Somewhere in the middle of the digital revolution, business leaders started noticing a strange phenomenon. The revolution, as it turned out, wasn't about technology--it was about people. Although digital seems to be pervading everything, with the global digital economy accounting for 22% of the world's economy in 2015, up from 15% in 2005, leading companies that place people first will find success in a world that continues to reinvent at an unprecedented rate. The 2016 Accenture Technology Vision highlights five emerging technology trends shaping the new landscape: intelligent automation, liquid workforce, platform economy, predictable disruption and digital risk. Despite the fact that each trend outlines a technology driver that will impact businesses for years to come, they are tied together by the central theme of people.

By using machine learning algorithms, we are increasingly able to use computers to perform intellectual tasks at a level approaching that of humans. Given that computers cost less than employees, many people are afraid that humans will therefore necessarily lose their jobs to computers. Contrary to this belief, in this article I show that even when a computer can perform a task more economically than a human, careful analysis suggests that humans and computers working together can sometimes yield even better business outcomes than simply replacing one with the other. Specifically, I show how a classifier with a reject option can increase worker productivity for certain types of tasks, and I show how to construct and tune such a classifier from a simple scoring function by using two thresholds. I begin with a parable featuring the same characters as the one from Part 1 of this Machine Learning Meets Economics series.

Welcome to the 10th part of our of our machine learning regression tutorial within our Machine Learning with Python tutorial series. We've just recently finished creating a working linear regression model, and now we're curious what is next. Right now, we can easily look at the data, and decide how "accurate" the regression line is to some degree. What happens, however, when your linear regression model is applied within 20 hierarchical layers in a neural network? Not only this, but your model works in steps, or windows, of say 100 data points at a time, within a dataset of 5 million datapoints.

Every once in a while a new algorithms comes and makes all others (in the same domain) seems kind of obsolete when it comes to the same domain. Will deep learning make that related algorithms (backpropagation NN, GMM, HMM, ...)? There are several reasons why there will always be a place for other algorithms to be better suited than deep learning in some applications. There are many cases where you need to have an understanding of the domain in order to have optimal results. While some proponents of Deep Learning describe their approach as being general-purpose, I don't think that will ever be true.

The third annual RE•WORK Future of Education workshop will take place in London on 20 June as part of London Technology Week, bringing together education practitioners, technologists, edtech startups, investors and policy leaders to discuss, explore and collaborate to discover how rapidly advancing technology will impact education. Topics explored will include: Wearable Technology, Augmented Reality, Artificial Intelligence, Gamification, Internet of Things, Robotics, Human-Computer Interaction and Facial Recognition. Over the past two years 200 attendees have come together to share their insights into technological advancements, as well as discuss key areas such as: What experience do we want students and teachers to have? How can we make these technologies purposeful? What problem are we trying to solve?

John Miranda, Software Engineering Manager at Intel, discusses machine learning, big data analytics, artificial intelligence and cognitive computing and how these areas are driving career opportunities and how they are improving the way Intel runs its business.

Unlike other technical computing applications that require high-precision floating-point computation, deep neural network architectures have a natural resilience to errors due to the backpropagation algorithm used in their training. Storing FP16 data compared to higher precision FP32 or FP64 reduces memory usage of the neural network, allowing training and deployment of larger networks. Using FP16 computation improves performance up to 2x compared to FP32 arithmetic, and similarly FP16 data transfers take less time than FP32 or FP64 transfers. The GP100 SM ISA provides new arithmetic operations that can perform two FP16 operations at once on a single-precision CUDA Core, and 32-bit GP100 registers can store two FP16 values. Atomic memory operations are important in parallel programming, allowing concurrent threads to correctly perform read-modify-write operations on shared data.

The other day, the National University of Science and Technology (MISiS) hosted a meeting on the development of robot technologies during the implementation of projects for the National Technology Initiative. Meeting participants watched a presentation of an international project to create an online site for testing unmanned equipment. Russia's KAMAZ Automotive Plant and IT solutions developer Cognitive Technologies have said they are ready to unveil the first Russian-made autonomous truck, an autopilot system that can detect road signs, lane markings and other vehicles. According to developers, the first autonomous commercial trucks could reach production by 2020. Some estimates show that the use of online testing sites will make it possible to save up to two billion rubles that would otherwise be spent on real-life tests and simulated real-life situations.

Google parent company Alphabet reports earnings after the bell Thursday, but investors may be paying more attention to what's ahead for the company's various "moonshots." "What's going on inside the X labs … is the stuff of science fiction, but increasingly the stuff of next quarter, next year's revenue," said Max Wolff, chief economist at Manhattan Venture Partners in an interview with CNBC on Thursday. Wolff said he hopes to hear an update on Alphabet's self-driving car development and virtual reality technology during the earnings call Thursday. Developments in VR, along with artificial intelligence, should be a major focus for Google as well as other major tech companies, Wolff said. "I think the era of the smartphone is mostly behind us," Wolff said.

The impact of Artificial Intelligence (AI) on the world must surely be one of the greatest contemporary puzzles. The spectrum of risk and the gamut of possible applications are significantly complex that almost any scenario can be envisioned, from robot apocalypse to workless utopia. The only assurance is that change is coming, and in my opinion, it is likely to be a revolution of a scale seen only during the onset of history-shifting events such as industrialization or farming. The timing is auspicious: Google AI unit DeepMind's AlphaGo has recently beaten South Korean professional game player Lee Sedol at the ancient Japanese board game Go--a game of huge potential complexity based on simple rules and considered one of the biggest challenges in AI, since it defies brute-force planning. As a milestone in machine intelligence (and good PR), it sits up there with IBM Watson's victory on Jeopardy in 2011 and IBM's earlier AI DeepBlue's victory over chess grandmaster Garry Kasparov in 1997.