In 2016, a machine beat the world's best (human) player at the ancient strategy game Go, which up to then was said to be too intuitive for a computer. AlphaGo's victory was doubly stunning because it taught itself Go by trial and error. Does the fact that computers can now learn mean that artificial intelligence (AI) has moved from science fiction to reality? The story of AlphaGo is told in a specially isolated display, almost like a little temple, at the heart of the Barbican's sprawling survey of the past, present and future of machines that can think for themselves. It's a show that's sorely needed.

One of the major challenges in most BI projects is to figure out a way to get clean data. This is true for both BI and Predictive Analytics projects. To improve the effectiveness of the data cleaning process, the current trend is to migrate from the manual data cleaning to more intelligent machine learning-based processes. Before we dig into figuring out how to handle missing values, it's critical to figure out the nature of the missing values. There are three possible types, depending on if there exists a relationship between the missing data with the other data in the dataset.

The purpose of this book is to provide an up-to-date and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call "learning" tasks, as we use the word in daily life. It is also broad enough to encompass computers that improve from experience in quite straightforward ways. The book will be ... The purpose of this book is to provide an up-to-date and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call "learning" tasks, as we use the word in daily life.

Forget all those whiskey brands from musicians and celebs -- there's A.I. whiskey now. Microsoft this week announced it has teamed up with Finnish tech company Fourkind and Sweden-based distillery Mackmyra Whisky to create the "world's first whisky developed with artificial intelligence." Here's how it will work. As part of the distillation process, whiskey first spends time -- typically years -- sitting in charred wooden casks. This turns the clear liquor a darker color, and gives it a unique flavor.

Self-driving vehicles move closer toward reality every day, but most of us will likely live through a few more presidential elections before driverless travel is routinely within reach through personal purchase (although availability through ride-hailing providers like Uber and Lyft could be here sooner). Even so, you're more likely to encounter such vehicles in your daily travel, because many states and cities are now serving as testing grounds for these rapidly advancing technologies, and the list of locations is growing steadily. Legislators in 29 states have passed laws related to autonomous vehicles (AV), and governors in 11 more states have issued AV-related executive orders. But regardless of where they're happening, there are two things to remember about these experiments: They are inescapably disruptive, and they are entirely necessary. Slightly more than half of U.S. adults are apprehensive about riding in a self-driving vehicle, citing safety concerns and a general lack of trust in the technology, according to a Pew survey in 2017.

Artificial Intelligence is a constantly transforming industry and is rapidly becoming a profitable one as well with many opportunities to invest at the ground level this year with potential for tremendous growth. Here are 5 AI companies you should investigate investing stock in this year. This is one of the largest social media companies to come out of China. They recently founded an AI lab which developed tools to process information across its ecosystem. It developed tools for natural language processing, news aggregators and facial recognition.

AI and People Analytics have taken off. As I've written about in the past, the workplace has become a highly instrumented place. Companies use surveys and feedback tools to get our opinions, new tools monitor emails and our network of communications (ONA), we capture data on travel, location, and mobility, and organizations now have data on our wellbeing, fitness, and health. And added to this is a new stream of data which includes video (every video conference can be recorded and more than 40% of job interviews are recorded), audio (tools that record meetings can sense mood), and image recognition that recognizes faces wherever we are. In the early days of HR analytics, companies captured employee data to measure span of control, the distribution of performance ratings, succession pipeline, and other talent-related topics. Today, with all this new information entering the workplace (virtually everywhere you click at work is stored somewhere), the domain of people analytics is getting very personal. While I know HR professionals take the job of ethics and safety seriously, I'd like to point out some ethical issues we need to consider.

"We have proposed a new framework for continual learning, which decouples network structure learning and model parameter learning," says Yingbo Zhou, co-lead author of the paper and a research scientist at Salesforce Research. "We call it the Learn to Grow framework. In experimental testing, we've found that it outperforms previous approaches to continual learning." To understand the Learn to Grow framework, think of deep neural networks as a pipe filled with multiple layers. Raw data goes into the top of the pipe, and task outputs come out the bottom.

But they'll have nothing on Rikard Grönborg, the head coach of Sweden's national hockey team who will log over 400 consecutive hours during the Ice Hockey World Championships in Slovakia … sort of. Using advanced 3D and voice technology, agency Perfect Fools created a virtual Grönborg to report live on YouTube 24 hours a day through the duration of the tournament. The coach spent hours in front of the camera so the virtual anchor could learn his voice and mannerisms. Additionally, 20 years of hockey data was analyzed so the fake Grönborg could make predictions for all of the tournament's games. The end product does look and sound a little robotic but in all fairness, it's an ambitious project, and the technology is still somewhat nascent. However, what's most impressive is that the AI will continuously learn and update itself so the world's first virtual sports anchor can make even more accurate predictions.

This summer, furniture company Kartell will start selling a new plastic chair designed by Philippe Starck – with some help. The system used – not, perhaps, strictly an AI – was a generative design software platform from Autodesk. Supplied with initial design goals, along with parameters such as materials, manufacturing methods and cost constraints, the software explores all the possible permutations of a solution to generate design alternatives. It tests and learns from each iteration what works and what doesn't. "As the relationship between the two matured, the system became a much stronger collaborative partner, and began to anticipate Starck's preferences and the way he likes to work," says Mark Davis, senior director of design futures at Autodesk.