Arvind Narayanan Profile picture
Jun 22, 2021 8 tweets 2 min read Read on X
Academia rewards clever papers over real world impact. That makes it less useful. But it also perpetuates privilege—those with less experience of injustice find it easier to play the game, i.e. work on abstruse problems while ignoring topics that address pressing needs.
I have no beef with fundamental research (which isn't motivated by applications). But most scholarship that *claims* to be motivated by societal needs happens with little awareness of what those needs actually are, and no attempt to step outside academia to actually make change.
Like many of academia's problems, this one is structural. Telling individual scholars to do better is unlikely to work when the incentives are all messed up. Here are some thoughts on what might work. I'd love to hear more.
1. Hiring/promotion committees and funding agencies should do less paper counting and work harder to assess impact. One example: the National Science Foundation's "Broader Impact" assessments of proposals. (I've found it to be 10% meaningful / 90% box checking, but it's a start).
2. Academic conferences should have (genuine, compensated) representation from communities supposedly impacted by the academic work being presented. Huge props to @KLdivergence for leading by example and showing me the importance of this.
3. Advocacy / civil society organizations could institute awards highlighting the academic work that they've found most impactful. In my experience, this type of recognition has a powerful and under-appreciated influence in shaping the priorities of academic communities.
While we're at it, I think we should get rid of most awards for just-published scholarship and make them retrospective, because impact can only be assessed in hindsight. The papers that were best at impressing peers at publication time aren't necessarily the most valuable.
In conclusion, complaints about the ivory tower are nothing new but the fact is that academia has always been in a state of flux.¹ The levers for change, while not obvious, are plentiful. The direction of change is up to us.


• • •

Missing some Tweet in this thread? You can try to force a refresh

Keep Current with Arvind Narayanan

Arvind Narayanan Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!


Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @random_walker

May 16
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.The image is a line graph titled "Top Airplane Speeds and Their Dates of Record, from Wright to Now," produced by the Mercatus Center at George Mason University. The graph tracks the progression of top airplane speeds from 1903 to around 2013. Here's a detailed description:  Y-Axis (Vertical Axis): Labeled "miles per hour (mph)," it ranges from 0 to 2,500 mph. X-Axis (Horizontal Axis): Labeled with years from 1903 to 2013 in increments of 10 years. Notable Annotations: Speed of Sound: Represented as a horizontal dashed line across the graph at approximately 760 mph. Reco...
By 1971, about a hundred thousand people had signed up for flights to the moon…
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…Image
Read 11 tweets
Apr 30
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 🧵 This image is a scatter plot titled "Our simple baselines beat current top agents on HumanEval." It charts the performance of various computational models based on their human evaluation accuracy and cost. The horizontal axis represents cost, while the vertical axis shows human evaluation accuracy ranging from 0.70 to 1.00. Different models, such as GPT-3.5, GPT-4, and those from the Reflexion series, are plotted as points. The Pareto frontier, depicted by a dashed line, shows the most efficient trade-offs between cost and accuracy. Points are colored differently to indicate the c...

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 ()…
Here are the five key takeaways.…
AI agent accuracy measurements that don’t control for cost aren’t useful.  Pareto curves can help visualize the accuracy-cost tradeoff.  Current state-of-the-art agent architectures are complex and costly but no more accurate than extremely simple baseline agents that cost 50x less in some cases.  Proxies for cost such as parameter count are misleading if the goal is to identify the best system for a given task. We should directly measure dollar costs instead.  Published agent evaluations are difficult to reproduce because of a lack of standardization and questionable, undocumented evaluati...
Read 12 tweets
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.…
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
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…
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
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.🧵…
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.
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.”…
Read 30 tweets
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…
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

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!


0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy


3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us!