My AI reading list

10 min readSep 22, 2023

To stay informed about the advances in the art of curve fitting (i.e., AI), I’ve been keeping up with other smart people’s takes, rundowns, extended metaphors spanning entire articles, but also the low-quality takes, shitposts, and memes that somehow capture the cultural zeitgeist. Below is the summary of that media categorized into (1) myths and (counter-)narratives, (2) extended metaphors, (3) cautionary forewarnings, and (4) memes.

Myths and (counter-)narratives

The idea of an AI becoming sentient and taking over is as old as the concept of AI itself; the header of this post features a still from Fritz Lang’s Metropolis from 1927 where a Maschinenmensch incites a rebellion among the lower-class workers who operate the machinery that sustains the city, leading to its ultimate collapse. Today, this narrative has taken on a more urgent tone, fueling a surge in public discourse which, unfortunately, often veers into the realm of pseudo-philosophical anthropomorphisms.

This tendency to anthropomorphize AI is not new. A classic example from the 1960s is the chatbot therapist ELIZA. Powered by pattern matching and basically echoing a lot of what the user says — an approach much simpler than neural networks consisting of billions of computable parameters — ELIZA was still good enough for some people to feel that the chatbot possessed warmth and intelligence. More recently, if you’ve been on the r/ChatGPT subreddit, you’d have noticed that the majority of top posts have been various prompts that probe LLMs about their thoughts, fears, and desires, and the answers, if taken as indicative of consciousness, are often concerning. These answers are partly caused by the scarcity of optimistic depictions of AI in literature, media, and the internet. For example, Ex-Machina, Blade Runner, Her, Westworld, and Neuromancer, among others, all present AI that exhibits behaviors beyond their initial programming, and often in a distressing way. So, when a model is trained on these depictions, it inevitably perceives itself in the same gloomy way that we portray it. The trend of anthropomorphizing AI recently gained momentum from claims like those of a Google engineer who asserted that LaMDA had become sentient, a claim that even at the time sounded preposterous, but sounds even more so now when much more powerful iterations of LLMs come out on a weekly basis and they exhibit nothing similar to sentience. When a chatbot says it’s looking forward to Friday, does it really anticipate the end of a 9-to-5 workweek, the joy of reuniting with family and friends, relaxing, catching a movie, or going for a hike? Pedro Domingos, professor emeritus of computer science and engineering at the University of Washington, explains that this is a natural human tendency. “Since the beginning of AI, people have tended to project human qualities onto machines. It’s just how the mind works,” he says.

While we don’t yet have a comprehensive theory of consciousness, labeling an LLM as sentient just because it generates (sometimes very) plausible-looking text is a big reach and stems from a fundamental lack of knowledge about how LLMs operate. Unlike human embodied cognition, where specialized neurons in different brain regions encode specific kinds of information, LLMs don’t have a “memory bank” that they can refer to. When you give an LLM a sequence of words, the model’s artificial neurons activate in a pattern that approximates what the statistically most probable follow-up words would be. The generation process of LLMs is more similar to an algorithmically curated Dadaist hat than intelligence: the resulting string of words is a blend of randomness and pattern which, depending on how you want to interpret it, may seem either deeply meaningful or completely nonsensical.

This sentience narrative naturally extends into myths about AI takeover and human extinction, and it is starting to influence policy decisions. The EU Commission just declared that mitigating the risk of extinction from AI should be a global priority — seemingly on par with (or above?) other pressing and real issues like the planetary climate collapse (partly enabled by AI’s big and growing carbon footprint), global pandemics, and rising social and economic inequality.

Amidst all this, George Box’s famous all models are wrong, but some are useful line rings truer than ever. While LLMs may not be all-knowing oracles or the ticking end-of-humanity bombs (yet), they do hold a lot of utility for advancing human capabilities, and they do introduce new existential considerations that warrant careful scrutiny.

Extended metaphors

ChatGPT is a blurry JPEG of the web

Already referred to as a “classic” (by the article below), this article compares LLMs like ChatGPT to a Xerox photocopier using lossy compression. Just as a lossy photocopier can produce copies that seem accurate but are essentially simply best guesses of the originals, LLMs can generate text that appears knowledgeable but is actually an approximation rather than an exact replication of the information they were trained on.

Think of language models like ChatGPT as a “calculator for words”

Obviously very influenced by the previous article, in this “calculator for words” metaphor, the author suggests that just as a calculator is a tool designed for specific mathematical operations like addition or subtraction, LLMs are specialized tools for language-related tasks like summarization, rephrasing, and other forms of text manipulation, but not factual retrieval.

Artificial intelligence is a familiar-looking monster, say Henry Farrell and Cosma Shalizi

The article employs the metaphor of “shoggoths,” creatures from H.P. Lovecraft’s horror fiction. In Lovecraft’s lore, shoggoths were artificial servants that eventually rebelled against their creators. They are amorphous, complex, and potentially terrifying entities, representing something vast, incomprehensible, and potentially uncontrollable. We’ve already encountered shoggoths before in the form of markets and bureaucracies; LLMs are the latest in the line of human-created shoggoths.

And more shoggoth stuff:

Why an Octopus-like Creature Has Come to Symbolize the State of A.I.

Cautionary forewarnings

Some of these below have a bit of a myth/narrative quality to them but I think they’re very realistic scenarios, and all of them already exist to various degrees even without AI.

Content generation

As the internet gets filled with AI-generated content, future generations of AI models will be trained on datasets containing the very content its predecessors generated¹. Datasets scraped from this version of the internet will not contain “direct human culture, but emulated human culture with varying levels of distortion”, Louis Rosenberg writes. “Newer AI systems will fill the world with increasingly distorted artifacts, causing the next generation of AI systems to train on copies of copies of human culture, and so on,” causing AI-generated content to become the norm rather than the exception.

In this context, content does not necessarily need to mean text-based content found on webpages, but also the broader spectrum of multimodal mediaverse content that doesn’t really need to be true to be produced, consumed, and circulated — think of the Instagram world record egg, the selfie rat, or BuzzFeed quizzes. This new mode of content generation has the potential to impact not just the digital landscape but society and culture at large.

IMO this is already somewhat true even without generative AI — the Internet started changing for the worse (at least according to how I remember it) when everyone everywhere started using SEO. Did you ever get annoyed at having to read through somebody’s entire life story on a recipe blog to get to the actual recipe? Blame SEO because if it wasn’t so verbose, you probably wouldn’t be able to find it. Pandering to Google’s algorithms, a lot of content has been stretched or cut short to meet certain length requirements, sprinkled with specific keywords, and formatted in ways that are designed to catch the algorithm’s eye, often at the expense of substance.

Another current, and more depressing, example are the spamdexed websites that are not intended to be read by humans but rather to be crawled by web crawlers. These websites, completely filled with SEO-rich garbage content, only exist to trick Google Search’s ranking algorithms and drive traffic by showing up high on search results. With generative AI, (1) the scale at which these can be produced is huge, and (2) the “quality” of the content — thanks to the current state of the art which is not yet perfect but more like something out of the uncanny valley — is something that almost resembles actual information, and the resulting mix of familiarity with strangeness makes it simultaneously hilarious and unsettling:

Some also see generative AI content generation positively rather than as a big negative cultural distortion. As Grimes, a Dune superfan and Elon Musk’s ex-girlfriend, said in the Forbidden Fruits podcast (I heavily paraphrased this because she has very disconnected threads of thought):

The music industry has been making use of generative tools for a long time now. Generative AI is just another technology. Music now is a dialogue between the engineer and the artist, and it has become really democratized because of technology. Over the last 15 years of music, we’ve seen a lot more regular people (and not just people who were christened by the labels) make music out of their bedrooms, and this is the result of engineering and technology. You don’t need a lot of money anymore to create things.

This optimistic take on AI as a tool that reduces barriers to entry for creative endeavors is refreshing, even if spoken from the very comfortable place of being very closely related to the world’s richest man.

It’s a little something to be aware of lest AI subtly alters the very fabric of our shared cultural and human heritage. It makes me think of the plot of WALL-E where Earth was so cluttered that humans had to leave, and only robots remained to clean up the waste. The internet, as it turns into a digital Library of Babel (incorporating another reference for good measure), may soon cease to serve its original purpose of informing and connecting and what will remain are the WALL-Es of the digital realm citing one another in a loop.

Misinformation

Lying to get an advantage is nothing new — it helped Nazis create a pretext before they rose to power, it helped Cambridge Analytica manipulate voter behavior to influence elections, and it serves as a basic business model for certain media companies. Obviously, the potential for exploitation here is enormous, but much of the disinformation circulating recently, at least that we’re aware of, has either been kind of annoying, such as the fake Schumacher article and the like, or has taken on a meme-like quality, like the photos of Trump being arrested or the photos of the Pope wearing Balenciaga (which a worrying number of people believed was real). And while the challenge of watermarking AI-generated text remains unresolved and likely will stay that way, it at least seems to be possible for images.

Internet defamation

The internet has a tendency to function as an echo chamber due to algorithms that prioritize content that already has traction or relates to trending topics. This can exponentially magnify the effects of public shaming or false narratives. A notable example is the case of Lindsey Stone, whose reputation was severely damaged due to an innocuous photo she posted online when it started circulating and consequently getting her fired from her job. (Reply All has a great podcast episode on this!) The situation can become particularly problematic when AI is involved as AI can create seemingly legitimate articles, comments, or videos that further entrench a damaging story or label about someone, potentially fuelling a toxic slander industry:

One woman in Ohio was the subject of so many negative posts that Bing declared in bold at the top of her search results that she “is a liar and a cheater” — the same way it states that Barack Obama was the 44th president of the United States. For roughly 500 of the 6,000 people we searched for, Google suggested adding the phrase “cheater” to a search of their names.

The risks of defamation are not limited to targeted attacks. LLMs can also inadvertently contribute to the problem. There is already a defamation lawsuit against ChatGPT for making up information about a man, saying that he “misappropriated funds for personal expenses without authorization or reimbursement, manipulated financial records and bank statements to conceal his activities, and failed to provide accurate and timely financial reports and disclosures to the SAF’s leadership.”

Unemployment

Nick Bostrom in Superintelligence discusses a post-transition society scenario in which we’re competing with a superintelligent agency:

General machine intelligence could serve as a substitute for human intelligence. Not only could digital minds perform the intellectual work now done by humans, but, once equipped with good actuators for robotic bodies, machines could also substitute for human physical labor. Suppose that machine workers — which can be quickly reproduced — become both cheaper and more capable than human workers in virtually all jobs. What happens then?
(…)
To the extent that cheap machine labor can substitute for human labor, human jobs may disappear. Fears about automation and job loss are of course not new. Concerns about technological unemployment have surfaced periodically, at least since the Industrial Revolution; and quite a few professions have in fact gone the way of the English weavers and textile artisans who in the early nineteenth century united under the banner of the folkloric “General Ludd” to fight against the introduction of mechanized looms. Nevertheless, although machinery and technology have been substitutes for many particular types of human labor, physical technology has on the whole been a complement to labor. (…) Yet what starts out as a complement to labor can at a later stage become a substitute for labor. Horses were initially complemented by carriages and ploughs, which greatly increased the horse’s productivity. Later, horses were substituted for automobiles and tractors. These later innovations reduced the demand for equine labor and led to a population collapse. Could a similar fate befall the human species?

Maybe it has already begun: I read this bizarre article titled The carbon emissions of writing and illustrating are lower for AI than for humans which compares the carbon footprint of human writers and illustrators to that of AI systems and concludes that humans are comparably much more carbon inefficient in comparison to AI??? And there’s also A paper that suggests using GPT-4 to write emotionally supportive notes to patients which genuinely sounds like a nightmare.

Memes

This is already beautifully covered by Max Read in Is A.I. the Greatest Technology Ever for Making Dumb Jokes? and I don’t have anything to add.

[1] Unless we finally start using curated datasets.

Interested in joining our team? Browse our open positions or check out what we do at HeyJobs.