Let the Great Messaging War of 2017 begin. Today, Google is launching Allo, a revamped messaging app meant to compete with the likes of Apple Messages and Facebook Messenger--two products that in recent months have steadily unveiled a slew of new features. Google thinks it could leapfrog them both, thanks to its most valuable assets: a wealth of data about what we do and search for online, and the billions of dollars it has invested in machine learning. When you message people, Allo creates smart replies akin to those found in Inbox--but those smart replies are carefully calibrated to both the content and context of your conversation. So, for example, if someone sends you a picture of them skydiving, you can immediately tap on a series of well-tuned responses: "So brave," "How fun," and "So exciting!"

Blizzard Entertainment announced it has restored access to its gaming servers. The login issues have been resolved. UPDATE: 7:30 a.m. EDT -- With about two hours since the DDoS attack began on the servers of Blizzard Entertainment, popular games like World of Warcraft and Overwatch are still inaccessible by players around the world. PoodleCorp, which took responsibility for the latest DDoS attack on the gaming company, said it would stop the attack when its tweet announcing the attack was shared 3,000 times. And as of 7:30 a.m. EDT, it had been retweeted just over 1,100 times. Some gamers, livid at the repeated interruption of Blizzard servers over the last few weeks, were angry with PoodleCorp for targeting the gaming company repeatedly.

Last week, I introduced the notion that businesses can gain deeper customer insights if they connect their disparate data silos. Similar to how oncologists can leverage information from genome sequencing to tailor cancer treatments for a specific patient in order to improve health outcomes, businesses can use all customer data from disparate data silos to personalize interactions with their customers to improve customer loyalty. Using the 2x2 graphical approach to understanding data size (i.e., number of customers and number of variables), we can see how the value of your integrated business data is greater than the sum of its parts. Figure 1 illustrates these two components of size by examining four different scenarios of how businesses use their data. In the lower right quadrant, it is business as usual; when departments keep their data siloed, each department only knows a few things about the customers.

The business world is full of streams of items that need to be filtered or evaluated: parts on an assembly line, resumés in an application pile, emails in a delivery queue, transactions awaiting processing. Machine learning techniques are increasingly being used to make such processes more efficient: image processing to flag bad parts, text analysis to surface good candidates, spam filtering to sort email, fraud detection to lower transaction costs etc. In this article, I show how you can take business factors into account when using machine learning to solve these kinds of problems with binary classifiers. Specifically, I show how the concept of expected utility from the field of economics maps onto the Receiver Operating Characteristic (ROC) space often used by machine learning practitioners to compare and evaluate models for binary classification. I begin with a parable illustrating the dangers of not taking such factors into account. This concrete story is followed by a more formal mathematical look at the use of indifference curves in ROC space to avoid this kind of problem and guide model development. I wrap up with some recommendations for successfully using binary classifiers to solve business problems.

Ever wish you could take some of your extra money and double or even triple it on the stock market? Unfortunately, the market is tricky, it can be hard to know what to do, how to read the trends and figure out where the money is to be made. Luckily, you can get started with all you need to know for only a small investment. With this complete Machine Learning bundle for just 40 you can get access to 64 lectures, 11 hours of content and more at anytime you want it. You can set up historical price databases in MySQL using Python, learn Python libraries and even access the source code any time as a continued resource.

Five years ago, music personalization at Spotify was a tiny team. The team read papers, developed models, wrote data pipelines and built services. Today personalization involves multiple teams in New York, Boston & Stockholm producing datasets, feature engineering and serving up products to users. Features like Discover Weekly and Release Radar are but the tip of a huge personalization iceberg. One thing we have noticed is the overhead of running services.

IBM and the Massachusetts Institute Technology (MIT) are teaming up to advance the development of machine vision using insights from the brain and cognitive research. The multi-year partnership will see IBM Research collaborate with MIT's Department of Brain & Cognitive Sciences (BCS) to advance frontiers of artificial intelligence in real-world audio-visual comprehension technologies. The organisations are building a research laboratory for brain-inspired multimedia machine comprehension (BM3C) in Cambridge, Massachusetts. Together they plan to develop cognitive computing systems that mimic the human ability to understand and integrate input from several sources for use in various computer applications in industries like healthcare, education, and entertainment. MIT researchers will work with IBM scientists and engineers, who will offer technology expertise and advances from the IBM Watson platform.

I listened to this podcast on my drive to Michigan last Friday. If you like to think about the future, and where the puck is going, put your headphones on and listen critically. What is revealed is the way machine learning can be used to create biased and unbiased conclusions. It's always been known that if you start with the wrong hypothesis when using statistical analysis, you will reach a bad conclusion. There is a humorous blog, Spurious Correlations, that turns statistics on its head.

In addition, the solution reduces friction for existing trustworthy customers by eliminating time consuming and step-up challenges. For more information about iovation, please check the profile in the company database.

The new way of thinking about AI is to see it doing time-consuming tasks, freeing up space for accountants to do the serious thinking and to exercise professional judgement on more complex matters. Despite some people's fears, AI's advocates say it can be a job-creator, not a job-killer. For years, there have been fears that Artificial Intelligence (AI) – smart machines that work and react like humans while having self-learning capabilities – will redefine the role of accountants. Now innovative firms are investing in AI so they can be at the forefront of cognitive technologies. What does the evolution from automation and data-analytics software to AI mean for accountants?