Statistics researcher working from Turin. Research on multiple testing, selective inference, resampling methods, random field theory, and large language models.
Are you at #OHBM2024? Come check out my talk on Localized Cluster Enhancement - TFCE revisited with valid error control, in the oral session on advanced statistical methods on Wednesday at 11.30. Or poster 1871, on Wednesday/Thursday to see how to improve the validity of TFCE (1)
Aliens reveal to Terry Tao a proof of the Riemann Hypothesis, which he understands. The rest of humanity gets to ask him 10,000 yes-or-no questions, which he'll answer to the best of his ability. How confident are you we would be able to prove RH within a year?
Italian efficiency when it comes to coffee should be studied.
In Italy:
- Walk into a bar and look at the guy
- Un caffe
- 30 seconds later it’s ready
- Shoot it
- Leave €1
- Walk out
In the US:
- Join a line
- Wait
- Order coffee
- Answer 12 questions: Size? Milk? Roast? Sugar? Temperature? Colombia beans? Name? How do you spell it?
- $12.34
- Ask for a 20% tip. Click 5 times on a ipad to have a custom tip
- Tap phone
- ask where to send the invoice
- Wait again on a different line
- Someone call a name that sounds similar to mine
- get the coffee
- too hot, can't drink it
- finally at temperature
taste like shit
@ziv_ravid Anyone upload a PDF to ChatGPT, but a reviewer who offloads their work to AI is not providing any additional value to the conference. Reviewers are domain experts with years in the field, a unique perspective, and a longer context window than any LLM.
@littmath@roydanroy Do you think this could indicate that the openai effort used smart mathematicians in the loop to guide/help the AI - which public efforts using the same tools would not have access to?
Yep, 100%.
I often tell young students/researchers/professionals (including my own son and his friends) that there are three groups of people when it comes to AI, and it’s clear which group they should aim to be in:
1. Those who ignore AI and don’t use it at all.
2. Those who learn to leverage AI effectively—not as a substitute for their own thinking, but as a supplement to it—to handle routine tasks (including coding), improve efficiency, and enhance the clarity of their writing.
3. Those who overuse AI—taking shortcuts, accepting its output uncritically, or relying on it as a replacement for their own thinking.
People in group 1 are putting themselves at a serious disadvantage. As others learn to use AI effectively, those who avoid it entirely may struggle to keep pace and remain competitive.
People in group 3 face a different risk: they fail to develop their own deep thinking skills and become overly dependent on the technology. The temptation to take shortcuts can be strong, especially when it yields short-term success. But over time, these individuals risk becoming interchangeable—simply “replacement-level AI users” who lack the independent insight and intellectual depth needed to distinguish themselves.
People in group 2 avoid both extremes. They learn how to use AI thoughtfully to increase productivity, clarity, and thoroughness, while continuing to cultivate their own reasoning and expertise. They use AI as a tool, not a crutch. These are the individuals who will remain valuable and in demand.
My advice to young researchers is simple: make sure you are in group 2. Learn how to leverage AI to enhance your work, but never let it replace your own thinking. If you do that, you will continue to develop durable, meaningful skills that will remain valuable no matter how the technology evolves.
Martin Hairer and colleagues released a set of hard maths problems, designed to be test cases for LLMs.
We have *one week* to solve them, using LLMs. They encrypted the solutions at https://t.co/EZNjVzFT9t and will reveal them just after.
https://t.co/20TaPDaSf2
(1/3)
Yuval Noah Harari at Davos: “We conquered the world with words—convincing millions of strangers to cooperate through stories, ideologies, religions. That was our superpower.”
“Now AI can use words better than us.”
Social media already reshaped everything. In 10 years, when AIs command language, what happens to Davos? To politics? To human cooperation?
Will the future still need physical humans at the table—or just their avatars?
This 2:43 clip is a chilling rethink of what made us dominant… and what might dethrone us.
Do you think AI mastering language ends the era of human persuasion? Or will words remain our last stronghold?
Many folks are yet to grasp the specification problem.
> Wouldn't AI write better code than us?
"Better" for what? Or with respect to what exactly? I'm building software because I have a specific vision/intent/need.
How should that intent be specified in the first place, so that a specific piece of code is better or worse with respect to it?
I'm saying: for many needs, conversation is not an efficient medium of expressing *what* I want. AI having excellent taste or horrible taste has no bearing on that.
I will be so disappointed if the way we build software with AI remains vibe coding rather than a genuinely higher level of abstraction.
I want to express the “code” of the system—but shorter and more pleasant—not manage an agent to write 100,000 lines of low-level slop for me.
Just listened to Richard Sutton on Dwarkesh's podcast (3 months late but my backlog is out of hand).
I never thought an intellectual conversation could make me cringe so hard.
Sutton was so unnecessarily adversarial. I'm not referring to the substance (which *is* contrarian, but that's all good). I'm talking about his demeanor. He treated Dwarkesh like the slowest idiot student he's ever had to deal with. Meanwhile, Dwarkesh was firm but extremely graceful.
I'd expect a university professor to be more... nurturing, more patient. I'm hearing a sort of bitterness that reminds me of Yann LeCun.
They're both insanely successful, respected, outwardly recognized (with Turing awards), and I can only assume extremely rich. So where is this bitterness coming from?