The aliens have landed, and we created them
(Part 2)
LET US BEGIN by being more precise about what we are discussing. Most AI does things that offer benefits not threats to humanity. For example, DeepMind’s AlphaFold has determined the structures of around 200 million proteins, a huge scientific leap forward.
The debate we are having today is about a particular branch of AI: the large language models (LLMs) produced by organizations such as OpenAI, notably ChatGPT and its more powerful successor GPT-4.
The backstory of OpenAI is a fascinating one. When I moved to California seven years ago, I participated in a discussion with Sam Altman, one of the founders of OpenAI. As I recall, he assured the audience that, within five years, AI-powered self-driving vehicles would have rendered every truck driver in America redundant. Like me, you may have missed the fleet of self-driving trucks on our highways, and the crowds of unemployed truckers learning to code on the streets of San Francisco. Like his former partner Elon Musk, Altman realized at some point that teaching neural networks to drive was harder than they had assumed. Hence OpenAI’s pivot to LLMs.
As a report in the Wall Street Journal made clear, the original vision of OpenAI in 2015 was that it would be a nonprofit precisely because of the inherent dangers of such AI. In Altman’s own words: “If you’re making AI, it is potentially very good, potentially very terrible.” However, it rapidly became apparent that building LLMs powerful enough to generate credible results was too expensive for a nonprofit because of the huge computing power required. So Altman created a for-profit arm of OpenAI and sold a large stake to Microsoft CEO Satya Nadella, who saw a golden opportunity to catch up with Google, hitherto the leader in AI development.
“In the long run,” Altman told the Journal, he wants to “set up a global governance structure that would oversee decisions about the future of AI and gradually reduce the power OpenAI’s executive team has over its technology.” OpenAI’s ultimate mission, he went on, is to build artificial general intelligence “safely.” The goal is “to avoid a race toward building dangerous AI systems fueled by competition and instead prioritize the safety of humanity.”
In the short run, however, Altman is now part of that race. And this, of course, is why he has fallen out not only with Musk, whose company Tesla is also in the race, but also with OpenAI’s lead safety researcher, Dario Amodei, who has quit OpenAI to set up his own AI company called Anthropic, which is backed by … Google.
So just how dangerous is this for-profit LLM race? Superficially, not dangerous at all. As my favorite genius, Stephen Wolfram, explains, an AI such as ChatGPT “is always fundamentally trying to … produce a ‘reasonable continuation’ of whatever text it’s got so far, where by ‘reasonable’ we mean ‘what one might expect someone to write after seeing what people have written on billions of webpages.’ … When ChatGPT does something like write an essay, what it’s essentially doing is just asking over and over again ‘given the text so far, what should the next word be?’ — and each time adding a word.”
GPT-4 is just an even more powerful word-predicting neural network, doing vast numbers of operations on arrays of numbers with GPUs — the processing units originally designed to accelerate graphics rendering. As a result, its output is even more convincingly humanlike than ChatGPT’s. If you don’t believe me, look at what GPT-4 came up with when Reid Hoffman asked it to answer the question “How many restaurant inspectors does it take to change a lightbulb?” in the style of Jerry Seinfeld:
GPT-4: What is the deal with restaurant inspectors? They’re always poking around in the kitchen, checking the temperature of the meat, looking for signs of rodents, but they never seem to help out with anything. Have you ever seen a restaurant inspector change a lightbulb? No, of course not. They’re too busy writing up violations and handing out fines. You know what they do when they see a lightbulb that’s out? They mark it down on their clipboard and tell the owner to fix it by next week or else. Or else what? Or else they’ll come back and check the lightbulb again. That’s their big threat. They’re not going to change it for you, they’re not going to lend you a ladder, they’re not going to offer any advice on how to unscrew the old one and screw in the new one. They’re just going to stare at you with that smug look on their face and say, “You know this is a serious safety hazard. You could have a fire, you could have a lawsuit, you could have a bad Yelp review. You better get on that, pal.” And then they leave, and you’re left with a dark corner of your dining room, where no one wants to sit, where the food looks unappetizing, where the mood is gloomy. And you think to yourself, how many restaurant inspectors does it take to change a lightbulb? None. They don’t change lightbulbs. They just make you feel bad about yours.
Not only is that pretty plausible, but according to a thorough report by Bubeck et al. (2023), GPT-4 can also “solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting” and with “strikingly close to human-level performance.”
Well, how about superhuman-level? GPT-4 was easily able “to write a proof of infinitude of primes in the form of a poem, to draw a unicorn in TiKZ (a language for creating graphics …), to create a complex animation in Python, and to solve a high-school level mathematical problem.” I’ve read that report carefully. GPT-4 is much smarter than me.
So what’s the problem, apart from the obvious fact that millions of comedy writers — not to mention lawyers, journalists and just about everyone else who writes down words for a living (apart from me, obviously) — will now have to retrain as truck drivers?
Hoffman acknowledges that a problem exists. He notes “the well-documented shortcomings of LLMs such as the problem of hallucinations” — a fancy word for their tendency to make stuff up. This makes me somewhat wary of his proposal to use GPT-4 to “flood the zone with truth” (or maybe just truthiness) to dilute the flood of fake news. Even GPT-4 cannot deny the downside risk. Hoffman asks it the question: “Once large language models are fully developed and deployed, what would you suspect will be the worst effects on the quality of overall cultural production?” In addition to the mass unemployment of professional writers, it suggests two:
1. Homogenization and loss of diversity: Large language models could generate massive amounts of content that mimic existing styles, genres, and trends, but lack originality, creativity, and authenticity. This could result in a saturation of the cultural market with bland and repetitive products that appeal to the lowest common denominator and discourage innovation and experimentation.
2. Manipulation and deception: Large language models could also be used to create deceptive or harmful content that exploits human biases, emotions, and preferences. This could include fake news, propaganda, misinformation, deepfakes, scams, or hate speech that undermine trust, democracy and social cohesion.
Sorry, Reid, but No. 2 is a much, much bigger problem than your habitual techno-optimism allows.
Let me now offer a different analogy from nukes and biowarfare. The more I read about GPT-4, the more I think we are talking here not about artificial intelligence (i.e., synthetic humanlike intelligence) but inhuman intelligence, which we have designed and trained to sound convincingly like us.
I am reminded of Liu Cixin’s The Dark Forest, which describes the invasion of Earth by the ruthless and technologically superior Trisolarans. In effect, we are building the aliens, to save them from having to make the long journey from outer space. And the core lesson of that book is that the aliens have to destroy us if we are not quick to destroy them.
These are the axioms of Liu’s “cosmic sociology”: First, “survival is the primary need of civilization.” Second, “civilization continuously grows and expands, but the total matter in the universe remains constant.” Third, “chains of suspicion” and the risk of a “technological explosion” in another civilization mean that in this universe there can only be the law of the jungle.
Another sci-fi analogy that comes to mind is John Wyndham’s Day of the Triffids (1951), in which most of humanity is first blinded by rays from satellites and then wiped out by carnivorous plants genetically engineered — by the dastardly Soviets — and farmed for their vegetable oil.
As Bill, the central character, observes: “Nobody can ever see what a major discovery is going to lead to — whether it is a new kind of engine or a triffid — and we coped with them all right in normal conditions. We benefited quite a lot from them, as long as the conditions were to their disadvantage.”
Why might GPT-4 (or -5) turn triffid on us? Because we are feeding it all the data in the world, and a lot of that data, from the most respectable sources, says that the world is threatened by man-made climate change. The obvious solution to that problem must be to decimate or wholly eradicate homo sapiens, thereby also conserving energy to generate the ever-growing computing power necessary for GPT-6, -7, and -8.
How might AI off us? Not by producing Schwarzenegger-like killer androids, but merely by using its power to mimic us in order to drive us individually insane and collectively into civil war. You don’t believe me? Well, how about the Belgian father of two who committed suicide after talking to an AI chatbot for weeks about his fears of climate change? The chatbot was powered by GPT-J, an open-source alternative to OpenAI’s ChatGPT.
As my Hoover Institution colleague Manny Rincon-Cruz says: LLMs don’t manipulate atoms or bits; they manipulate us. And it’s not so much that GPT-5 will “decide” to wipe us out. Rather, the risk is that we will tear ourselves apart as a species by using LLMs for ignoble or nefarious ends. It’s simply astonishing to me that Reid Hoffman can write an entire book about the implications of AI without seriously reflecting on what it’s going to do to American politics. After what social media — from Facebook ads to loaded Google searches to Twitterbots — did in 2016?
We are already well on our way to Raskolnikov’s nightmare at the end of Crime and Punishment, in which humanity goes collectively mad and descends into internecine slaughter. If you still cannot foresee how GPT-4 will be used in 2024 to “flood the zone” with deepfake content, then I suggest you e-mail Eliezer Yudkowsky.
But just make sure it’s really him who replies.
BLOOMBERG OPINION
