In his book Outliers, Malcolm Gladwell repeatedly mentions what has become known as the "10,000-hour rule", which states that to become world-class in any field you must devote 10,000 hours of "deliberate practice". Whether or not you believe the 10,000-hour figure, many would acknowledge that to become an accomplished legal professional requires considerable legal, communicative and, particularly in in-house environments, interpersonal skills that are often acquired after a tremendous amount of effort exerted over many years. There has been much hoopla about AI-based legal systems that, some might have you believe, may soon replace lawyers (no doubt causing a degree of anxiety among some legal professionals). There is some misunderstanding among many lawyers, and much of the public, about what AI systems are presently capable of. Can a legal AI, based on current technology, actually "think" like a lawyer?

Fraudulent credit card transactions are a pressing concern among retailers, card issuers, and consumers alike. With a reported 31.8 million U.S. consumers experiencing credit card fraud in 2014 alone, sweeping changes have been put in place to thwart its devastating effects. Technology enhancements like EMV chip-enabled cards, fraud alerts from card issuers and banks, and dozens of no-cost credit monitoring services all work together to fight the ongoing issue of credit card fraud throughout the country. Despite good intentions, some consumers desire more from card issuers in accurately detecting – and halting – fraudulent activity on credit or debit cards. The majority of consumers can share at least one awkward moment when a credit card purchase was declined in error.

Today, there is incredible interest in anything that is even remotely related to Artificial Intelligence. AI is dominating the conversation on a variety of levels. Philosophers and thinkers are debating the moral implications and risks for human kind of a world where intelligent machines are ubiquitous. In the media, it seems that a new movie or TV series on AI is launched every month. Academic papers on the topic are receiving attention from far beyond the scope of the usual research audience.

Bosch is creating the brain for the self-driving cars of the future. At the international Bosch ConnectedWorld 2017 conference in Berlin this week, the supplier of technology and services presented an onboard computer for automated vehicles. Thanks to artificial intelligence, the computer can apply machine learning methods. The AI onboard computer is expected to guide self-driving cars through even complex traffic situations, or ones that are new to the car. "We are teaching the car how to maneuver through road traffic by itself," said Dr. Volkmar Denner, chairman of the Bosch board of management.

Samsung Electronics is taking on Google and Apple to grab the lion's share in the highly potential artificial intelligence (AI) industry. The Seoul-based tech behemoth confirmed this week that its upcoming flagship smartphone, the Galaxy S8, would be equipped with its new AI assistant, Bixby. Samsung is a latecomer in the AI industry, compared with its handset rivals such as Apple and Google. Google, for its part, is also seeking to expand its AI presence, backed by its dominant Android mobile operating system (OS), allowing Nougat 7.0 users to access its AI service, Google Assistant. Samsung, though, remains confident about its success in AI, largely due to its global influence as the world's largest consumer electronics manufacturer.

Learn how SAP customers are experiencing some of today's hottest technologies and exploring how to reimagine business models, processes, and the future of work.

"Scientia potentia est" is a Latin adage that means "knowledge is power". This phrase is commonly attributed to Sir Francis Bacon and its most common modern interpretation is'information is power'. There has never been a time in human history when this phrase was more relevant, as each day humanity creates over 2 Quintillion bytes of data. This reality has manufactured the big data boom that the world is currently experiencing. All of this data has to be processed, analyzed and stored in some way.

Deep Learning is not just for super genius Data Science PhDs. Someone needs to get this code running, scale it, distribute it, install it, integrate it, and have it executed. Here is the place where the Big Data engineers and data DevOps teams come into play. I have been integrating some of these frameworks, libraries, models and tools into existing Hadoop, Big Data, Spark, and Machine Learning pipelines. Follow the instructions for PyTorch, TensorFlow, and MXNet.

And last year, one tech company, Alphabet's Google, published papers in all of them. According to the tally Google provided to MIT Technology Review, it published 218 journal or conference papers on machine learning in 2016, nearly twice as many as it did two years ago. "The top people care about advancing the world, and that means writing papers the world can use, and writing code the world can use." So when Apple hired computer scientist Russ Salakhutdinov from Carnegie Mellon last year as its new head of AI, he was immediately allowed to break Apple's code of secrecy by blogging and giving talks.

These are some the most elite academic journals in the world. And last year, one tech company, Alphabet's Google, published papers in all of them. The unprecedented run of scientific results by the Mountain View search giant touched on everything from ophthalmology to computer games to neuroscience and climate models. For Google, 2016 was an annus mirabilis during which its researchers cracked the top journals and set records for sheer volume. Behind the surge is Google's growing investment in artificial intelligence, particularly "deep learning," a technique whose ability to make sense of images and other data is enhancing services like search and translation (see "10 Breakthrough Technologies 2013: Deep Learning").