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.

Artificial Intelligence in the context of Alternative Asset management will manifest itself across a number of dimensions; the need to understand the vast quantities of data being generated by economic activity moving online, the ubiquity of networks for conducting these online transactions, and the emergence of algorithms for seeking investment opportunities and making investment decisions. There has already been a rise in the number of Quantitative Funds – such as Aidyia- relying on machine intelligence, neural networks and the ability to mine Big Data to compete alongside the likes of Renaissance Technologies, DE Shaw, Two Sigma and a host of other brand name funds such as Bridgewater Associates and Point72 Asset Management that investigating the scope for AI to generate Alpha. The rise of this machine intelligence can be broken down into two categories: "Active" AI uses technology to seek out opportunities for hedge fund alpha by analyzing data in unconventional ways, uncovering hidden correlations in market data or interpreting "signals" in the marketplace which then generate trading ideas. "Passive" AI takes place in the post-trade arena, opening up time-series of data, connecting flows between markets, counter parties and assets to support Portfolio Managers and Traders in their decision making. By using AI to open up post trade data more aggressively, funds could find their optimal financing mix or most credit-worthy counter parties by searching the market for news that may impact their credit rating or actively size/shape trading portfolio positions in real-time.

We've all experienced grabbing drinks with friends and getting stuck talking to someone where the conversation seems to go on too long. That same dynamic plays out in voice skills, and it's really important to measure, test, and refine your overall session length. You don't want your voice skill to be the bore of the party. The best way to measure when your app gets boring is to use a tool like VoiceLabs.co to track when the majority of your users start to drop off your user flow. For every voice skill, you'll hit a point where you've asked users too many questions and they drop off.

Google's self-driving cars have graduated from the company's "moonshot division", X labs, to become a full-blown subsidiary of umbrella group Alphabet, called Waymo. The new company, headed by X alumni John Krafcik, is charged with turning the self-driving car technology that Google has been developing behind closed doors into a viable business for the future. How that will actually happen, though, Waymo has apparently not yet decided. Mooted revenue streams include Uber-style driverless ride sharing, disrupting the trucking and logistics sector, fitting self-driving technology into public transit vehicles, improving the "last mile" of postal delivery or even simply licensing the software to car manufacturers who will sell cars to end users. "Self-driving technology is awesome in all these categories," said Krafcik at an event to announce the launch of Waymo.

Snapchat has finally launched group conversations, letting the app take on competitors like WhatsApp. The company will finally let people talk to a group of friends – up to 16 of them – rather than starting individual conversations with a range of different people. The move is perhaps one of its biggest launches in recent years. The company has been gradually looking to become more of a centre for people to have conversations, something that was severely limited by its inability to have group conversations like competitors including WhatsApp and Facebook Messenger. In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module'Kibo' of the International Space Station (ISS) and develop launch vehicles 32/39 The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight.

The Universal Store team in WDG is developing some of Microsoft's largest scale and business critical cloud services. These services have a huge global footprint of over 240 markets and process millions of transactions daily, with loads growing linearly as Microsoft moves to a "cloud first", "mobile first" strategy. The platform powers all of Microsoft's key services Windows App Store, Windows Phone, XBOX, Bing Ads, Office 365, Azure to name just a few. Whether renting a movie or buying a game on Xbox LIVE, purchasing an app on a Windows or Windows Phone device, signing up for an Office 365 subscription or paying for Azure services, you are using the Universal Store platform. We are the Developer Engagement & Monetization team in Bangalore with in the Universal Store organization in OSG responsible for providing the high yield monetization, data platform, developer knowledge graph, data/ML models, insights, intelligence, engagement & Monetization.