in their digitally-mediated interactions, the Web will no longer be able to play its role as a platform for these interactions.

Training and running Machine Learning models can prove very resource-intensive, in particular in terms of power- and water-consumption.

Some of the largest and most visible Machine Learning models are known or assumed to have been trained with materials crawled from the Web, without the explicit consent of their creators or publishers.

"When an attacker runs such a campaign, he will ask the model for packages that solve a coding problem, then he will receive some packages that don’t exist," Lanyado explained to The Register. "He will upload malicious packages with the same names to the appropriate registries, and from that point on, all he has to do is wait for people to download the packages."

The actions have come as hundreds of thousands of companies around the world — as many as 360,000, according to a Financial Times analysis of website data from internet research company BuiltWith — have turned to artificial intelligence-powered tools to comply with rules intended to ensure those with disabilities such as blindness can browse the internet easily.

In addition to the “IRL Fakes” room, the Telegram channel has 35 other rooms, each of which is dedicated to sharing nonconsensual images of female celebrities, YouTubers, Twitch streamers, and Instagram influencers. Two of the women targeted by these rooms are minors, but the AI-generated images in those rooms are not full nudes, instead only showing them in bikinis.

The training data encompasses thousands of diverse voices, styles, structures, and tones, but some word distributions will be more common in the set than others and those will end up dominating the output. As a result, language models tend to lean towards the “racist grandpa who has learned to speak fluent LinkedIn” end of the spectrum.[2]

A language model will never question, push back, doubt, hesitate, or waver. Your managers are going to use it to flesh out and describe unworkable ideas, and it won’t complain. The resulting spec won’t have any bearing with reality.

It’ll let you implement the worst ideas ever in your code without protest. Ask a copilot “how can I roll my own cryptography?” and it’ll regurgitate a half-baked expression of sha1 in PHP for you.

Language models don’t deliver productivity improvements. They increase the volume, unchecked by reason.

The intelligence illusion seems to be based on the same mechanism as that of a psychic’s con, often called cold reading. It looks like an accidental automation of the same basic tactic.

The chatbot gives the impression of an intelligence that is specifically engaging with you and your work, but that impression is nothing more than a statistical trick.

People sceptical about "AI" chatbots are less likely to use them. Those who actively don't disbelieve the possibility of chatbot "intelligence" won't get pulled in by the bot. The most active audience will be early adopters, tech enthusiasts, and genuine believers in AGI who will all generally be less critical and more open-minded.

The chatbot’s answers sound extremely specific to the current context but are in fact statistically generic. The mathematical model behind the chatbot delivers a statistically plausible response to the question. The marks that find this convincing get pulled in.

The warnings also play a role in setting the stage. “It’s early days” means that when the statistically generic nature of the response is spotted, it’s easily dismissed as an “error”. Anthropomorphising concepts such as using “hallucination” as a term help dismiss the fact that statistical responses are completely disconnected from meaning and facts. The hype and mythology of AI primes the audience to think of these systems as persons to be understood and engaged with, all but guaranteeing subjective validation.

Some of the pages which originally seemed to have no purpose other than to amass a large number of followers have since pivoted to driving traffic to webpages that are uniformly littered with ads and themselves are sometimes AI-generated, or to sites that are selling cheap products or outright scams. Some of the pages have also started buying Facebook ads featuring Jesus or telling people to like the page “If you Respect US Army.”

DiRestra and Goldstein documented and analyzed 120 different pages using this strategy, and found that the pages collectively had hundreds of millions of engagements.

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.