Cognitive Scientist and Assistant Professor @Illinois_Alma. Studies language and semantic development and computational models.
Champaign, IL
Joined February 2011
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Jon Willits retweeted
You can tell when someone has actually understood the hard problem etc. because they’re like a timid abused child when it comes to Philosophy of Mind questions, curling up in a corner and hoping no one asks them to speak decisively. Confidence comes from the untraumatized.
Jon Willits retweeted
AI can be a really wonderful tool for exploring topics far from coding.
I had Claude Fable 5.1 put together an annotated guide to Eliot's poem "The Wasteland," with multiple pathways through the poem, recordings, scholarship, etc. I am quite impressed. the-waste-land.netlify.app/
Jon Willits retweeted
Writing is fun. You endure hours of excruciating pain and self-doubt. But in the end, you’ve created something. And then you get to hate that thing.
Jon Willits retweeted
I’ve given Claude Opus various published papers (and my own unpublished ones) and asked it to review them. (Don’t panic—I’m not uploading confidential manuscripts.)
The reviews it produces are excellent. It finds statistical errors, internal contradictions, and conceptual confusions that human reviewers miss; it provides useful suggestions; it has a good sense of what counts as significant and novel and what doesn’t; and it’s generous in its appreciation for what the authors get right. And it doesn’t suffer from the strong “myside bias” humans have; it doesn’t recommend rejection because the author didn’t cite it enough; it doesn’t insult the authors; and it doesn’t agree to write a review and then never send anything in.
As a journal editor, I’m not sure what to do with this. If I gave up on human reviewers and used GPT-5.6 Sol and Claude Opus instead, my bosses would fire me, probably rightfully so. The only constructive thing I can recommend is that, before submitting something to a journal, researchers and scholars use this tremendously valuable resource. (Just don’t tell the models that it’s your paper, because they do suffer from sycophancy.)
Jon Willits retweeted
We created this problem ourselves by setting up “striving” and “consumption” as the only two categories of possible activities. The rub is that this invariably leads to floundering lives regardless of the existence of AI.
Jon Willits retweeted
No one in the upper administration is requiring this. And it not required by law or demanded by the board of regents.
Just to be safe we want to go above and beyond when it comes to compliance!
Proudly, this college has always been about overachieving on rules & regulations!
Jon Willits retweeted
A strange paradox I've with noticed with the new models:
1.) AI has dramatically improved in the evaluation of writing.
2.) AI has dramatically deteriorated in the production of writing.
Jon Willits retweeted
Myth: “Colleges should teach AI because industry wants AI skills”
1. You can acquire AI-use skills almost overnight. Install Codex, start talking, you catch up fast
2. College class aren’t nimble. “Relevant AI skills” change too fast—AI skill classes will be obsolete fast
3. 🧵
Jon Willits retweeted
This week I had the honor of speaking to Princeton’s entire incoming undergraduate class to address their AI anxieties. I had three messages for them — good news, bad news, and a note of optimism. Here’s a condensed version.
The good news
We have enough evidence now to conclude that the shrill predictions of rapid, massive job loss were misplaced. Even in a field like software engineering where AI has been rapidly adopted, its effect has been to shift, not replace the role of the human (see the “decide-execute-deliver” framework normaltech.ai/p/why-ai-hasnt…)
Similarly, the panic about what to major in is also misplaced. There will be enduring demand for computer science, philosophy, and just about everything else. (In fact, AI companies hiring philosophers has been a big recent trend.)
The bad news
AI seems to help senior people much more than juniors. I can use AI for coding because I spent 25 years learning how to code, which lets me supervise coding agents effectively. (See my post on the “growth cycle” vs the “dependence spiral” xitter.cf/random_walker/status/2…)
You are in a bind — you can’t offload your skill-building to AI, but you’ll graduate into a market where employers will expect you to get work done with AI. We never faced this dilemma. As a result we haven’t figured out how to revamp our classes to help you do both. You’ll have to help us figure it out. And you’ll need to somehow resist the constant temptation to turn to the shortcut machine.
The hope
My point is not that AI is bad for learning. It’s an incredibly flexible tool. Is the internet good or bad for learning? Depends — are you using it to find research papers or waste time scrolling? I use AI every day for learning. The key is to use it to increase, not decrease your cognitive load. To learn deeper, not faster. There is no learning without the cognitive sweat. I try to make sure I’m mentally exhausted at the end of the day. I do feel that AI lets me push myself harder than I ever could before, and I have a vision that as AI continues to advance it will enable human-AI “co-superintelligence“. (I talked about this at the end of my ICML keynote. normaltech.ai/p/what-will-be…)
There’s a big, under-appreciated reason why people may have very different experiences and opinions about using AI for work — are they using it for tasks they’re already an expert at, or tasks they can’t do themselves? The former leads to a *growth cycle* and the latter leads to a *dependence spiral*.
When I use AI to do something I’m an expert at, like coding, I treat it as a tool. I can build quickly, maintaining an understanding of the code, knowing that if necessary, I can fix the code myself. It feels empowering. It frees up my time to think about the complex, judgment-oriented parts of software engineering that I can’t or won’t delegate to AI. That means my own skills improve rapidly, and I get to climb the ladder of complexity and develop higher-level skills, much more so than when I write the code myself. I feel in control. I can lock in and achieve a flow state — when AI is working, I’m reviewing, building understanding, and planning the next steps. I never get the feeling that the tool is about to replace me. This is the growth cycle.
(Of course, the growth cycle is not automatic. I still need to exercise agency to use AI responsibly. But it’s the same challenge with any productivity-enhancing technology, and those who’ve navigated such transitions before are well-equipped to navigate it with AI as well.)
On the other hand, if I use it for tasks I don’t understand and haven’t learned to perform myself, I have no choice but to treat it as a superintelligence. If something breaks, the best I can do is ask AI to fix it and hope for the best. I generally can’t evaluate the quality of the output myself. The only way to find out if it's any good is if and when the work is ultimately reviewed by an actual expert. The experience is confusing, unsettling and disempowering. And forget about flow state. By over-relying on AI, I risk losing whatever skill I had at the task in the first place, even if it boosts productivity in the short term. This is the dependence spiral.
It’s no wonder that entry-level workers and students preparing to enter the workforce find themselves in a bind. To compete with the AI-enabled productivity of more seasoned workers, they must adopt AI themselves, but doing so risks the dependence spiral. I have some thoughts on solutions that I will share in later posts, but I think having a clear diagnosis of the problem is a useful first step.
Jon Willits retweeted
Hot take: Realtors are a profession that should have been eliminated thanks to Redfin, Zillow etc
Their persistence is a good reminder to be generally skeptical of AI “disruption” to customer-facing professions that can circle the wagon - law, medicine, accounting, sales, etc
Jon Willits retweeted
now that AI makes information consumption and transformation easier than ever I would like to bring back this old banger by Sasha Chapin about how books are not information transfer devices but subjectivity-merging devices
in fact I would say content consumption in general is more about subjectivity-merging than information transfer, which is why I am generally much more interested in writing by humans than by AI
Jon Willits retweeted
GPT reviewer "scores above each paper's top-rated human reviewer" but AI review agents "overlap far more than humans do...and exhibit 16 recurring weaknesses humans do not share..." Results "position current AI reviewers as complements to, not substitutes for, human reviewers."
Jon Willits retweeted
asking people to read ai-generated text is offensive.
this is not because ai text is intrinsically bad. rather, the author has not paid a cost to write the text himself. this cost is a credible signal he finds its communication important.
so: not paying that cost is telling
Jon Willits retweeted
Let me elaborate on why I think imposing minor but tangible penalties for hallucinations on a preprint server is appropriate (one-year bans in any single outlet are really not a huge deal).
As you all know, I've been an "AI booster" writing on human slop before it was cool and getting flak for it. But journal editors really don't have a good option now. No matter what you do, you lose. Explicitly imposing some cost on careless submissions is a good idea.
Should we have done it earlier for similar and more serious human errors in data analysis? Sure. But as many pointed out, the great thing about AI is that it gives us a good excuse to improve things in conservative academic institutions resistant to any change.
My hope is that in equilibrium, when everyone is aware of the one-year ban rule, people will be less inclined to submit clear AI slop, or human slop for that matter. Or at the very least, they will go over their references and double-check everything. But if more outlets implement this rule and things go insane, I'm totally willing to change my mind.
Jon Willits retweeted
Here are 14 images to better understand cognitive science visually 🧵
(From a lecture I gave for @pgmid's BrainInspired discord community)
