An example of the prompt engineering phenomenon: my wife and I were recently discussing a financial decision. I'd offered my arguments in favor of one choice and she was mostly persuaded but decided to check in with ChatGPT to help reassure herself that I was right. She asked the financial question in layman's terms and got the opposite answer that I had given.<p>She showed me the result and I immediately saw the logical flaws and pointed them out to her. She pressed the model on it and it of course apologized and corrected itself. Out of curiosity I tried the prompt again, this time using financial jargon that I was familiar with and my wife was not. The intended meaning of the words was the same, the only difference is that my prompt sounded like it came from someone who knew finance. The result was that the model got it right and gave an explanation for the reasoning in exacting detail.<p>It was an interesting result to me because it shows that experts in a field are not only more likely to recognize when a model is giving incorrect answers but they're also <i>more likely to get correct answers</i> because they are able to tap into a set of weights that are populated by text that knew what it was talking about. Lay people trying to use an LLM to understand an unfamiliar field are vulnerable to accidentally tapping into the "amateur" weights and ending up with an answer learned from random Reddit threads or SEO marketing blog posts, whereas experts can use jargon correctly in order to tap into answers learned from other experts.
I'm generally quite skeptical of AI, but this overstates its case. Two things stand out:<p>> <i>what LLMs do is string together words in a statistically highly probable manner.</i><p>This is not incorrect, but it's no longer a sufficient mental model for reasoning models. For example, while researching new monitors today, I told Gemini to compare $NEW_MODEL_1 with $NEW_MODEL_2. Its training data did not contain information about either model, but it was capable of searching the Internet to find information about both and provide me with a factual (and, yes, I checked, accurate) comparison of the differences in the specs of the models as well as a summary of sentiment for reliability etc for the two brands.<p>> <i>Currently available software may very well make human drivers both more comfortable and safe, but the hype has promised completely autonomous cars reliably zipping about in rush hour traffic.</i><p>And this is already not hype, it's reality anywhere Waymo operates.
I love reading, I enjoy long-form articles, but I really wish technical bloggers especially would practice distilling their point into shorter posts. I notice it a lot with (older) scott alexander articles, this implicit assumption that your writing is informative/entertaining enough that you can stretch a simple idea to many pages.<p>I want to reitterate that I don't want dull, minimal, writing. I don't subscribe to the "reduce your wordcount until it can't be reduced any further" style of writing advice. I just think that many people have very similar ideas about ai (and written very similar things), and if you have something to say that you haven't seen expressed before, it is worthwile (imo) to express it without preamble.
A different kind of AI winter is already here. This "winter" is associated with companies laying people off and then lazily waiting around for AGI to emerge. This is leading to a kind of malaise that I think will ultimately be bad for economies. It is fine to use any available tool to boost productivity, but magical thinking is not sound management.
Orthogonal: the lemons in the picture, from Palermo (Sicily), could not only being lemon or lemon-shaped soap, but also a sweet, our very famous "frutta martorana": <a href="https://en.wikipedia.org/wiki/Frutta_martorana" rel="nofollow">https://en.wikipedia.org/wiki/Frutta_martorana</a>
Winter seems unlikely in the near term because we are around the level in the steady increase in computing power where it's around human level, like this cartoon <a href="https://x.com/waitbutwhy/status/1919870578502021257" rel="nofollow">https://x.com/waitbutwhy/status/1919870578502021257</a>
A predict a lot of circumlocutions about AI but most people not noticing since they blindly follow the TV/politics as-is anyway.<p>A lot of people (still a tiny proportion of the population) will be loud in opposition but ultimately overwhelmed by the nihilism and indifference of the aggregate.<p>The loudest will be those who perceive some loss to their own lifestyle that relies on exploiting other’s attention, as AI presents new risk to their attention grabbing behaviors.<p>Then they will die off and humanity will carry on with AI not them.<p>Circle of life Simba.
AI winters will keep coming as long as the definition of AI stays relative. We used to call chess programs chess "AIs", but hardly anyone says that anymore. We call LLMs "AIs" now, but let's be real: a few decades from now, we'll probably be calling them <i>token predictors</i>, while some shiny new "AIs" are already out there kicking asses.<p>At the end of the day, "AI" really just means throwing expensive algorithms at problems we've <i>labeled</i> as "subjective" and hoping for the best. More compute, faster communication, bigger storage, and we get to run more of those algorithms. Odds are, the real bottleneck is hardware, not software. Better hardware just lets us take bolder swings at problems, basically wasting even more computing power on nothing.<p>So yeah, we’ll get yet another AI boom when a new computing paradigm shows up. And that boom will hit yet another AI winter, because it'll smack into the same old bottleneck. And when that winter hits, we'll do what we've always done. Move the goalposts, lower the bar, and start the cycle all over again. Just with new chips this time.<p>Ah, Jesus. I should quit drinking Turkish coffee.
Let's just say that people once thought that Big Tech was once invincible, until it wasn't.<p>Obviously those exposed in the AI hype will tell you that there is no winter.<p>Until the music stops and almost little to no-one can make money out of this AI race to zero.
>Spring 2024<p>Reasoning models like o1 had not yet been released at that time. It's amazing how much progress has been made since then.<p>Edit: also Search wasn't available as the blog mention "citation"s.