The amount of energy consumed by big data centers has always been a headache for tech companies. Keeping the servers cool as they crunch numbers is such a challenge that Facebook even built one of its facilities on the edge of the Arctic Circle. Well, Google has a different solution to this problem: putting its DeepMind artificial intelligence unit in charge and using AI to manage power usage in parts of its data centers. A 40 percent reduction in the amount of electricity needed for cooling, which Google describes as a "phenomenal step forward." After accounting for "electrical losses and other non-cooling inefficiencies," this 40 percent reduction translated into a 15 percent reduction in overall power saving, says Google.

First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.

From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world's most challenging physical problems -- such as energy consumption. Large-scale commercial and industrial systems like data centers consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world's increasing need for computing power. Google is taking many steps to reduce energy consumptions . Compared to five years ago, Google now get around 3.5 times the computing power out of the same amount of energy.

It's fair to say that Google-owned AI company DeepMind's big push into the health space via data-access collaborations with the UK's National Health Service -- announced with much fanfare in February this year -- has not been running entirely smoothly so far. But there are more regulatory bumps in the road ahead for DeepMind Health. TechCrunch has learned the company won't continue using one of the apps it co-designed with the NHS until the software has been registered as a medical device with the relevant regulatory body, the MHRA. That's especially interesting given that this app, called Streams, has already been used for patient care in multiple NHS hospitals. The Royal Free NHS Trust previously told TechCrunch the app had been used by up to six of its clinicians in three "user tests" in its London hospitals. Which, put another way, means a profit-driven commercial entity has been involved in a real-world test of an unregistered medical device on actual hospital patients.

Google is putting DeepMind's machine learning to work on managing their sprawling data centers' energy usage, and it's is performing like a boss -- the company reports a 15% drop in consumption since the AI took over. Google is undeniably a huge part of western civilization. The company's data servers pretty much handle all of my mail at this point, along with YouTube, social media platforms and much more. But even so, it's easy to forget that the Google we know and interact with every day is just the tip of the iceberg; it relies on huge data servers to process, transfer and store information -- and all this hardware needs a lot of power. So much power, in fact, that the company decided to do something about it.

Google is using a powerful new machine-learning approach to save huge amounts of energy (and hundreds of millions of dollars) each year at its vast data centers. It might not be long before the technique, which involves a machine-learning algorithm gradually learning to perfect a task with positive reinforcement, catches on in a range of other areas. Demis Hassabis, the CEO of Google DeepMind, a subsidiary based in the U.K. that is focused on artificial intelligence, said at a conference recently that Google was using techniques developed by his company to improve the energy efficiency of its data centers. Because Google spends so much on electricity for the buildings that house its massive server farms, a savings of just a few percent equals hundreds of millions of dollars per year. DeepMind, which Google acquired in 2014 for around 600 million, has shown how large artificial neural networks combined with reinforcement learning can train computers to perform complex tasks incredibly well.

With the push of a button this spring -- Google was instantly using 40% less energy to cool a handful of its data centers. The achievement, which Google hails as a major breakthrough, points to how artificial intelligence can be used to make data centers, power plants, energy grids and manufacturing plants more efficient. As these huge, energy-intensive operations use power more efficiently, fewer greenhouse gases are emitted. "We're really thrilled about the environmental impact," said Mustafa Suleyman, who leads applied AI at Google DeepMind, a group of London researchers behind the project. DeepMind has leapt to prominence by building computer systems capable of mastering everything from Atari games to the board game Go.

The blockish and slightly dorky computer game Minecraft may turn out to be a great place for humans and AI to learn how to work together. An experimental new version of the game, released by Microsoft researchers this month, can be used to train an AI to perform all sorts of tasks, from crossing bridges to building complex objects. The new platform, called Project Malmo, makes it possible for a learning algorithm to control a Minecraft character that's normally operated by a human player. But it also provides ways for human players and AI agents to work together, and a chat window through which a person can talk with a nascent AI. "In the long run I want to work toward AI that can be taught by any user to help them achieve their goals," says Katja Hoffman, a researcher at Microsoft Cambridge in the U.K. who leads the project. Hoffman, who gave a demo of the software to AI researchers at an academic conference in New York last week, says that human-AI collaboration is a key goal for the project: "We've built in all the capabilities that a researcher would need in order to work toward collaborative AI." Malmo is geared toward testing reinforcement-learning algorithms, a way of training a computer to perform a task by providing simulated rewards.

The 4U4 card design of Inspur NF5568M4 is applicable to present electric power and heat dissipation designs of the data center, and is scalable to multi-machine and multi-card CPU computing clusters via the open-source Inspur Caffe-MPI becoming the mainstream CPU server used presently in the internet industry. Currently, Inspur's deep learning solution is being applied at Tencent, Baidu, Alibaba, Qihoo, iFLYTEK and JD and is supporting the "super brains" of various types of intelligentized services. As the neural network model grows in complexity, the computing performance necessary for the model training increases dramatically. The cluster-edition Caffe-MPI computing frame launched by Inspur achieves parallel computing of GPU server. It adopts high-performance mature MPI technology in computing -- carrying out parallel data optimization to the Caffe edition -- with the ability to organize multiple NF5568M4 into CPU parallel computing clusters via IB network.

"Mastering a field of knowledge involves not only'learning about' the subject matter but also'learning to be' a full participant in the field." This quote is a passage from the readings this week that really resonated with me. Social learning is a way to really engage in the subject matter because you are not only passively participating in the subject matter by reading it but also engaging in the subject by discussing it with your peers and in some instances with the instructor. The chapter also discusses surface learning in comparison with deep learning. Surface learning is when you know the basic facts of the material but don't understand it enough to use it out of direct context, or at least that was my understanding.