AlphaGo, Google Deepmind's AI, defeats champion Go-player Lee Sedol (right) in a 5-game Go match in 2016. Go had been one of the few games remaining where AI could not beat humans. The rise of artificial intelligence (AI) in machine learning and natural language processing technologies will have a huge impact on the media, advertising, retail, finance, and healthcare industries in the near future. Technology research firm Gartner claims that 85% of all customer interactions won't require human customer service reps by the end of this decade. You may be thinking creativity makes you robot proof, because robots cannot be creative, but that notion is no longer true.

Artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other. Andrew Ng, chief scientist at Chinese internet giant Baidu Inc. and co-founder of education startup Coursera, and Neil Jacobstein, chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University, sat down with The Wall Street Journal's Scott Austin to discuss AI's opportunities and challenges. What is Baidu focused on? NG: For large enterprises like Baidu, AI creates two big pockets of opportunities. One is our core business.

I am sometimes asked whether I am working on the stats, whether I am making progress on the stats, and what I do with all of the stats. People are also prone to hyperbole. I am told that I sure work on a lot of stats, I am always keeping myself busy doing stats, and I am the person to go to for stats. I suppose my real job is more mysterious than the one others imagine that I do. I first want to explain that for everyday people, the term "stats" or "statistics" often means historical data rather than statistics in a substantive sense. So people actually mean that I surround myself with data, which is certainly true.

The sheer scale of SXSW can be overwhelming for those looking for guidance on the next big thing. I went to Austin with one mission in mind: to zero in on conversational interfaces. It follows promising work by early adopters such as Activision, Burberry, and H&M, which proved the early promise of the humble chatbot. Meanwhile, voice assistants like Alexa and Google Assistant are shaping the connected home, providing us with fluid, intuitive access to services and products. But there's more still to come as the field develops.

Automakers and suppliers gave widely differing timelines for the introduction of self-driving vehicles on Thursday, showing the uncertainties surrounding the technology as well as a split between cautious established players and bullish new entrants. Chipmaker Nvidia, facing direct competition with the world's top chipmaker after Intel's $15 billion deal to buy autonomous driving technology firm Mobileye this week, gave the most optimistic predictions. Chief Executive Jen-Hsun Huang forecast carmakers may speed up their plans given technological advances and that fully self-driving cars could be on the road by 2025. "Because of deep learning, because of AI computing, we've really supercharged our roadmap to autonomous vehicles," he said in a keynote speech to the Bosch Connected World conference in Berlin. Germany's Bosch, however, the world's biggest automotive supplier, gave a timetable as much as six years longer to get to the final stage before fully autonomous vehicles, and declined even to forecast when a totally self-driving car might take to the streets.

Billionaire entrepreneur Mark Cuban's prediction for the future of the workforce includes more robots and less human workers. "We're about to go into a period with artificial intelligence, machine learning, deep learning, those things where we literally are going to see a change in the nature of employment," Cuban said in an interview with CNN's Jake Tapper. He used that claim as a lunch point to discuss the swiftly evolving nature of jobs due to automation, using Trump's work with US factories to underscore his point. "Pay attention to the number of jobs in those companies two and three years out," Cuban said. "I guarantee they are going to be 30 and 40% lower."

Advances in applied machine learning fueled our enthusiasm for smarter, more talkative devices. It impacted how we learned about and processed the news--and fake news--of the national elections. Automation grew in sophistication: detecting financial fraud, improving healthcare, deepening business intelligence, and disrupting human work models. While significant flux continues, the fact is that machine learning is now firmly established in business technology. Some obvious big data categories are already dominated, but you need only ingenuity to start putting machine learning insights to new and quantifiable uses.

This month IBM and Google both said they aim to commercialize quantum computers within the next few years (Google specified five), selling access to the exotic machines in a new kind of cloud service. The competitors predict a new era in which computers are immensely more powerful, with dividends including more efficient routing for logistics and mapping companies, new forms of machine learning, better product recommendations, and improved diagnostic tests. But before any of that, the first quantum computer to start paying its way with useful work in the real world looks likely to do so by helping chemists trying to do things like improve batteries or electronics. So far, simulating molecules and reactions is the use case for early, small quantum computers sketched out in most detail by researchers developing the new kind of algorithms needed for such machines. Quantum computers, which represent data using quantum-mechanical effects apparent at tiny scales, should be able to perform computations impossible for any conventional computer.

A recent survey conducted by MIT Technology Review Custom and Google Cloud reveals that while the majority of businesses are struggling to apply machine learning, others are hard at work developing strategies for the technology -- and are already realizing genuine ROI. A recent survey conducted by MIT Technology Review Custom and Google Cloud reveals that while the majority of businesses are struggling to apply machine learning, others are hard at work developing strategies for the technology -- and are already realizing genuine ROI. The business world's focus on machine learning (ML) may seem like an overnight development, but the buzz around this technology has been steadily growing since the early days of big data. ML is beginning to deliver on the potential created by big data and analytics by turning raw data into useful, predictive tools for business. Innovation-minded business leaders are embracing ML as "the next big thing" and have already crafted ML strategies and initiatives that promise real benefits and return on investment (ROI).

They learn the same way as humans. Humans learn from experience and so do machines. For machines, experience is in the form of data. Machines use powerful algorithms to make sense of the data. They identify underlining patterns within the data to learn things about the world.