I see your point, but something still seems different. Yes we bred plants and animals, but we did not create them. Yes we did build steam engines before understanding thermodynamics but we still understood what they did (heat, pressure, movement, etc.)
Fun fact: we have no clue how most drugs works. Or, more precisely, we know a few aspects, but are only scratching the surface. We're even still discovering news things about Aspirin, one of the oldest drugs: https://www.nature.com/articles/s41586-025-08626-7
I’ve only skimmed the paper - a long and dense read - but it’s already clear it’ll become a classic. What’s fascinating is that engineering is transforming into a science, trying to understand precisely how its own creations work
This shift is more profound than many realize. Engineering traditionally applied our understanding of the physical world, mathematics, and logic to build predictable things. But now, especially in fields like AI, we’ve built systems so complex we no longer fully understand them. We must now use scientific methods - originally designed to understand nature - to comprehend our own engineered creations. Mindblowing.
Agreed. But just say that. No need to pretend taking responsibility, which is defined as facing consequences when things go bad.
“As CEO, I’m truly sorry to those impacted. But I strongly believe that this change is what is needed now to make sure Dropbox can thrive in the future.”
I completely agree. LLMs are incredibly useful for improving the flow and structure of an argument, not just for non-native speakers, but even for native English speakers.
Making texts more accessible through clear language and well-structured arguments is a valuable service to the reader, and I applaud anyone who leverages LLMs to achieve that. I do the same myself.
I'm a bit puzzled why one would hold a press conference for a trial that hasn't happened yet - especially as this seems to be a phase 1 trial to test safety in humans.
The development of medical interventions typically goes through a number of stages.
The earliest stage is called the pre-clinical phase, where a candidate intervention is tested outside of humans, either in vitro or in animals. The images of the mouse and ferret teeth suggest this has been done.
Phase 1 is when the intervention is first tested in humans, typically in small groups of usually dozens of participants. The aim here is not to evaluate efficacy - phase 1 studies are often too small for that - but to assess tolerability and safety, and to find the optimal dosing with respect to side effects.
If an intervention appears safe in certain doses, it can then be evaluated for initial efficacy and continued safety in a phase 2 trial, which is larger (usually hundreds of participants). Think of it as a kind of pilot trial to see if the intervention has the desired beneficial effects without serious negative side effects, and to identify the dose with the best benefit/risk ratio. About half of the studies get past this stage.
Those that do can enter phase 3, which is the full efficacy and safety assessment of the intervention. Again, about half of the phase 3 trials eventually make it to market. Of course, the intervention first needs to go to the health authorities for regulatory approval before it can be offered on the market.
At EPFL we're trying a model with the Extension School that is somewhere between 2 and 3. We don't quite do flying cars (yet) but we're developing more and more advanced material - and the big plus being that learners in the programs get a diploma (e.g. https://exts.epfl.ch/courses-programs/applied-data-science-m...).
I do think that universities should invest more heavily in this mix, obviously without losing their strong standing in 1.
The non-profit platform https://www.crowdAI.org currently hosts a NIPS 2017 challenge as well as a genetic challenge, and constantly adds new challenges. Btw, it's completely open source, and keen on having contributors to help build the open challenge platform the community wants the most.
I see comments like this all the time, and while what you say is correct, I think committees increasingly appreciate this sort of thing - frankly they have to or they will miss out on some of the most innovative people. There is plenty of "standard" already (nothing wrong with that of course).
With new things, what you need is at least one person on the committee to fight and convince the others why this new thing is awesome. As someone who is now on some of these committees, I would put all my weight behind something like this should I encounter it (assuming of course it has the relevant quality).
The platform itself is funded by institutional research funding we get at EPFL. For some of the monetary prizes, these typically come from the corresponding projects.
https://www.crowdAI.org is an open source alternative. Disclaimer, my research group at EPFL started the platform, because we think there should be a community-based open source version that is open to anyone. Always looking for contributors!
EPFL Extension School | Geneva & Lausanne, Switzerland | ONSITE | Full Stack Web Developer
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- I have thought about destroying / recycling the car. I am not sure this would be the most meaningful action. At this point, I simply want to go to the seller and return a product that is not as advertised.
- I certainly agree that there are some intermediary steps that need to be taken with the existing pipeline. I simply wanted to express that the future has to be about batteries & software, and radically so given the current scandal.
Fun fact: we have no clue how most drugs works. Or, more precisely, we know a few aspects, but are only scratching the surface. We're even still discovering news things about Aspirin, one of the oldest drugs: https://www.nature.com/articles/s41586-025-08626-7