Recently, we had written about how Expedia is planning to become the first $100 billion online travel company. One of the main drivers to help Expedia reach its target is technological investments. In one of its moves towards technological advancements, Expedia has recently announced voice activated searches on its platform through Amazon Alexa. This is the company's first move towards introducing a voice activated search product and it marks progress in Expedia's ongoing work on the processing of natural language. The feature will enable Expedia customers to ask Alexa about the status of their existing flight reservations, it will help them rent cars and also answer queries about the customers' loyalty program numbers.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference? There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields.

The Minecraft video game is popular with children; now a version is being used to test artificial-intelligence programs. The Minecraft video game was familiar to José Hernández-Orallo long before he started using it for his own research. The computer scientist, who devises ways to benchmark machine intelligence at the Polytechnic University of Valencia in Spain, first watched his own children play inside the 3D virtual world, which focuses on solving problems rather than shooting monsters. In 2014, Microsoft bought Minecraft, and its science arm, Microsoft Research, gave its own researchers access to a new version of the game that allowed computer programs, as well as people, to explore and customize the 3D environment. Then, after inviting a small group of outside researchers that included Hernández-Orallo to download the machine-friendly version of the world, last July, Microsoft made it freely available to anyone, with the goal of speeding up progress in artificial intelligence (AI).

Business as we know it is changing. But some business leaders aren't convinced that it will affect them or their business. But the truth is, business is changing at a fundamental level. Some of the key leadership challenges of today include the advent of big data, data becoming a major business asset, AI taking over, platform businesses challenge traditional suppliers, the sharing economy, 3 D printing, robots becoming more intelligent, and more. These eye-opening facts show the importance of all the things I continue to write about here, capturing the key tech trends and business challenges every leader should reflect on...

M6 Publicité and M6 Web Select Accenture's New Advertising Offering, Powered by Artificial Intelligence, To Target Ads More Effectively NEW YORK; Dec. 13, 2016 – M6 Publicité and M6 Web, Group M6 subsidiaries, have chosen Accenture's (NYSE: ACN) new Advanced Video Analytics offering that uses artificial intelligence to more accurately predict customer advertising preferences and serve targeted ads more effectively. M6 Group is the second largest private TV Group in France with major traditional TV channels such as M6, W9, and 6ter, as well as 6play, a free digital TV platform. M6 Publicité and M6 Web chose Accenture to implement a powerful data-driven advertising optimization tool run by a team of data scientists, analysts and developers. This data tool integrates several Accenture offerings including artificial intelligence that increases the number of users reached with targeted ads. Processing large amounts of historical user data, artificial intelligence can predict with more precision users' interests, behaviors, and purchasing actions.

There is a lot of buzz around the word „AI" in legal tech at the moment. AI is portrayed as the number one trend and disruptive technology in the legal industry and beyond. Consequently, many lawyers want to understand how AI might change or disrupt the legal industry. Some are already a step ahead and posing the question how they can use it to reinvent their own law firms. The hype (or hysteria) around AI seems to be sometimes founded in an unrealistic picture of the capabilities of current AI technology. AI seems to be almost magical. A technology we do not really understand but with a potential to heal all our problems and inherent human limitation. Today's AI is far away from being magical or a „strong AI" (or full AI).

"The only function of economic forecasting is to make astrology look respectable" John Kenneth Galbraith Predictive modeling and traditional ratemaking is an exercise of forecasting the future, whether directly or indirectly (indirectly as generalizing historical lessons to the future). But is such forecasting so hopeless as being same as seeing into a crystal ball? The advantage of data scientist and actuaries is their close in contact with other business professionals. That means leverage to obtain, generate and apply a lot of expert judgment to try to overcome the shortcomings of quantitative modeling. Perhaps that is why such methods have been so successful over time. 'Data scientist' and'actuary' will be used synonymously here.

After a potential customer has first contact with a business, sales gets in gear, often responding with an introductory email. Conversica has developed an artificial intelligence system designed to automate these early contact emails, and pass it off to a human salesperson when the time is right. Today, it announced a $30 million investment. The round was led by Providence Strategic Growth. Toba Capital, Wellington Financial LP, Recruit Strategic Partners and Series A investor Kennet Partners also participated. Today's investment brings the total to $56 million.

The Allen Institute for Artificial Intelligence has organized a 4 month contest in Kaggle on question answering. The aim is to create a system which can correctly answer the questions from the 8th grade science exams of US schools (biology, chemistry, physics etc.). DeepHack Lab organized a scientific school hackathon devoted to this contest in Moscow. Our team decided to use this opportunity to explore the deep learning techniques on question answering (although they seem to be far behind traditional systems). We tried to implement Dynamic memory networks described in a paper by A. Kumar et al.

It's not just for developers with a farm of servers and GPUs, though--hobbyists and modest users can be equally competitive because the Toolkit is flexible enough to run on a single laptop. Developers can also integrate into the Toolkit their own Python or C code.