When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. New York, meet the world's tech scene This is your chance to join them. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination.

The answer to the question "What machine learning algorithm should I use?" is always "It depends." It depends on the size, quality, and nature of the data. It depends what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Even the most experienced data scientists can't tell which algorithm will perform best before trying them. The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Microsoft Azure Machine Learning library of algorithms.

Stephanie Yee and Tony Chu created a step-by-step visual explanation of what actually happens as a machine "learns." It's part one of an interesting exercise to explain statistical concepts through interaction design.

Succeeding in today's fast-paced business economy requires companies to harness data quickly and at scale. As the volume, velocity, and variety of data increase, it's becoming necessary to use machine learning and artificial intelligence (AI) to sift through all the incoming information, make sense of it, and accurately predict future business direction. It takes the right expertise, the right tools, and the right data to achieve the promise of machine learning. Even with all of those factors in place, it's still easy to get it wrong. "Machine learning gives us a very powerful set of techniques for making predictions, but it can also lead to disastrous results if you don't understand what your machine learning algorithm is doing," said Spencer Greenberg, a mathematician and founder of decision-making website ClearerThinking.org, in an interview.

Three years ago, Jeff Bezos announced that drones are eventually going to deliver Amazon orders. In the past year, he brought out Amazon's Alexa artificial intelligence service, which understands speech well enough that you can say, "Alexa, I really need a waffle cone maker," and she'll put one in your Amazon online shopping cart, even though nobody needs a waffle cone maker. Both of these technologies--drones and cloud AI--are exciting today, yet still wobbly works in progress. But in coming years, Amazon or some other company is going to put them together. And that, finally, will evolve into a technology that could become as significant to humans as domesticated dogs.

Bengaluru: Wipro Ltd, which launched its artificial intelligence (AI) platform Holmes 18 months ago, is now making its biggest push to embrace automation by allowing more of its managers to identify work which will not require engineers in each of the over 20,000 projects currently underway. This "bold" development, according to one executive who did not want to be identified will mean Wipro doesn't just save on costs (thereby arresting falling profitability), but fundamentally alters the traditional model of deploying armies of engineers to undertake maintenance work. To be sure, that could also mean that its current workforce needs to learn new technology skills swiftly to stay relevant. Wipro is in the process of appointing individual leaders in each of the six industry-serving segments (which the company calls strategic business units) and five solution offering verticals or practices, who will be entrusted with the job of automating mundane maintenance work. Until now, Wipro's chief technology officer K.R. Sanjiv, in consultation with various segment heads used to decide which projects could use Holmes.

The Spain-headquartered startup, which today is announcing $6 million in Series A funding, is using deep learning to help startups and larger companies recruit developers. Specifically, its AI tech is analysing the code of millions of developers via their open source contributions in order to match them to appropriate job openings. "We use this analysis to understand how good they are at any given language and framework and match them with companies looking for developers," is how Source{d} co-founder and COO Jorge Schnura explains it. He also says it isn't just about identifying code quality or a developer's ability, but also coding style and other nuances that differentiates one developer from another. "We can [find] people who are similar to your team," adds Schnura. "This is all unsupervised learning since we don't tell our algorithms which features to look for, it defines them itself".

Greetings and welcome to the PROS Holdings Inc Third Quarter 2016 Earnings Call. At this time, all participants are in a listen-only mode. A brief question-and-answer session will follow the formal presentation. It is now my please to introduce your host Stefan Schulz, Chief Financial Officer. Good afternoon, everyone and thank you for joining us. With me on today's call is Andres Reiner, President and Chief Executive Officer. Before we begin, we must caution you that some of today's remarks, including our guidance, our strategy, our competitive position, future business prospects, revenue, bookings, market opportunities, as well as statements made during the question-and-answer session, contain forward-looking statements. These statements are based on present information and are subject to numerous and important factors, risks and uncertainties, which could cause actual results to differ materially from the results implied by these or other forward-looking statements. PROS does not assume any obligation to update the forward-looking statements provided to reflect events that occur, or circumstances that exist, after the date on which they are made. Additional information concerning risks and other factors that may cause actual results to differ can be found in the Company's filings with the SEC. Also, please note that a replay of today's webcast will be available in the Investor Relations section of our website at pros.com. We encourage everyone to review this additional information. Finally, I would like to point out that in addition to reporting financial results in accordance with Generally Accepted Accounting Principles, or GAAP, PROS reports certain financial results, as well as forward-looking guidance, on a non-GAAP basis. A reconciliation of each non-GAAP measure to the most directly comparable GAAP measure, to the extent available without unreasonable efforts is available on the press release distributed earlier today, and in the Investor Relations section of our website. Good afternoon, everyone and thank you for joining us on today's call.

Is artificial intelligence tech quickly becoming enterprise tech? Vendors are betting on it. Last week at IBM World of Watson, IBM CEO Ginni Rometty laid out her vision for the technology: Namely, that Watson will reach a billion users by the end of 2017, and that the technology will underpin every major personal and corporate decision. Last month, Salesforce rolled out Salesforce Einstein, an AI system that analyzes data to identify trends in marketing and sales. Having a clear-cut IT strategy is key establishing a competitive advantage over any competition.

All you hear these days is how machine learning and artificial intelligence are the next big things. Your fellow executives, managers, vice presidents and directors are all talking about the need to think about an AI strategy to build business intelligence and create a competitive advantage. Unless you are among a handful of sophisticated technology companies, machine learning and artificial intelligence are way beyond the level of your core competence. You don't have a neural network and wouldn't know what to do with one even if you did. Your IT department just finished (finally) setting up your cloud platform and migrating all of the old company resources to a fancy new public/private hybrid cloud that the executive team spent three years talking about.