Economist Andrew McAfee concludes in his TedTalk "What will future jobs look like?" (already 3yrs old) that "The new'algorithm enabling' technology is here today, and banks could use it to fundamentally change the value proposition for their customers." "The future belongs to those that can recognize opportunities before they become obvious". Artificial intelligence, which encompasses these days all sorts of'algorithm enabling' technology, is creeping into our lives. Asset management and wealth management is no exception. Mentors at Fintech accelerators are advising entrepreneurs to drop the idea of creating the next Bloomberg and are suggesting a focus on AI finance.

Microsoft has created a strange robot that can converse with people on Twitter. The robot, apparently named Tay, is artificially intelligent and speaks with people who send messages to it. It appears to be based on Microsoft's machine learning work and claims that it will get better as it is used. The account, found at @TayandYou, responds automatically to all tweets. But it remains a complete mystery why Microsoft created the account – which has been verified by Twitter – and what it plans to do with it. Boston Dynamics describes itself as'building dynamic robots and software for human simulation'.

On one otherwise unremarkable day in May 2013, August de los Reyes fell out of bed and hurt his back. Forty-two years old at the time, he was just six months into his dream job at Microsoft: running design for Xbox and righting a franchise that was drifting due to mission creep. At first, de los Reyes was worried that the fall was serious; he went to the ER and was assured that he was fine. Yet several hospital trips later, he found himself undergoing emergency surgery. His spine had been fractured all along. His spinal cord had been damaged. With breathtaking quickness, he was unable to walk ever again. De los Reyes has the reassuring smile and steady calm of a high-school guidance counselor, and an almost-spiritual attachment to video games. He likes to tell people that the universe is play, and that we all have a moral imperative to play.

Intelligence TV is a world?class team of dedicated machine learning entrepreneurs, luminaries and scholars -- a cogent growing force of true connoisseurs, innovative organizations and ultra-affluent leaders -- offering state of the art techniques from artificial intelligence to build the best deep learning algorithms: The experienced team at Billionaire Business Enterprises Inc. acts as a seasoned and trustworthy resource. Intelligence TV, in compliance with Executive Order EO: III EX[1-I], has a Jewel status in Billionaire Business Enterprises Inc.'s portfolio. Update: October 2015 -- We are preparing a worldwide PR campaing with major talk show appearances, a feature film and a television documentary. A tribute to Artificial Intelligence (AI) Bringing contributions by scholars recognized as the foremost authorities in AI, Big Data and Deep Learning, exalting ground?breaking Terms of Reference are developed in consultation with the Intelligence TV Stakeholders, the Program Overview Committee and the Service Line Advisory Group.

Are we looking for an algorithm that "If we all die, it would keep trading"? Should we be worried that electronic trading is mushrooming like airplane traffic, while we are not paying that much attention? Today, I'll look for AI pigments of incremental changes in algorithmic trading, first on Wall Street and then outside, in the Fintech startup world. I am not including the HFT space because it is a particular space driven by speed and merits a separate post because of its politically sensitive angle (Michael Lewis's babe). Renaissance Tech and Two Sigma, are probably the most recognizable names in old fashioned quant trading space.

Cherubic Ventures, an early-stage VC firm with a focus on the U.S. and Asia, has announced a new fund targeted at 120 million. Fundraising is still ongoing, and founder-managing partner Matt Cheng told TechCrunch that the firm currently has 80 million in confirmed commitments from LPs. Cheng started Cherubic back in 2010 after spending time with Chinese social portal Tian Ge Interactive and C Squared Venture Capital. Today, its portfolio includes close to 90 companies worldwide, such as Hyperloop, Luxe and Wish in the U.S., Cloud Union and Liulishuo in China, and Pinkoi and Chadal in the wider Asian continent. The firm has offices in Beijing, Taipei and San Francisco but, alongside the announcement of the fund, Cheng said it had opened a new headquarters in Shanghai. That will house around half of the firm's team, which Cheng expects will increase to 12 this year.

Today is a big day for whatGoogle just bought a startup that takes the pain out of editing videos. Read more ... » very well could be the next big business at GoogleGoogle Acquires Fly Labs To Bring Photo Editing To Google Photos. In San Francisco, the company is holding its first cloud userMicrosoft Dynamics AX Upgrade Integrates Power BI, Machine Learning. Read more ... » conference, a perch to show off its product and win more paying customers. Keynotes will come from new enterprise boss Diane Greene and Urs Hölzle, its veteran infrastructureGoogle Just Open Sourced TensorFlow, Its Artificial Intelligence Engine. Also on the slate is Nicholas Harteau, the engineeringMicrosoft Invests 1 Billion In'Holistic' Security Strategy.

All around us people are slowly (or sometimes quickly) transitioning into the future of work. The full-time job (9 to 5, traditional career, etc.) is about to become a rarity; only available to a select group of people who represent the core of an organization, or who possess a very specific skill set. Because we live in a society increasingly shaped by tech. Automation will take over many of the tasks previously assigned to people. And the youth of today (and tomorrow) will have no problem transitioning into that situation.

'Organizations are pivoting towards operational analytics as it can both increase the efficiency and performance of the back office as well as boost the customer experience in the front office.' comments Anne-Laure Thieullent, Head of Big Data in Europe, for Capgemini's Insights & Data global practice. 'However, despite the focus, there are factors limiting the success of these projects; specifically siloed datasets, fragile governance models, inability to harness third party data sources, and an absence of a strong mandate from leadership teams.' 'Going Big: Why Organizations Need to Focus on Operations Analytics' from Capgemini Consulting's Digital Transformation Institute mapped organizations based on the extent to which their analytics initiatives were integrated with core operations processes and their success rate with initiatives, identifying four stages of operational analytics maturity: Capgemini Consulting's Digital Transformation Institute applied the four stages of operational analytics maturity to build up a geographic picture of adoption and success rates around the world. US companies are not only the most advanced with their analytics initiatives but also the most successful; 50 percent have successfully realized the desired benefits from operational analytics compared to only 23 percent of Chinese respondents, despite China ranking highly for level of implementation. A strong contributing factor of the success of US companies is their focus on setting up effective data and governance processes. The prominence of US organizations tallies with a recent resurgence in US manufacturing and will drive US manufacturing competitiveness in the coming years.

A small step for Google may very soon become a giant step for mankind. An artificially intelligent computer system built by Google has just beaten the world's best human, Lee Sedol of South Korea, at an ancient strategy game called Go. Go originated in Asia about 2,500 years ago and is considered many, many times more complex than chess, which fell to AI back in 1997. Google's programmers didn't explicitly teach AlphaGo – that's what the system is called - to play the game. Instead, they built a sort of model brain called a neural network that learned how to play Go by itself. As it studied a database of about 100,000 human matches, and then continued by playing against itself millions of times, it constantly reprogrammed itself and improved.