MENLO PARK (Web Desk) – Facebook has begun using artificial intelligence to help people with visual impairments to recognize objects in pictures and then describe photos aloud. Blind Facebook users scrolling through their feed have known for a while exactly what they were missing. Text-to-speech dictation software that describes the back-and-forth comments and recites friends' status updates would offer little when users came across an image: "Photo," the machine would say. Maybe a name, if the photo was tagged with a person. The feature was being tested on mobile devices powered by Apple iOS software and which have screen readers set to English.

The multibillion-dollar business plans being hatched by global automakers and technology companies for self-driving vehicles depend on a sensor that's less than 1 inch wide and costs all of 92 cents. And a company you've probably never heard of, Nippon Ceramic Co., controls about half of that market. Its stock is up more than 40 percent over the past three years and analysts expect profits to nearly double by 2018. In other words, it's good to be Nippon Ceramic right now. The Japanese maker of ultrasonic sensors, which help autonomous vehicles avoid crashes and fit into tight parking spaces, expects demand to double in the next five years and is expanding its production lines to keep up, President Shinichi Taniguchi said in an interview.

On the 10th day of every month, Junsuke Senoguchi has just one thing on his mind -- the closing level of the Nikkei 225 Stock Average. That's because Mr Senoguchi, an unassuming man in his late forties, has built a machine that's been predicting the direction of Japanese shares, and once a month he gets a progress report on its success. The model makes a simple call -- whether the equity index will be higher or lower after 30 days -- and over almost four years it's been right 68% of the time. "I'm so happy when it works", said Mr Senoguchi, a senior equity strategist at Mitsubishi UFJ Morgan Stanley Securities in Tokyo. "It's because I feel I can predict the future." Algorithms have invaded global share markets, used by everyone from high-frequency traders closing bets in fractions of a second, to specialist asset managers whose strategies are determined by complex quantitative analysis.

From the increasing use of contingent freelance workers to the growing role of robotics and smart machines, the corporate workforce is changing--radically and rapidly. These changes are no longer simply a distraction; they are now actively disrupting labor markets and the economy. Three years ago, Deloitte introduced the concept of the open talent economy, predicting that new labor models--on and off the balance sheet--would become increasingly important sources of talent.2 Granted, respondents to this year's survey rated workforce management the least important of the trends we explored. At an even more basic level, companies are struggling to understand who (and what) their workforces are composed of and how to manage today's incredibly diverse combination of worker types.

Naive Bayes is a simple but surprisingly powerful algorithm for predictive modeling. In this post you will discover the Naive Bayes algorithm for classification. This post is written for developers and does not assume any background in statistics or probability, although knowing a little probability wouldn't hurt. Naive Bayes for Machine Learning Photo by John Morgan, some rights reserved. In machine learning we are often interested in selecting the best hypothesis (h) given data (d).

REUTERS/Joshua RobertsAn engineer makes an adjustment to the robot "The Incredible Bionic Man" at the Smithsonian National Air and Space Museum in Washington October 17, 2013. It's the latest in a series of figures economists have released projecting the impact that AI systems and machines will have on the human workforce, and this one, from the World Economic Forum, predicts a "Fourth Industrial Revolution," characterized by unprecedented "developments in genetics, artificial intelligence, robotics, nanotechnology, 3D printing, and biotechnology." And while previous industrial revolutions have catapulted the human workforce forward, this one may set us back -- at least in the short term. According to the researchers at the WEF, "current trends could lead to a net employment impact of more than 5.1 million jobs lost to disruptive labor market changes over the period 2015–2020." The Forum estimates that a grand total of 7.1 million jobs will be lost as a direct result of many of our proudest innovations, and that two-thirds of these jobs will be "concentrated in the Office and Administrative job family."

Nvidia chief executive Jen-Hsun Huang has ridden the game industry to glory in the graphics chip business. But now those chips are being used for more than just games. And Nvidia now has a software development kit for deep-learning A.I. developers. That software kit will enable developers to create better deep-learning applications to solve problems such as enabling self-driving cars to recognize pedestrians. Nvidia is creating its own deep-learning chip and technology for self-driving cars, Huang said in a keynote speech at the GPUTech conference in San Jose, California.

Top-level certification means that the DataRobot predictive analytics engine can now be managed from the Cloudera management console and that it complies with Cloudera's preferred security and resource-management tools. DataRobot said certification status should help accelerate adoption of its machine learning algorithm platform by enterprises, which are typically reluctant to work with tools that haven't passed muster with key vendors. DataRobot has created a stir since it came out of stealth in February with a 33 million funding round from investors that included Accomplice LLC (formerly Atlas Ventures), Intel Capital Corp., IA Ventures, Recruit Strategic Partners Inc., and New York Life Insurance Co. The Boston-based company has raised a total of 57 million over the past three years. Its products and services address the pain being felt by many corporations over the shortage of skilled data scientists needed to drive their big data analytics efforts.

The Brand AI can analyse this liquid big data using its machine learning capabilities to create dynamic real-time personalised actionable insights seamlessly across a customer's physical and digital experience – it is the heartbeat of the retailer's invisible omni-channel offering. For example, the Brand AI can advise in-store sales staff in advance what specific products a customer wants or needs that particular day to help personalise this human interaction, provide on the spot guidance and critical feedback about products available immediately to drive a purchasing decision, or tailor in-store digital experiences such as virtual reality or media walls to create genuine moments of customer delight. In addition, the AI can capture the customer's emotional and physical reactions via wearables to these experiences (such as a raised heartbeat when seeing a new product for the first time); such insights can then be explored later by the customer (including socially with family and friends) using the AI on the retailer's integrated digital channel to sustain their retention. A further opportunity for using Brand AI is its potential ability to streamline inventory management to improve the customer experience and reduce operating risk.

So how could a scalable retail artificial intelligence in the cloud – Brand AI – turn these challenges into unique opportunities for competitive advantage? But unlike today's arguably bland, soulless smartphone versions that focus on delivering simple functionality; Brand AI would have a unique, human character that reflects the retailer's values to inform its interactions and maturing relationship with an individual customer. Intended to be more than another'digital novelty', this disruptive form of customer engagement builds on and enhances a B&M's traditional brand as a trusted long term friend throughout the entire customer journey by offering compelling, timely presale insights, instant payment processing and effective after sales support and care. A customer is empowered to select what personal data they choose to share (or keep private) with the Brand AI to enrich their relationship. Social, location, wearable or browsing and buying behaviour data from complementary or even competing retailers could potentially be shared via its cloud platform.