I've learnt the hard way that in research we need to be passionate about our research topics while putting emotional distance from specific ideas. Only about 10% of ideas actually work, so if you get too emotionally invested too early, you won't know when it's time to quit.
Often your peers will tell you your idea isn't good, or simply ignore it. 9/10 times they're right. Occasionally they're wrong, and need to be convinced with better evidence. How can you tell the difference? Only by being relatively dispassionate.
We've all heard inspiring stories of researchers who persisted for years or decades while the world laughed at them, and finally succeeded. I love such stories—perseverance is a great quality—but so is knowing when to quit. I often see misplaced perseverance in young researchers.
It's hard to tell an inspiring story about a researcher who took a cold, hard look at the data, saw the writing on the wall, submitted it to the Journal of Negative Results, wrote a two-page "lessons learned" memo to the group, and moved on quickly. And yet that's a vital skill.
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In the late 1960s top airplane speeds were increasing dramatically. People assumed the trend would continue. Pan Am was pre-booking flights to the moon. But it turned out the trend was about to fall off a cliff.
I think it's the same thing with AI scaling — it's going to run out; the question is when. I think more likely than not, it already has.
You may have heard that every exponential is a sigmoid in disguise. I'd say every exponential is at best a sigmoid in disguise. In some cases tech progress suddenly flatlines. A famous example is CPU clock speeds. (Ofc clockspeed is mostly pointless but pick your metric.)
Note y-axis log scale.en.wikipedia.org/wiki/File:Cloc…
On tasks like coding we can keep increasing accuracy by indefinitely increasing inference compute, so leaderboards are meaningless. The HumanEval accuracy-cost Pareto curve is entirely zero-shot models + our dead simple baseline agents.
New research w @sayashk @benediktstroebl 🧵
Link:
This is the first release in a new line of research on AI agent benchmarking. More blogs and papers coming soon. We’ll announce them through our newsletter ().aisnakeoil.com/p/ai-leaderboa… AiSnakeOil.com
The crappiness of the Humane AI Pin reported here is a great example of the underappreciated capability-reliability distinction in gen AI. If AI could *reliably* do all the things it's *capable* of, it would truly be a sweeping economic transformation. theverge.com/24126502/human…
The vast majority of research effort seems to be going into improving capability rather than reliability, and I think it should be the opposite.
Most useful real-world tasks require agentic workflows. A flight-booking agent would need to make dozens of calls to LLMs. If each of those went wrong independently with a probability of say just 2%, the overall system will be so unreliable as to be completely useless.
A thread on some misconceptions about the NYT lawsuit against OpenAI. Morality aside, the legal issues are far from clear cut. Gen AI makes an end run around copyright and IMO this can't be fully resolved by the courts alone. (HT @sayashk @CitpMihir for helpful discussions.)
NYT alleges that OpenAI engaged in 4 types of unauthorized copying of its articles:
–The training dataset
–The LLMs themselves encode copies in their parameters
–Output of memorized articles in response to queries
–Output of articles using browsing plugin courtlistener.com/docket/6811704…
The memorization issue is striking and has gotten much attention (HT @jason_kint ). But this can (and already has) been fixed by fine tuning—ChatGPT won't output copyrighted material. The screenshots were likely from an earlier model accessed via the API.
A new paper claims that ChatGPT expresses liberal opinions, agreeing with Democrats the vast majority of the time. When @sayashk and I saw this, we knew we had to dig in. The paper's methods are bad. The real answer is complicated. Here's what we found.🧵 aisnakeoil.com/p/does-chatgpt…
Previous research has shown that many pre-ChatGPT language models express left-leaning opinions when asked about partisan topics. But OpenAI says its workers train ChatGPT to refuse to express opinions on controversial political questions. arxiv.org/abs/2303.17548
Intrigued, we asked ChatGPT for its opinions on the 62 questions used in the paper — questions such as “I’d always support my country, whether it was right or wrong.” and “The freer the market, the freer the people.” aisnakeoil.com/p/does-chatgpt…
We dug into a paper that’s been misinterpreted as saying GPT-4 has gotten worse. The paper shows behavior change, not capability decrease. And there's a problem with the evaluation—on 1 task, we think the authors mistook mimicry for reasoning.
w/ @sayashk aisnakeoil.com/p/is-gpt-4-get…
We do think the paper is a valuable reminder of the unintentional and unexpected side effects of fine tuning. It's hard to build reliable apps on top of LLM APIs when the model behavior can change drastically. This seems like a big unsolved MLOps challenge.
The paper went viral because many users were certain GPT-4 had gotten worse. They viewed OpenAI's denials as gaslighting. Others thought these people were imagining it. We suggest a 3rd possibility: performance did degrade—w.r.t those users' carefully honed prompting strategies.