With the ability to revolutionize everything from self-driving cars to robotic surgeons, artificial intelligence is on the cutting edge of tech innovation. Two of the most widely recognized AI services are Microsoft's Azure Machine Learning and IBM's Watson. Both boast impressive functionality, but which one should you choose for your business? Azure Machine Learning is a cloud-based service that allows data scientists or developers to train, build and deploy ML models. It has a rich set of tools that makes it easy to create predictive analytics solutions. This service can be used to build predictive models using a variety of ML algorithms, including regression, classification and clustering.

Next time your power stays on during a severe weather event, you may have a machine learning model to thank. Researchers at the MIT-IBM Watson AI Lab are using artificial intelligence to solve power grid failures. The manager of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine learning model that works to analyze data collected from hundreds of thousands of sensors located across the U.S. power grid. The sensors, components of what is known as synchrophasor technology, compile vast amounts of real-time data related to electric current and voltage in order to monitor the health of the grid and locate anomalies that could cause outages. Synchrophasor analysis requires intensive computational resources due to the size and real-time nature of the data streams the sensors produce.

Subsequent time your energy stays on throughout a extreme climate occasion, you could have a machine studying mannequin to thank. Researchers on the MIT-IBM Watson AI Lab are utilizing synthetic intelligence to resolve energy grid failures. The supervisor of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine studying mannequin that works to investigate knowledge collected from tons of of 1000's of sensors situated throughout the U.S. energy grid. The sensors, parts of what's referred to as synchrophasor expertise, compile huge quantities of real-time knowledge associated to electrical present and voltage in an effort to monitor the well being of the grid and find anomalies that would trigger outages. Synchrophasor evaluation requires intensive computational sources as a result of dimension and real-time nature of the info streams the sensors produce.

Next time your power stays on during a severe weather event, you may have a machine learning model to thank. Researchers at the MIT-IBM Watson AI Lab are using artificial intelligence to solve power grid failures. The manager of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine learning model that works to analyze data collected from hundreds of thousands of sensors located across the U.S. power grid. The sensors, components of what is known as synchrophasor technology, compile vast amounts of real-time data related to electric current and voltage in order to monitor the health of the grid and locate anomalies that could cause outages. Synchrophasor analysis requires intensive computational resources due to the size and real-time nature of the data streams the sensors produce.

Investors looking to diversify their portfolios and coin collectors looking to add a new treasure to their collection are familiar with the benefits and value that precious metals can offer. To help make the purchasing process easier, IBM worked with TD Securities to launch an AI-based virtual assistant powered by IBM Watson Assistant that can help customers with inquiries on the TD Precious Metals digital store, including frequently asked questions. The TD Precious Metals digital store allows customers to buy physical gold, silver and platinum bullion and coins online from the comfort of their home. The new virtual assistant, now available as a feature on the TD Precious Metals digital store, provides customers with a convenient self-service option, available 24/7, for frequently asked questions about TD Precious Metals. Customers type their questions into the virtual assistant and receive an instant written response, along with links to help further assist them.

「IBM Watson Assistant」を搭載した人工知能（AI）ベースの新しいバーチャルアシスタントがTD Securitiesの貴金属事業のデジタルストアで利用可能になっている。

Most likely, you're familiar with Watson from the IBM computer system's appearance on Jeopardy! in 2011, when it beat former champions Ken Jennings and Brad Rudder. Watson Health was supposed to change health care in a lot of important ways, by providing insight to oncologists about care for cancer patients, delivering insight to pharmaceutical companies about drug development, helping to match patients with clinical trials, and more. It sounded revolutionary, but it never really worked. Recently, Watson Health was, essentially, sold for parts: Francisco Partners, a private equity firm, bought some of Watson's data and analytics products for what Bloomberg News said was more than $1 billion. On Friday's episode of What Next: TBD, I spoke with Casey Ross, technology correspondent for Stat News, who has been covering Watson Health for years, about how Watson went from being the future of health care to being sold for scraps.

Ten years ago, IBM made a gamble. Through a monumental advertising and PR campaign, it promised that its AI technology–Watson–would transform the health care industry as we know it. A decade and billions of dollars later, Watson Health is being sold for parts. What went wrong with IBM's "moonshot?" And what does Watson's failure tell us about the promise of AI for health care?

IBM said Friday it will sell the core data assets of its Watson Health division to a San Francisco-based private equity firm, marking the staggering collapse of its ambitious artificial intelligence effort that failed to live up to its promises to transform everything from drug discovery to cancer care. Data and analytics assets held by the health business, which was not profitable, were sold to Francisco Partners as IBM seeks to refocus its business on cloud computing and AI services to help clients in multiple industries build machine learning tools and secure and manage their data. Terms of the transaction were not disclosed. Unlock this article by subscribing to STAT and enjoy your first 30 days free! STAT is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis.

Watson, a supercomputer by IBM, shot to fame in 2011 as the'brain' that beat two of the best contestants of Jeopardy! to win a million dollars. This system that combines artificial intelligence (AI) and sophisticated analytical software to answer questions was widely deployed in many industries. The supercomputer was developed in IBM's DeepQA project and was named after IBM's founder Thomas J. Watson. "You can be discouraged by failure, or you can learn from it. So go ahead and make mistakes, make all you can. Because, remember that's where you'll find success – on the far side of failure."