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DeepMind in “very early stage” talks with National Grid to reduce UK energy use

arstechnica.co.uk

61–70 of 115 posts

Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use

#61

Predicting peaks in supply and demand for energy is essentially turning the utility into a hedge fund. I think it's worth considering the overall characteristics of the "improved" algorithm, since the ML optimizations are likely analogous to leveraging based on an overfitted predictive model, and the objective of a power grid is resilience as well as efficiency. Also, depending on how you define efficiency, it may be…

Well, the grid already has a prediction system, this is just supposedly improving that. Which makes me question whether it can actually reduce overall consumption, because surely that requires some of the consumers to change behaviour?

Dispatch priority already ensures that if wind is available it will be used in preference to coal: https://www.economy-ni.gov.uk/consultations/priority-dispatc...

Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use

#62
post #45

The DeepMind datacenter project was very interesting, but a lot of the ML people I spoke to were quite dubious about how much of it was genuinely down to new AI/neural networks, and how much of it was Google PR to justify how much they spent on DeepMind. > DeepMind trained a neural network to more accurately predict future cooling requirements, in turn reducing the power usage of the cooling system by 40 percent. But…

Not an expert but I expect that what AI/machine learning/NN shines is the ability to produce very high quality solutions (or approximations) for large-scale NP-hard problems. With regard to the "NP-hard" part, I don't think regular data science can be a sufficient substitute.

What do you mean by "regular data science"? Deep learning is just another tool in the belt of a data scientist. A deep learning solution doesn't just spring up on its own. At this point in time anyway, all solutions are engineered by engineers and data scientists.

Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use

#63
post #54

On the photo #4 you can see a man pointing a pretty insecure computer, windows xp :) Should Not be used xp anymore because there is no more security updates or I'm wrong? The direct link of the image is: https://cdn.arstechnica.net/wp-content/uploads/sites/3/2017/...

It's also possible that these are library photos, and not up to date.

Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use

#64
post #54

On the photo #4 you can see a man pointing a pretty insecure computer, windows xp :) Should Not be used xp anymore because there is no more security updates or I'm wrong? The direct link of the image is: https://cdn.arstechnica.net/wp-content/uploads/sites/3/2017/...

The image has been used in a number of articles, the oldest of which I was able to find was from 2013 - I would assume the image is older than that even.

Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use

#65

I don't want to give any hint to the DeepMind, since I an inclined to think that in this field an expert assessment can be better that DeepMind advice. Just to give a simple question or example of the kind of knowledge involved in those predictions: Since energy generation and demand depends a lot of weather conditions, do they have any state of the art machine learning model to forecasting weather conditions?, can t…

Reinforcement learning is used in spam detection and control amongst other things. You can do data augmentation and also do transfer learning.

Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use

#66

Earlier quoted context omitted.

I suppose you are joking, as double-glazing seems to be a popular meme in the UK (with people being convinced they don't need it and scammy vendors pushing it)? Must admit I never completely understood it, coming from a country where double glazing is the norm.

Can't understand your comment. You are agreeing with me that, in most other parts of the world, double-glazing (aka a glass that won't break if you elbow it by mistake!) is the norm. But in the UK, it's sadly the opposite. So we agree that they should replace their crappy windows with double-glazed, properly insulated, ones...

I meant I never understood why people in the UK dislike double glazing and think of it as a kind of joke. Don't know much about the story of double glazing in the UK, though.

The only thing that comes to mind is that climate is supposed to be mild in the UK, so maybe double glazing makes less sense than in colder countries? But it can get pretty cold in the UK...

My comment wasn't meant as a criticism of your comment.

Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use

#67
post #4

This title misses the following words; "suggests they can", "proposes to", or my personal favourite "something something ai solves all your problems" come on folks, we can be better than this.

We added "very early stage" to the title since that's what the National Grid is quoted as saying in the (you're right, extremely frothy) article.

Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use

#68

Heh. If only they mandate to replace a majority of the crappy wooden + single-pane windows they have in most houses, that let all of the heat out and wind and noise in, with normal, double-glaze windows with good insulation, they would cut the energy footprint in HALF, I'm sure...

I think 'Most' is a huge overstatement, the last house I lived in without it was a student dive in 1996.

Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use

#69

Earlier quoted context omitted.

I think this question applies to all applications of machine learning. PR pieces love to label every application of ML to a novel field as "artificial intelligence," even when the predictions are simple enough to be predicted by statistical regression (and in many cases that's effectively what is happening). So yes, many applications of ML are something "a regular data science team could do." However, isn't the main…

I don't think your second point follows from your first. ML is not easy to set up or maintain, even if we're just talking about linear regression. Companies trying to make 'data scientists obsolete', usually frame ML as a black box that you can plug data in one end and spit out profits the other. But in reality it doesn't work like that, significant effort has to build and maintain data pipelines, to make sure data q…

It can be easier to set up and maintain than hand-rolled solutions. Best example I can think of is speech recognition - previous systems used hidden markov models, gaussian mixtures, triphones, and all sorts of complex and obscure things that had to be tuned by experts. Now they just use an end-to-end neural network that goes directly from sound to letters (almost anyway; I believe the input is still MFCCs).

I agree it's not magic but it neither are 'manual' methods and at least the maths is a lot simpler.

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