11–20 of 129 posts
Google might try to become the conglomerate of all forward-facing things but it is somewhat funny to see how through it all, it's their advertising revenues that form the core of the business.
Which is exactly what they're trying to change - both with "conventional" offerings like Android and Cloud, as well as with what they call "moonshots".
The millions of possible "solutions" and algorithms for working fusion reactors may be what has made fusion research so expensive and fusion reactors seem so far away. Quantum computers may be able to cut right through that hard problem, although we may have to wait a bit more until quantum computers are useful enough to make an impact on fusion research. I don't know if that's reaching 1,000 qubits or 1 million qubits.
Google might try to become the conglomerate of all forward-facing things but it is somewhat funny to see how through it all, it's their advertising revenues that form the core of the business.
Google might try to become the conglomerate of all forward-facing things but it is somewhat funny to see how through it all, it's their advertising revenues that form the core of the business.
Microsoft: They make an Operating System and Office Suite. From Microsoft Research they have labs on Quantum Computing, they have five Turing Award winners (One is Leslie Lamport) and he developed TLA+ while employed there.
Facebook: A social network Funds a bunch of Deep Learning Research and NLP.
Elon Musk: Helped create PayPal, now does electric cars and rockets, (Tesla, SpaceX)
NVIDIA: Made graphics cards for video games. Now those same devices allow for deep learning.
I can see this being coupled with simulations as well to understand sources of systematic errors, create better simulations which can then be used as a stronger source of truth for "offline" (computation-only) experiments.
The biggest challenge of course becomes interpreting the results. So you got better performance, what parameters really made a difference and why? But that is at least a more tractable problem than "how do we make this better in the first place?"
"Google and a nuclear fusion company have developed a new algorithm"
sounds way better than:
"Nuclear fusion company has developed a new algorithm using Google"
They may not mean the same, but in today's world faking it until you make it might pay off.
Outside of the title being misleading, I'm sceptical. It's one thing to have the hardware for research, and completely other to have the expertise for the research. Google entered the self driving cars research, and we have yet to see them driven around. This heavily reminds me of Intel and their diversification, up until recently, they were in IoT, makers market and what not. One solid push from AMD and they jumped…
https://www.slashgear.com/waymo-launches-early-rider-beta-pr...
They're easily 5 years ahead of the competition.
Outside of the title being misleading, I'm sceptical. It's one thing to have the hardware for research, and completely other to have the expertise for the research. Google entered the self driving cars research, and we have yet to see them driven around. This heavily reminds me of Intel and their diversification, up until recently, they were in IoT, makers market and what not. One solid push from AMD and they jumped…
In fact the more I think about it the more I'm confused by your comment. What are you skeptical about? Google here has demonstrated their computational resources can be of great benefit to scientific causes such as nuclear fusion. If you're saying that you're skeptical Google can do nuclear fusion, I think you're missing the point.
> Two additional complications arise because plasma fusion apparatuses are experimental and one-of-a-kind. First, the goodness metric for plasma is not fully established and objective: some amount of human judgement is required to assess an experiment. Second, the boundaries of safe operation are not fully understood: it would be easy for a fully-automated optimisation algorithm to propose settings that would damage the apparatus and set back progress by weeks or months.
> To increase the speed of learning and optimisation of plasma, we developed the Optometrist Algorithm. Just as in a visit to an optometrist, the algorithm offers a pair of choices to a human, and asks which one is preferable. Given the choice, the algorithm proceeds to offer another choice. While an optometrist asks a patient to choose between lens prescriptions based on clarity, our algorithm asks a human expert to choose between plasma settings based on experimental outcomes. The Optometrist Algorithm attempts to optimise a hidden utility model that the human experts may not be able to express explicitly.
I haven't read the full article nor do I understand the problem space, but the novelty seems overstated based on this. Maybe they can eventually collect metadata to automate the human intuition.
Edit: here's their formal description of it: https://www.nature.com/articles/s41598-017-06645-7/figures/2