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Arvind Narayanan Profile picture
@random_walker
Jan 11, 2021 • 4 tweets • 1 min read • Read on X
Professors at top universities are lottery winners, but rarely acknowledge the role of luck in their success. Be skeptical when they give you advice suggesting that the path they took is a repeatable one. If you aspire to an academic research career, have a backup plan.
Deciding to go into academia because all your professors said it worked out pretty well for them is also known as the statistical fallacy of sampling on the dependent variable.
I'm reviewing pre-doctoral, doctoral, post-doctoral, and faculty applications. I'm amazed by how much more these candidates have accomplished than I had at the corresponding stage of my career, and how many more qualified candidates there are compared to available positions.
This thread is about one of the many improbable things that happened to me, without which I wouldn't be where I am today.

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More from @random_walker

Arvind Narayanan Profile picture
Apr 12
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.
Read 7 tweets
Arvind Narayanan Profile picture
Dec 29, 2023
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.

Screenshot from lawsuit: output from GPT-4 identical to actual text from NYT
Read 13 tweets
Arvind Narayanan Profile picture
Aug 18, 2023
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…
Read 30 tweets
Arvind Narayanan Profile picture
Jul 19, 2023
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. Among those skeptical of the intentional degradation claim, the favored hypothesis for people’s subjective experience of worsening performance is this: when people use ChatGPT more, they start to notice more of its limitations. But there is another possibility. The user impact of behavior change and capability degradation can be very similar. Users tend to have specific workflows and prompting strategies that work well for their use cases. Given the nondeterministic nature of LLMs, it takes a lot of work to discover these st
Read 9 tweets
Arvind Narayanan Profile picture
Jul 19, 2023
This is fascinating and very surprising considering that OpenAI has explicitly denied degrading GPT4's performance over time. Big implications for the ability to build reliable products on top of these APIs.
This from a VP at OpenAI is from a few days ago. I wonder if degradation on some tasks can happen simply as an unintended consequence of fine tuning (as opposed to messing with the mixture-of-experts setup in order to save costs, as has been speculated).
If the kind of everyday fine tuning that these models receive can result in major capability drift, that's going to make life interesting for application developers, considering that OpenAI maintains snapshot models only for a few months and requires you to update regularly.
Read 11 tweets
Arvind Narayanan Profile picture
Jul 9, 2023
ChatGPT with Code Interpreter is like Jupyter Notebook for non-programmers. That's cool! But how many non-programmers have enough data science training to avoid shooting themselves in the foot? Far more people will probably end up misusing it.
The most dangerous mis- and dis-information today is based on bad data analysis. Sometimes it's deliberately misleading and sometimes it's done by well meaning people unaware that it takes years of training to get to a point where you don't immediately shoot yourself in the foot.
I have no doubt that the capabilities will continue to improve and that people will gradually find many good uses for it (much like ChatGPT itself). The problem is that these tools are also prone to misuse and harmful use, and AI companies externalize those costs.
Read 14 tweets

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