Wait, what if I want to know and I don't follow you on other social media? :P

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.

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.

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.