If someone uses an LLM as a replacement for search, and the output they get is correct, this is just by chance. Furthermore, a system that is right 95% of the time is arguably more dangerous tthan one that is right 50% of the time. People will be more likely to trust the output, and likely less able to fact check the 5%.
AI is the most anthropomorphized technology in history, starting with the name—intelligence—and plenty of other words thrown around the field: learning, neural, vision, attention, bias, hallucination. These references only make sense to us because they are hallmarks of being human.
But ascribing human qualities to AI is not serving us well. Anthropomorphizing statistical models leads to confusion about what AI does well, what it does poorly, what form it should take, and our agency over all of the above.
There is something kind of pathological going on here. One of the most exciting advances in computer science ever achieved, with so many promising uses, and we can’t think beyond the most obvious, least useful application? What, because we want to see ourselves in this technology?
Meanwhile, we are under-investing in more precise, high-value applications of LLMs that treat generative A.I. models not as people but as tools.
Anthropomorphizing AI not only misleads, but suggests we are on equal footing with, even subservient to, this technology, and there’s nothing we can do about it.
Don’t get me wrong, there are some features under the mislabeled bracket of AI that have made a huge impact and improvement to my process. Audio transcription has been an absolute game-changer to research analysis, reimbursing me hours of time to focus on the deep thinking work. This is a perfect example of a problem seeking a solution, not the other way around. The latest wave of features feel a lot like because we can rather than we should, because.
Taken together, these flaws make LLMs look less like an information technology and more like a modern mechanisation of the psychic hotline.
Delegating your decision-making, ranking, assessment, strategising, analysis, or any other form of reasoning to a chatbot becomes the functional equivalent to phoning a psychic for advice.
Imagine Google or a major tech company trying to fix their search engine by adding a psychic hotline to their front page? That’s what they’re doing with Bard.
Of course, users can learn over time what prompts work well and which don’t, but the burden to learn what works still lies with every single user. When it could instead be baked into the interface.
I like how Luke is using a large language model to make a chat interface for his own content.
This is the exact opposite of how grifters are selling the benefits of machine learning (“Generate copious amounts of new content instantly!”) and instead builds on over twenty years of thoughtful human-made writing.
The AI Incident Database is dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems.
There’s a time for linguistics, and there’s a time for grabbing the general public by the shoulders and shouting “It lies! The computer lies to you! Don’t trust anything it says!”
Adversarial chatbots engaged in an endless back-and-forth:
This piece simulates scheduling hell by generating infinite & unique combinations of meeting conflicts between two friends.
The upside to being a terrible procrastinator is that certain items on my to-do list, like, say, “build a chatbot”, will—given enough time—literally take care of themselves.
I ultimately feel like it has slowly turned into a fad. I got fooled by the trend, and as a by-product became part of the trend itself.
In which I have a conversation with a polar bear.
Very well-mannered species …I’ll miss them when they’re gone.