It seems like only yesterday when everyone was talking about the impact of Big Data on the insurance industry. Talk about Big Data now and you will almost seem old-fashioned – it's all about artificial intelligence and InsurTech. Artificial intelligence (AI) will soon be everywhere. It is making decisions about what we buy, when we buy it and how much it costs. It is controlling interactions between customers and suppliers.

At the moment, legal AI is at the "frothy" part of the hype cycle. It's a bit like the scene What did the Romans ever do for us? in the film the Life of Brian, but in reverse. The scene's premise is that the Romans did little, but of course they did a lot. Legal AI promises a lot but at the moment has delivered little. Somewhat frustratingly, while a decade from now it will be obvious in hindsight that the world beater was going to be "X", right now we don't know what that "X" will be.

Summary: IBM's Watson now to do your taxes at H&R Block? This is a good opportunity to explore the differences between Question Answering Machines (Watson) and Expert Systems. If you were paying attention during the Super Bowl you saw something unprecedented, an advertisement aimed at data scientists. It was the H&R Block announcement that it was rolling out IBM's Watson to all 80,000 of its tax preparers. So far we've seen Watson deployed primarily on more complex and obscure data like chemical reactions, cancer diagnoses, and environmental engineering.

While artificial intelligence applications in business and industry remain limited to narrow machine learning tasks, we are seeing progressive improvements in the convergence of algorithms and hardware that will have significant implications for how well and how quickly we can implement AI. Researchers can now train neural networks within a few hours or days, which opens up an amazing range of possibilities, products, and things to learn -- as well as challenges -- that we could not have even considered before. For example, Google's AI group, DeepMind, is hard at work unraveling the mysteries of how proteins fold themselves, a discovery that could have far-reaching implications for health care. It is also very much involved with the research community in working through the ethical issues of AI. As I see it, 2018 will be the year AI will meet a crossroads -- when companies are better able to skim the hype from the reality, and when we can apply AI for both the good and the bane of humanity.

Chan Zuckerberg Initiative awards $5.5 million to UMass for artificial intelligence project … The project's goal is to create an intelligent and navigable map of scientific knowledge using a branch of artificial intelligence called "knowledge representation and reasoning," according to a UMass press release.

Health care providers enter into dozens of contracts with outside companies, like medical record disposal or storage entities, that have access to patients' medical information. Dan Mulholland, senior partner at the health law firm Horty Springer, said these contracts come and go so frequently that they're …

We have long been used to manual work being automated. We are finally at a juncture where even knowledge work is starting to see the same fate. Weren't engineering, medicine and law supposed to be future-proof careers? For example, the world's largest hedge fund Bridgewater Associates is building a piece of software to automate the day-to-day management of the firm, including hiring, firing and other strategic decision-making. "The role of many remaining humans at the firm wouldn't be to make individual choices but to design the criteria by which the system makes decisions, intervening when something isn't working," wrote the Wall Street Journal, which spoke to five former and current employees.

The Waymo v. Uber trial is set to finally get started next month, but Anthony Levandowski, the man who has been accused of taking 14,000 files from Google's self-driving outfit when he left the company for his own startup Otto, has been hit with a lawsuit that may affect Waymo's. Wired reports that Levandowski's former nanny, Erika Wong, has filed a suit against him claiming Levandowski failed to pay her wages, violated labor and health codes and inflicted emotional distress. But the complaint also includes details of Levandowski's business practices, which suggest that he might have been paying off employees of other autonomous vehicle companies and that he considered fleeing to Canada when Waymo first filed its lawsuit. In the filing, Wong says that the day Waymo filed its suit against Uber, Levandowski was noticeably agitated while he spoke to his lawyer over the phone -- sweating and pacing as he cursed into the phone. When Waymo filed for an injunction against Uber, former Uber CEO Travis Kalanick visited Levandowski's house, according to Wong, with legal documents for Levandowski to sign as well as a bucket full of circuit boards and lenses.

Artificial intelligence (AI) is making significant waves across the globe, with experts predicting that it will increasingly change and reshape the way people live their daily lives. AI is also likely to shake up the legal industry; triggering a profound shift in the delivery of legal services. However, with such potential and power to drive seismic change to ordinary life and professional services, it has led to some debate over whether AI should be regulated. Professor Sylvie Delacroix, of the University of Birmingham, spoke to Thomson Reuters' Legal Solutions UK & Ireland Blog about her views on AI and the call for regulation. How significant is artificial intelligence and its role within society? The most significant development today is the extent to which we are capable of gathering and exploiting data to develop new kinds of knowledge which radically transform the way we live, for better or for worse.

Algorithms are everywhere in our world, and so is bias. From social media news feeds to streaming service recommendations to online shopping, computer algorithms--specifically, machine learning algorithms--have permeated our day-to-day world. As for bias, we need only examine the 2016 American election to understand how deeply--both implicitly and explicitly--it permeates our society as well. What's often overlooked, however, is the intersection between these two: bias in computer algorithms themselves. Contrary to what many of us might think, technology is not objective. AI algorithms and their decision-making processes are directly shaped by those who build them--what code they write, what data they use to "train" the machine learning models, and how they stress-test the models after they're finished.