DailyNous, a philosophy blog, invited a bunch of researchers to share their opinion about GPT-3 and related questions. Shortly after the essays appeared, someone claimed in a clickbait tweet (GPT-3 clickbait tweets are now a genre of their own, but we will use this one for illustration):
“I asked GPT-3 to write a response to the philosophical essays … It’s quite remarkable!”
Folks on Twitter did not disappoint. The tweet and its enclosing 4-page “Response” were retweeted more than a thousand times because the clickbait language and the presentation in the Response document probably made people go, “ZOMG! I can’t believe an AI did this.” To aid that, the Response came with a misleading (and an ethically dubious) “NOTE” to prime the readers into thinking that.
What was not covered in the original tweet or the Response document was the amount of human involvement needed to produce a text of that length and clarity — with multiple generations for each sentence and careful picking and choosing that went in the composition of the generated text. A more likely scenario for the cogent text in the Response is illustrated here, which raises an interesting design question of how best to faithfully portray generated content (not a topic of this issue but worth exploring from a safety/trust point of view).
Raphaël Millière, the author of the “Response”, to his credit, published the details of the production process later, which was only shared a few dozen times as opposed to more than a thousand or so times for the original misleading clickbait. As usual, misinformation flies, and the truth comes limping after it.
Aside: The word misinformation means many things. Withholding some facts to misrepresent something is a kind of misinformation. For a good taxonomy of misinformation, I recommend this article from Claire Wardle, a fact-checker and misinformation researcher.
The Turk’s Gambit
Such sensational overrepresentations of technology are commonplace in the Valley. Many demos in VC pitches are carefully orchestrated wizard-of-oz shows, much like Von Kempelen impressing Maria Theresa’s court with his chess-playing “automaton” — The Turk.
There are accounts (personal favorite is by Tom Standage) of how Kemplen, and later Mälzel, captivated audiences ranging from peasants to nobility to the scholars at the time on the Turk’s abilities for almost a century across Europe and America before its limits and workings were discovered. The Turk was a marvel of engineering and ingenuity, but more importantly, a storytelling device. It captivated generations to come — e.g., Charles Babbage was impressed by it — and raised questions that weren’t asked frequently before, much like the GPT-3 demos are asking of us now:
While Kemplen and Mälzel were showmen and some trickery was expected of them, how does one ethically present results for technologies like GPT-3? As we will see, this is not just a question of ethics and attribution, but also a question of AI Safety — i.e., preventing AI models from being harmfully utilized.
How do we avoid the steep descent into the “trough of disillusionment” that inevitably comes after peak hype and fast-forward our way to the “slope of enlightenment” and the “plateau of productivity”? If we clear the clouds of hype, the resulting clarity will make us ask the right questions about the technology.
AI Model Capability Hype is Fundamentally an AI Safety Issue
Bringing clarity amid a model capability hype is useful for identifying true product/business opportunities. But a more critical purpose is to ensure our implementation choices lead to products that are safe for the consumer and the world we inhabit.
Safety issues from the AI model hype can arise in two different ways. The first is when product builders overreach model capabilities and, either knowingly or unknowingly, set wrong expectations with the customers. These are usually self-correcting (unless you’re on a 4y startup exit trajectory) as customers inevitably complain and regulatory bodies step in, but not without significant harm to the company building the product and its customers. Tesla overreaching the driver-assist feature of its cars to “Fully Self Driving” in its product promotion materials (and in tweets from Elon Musk himself) is an example.
Customers mislead into believing these hyped-up capabilities could potentially endanger themselves and others due to misplaced trust. As AI models become easier to use (as GPT-3’s few-shot examples promise), folks building with AI models will increasingly not be AI experts who designed those models. Building appropriate safety valves in the product and regulatory framework around its use becomes critical.
The second way AI models can become unsafe due to hype is by customer overreach. People are inherently creative in how they use their tools. Folks using AI models outside of the advertised purposes for fun or entrepreneurial reasons can similarly bring harm.
Good policies, responsible communication practices, regulation, and consumer education are indispensable for creating an environment of safe consumption of AI technologies. Many of these practices are often at odds with short-term gains but not necessarily so with long-term rewards. There is a lot more to talk about AI Safety, but in next post, I will focus on the question: how do we free ourselves from the tyranny of appearances of AI models and truly understand their automation capabilities.
