Artificial intelligence (AI) on its own isn't enough to compete -- companies need industry-specific solutions to business problems. So said Martin Schroeter, IBM Corp. (NYSE: IBM)'s company senior vice president and chief financial officer, on the company's quarterly earnings call Thursday afternoon. Cognitive computing technology (IBM's term for AI) is just "table stakes," said Schroeter, claiming that his company is going the extra mile. IBM is building datasets for Watson to serve specific industries, including healthcare and finance. "You need more than public data or algorithms to solve real-world problems," Schroeter said.

Artificial intelligence (AI) has increasingly become one of the hottest topics in both business and science. More leading tech companies are showing their interest in AI investment, from Google's $400 million acquisition of DeepMind and Faraday Future's unveiling of self-driving supercars at CES 2017. These are just a few examples of the commitment companies have towards this cutting-edge technology, but one of the most promising areas for AI is in mobile. The idea of having a personal assistant to help tackle everyday tasks is becoming more appealing to users everywhere. However, intelligent apps are not just limited to digital assistants but for a variety of purposes from security to e-commerce.

Crowdfunding, blockchain and quantum computers: Why we've only just seen the start of the new...

Kristen Stewart – the actress best known for "Twilight" – has co-written a paper on machine learning. The paper outlines the use of neural style transfer in Stewart's directorial debut, "Come Swim", which is about to premiere at Sundance Film Festival. Neural style transfer turns normal images into impressionist art, and is used by popular photo app Prisma. The paper, first spotted by Quartz, is co-bylined with Adobe research engineer Bhautik J. Joshi and producer David Shapiro. It was published yesterday on ArXiv, a repository run by Cornell University for scientific papers that are not yet peer-reviewed.

Heading into HIMSS17, GE Healthcare is developing a range of technologies, from analytics and cloud-based imaging to machine learning. Building on its GE Health Cloud, in fact, the company is enabling care teams to store, view, analyze and share images in ways they could not before storage and compute power were available in the cloud. "We're working at building analytics into every part of our business and applying digital from a horizontal approach across all of GE Healthcare," GE Healthcare spokeswoman Kelley Sousa said. Take Project Northstar, for instance. GE's solution for helping practices transition to value-based care, which was announced last year, combines population health, care delivery, patient engagement and financial management in an integrated, interoperable software solution.

Banks tend to use customer analytics to cross-sell products to consumers and do things that benefit their own bottom line. Doing so might not help profits, but it might make for more loyal customers, says wallet.ai's Green, who formerly worked at Intuit, wants to help consumers benefit from their account data, just as Wall Street firms benefit from buying anonymized data. Banks' customer analytics programs are typically set up to learn about customer behavior in order to market them certain products. Green recalled an event where he spoke on this topic -- bankers in the audience accused him of being anti-marketing.

If you use your credit card to buy a latte in Vancouver and a couple of minutes later that card is making a purchase in Singapore, that's a red flag for fraud. But increasingly sophisticated fraud calls for more sophisticated measures to deal with it, and that is among the challenges behind RBC Research's announcement today that it is launching a new lab to explore the use of artificial intelligence and machine learning in the financial sector. Richard Sutton, a computer scientist and pioneer in artificial intelligence, has been named head academic advisor to RBC Research in machine learning. The new lab will work with the Alberta Machine Intelligence Institute at the University of Alberta, where Sutton is a professor. Foteini Agrafioti, head of RBC Research, which was launched last fall in Toronto, said the announcement will help her organization to play a major role in advancing AI research in the future of banking. Agrafioti said that as the complexity of fraud evolves over time, it becomes increasingly difficult to detect it.

Mountain View, California-based Drive.ai is a startup created by former lab mates from Stanford University's Artificial Intelligence Lab. Originally founded in 2015 by Carol Reiley and Fred Rosenzweig, Drive.ai raised $12 million in Series A funding earlier this year to develop deep learning algorithms to control the operation of autonomous vehicles. Building on experience gained from the DARPA Grand Challenge, Google and other self-driving pioneers programmed the first self-driving car to rely primarily on light detection and ranging (LIDAR), which is a remote sensing method that uses pulses of laser light to measure distances, and detailed mapping. Although this has worked pretty well, the current technology is expensive. Making autonomous vehicles easier to manufacture with less expensive parts will make them more affordable.

Putting your future at your fingertips: read this article and thousands more about emerging technology and it's impact on your life, your business and your society at www.globalfuturist.org. As many companies, large and small will tell you, creating a good artificial intelligence (AI) is difficult, really difficult, and for companies such as Alibaba, Facebook, Google, IBM and Microsoft where AI could literally make or break their futures it's a battle they have to win. And now the team at Google's AI research lab, Google Brain, think they have the answer – get AI's to design and build new AI's, and they're not alone – late last year Facebook also hoped on the same bandwagon and were already ahead of the curve. What could possibly go wrong with that great idea!? If you were one of the people who said "nothing" and "that sounds like a great idea" then you might want to go and stand in the corner with everyone else who said that – and it's a lonely corner.

Reading through all of the 2017 predictions about Artificial Intelligence set out by tech publications the world over, it can be easy to think that we are all about to experience a series of seismic changes. The impact of the upcoming discoveries and inventions can feel so overwhelming, so immeasurable that, really, you're more than likely setting yourself up for disappointment by virtue of allowing yourself to dream. We've been promoting improved digitalization in the workplace, and extolling the virtues of Artificial Intelligence tools for a while. With that experience in mind, we like to think that we're fairly grounded when it comes to thinking about the possible impact of AI on wider industries over the coming year. From experience, we have seen that more companies have been switching on to the power of Artificial Intelligence, and to the importance of digital transformation, over the last few years.