Vincent Mai

To be honest, I hope you are right. I believe that there is high economic value in current and slightly better AI once correctly integrated. But there are many risks with exponential AI improvements, and I hope your argument of lack of economic sustainability stands and this progress slows down.

I also believe AI's current costs can and will be reduced significantly (per ill-defined unit of performance), the current models being non-optimal algorithms running on non-optimal hardware.
Every year, smaller models catch up to last year's champions. There is a lot of space to reduce costs.

Absolutely!
We are talking about what happens in the future - there are multiple assumptions and bets. I would not bet my house on everything I said ^^
Yet it seems to be a very plausible scenario.

Also, they have plenty of monetization schemes in the sleeves. Social media style/search engine style - gather data from users, make companies pay for their products to come out in the results, etc.
This is like the early internet. It will grow, become necessary, and then get monetized.

I think you may be underestimating the usage of these tools given for very low cost to other parts of the economy. Once everyone has found a way to use AI for value in their specific domain, the prices will get higher without hindering adoption.

LawZero <p>&nbsp;</p> <p><strong>About LawZero</strong></p> <p>LawZero is a non-profit organization committed to advancing research and creating technical solutions that enable safe-by-design AI systems. Its ...

LawZero is growing fast, and we're always looking for dedicated people to join our team.
If you're interested in working on technical safeguards to create safe-by-design AI systems, check out the openings on our website and don't hesitate to reach out to our team!
job-boards.greenhouse.io/lawzero

Mila's annual supervision request process is now open to receive MSc and PhD applications for Fall 2026 admission! For more information, visit mila.quebec/en/prospecti...

200+ prominent figures endorse Global Call for AI Red Lines AI could soon far surpass human capabilities and escalate risks such as engineered pandemics, widespread disinformation, large-scale manipulation of individuals including children...

Establishing where we collectively draw red lines is essential to prevent unacceptable AI risks.

See the statement signed by myself and over 200 prominent figures:
red-lines.ai

You are right. I am sorry. I will remove my post.

It is very difficult, and stressful, to see the US going there. Being outside and therefore unable to do anything about it makes me feel powerless. I wish more would be done by those who can.
It is unfair to judge though. I am not in this position.

To friends worried about the US some months ago, I said things were OK as long as people like Kimmel could make fun of the president night after night in a large audience TV show.

This is not OK anymore. This country is running at fast speed into dark territory. Gotta wake up, American people. Now.

L’ONU se dote d’un groupe d’experts scientifiques sur l’intelligence artificielle Le secrétaire général des Nations unies, Antonio Guterres, va lancer un appel à candidatures pour identifier les quarante futurs membres du groupe, qui siégeront pour trois ans.

L’ONU se dote d’un groupe d’experts scientifiques sur l’intelligence artificielle

Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage Hydro-generating units (HGUs) play a crucial role in integrating intermittent renewable energy sources into the power grid due to their flexible operational capabilities. This evolving role has led to...

The article is also available on arXiv for when the Elsevier free sharing is over: arxiv.org/abs/2411.14618

It's among my favorite projects because 1) we used DL to reduce the costs of clean energy production 2) we managed to deal with many real-world and real-time constraints, and 3) it's fun to use neural nets to control enormous machines!

We ran this on a real turbine and achieved a 42% reduction of strain amplitude after only 7 startups, compared to the standard one. It's significant! We now aim at making it generally applicable for different types of turbines (this was for a Francis, Kaplans have an additional dimension).

Once the model is trained with enough data (or when we have used most of the startup budget), we finalize by optimizing the strain only. The parameters are always tested on the turbine, allowing to confirm the predictions with ground truth measurements.

The virtual sensors are integrated in a black box optimization loop, which also includes a turbine dynamics simulator. For several startups, we use active learning, with an ensemble and optimism under uncertainty, to output the parameters to run next to get the most informative data for the model.

This is where machine learning comes into play. We use a neutral networks to create a "virtual sensor" predicting the strain amplitude for a given turbine strain. It is trained on the trajectories we currently have measured.

The challenge is that the sensors for strain (which leads to fatigue and cracks) don't last long on such turbines. They get flushed away! We have ~10 tries only to find the best set of parameters which will reduce the strain amplitude.

The strain is what leads to fatigue and cracks. As every turbine is different, it's hard to know in advance which startup parameters lead to the lowest strain. We have to figure it out during the commissioning campaign, when the turbine is placed in the dam.

There are different ways to start a turbine. Startup parameters control the vane opening/rotation speed trajectory until the desired speed is reached. They lead to different strains on the turbine blades.

As renewable energies are integrated in the power grid, hydroelectric turbines are expected to be stopped and started more frequently. But startups are a major source of fatigue on the turbine blades and reduce turbines' lifetime.

One of the most fun projects I worked on.
It's not everyday you can train a neural net on a laptop in a hydroelectric dam to provide commands in real time to start a gigantic turbine!
authors.elsevier.com/a/1lfNk3QJ-d...

It was a pleasure to meet Prime Minister @mark-carney.bsky.social and Minister Evan Solomon today as part of an official visit at @mila-quebec.bsky.social with many collaborators from our AI ecosystem. We discussed AI risk mitigation, sovereignty & economic potential among other important topics.

This project was 4 years in the making and it's finally out!

We found that controlling vehicle speeds to mitigate traffic across a city can cut carbon emissions between 11 and 22 percent. To do this, we used deep reinforcement learning to optimize one million eco-driving scenarios. 🚗🤖🧠

It is not only impressive, it is absolutely fascinating. One cannot be scientifically curious and not be thrilled. Yet the consequences of this new knowledge may be harmful, because of society.
This is hard to grapple with for someone who believes knowledge should be inherently good.

Eugene Vinitsky: Building capable, controllable simulation agents through reinforcement learning

RL4RS Workshop at @rl-conference.bsky.social

📍 The RL4RS Workshop is happening tomorrow at
University of Alberta, as part of @rl-conference.bsky.social.

Join us for a focused day on real-world applications of Reinforcement Learning.

🗓️ Full Schedule: rl4rs.github.io/RL4RS/schedu...

We hope to see you there.

Propose some socials for RLC! Research topics, affinity groups, niche interests, whatever comes to mind!

rl-conference.cc/call_for_soc...

We’ve received several requests from authors, and in response, the submission deadline for the RL4RS Workshop at RLC has been extended to June 2nd (Anywhere on Earth).

Check out the Call for Papers and submission link below — we look forward to your contributions!

rl4rs.github.io/RL4RS/

Reminder that early registration for RLC closes on the 30th! Please register early to save yourself some money and help us get the word out.