Young scientists regularly ask me for career advice. Academia or industry? Big company or startup? US or Europe?  Good scientists in AI disciplines are fortunate to have many choices. But choosing can be stressful. I always give the same advice. 1/10 There is no globally-optimal life. There is no sequence of choices in life that will produce the "perfect life" or "perfect career". This is hard to accept but, once you accept it, it's very freeing. 2/10

Build what you need and use what you build. This is a core philosophy of my research. It shifts the focus away from publishing “papers” to what really matters — impact. This thread unpacks why I think this is a successful approach to science. 1/10 Or see:
perceiving-systems.blog/en/post/build-… At the start of any research project, I ask my students “Who’s your customer?” By customer, I don’t mean “paying customer”. I mean “who needs what you’re proposing?” Who will use it? Who cares? If you can’t answer this, then the work is likely to be irrelevant. 2/10

These are exciting times. There's a sense that AI will change everything, including how science is done. Implicit in this excitement is the hope that everything will change for the better. Let’s look at that. First, we need to define “better.” Here, it’s the idea that science serves people to produce new artifacts (drugs, technologies, etc) that improve our lives. Behind this definition is a utilitarian view of science that is not quite correct and doesn't apply to all disciplines but that's a complex story for later.

.@ylecun writes “science must solely evaluate *impact*” and “evaluating work done by humans has ABSOLUTELY NOTHING TO DO with scientific publication.” Original emphasis retained. Let’s unpack the notion of impact and the evaluation of humans in science. Consider the claim that all that matters in science is impact. This sounds sensible but requires the ability to *measure* impact. In ML today, the turnaround time between innovation and application is short. the “customer base” is huge, and impact may seem easy to evaluate.

In the LLM-science discussion, I see a common misconception that science is a thing you do and that writing about it is separate and can be automated. I’ve written over 300 scientific papers and can assure you that science writing can’t be separated from science doing. Why? 1/18 Anyone who has taught knows the following is true. You think you understand something until you go to teach it. Explaining something to others reveals gaps in your understanding that you didn’t know you had. Well, writing a scientific paper is a form of teaching. 2/18

I repeat: Easily produced science text that's wrong does not advance science, improve science productivity, or make science more accessible. I like research on LLMs but the blind belief in their goodness does a disservice to them and science. Here is an example from #ChatGPT 1/5 SMPL is actually short for Skinned Multi-Person Linear model. #SMPL is a popular 3D model of the body that's based on linear blend skinning with pose-corrective blend shapes. It's learned from 3D scans of people, making it accurate and compatible with rendering engines. 2/5

With LLMs for science out there (#Galactica) we need new ethics rules for scientific publication. Existing rules regarding plagiarism, fraud, and authorship need to be rethought for LLMs to safeguard public trust in science. Long thread about trust, peer review, & LLMs. (1/23) I’ll state the obvious because it doesn’t seem to be obvious to everyone. Science depends on public trust. The public funds basic research and leaves scientists alone to decide what to study and how to study it. This is an amazing system that works. (2/23)

I asked #Galactica about some things I know about and I'm troubled. In all cases, it was wrong or biased but sounded right and authoritative. I think it's dangerous. Here are a few of my experiments and my analysis of my concerns. (1/9) I entered "Estimating realistic 3D human avatars in clothing from a single image or video". In this case, it made up a fictitious paper and associated GitHub repo. The author is a real person (@AlbertPumarola) but the reference is bogus. (2/9)

Who should be the last/senior author on a paper? How do you decide? What does being last entail? I get these questions a lot and it’s confusing because the last author is often a senior person, running a group & raising money. Do those things determine last authorship? No. (1/7) The last author is ultimately responsible for the paper throughout the process including conception of the idea, writing, rebuttal, camera ready, talk, video, code, website, tweet, dataset, etc. They don’t do everything but make sure it all gets done. Like a conductor. (2/7)

Avatars are central to the success of the #metaverse and #metacommerce. We need different #avatars for different purposes: accurate #3D digital doubles for shopping, realistic looking for #telepresence, stylized for fun, all with faces & hands. @meshcapade makes this easy. (1/8) For on-line shopping, clothing try-on, and fitness, an avatar should be realistic – your digital twin. You need a true digital double to see how clothing will look in motion. But, creating avatars that are accurate enough for shopping is hard. (2/8)

The 5 stages of rebuttal grief.
(1) Denial
The reviewers totally misunderstood my paper. The review process is broken. R1 was clearly a student who has never reviewed before. R2 doesn’t know what they are talking about. R3 hates me. (2) Anger
I’m going to withdraw my paper. I’ll submit it somewhere else where other people will love it. I hate this conference and this field. The whole process is broken. Reviews are random.

There is a lot of good thought going into how to make @siggraph more attractive for authors of technical papers (e.g. from @AaronHertzmann). All good. But the differences between the physical @siggraph and @CVPR/@iccv/@eccv conferences also matter. (1/8) 1. Remember being a grad student? If you write a paper, your advisor sends you on a free trip. So cool. CVPR/ICCV/ECCV are in different and exciting places. SIGGRAPH is mostly in LA. Boring. Branch out! (2/8)