Google's data centers hold thousands of servers and they power everything from Google Search to Gmail to YouTube. But those data centers need to be kept cool in order for those servers to run reliably. A couple of years ago, Google began applying AI to its data center cooling systems and it offered system controllers recommendations about how to boost energy efficiency while maintaining optimal temperatures. Now, Google says its AI is running the show. When the company developed its AI-powered recommendation system, it said the thinking behind the move was simple.

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Automated machine learning is the new kid in town, and it's here to stay. It is helping us create better and better models with easy to use and great API's. AutoML is not automated data science. While there is undoubtedly overlap, machine learning is but one of many tools in the data science toolkit, and its use does not actually factor in to all data science tasks. For example, if prediction will be part of a given data science task, machine learning will be a useful component; however, machine learning may not play in to a descriptive analytics task at all.

Artificial intelligence (AI) is running one of Google's data centers -- or at least the cooling system in said data center. Today in a blog post, the Mountain View, California company said that it has turned over management of cooling controls to an AI-powered recommender system it jointly developed with DeepMind, its U.K.-based AI research subsidiary. Google claims it's the first fully autonomous model of its kind. "We wanted to achieve energy savings with less operator overhead," Dan Fuenffinger, a data center operator at Google, said. "Automating the system enabled us to implement more granular actions at greater frequency, while making fewer mistakes."

A list of machine learning frameworks has come into the picture for the development and deployment of the AI apps. Developers are quite bewildered which framework to pick and which to ditch. Some of the frameworks would focus on the easy usability while others may put emphasis on the production deployment and parameter optimization. Every framework will have highs and lows of their own. They would have their own areas of excellence and downfalls making the choice for the developers even more difficult.

I love technology that's earnestly bad at doing things. So a newish AI called AttnGAN makes me a very happy human. It's a machine learning algorithm that was trained to produce images based on text input. The algorithm, a Generative Adversarial Network (GAN), was published in January by researchers at Microsoft's Deep Learning Technology Center. Their work was also detailed in a paper posted to arXiv.org.

Researchers from the Institute of Cyber Intelligence Systems at the National Research Nuclear University MEPhI (Russia) have recently developed a new learning model for the restricted Boltzmann machine (a neural network), which optimizes the processes of semantic encoding, visualization and data recognition. The results of this research are published in the journal Optical Memory and Neural Networks. Today, deep neural networks with different architectures, such as convolutional, recurrent and autoencoder networks, are becoming an increasingly popular area of research. A number of high-tech companies, including Microsoft and Google, are using deep neural networks to design intelligent systems. In deep learning systems, the processes of feature selection and configuration are automated, which means that the networks can choose between the most effective algorithms for hierarchal feature extraction on their own.

Kingsoft Corp. cloud's computing brand is the world's leading cloud computing service provider and China's Top 3 cloud computing company. Founded in 2012, it has established data centers and operations in Beijing, Shanghai, Chengdu, Guangzhou, Hong Kong and North America. At present, Kingsoft has reached a valuation of 2.373 billion US dollars, becoming the independent cloud service provider in China with the highest market capitalization. Kingsoft cloud products include cloud service solutions for side industries such as games, video, government, healthcare, and finance. Kingsoft has been conducting research and practical applications of artificial intelligence, launching the four layered IaaS, Paas, SaaS industry solutions, which are applicable to various combined AI solutions and services in various industries. In 2018, Kingsoft launched the blockchain ecosystem plan, "Project-X", making full use of the advantages of the cloud to promote the development and application of blockchain technology. Bottos is an infrastructure that focuses on artificial intelligence. It possesses both an underlying public chain designed specifically for data property and a data flow platform for the entire artificial intelligence and its derivatives. A consensus-based, scalable, easy-to-develop, and collaborative one-stop application platform for data, models, computing power and storage of multi layered shared services through data mining and smart contracts.

DeepMind, a Google-owned artificial intelligence company, has developed an AI system that can accurately identify 50 different types of eye condition as accurately as a doctor. The system -- capable of analysing 3D retinal OCT scans for early signs of conditions like glaucoma, diabetic eye disease and macular degeneration -- has been developed through a joint research partnership with Moorfields Eye Hospital in London over the last 18 months. The AI, which can correctly identify types of eye disease from OCT scans 94.5% of the time, learned how to detect eye conditions by studying approximately 15,000 anonymous eye scans. The results of the trial were published in the journal Nature Medicine on Monday. DeepMind cofounder Mustafa Suleyman, who leads DeepMind Health, claimed in a blog post that the AI system could help to save people's sight, adding that it could one day be rolled out in hospitals around the world.

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