Research provider Morningstar recently introduced a quantitative equity rating generated by a machine learning statistical model that tries to replicate the results produced by a human analyst. Lee Davidson, head of quantitative research, and Zurab Margvelashvili, a quantitative performance analyst, wrote in the August/September 2016 issue of Morningstar magazine that the firm has approximately 120 equity and credit analysts, who cover about 1,700 equities, and produce a Morningstar Rating for Stocks. "To complement our analysts' work and extend the coverage universe, Morningstar recently introduced the Morningstar Quantitative Equity Rating – a forward-looking measure that is generated by a machine learning statistical model that attempts to produce ratings and statistics that would have been produced by Morningstar analysts," said the article. The machine learning algorithm, called random forest, has increased Morningstar's coverage to 50,000 companies in 86 countries that trade on 64 exchanges. "The ratings provide additional benefits, including the ability to analyse portfolios by aggregating data and providing daily history to track changes over time," they wrote.

The estimate of the gradient from just a mini-batch is usually good enough to point you in the right descent direction. It doesn't make sense to do the extra computation for a marginally better estimate. Plus, the inaccuracy or noise introduced by the mini-batch approximation can act as a regularizer. Here is an interesting paper that performs statistical tests during optimization: if the gradient is not statistically significant, more samples are added to the mini-batch.

The latest version of Facebook Messenger adds a new feature to the 30,000 or so chatbots that currently inhabit its platform. Starting today with version 1.2, those Messenger bots can now accept payments directly in the chat without sending users to an external website. The feature was announced today at TechCrunch Disrupt by Facebook's Messenger chief David Marcus, who noted that the payments system will support most credit cards and payment services including Visa, MasterCard, American Express, Stripe, PayPal and Braintree. From the consumer side, users will see "Buy Now" links popping up in relevant chats. Tapping the link will bring up a quick payment confirmation tab that automatically defaults to whichever credit card a user has tied to their Facebook or Messenger account.

"By 2020 customers will manage 85 percent of their relationship with an enterprise without interacting with a human." "Why Salespeople Need to Develop Machine Intelligence" Harvard Business Review The answer is yes . . . It depends on an organization's strategy for leveraging AI as a competitive advantage. As human and machine become more integrated in sales and marketing, companies exploring AI for sales are focused on where, when and how AI can replace the human element. This requires CEOs to work with company leadership to evaluate sales and marketing ecosystems that have become a patchwork of investments in sometimes outmoded, "aggregation-based" software platforms for lead generation, pipeline management and forecasting.

On Friday International Business Machines Corporation (NYSE:IBM) share price closed at 155.69. Company net profit margin stands at 14.90% whereas its return on equity (ROE) is 82.00%. International Business Machines Corporation (NYSE:IBM) is -5.61% away from its 52 week high and its 52 week range is 116.90 – 164.95. International Business Machines Corporation (NYSE:IBM) announced the launch of the aptly-named IBM Power Systems S822LC for High Performance Computing. Its unwieldy name betrays the fact that this is a really interesting product.

When we see two people meet, we can often predict what happens next: A handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences. Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations. This is your chance to join them.

The WaveNet system can be thought of as an improvement upon concatenative text to speech, in that it still employs recordings of real human voices. But instead of chopping these up and reorganizing them in the old way, it uses an artificial neural network to generate synthetic utterances based upon the voices it was trained with. The downside is that this system is computationally intensive. Modeling raw audio typically requires 16,000 samples per second, with each sample being influenced by all the previous ones. This is well beyond the processing power of a typical smartphone, but not unthinkable for GPUs like Nvidia's DGX-1 deep learning supercomputer.

As the enterprise becomes more steeped in machine learning, cognitive computing and other forms of autonomous infrastructure, the issue of artificial intelligence ethics keeps coming up. Science fiction abounds with tales of benign computer systems that suddenly conclude people are simply inadequate and set about destroying humanity. As amusing as it is, though, how realistic is it that artificial intelligence (AI) could someday pose a hazard due to its development as it continues to evolve? Are there any reasons (and surefire means) to keep it in check? According to WebVisions, there are two ways to program ethics into machines: Hard code them into their operating systems or establish guidelines that allow these machines to reach ethical conclusions on their own.

Blog Post:Since the early 2000s, brands have experimented with social media platforms and networks to communicate with customers and prospects -- first through weblogs, then eventually through social networks such as Facebook and Twitter. Although the capabilities and sophistication have continued to evolve, at its core, social media has remained a platform to facilitate human-to-human communication. But then the robots moved in. Robots, though more specifically virtual robots or chatbots powered by artificial intelligence (AI), are transforming the way brands do business with their customers. Domino's was one of the first companies to dabble in AI, allowing customers to order pizza by tweeting a pizza emoji to @Dominos.

Ray Dalio, the founder of Bridgewater Associates, the largest hedge fund in the world, hired IBM's Watson programming team to incorporate artificial intelligence into the investment process. Later this year, I will be doing something similar so stay tuned to learn more about this exciting endeavor. Today, we have Jamie Wise from BUZZ Indexes, based in Toronto, who recently launched an Artificial Intelligence driven ETF based index fund BUZ (US). The investment process uses natural language processing software and artificial intelligence to scour the Internet for tweets, blogs, and searches for company names. One of the most interesting things is that the software is able distinguish between someone typing amazon when talking about a bad product they bought on line and AMZN and a bad outlook for the stock.