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

arstechnica.co.uk

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

#81

Post by iagovar »

SolaceQuantum wrote: post #76
iagovar wrote: post #74

Earlier quoted context omitted.

What I'm wondering is why I can't find anything about ML applied to finding and curating data, which is the most tedious part of data science. That would be an interesting way of using ML without fuzzy stuff.

There are a few current projects, particularly in database research. I don't know how many of them use ML in the traditional understanding. Current projects I know of are Wrangler, Mimir, Katara, MayBMS (in no particular order).

Did you try any of them. Wrangler seems like a tool similar to OpenRefine to me.

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

#82

Post by imranq »

sweezyjeezy wrote: post #5

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…

I think you could make a similar argument for a lot of important choices: when you pick a doctor, an employee, a job, a computer, you're going to pick the best you can afford even if the middle of the road doctor or computer could get the job done. Generally people want the satisfaction that they chose the best in the business and so aren't leaving anything in the table.

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

#84

Post by ucaetano »

sweezyjeezy wrote: post #5

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…

From the blog post, it seems that the 40% reduction is compared to the current utilization:

"by applying DeepMind’s machine learning to our own Google data centres, we’ve managed to reduce the amount of energy we use for cooling by up to 40 percent. [...] Given how sophisticated Google’s data centres are already, it’s a phenomenal step forward."

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

#86

Post by deepnotderp »

chatmasta wrote: post #75
deepnotderp wrote: post #71

Earlier quoted context omitted.

You're aware that numenta is considered a joke in the serious machine learning community, right?

Yes, and quite unjustifiably so in my opinion, which is why I sourced all those links in my comment. From what I've seen, Numenta is very good at a specific class of problems (time series prediction and anomaly detection). Feel free to underestimate them. It's really not my problem. And do pompous, abrasive comments like yours really add anything to the discussion? Why don't you elaborate on why they are a "joke" to…

Because they continously claim they're "ahead of everyone" and how everyone else is stupid and their glorious htm will beat everyone....and then proceeds to get destroyed by convnets...and then moves the goalposts to anomaly detection only...and then gets beaten on their own heavily rigged benchmark dataset.

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

#87

Post by KCFforecast »

computerex wrote: post #65
KCFforecast wrote: post #55

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.

In this concrete case, how do you do transfer learning? what is the domain you have experience to transfer to the energy of energy? Also, Bayes's naive algorithm can be used in spam detection and usually it gives good results, is RL such a great tool in spam filtering when there is moderate data?

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

#88

Post by m-i-l »

javiermaestro wrote: post #36
poooogles wrote: post #21

Earlier quoted context omitted.

>crappy wooden I'm not sure where you got the gripe against wood from. It lasts longer than uPVC given maintenance every 5 years, looks better, is more environmentally friendly and has similar thermal properties. Oh and it doesn't go yellow.

Nothing against wood, really. But if you've been to the UK, you should agree that the unmaintained, not-properly-fitted, wooden frames that they use in most old windows are just crap. They let in huge amounts of wind, cold, etc, through the unsealed seams. Anything that improved that would be 10 times better, regardless of the yellowing, etc.

I had my sash windows refurbished a few years back, adding double glazing and draft proofing. They said the frames were the original wood from around 150 years ago, although they said the glass was from the post-war period (in common with most windows in London, given most were shattered at some point during the war). There's no way uPVC frames will last anything remotely approaching that amount of time.

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

#89

Post by kylebgorman »

IshKebab wrote: post #69
sweezyjeezy wrote: post #56

Earlier quoted context omitted.

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…

Unless you know something I don't, the SOTA in ASR is a hybrid system that still has all the complexity of the traditional generative model plus NNs on top.

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

#90

Post by ZeroGravitas »

ucaetano wrote: post #84
sweezyjeezy wrote: post #5

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…

From the blog post, it seems that the 40% reduction is compared to the current utilization: "by applying DeepMind’s machine learning to our own Google data centres, we’ve managed to reduce the amount of energy we use for cooling by up to 40 percent. [...] Given how sophisticated Google’s data centres are already, it’s a phenomenal step forward."

That "up to" renders the rest of the phrase meaningless. Was it 40% less for one second/minute/hour/day?

I assume, because they haven't actually given an impressive number without massive wiggle room, that it's because they are clever enough to know the real number is embarrassingly low, but would welcome corrections.

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