Large Language Models (LLMs) and tools based on them (like ChatGPT) are all some in tech talk about today. I struggle with the optimism I see from businesses, the haphazard deployment of these systems and the seemingly ever-expanding boundaries of what we are prepared to call “artificially intelligent”. I mean, they bring interesting capabilities, but arguably they are neither artificial, nor intelligent.
Let me start with where I'm coming from. In 2008 I started studying for a degree called Cognitive Artificial Intelligence. Of a number of Dutch universities offering degrees in artificial intelligence, mine was the one most focused on psychology and philosophy. Others were more geared towards linguistics and/or computer science. We had courses all across these different fields, as well as mathematics. This made it super interesting. Case in point: I learned then that artificial intelligence, as a field, isn't easily defined. It comprises a lot of things. It attracts people with a wide range of interests. And it has all sorts of applications, from physical robots to neural networks and natural language processing. Towards the end of the first year, I realised I had different interests (primarily in philosophy) and skills (not a programmer by heart, or a mathematician, and my grades and a BSA agreed). I ended up switching to philosophy full time, specialised in AI, language and ethics (of course, philosophy is great for generalists, too).
Fast-forward almost 15 years… my knowledge at this point is a bit rusty, but I am not less interested in the subject. Today, there is a lot of hype around Language Models (LMs), a specific technique in the field of “artifical intelligence”, which Emily Bender and colleagues define as ‘systems trained on string prediction tasks’ in their paper ‘On the dangers of stochastic parrots: can language models be too big?’ (one of the co-authors was Timnit Gebru, who had to leave her AI ethics position at Google over it). Hype isn't new in tech, and many recognise the patterns in vague and overly optimistic thoughtleadership (‘$thing is a bit like when the printing press was invented’, ‘if you don't pivot your business to $thing ASAP, you'll miss out’). Beyond the hype, it's essential to calm down and understand two things: do LLMs actually constitute AI and are what sort of downsides could they pose to people?
Artificial intelligence, in one of its earliest definitions, is the study of things that are in language indistinguishable from humans. In 1950, Alan Turing famously proposed an imitation game as a test for this indistinguishability. More generally, AIs are systems that think or act like humans, or that think or act rationally. According to many, including OpenAI, the company behind ChatGPT and Whisper, large language models are AI. But that's a company: a non-profit with a for-profit subsidiary—of course they would say that.
In one sense, “artificial” in “AI” means non-human. And yes, of course, LLMs are non-human. But they aren't artificial in the sense that their knowledge has clear, non-artificial origins: the input data that they are trained with.
OpenAI stopped disclosing openly where they get their data since GPT-3 (how Orwellian). But it is clear that they gather data from all over the public web, places like Reddit and Wikipedia. Earlier they used filtered data
from Common Crawl.
First, there is the long term consequences for quality. If this tooling results in more large scale flooding the web with AI generated content, and it uses the contents of that same web to continue training the models, it could result in a “misinformation shitshow”. It also seems like a source that can dry up once people stop asking questions on the web to interact directly with ChatGPT.
Second, it seems questionable to build off the fruits of other people's work. I don't mean off your employees, that's just capitalism—I mean other people that you scrape input data from without their permission. It was controversial when search engines took the work from journalists, this is that on steroids.
What about intelligence? Does it make sense to call LLMs and the tools based on them intelligent?
Alan Turing suggested (again, in 1950) that machines can be said to think if they manage to trick humans such that ‘an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning’. So maybe he would have regarded ChatGPT as intelligent? I guess someone familiar with ChatGPT's weaknesses could easily ask the right questions and identify it as non-human within minutes. But maybe it's good enough already to fool average interrogators? And a web flooded with LLM-generated content would probably fool (and annoy) us all.
Still, I don't think we can call bots that use LLMs intelligent, because they lack intentions, values and a sense of the world. The sentences systems like ChatGPT generate today merely do a very good job at pretending.
The Stochastic Parrots paper (SP) explains why pretending works:
our perception of natural language text, regardless of how it was generated, is mediated by our own linguistic competence
We merely interpret LLMs as coherent, meaningful and intentional, but it's really an illusion:
an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.
They can make it seem like you are in a discussion with an intelligent person, but let's be real, you aren't. The system's replies to your chat prompt aren't the result of understanding or learning (even if the technical term for the process of training these models is ‘deep learning’).
But GPT-4 can pass standardised exams! Isn't that intelligent? Arvind Narayanan and Sayash Kapoor explain the challenge of standardised exams happens to be one large language models are good at by nature:
professional exams, especially the bar exam, notoriously overemphasize subject-matter knowledge and underemphasize real-world skills, which are far harder to measure in a standardized, computer-administered way
Still great innovation?
I am not too sure. I don't want to spoil any party or take away useful tools from people, but I am pretty worried about large scale commercial adoption of LLMs for content creation. It's not just that people can now more easily flood the web with content they don't care about just to increase their own search engine positions. Or that the biases in the real world can now propagate and replicate faster with less scrutiny (see SP 613, which shows how this works and suggests more investment in curating and documenting training data). Or that criminals use LLMs to commit crimes. Or that people may use it for medical advice and the advice is incorrect. In Taxonomy of Risks Posed by Language Models, 21 risks are identified. It's lots of things like that, where the balance is off between what's useful, meaningful, sensible and ethical for all on the one hand, and what can generate money for the few on the other. Yes, both sides of that balance can exist at the same time, but money often impacts decisions.
And that, lastly, can lead to increased inequity. Monetarily, e.g. what if your doctor's clinic offers consults with an AI by default, but you can press 1 to pay €50 to speak to a human (as Felienne Hermans warned Volkskrant readers last week)? And also in terms of the effect of computing on climate change: most large language models benefit those who have the most, while their effect (on climate change) threatens marginalised communities (see SP 612).
I am generally very excited about applications of AI at large, like in cancer diagnosis, machine translation, maps and accessibility. And even of capabilities that LLMs and tools based on them bring. This is a field that can genuinely make the world better in many ways. But it's super important to look beyond the hype and into the pitfalls. As a lot of my feed naturally have optimist technologists, I have consciously added many more critical journalists, scientists and thinkers to my social feeds. If this piques your interest, one place to start could be the Distributed AI Research Institute (DAIR) on Mastodon. I also recommend the Stochastic Parrots paper (and/or the NYMag feature on Emily Bender's work). If you have any recommend reading or watching, please do toot or email.
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