Microsoft is positioning itself as the world's platform for artificial intelligence, and that's a smart move. In 2014 I wrote that Microsoft's Cortana would be the next big thing. Redmond's vision for its johnny-come-lately AI is that it, like the GUI before it, will be pivotal in the evolution of the personal computing user interface. Microsoft's ambitions for Cortana were evident in 2014. Microsoft envisions an unbounded AI that developers and partners will incorporate into a range of everyday and innovative devices enabled by the Cortana SDK.

Job automation predictions from an individual expert typically draw from years of academic research experience, or time "in the trenches" of industry. With growing interest and speculation on the job market of the next decade, we set out to garner a perspective as to what Silicon Valley thinks about the possibilities of automations in various business tasks. In the infographics and article below, we explore the survey responses from nearly 80 Bay Area investors, founders, and tech folks – on which business functions have the greatest potential for automation today, and in the coming five years ahead. Together with San Fransisco-based venture firm BootstrapLabs, we designed a simple survey that was handed out during their "Autonomous Corporation" event in November 2016. It is interesting to note all three groups of respondents considered business intelligence to be the business function with the most current automation potential.

NAGOYA – Major auto parts maker Denso Corp. will team up with NEC Corp. on developing self-driving automobile technology that makes use of artificial intelligence, sources at the two companies said. With NEC boasting expertise in developing artificial intelligence and software, Denso plans to use its information technology expertise to accelerate research into automotive parts for next-generation vehicles. Denso hopes to marry its camera and sensor technology with NEC's "deep-learning" AI technology to develop systems that will enable cars to sense danger and avoid people and obstacles in their way, the sources said Saturday. The deal will also work toward enhancing cybersecurity due to the surge in data leaks and other dangers expected as automakers keep pushing to link cars and trucks to the internet. With alliances between the automotive and IT industries playing important roles in developing next-generation vehicles, Denso has moved to tie up with Toshiba Corp. and Sony Corp. to compete with overseas.

On Tuesday, the White House released a chilling report on AI and the economy. It began by positing that "it is to be expected that machines will continue to reach and exceed human performance on more and more tasks," and it warned of massive job losses. Yet to counter this threat, the government makes a recommendation that may sound absurd: we have to increase investment in AI. The risk to productivity and the US's competitive advantage is too high to do anything but double down on it. This approach not only makes sense, but also is the only approach that makes sense.

Regression is a modelling technique for predicting the values of an outcome variable from one or more explanatory variables. Logistic Regression is a specific approach for describing a binary outcome variable (for example yes/no). Let's assume you are own a new boutique shop. You have a list of potential clients you are thinking of inviting to a special event with the aim of maximizing the number of sales – who should you invite? Data on previous events you have run is a great starting point here, allowing you to predict an individual's likelihood of buying given the information you have on them.

By Data Munging we mean the process of taking raw data, understanding it, cleaning it and preparing it for analysis or modelling. It is by no means the glamorous part of data science however if done well it plays a more important role in getting to powerful models and insights than what algorithm you use. So, you've been given a new dataset and are looking to model some behaviors in the data. It is really easy to jump straight in and start running regression or machine learning but this is a mistake. The first step is to really understand the data, starting from a univariate view and slowly building out.

Clustering using tagging or indexation methods (see section 3 after clicking on the link), allowing you to cluster text (articles, websites) much faster than any traditional statistical technique, with a scalable algorithm very easy to implement Bucketization - the science and art of identifying the right homogeneous data buckets (millions of buckets among billions of observations), to provide highly localized (or segment-targeted) predictions, or to smooth regression parameters across similar buckets, with strong statistical significance. It is equivalent to joint (not sequential) binning in multiple dimensions, which is a combinatorial optimization problem. While decision trees also produce some bucketization, the data science approach is more robust, simple, scalable and model-free. It does not directly produce decision trees, and lead to easy interpretation (each data bucket corresponding to a specific type of fraud, in a fraud detection problem). A related problem is bucket clustering, via standard hierarchical clustering techniques.

Starbucks has led the way for not just fast-casual restaurants, but all of retail when it comes to using customer-facing technology in its stores. The company was the first major chain to integrate digital payment into its app, making it a common sight to see people pay by holding up their phones to a scanner. That happened well before payment via phone become a relatively common thing, and it forced other chains to follow. Starbucks also led the way with Mobile Order & Pay. That technology allows people to skip the line, creating a better experience for regular customers while offering shorter lines for casual visitors.

The diagram above uses synthetic data to show how a threshold classifier works. As you can see, picking a threshold requires some tradeoffs. Too low, and the bank gives loans to many people who default. Too high, and many people who deserve a loan won't get one. So what is the best threshold?

The ASA contacted the Statistical Consulting and Survey Center in the Augusta University Department of Biostatistics to help design and analyze the data for a survey of the association's nonacademic members in the United States employed by business, industry, or government. Members were asked to report their annual base salary (in dollars) and instructed to include bonuses, incentives, or other forms of monetary reward. Salary was "annualized" for part-time employed respondents. All salary statistics are reported as full-time equivalents in dollars per year. Salary information, in the form of percentiles, is for a 12-month period.