Microservers are a system-on-chip device featuring multiple single-socket servers sharing cooling fans, power supply, chassis, and other such hardware. This common platform allows microprocessors to achieve much higher power efficiency than conventional servers, since more tasks can be carried out on the same volume of power. Microservers were developed in order to integrate server motherboard tasks on to an easily portable and power-efficient unit. This eliminates the need for support chips complementing the server function, which has resulted in the power efficient and space-saving design of microservers. According to Transparency Market Research, the global microservers market was valued at more than US 1 bn in 2012. Due to the rapidly increasing adoption of microservers in diverse industries, the market is expected to exhibit a stellar 43.4% CAGR from 2013 to 2019, with the market's valuation expected to rise to US 30.2 bn over the period.

Is there anything that Google DeepMind can't do? It can defeat Go champions, potentially help those fighting blindness, and now it's helping Google itself to become more environmentally friendly. Bloomberg reported that the technology giant was able to use the artificial intelligence to reduce power consumption in its data centers. According to a blog post, DeepMind was able to help reduce the amount of energy used for cooling by up to 40 percent, which equates to around an overall 15 percent reduction. This is impressive considering Google said it used around 4,402,836 MWh (megawatt-hours) of electricity in 2014. For perspective, that's around what 366,903 US homes use yearly.

Raw data in its unprocessed state does not offer much value, but with the right analytics techniques can offer rich insights that can aid various aspects of life such as making business decisions, political campaigns, and advancing medical science. As shown in Figure 1, the analytics cycle can be broadly classified into four categories or phases: descriptive, diagnostic, predictive and prescriptive. Machine Learning is an approach to data analysis that automates analytical model building and is used in all four types of analytics. The relevance and the growing use of analytics using machine learning can be demonstrated by its widespread use in the 2016 US presidential election campaign. Unprecedented growth in the availability of useful information coupled with advancements in technology are making it attractive to use analytics to build and run a better campaign.

R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science platform. Big Data tools used by almost 40%, and Deep Learning usage doubles.

Chinese technology company Baidu today announced the launch of a few new services within its Baidu Open Cloud public cloud infrastructure portfolio. Baidu TianSuan (Smart Big Data) lets customers "collect, store, process and analyze big data," Baidu said in a statement. Baidu TianXiang (Smart Multimedia Cloud) includes face recognition and live video streaming, while Baidu TianGong (Intelligent IoT Service) is a full-stack platform for integrating cloud applications with internet-connected devices. The additions bring Baidu more in line with the world's leading cloud infrastructure providers, including Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Amazon and Microsoft have both introduced Internet of Things (IoT) services.

Google has finally revealed a commercial use for DeepMind -- a British artificial intelligence company it acquired for over 600 million in 2014. DeepMind made headlines for beating the best human in the world at the notoriously complex board game Go and it's recently started working with hospitals in the UK on a number of healthcare projects but the startup is yet to make any money for Google, until now. Google announced on Wednesday that it has been using a DeepMind-built AI system to control certain parts of its power-hungry data centers over the last few months as it looks to make its vast server farms more environmentally friendly. Last year, a Greenpeace report predicted that the electricity consumption of data centers is set to account for 12% of global electricity consumption by 2017 and companies like Google, Amazon, Facebook and Apple have some of the biggest data centers in the world. Google said it has been able to reduce the energy consumption of its data center cooling units -- used to stop Google's self-built servers from overheating -- by as much as 40% with the help of a DeepMind AI system.

Yelp has always relied on its users to share their dining experience with reviews. But now, it is experimenting with an AI to help it out. The San Francisco firm has developed software that uses image analysis to detect colour, texture and shape of objects in pictures in order to gather more information about a restaurant. As we live in an era where taking pictures before eating is part of the meal, the San Francisco firm has shifted its focus to images. Yelp has developed software that uses image analysis to detect colour, texture and shape of objects in pictures in order to gather more information about a restaurant.

Google tapped into the superior intelligence of its DeepMind neural network to find ways to vastly reduce the energy it uses in its data centers, which make up 40% of the worldwide Internet. "This will also help other companies who run on Google's cloud to improve their own energy efficiency," Google said in a blog about the achievement. "While Google is only one of many data center operators in the world, many are not powered by renewable energy as we are." Google has set a goal to eventually power its data centers using 100% renewable energy. Today, the company claims, renewable energy is used for 35% of its power needs.

Google tapped into the superior intelligence of its DeepMind neural network to find ways to vastly reduce the energy it uses in its data centers, which make up 40% of the worldwide Internet. "This will also help other companies who run on Google's cloud to improve their own energy efficiency," Google said in a blog about the achievement. "While Google is only one of many data center operators in the world, many are not powered by renewable energy as we are." Google has set a goal to eventually power its data centers using 100% renewable energy. Today, the company claims, renewable energy is used for 35% of its power needs.

Google has created artificial intelligence that's able to save the amount of electricity it uses to power its data centres. Using machine learning developed by the firm's AI research company, DeepMind, it was possible to reduce the energy used for cooling the centres by a staggering 40 per cent. By applying machine learning to its own centres, which power Google Search, Gmail, YouTube and all of Google's services, it was able to improve their efficiency. The algorithms and methods used could also be transferred to air conditioning systems in large manufacturing plants or, on an even larger scale, to reduce wastage in the energy grid. "What we've been trying to do is build a better predictive model that essentially uses less energy to power the cooling system by more accurately predicting when the incoming compute load is likely to land," Mustafa Suleyman, the co-founder of DeepMind told WIRED.