Companies, investors and regulators recognize the importance of an effective board of directors, with an ever-greater emphasis placed on board structure, composition and refreshment. Beyond the need for a variety of skills and experience, establishing a vital mix of personality traits can also be an important consideration, as this can greatly impact the board's effectiveness. Interacting with diverse personalities comes with the territory, but how well directors relate to conflicting personalities can mean the difference between collegial and respectful interactions--or clashes of opinions that stifle creative thinking.

Inc.'s 11th annual 30 Under 30 list features the young founders taking on some of the world's biggest challenges. In 2013, a high-frequency trader named Michael Coscia was arrested in New Jersey for an activity called "spoofing"--essentially manipulating the market by flooding trading systems with future orders he had no intention of completing. He was fined 6 million--with the possibility of jail time. It was the first such prosecution under a new set of financial regulations from the 2010 banking reform law called the Dodd-Frank Act. That was an aha! moment for David Widerhorn, 28, and it became his reason for founding Neurensic.

"Excuse me," the former vice president says, dabbing a tissue at his nose before offering up an explanation. Outside Gore's New York City office, spring has certainly sprung--early too. This March was the hottest one ever, beating the prior record set in March 2015. The same goes for February and January of this year, and, oh, the eight consecutive months before. Gore knows these statistics by heart. The fact that you might know them too is likely because of him.

Before you even ask, the name has no meaning. When Google was trying to figure out what to call its language parsing technology, someone suggested Parsey McParseface; it's a bit like Apple's Liam, which has no clever backstory either. The overall AI model is called SyntaxNet (please make your SkyNet jokes now); 'ol Parsey is just for English. Combining machine learning and search techniques, Parsey McParseface is 94 percent accurate, according to Google. It also leans on SyntaxNet's neural-network framework for analyzing the linguistic structure of a sentence or statement, which parses the functional role of each word in a sentence.

In machine learning, a programmer might develop an AI that can calculate all possible consequences of a single action. Humans, however, don't have the same raw computational power; we have to efficiently create and execute a plan. We mentally invent different "layers" to organise our actions and then think about the higher levels rather than individual steps, according to a Neuron study from members of Google DeepMind and the University of Oxford. "The idea is basically to understand how humans or animals make long-term decisions," says Jan Balaguer, a PhD student at University of Oxford and member of Google DeepMind. "We're interested in trying to find machine-learning solutions to difficult tasks and real-life problems. Quite often it can be useful to draw inspiration from neuroscience."

There's been a perception that the adoption of technology in legal services has been slow over the past few years. However, according to the ALM 2015 Law Tech Survey this is certainly beginning to change as 95% of firm leaders and technologists respondents agreed or mostly agreed with recent decisions by management regarding the firm's technology. Law firms have faced a number of challenges over recent years. Although the UK is recovering well from the 2009 financial crisis this also led to stricter compliance regulations. With the increase in regulatory demands there is a need for better finance and risk mitigation.

Microsoft unveiled Twitter artificial intelligence bot @TayandYou yesterday in a bid to connect with millennials and "experiment" with conversational understanding. Billed as'AI fam from the internet that's got zero chill!' Tay was meant to engage with her peers help the tech giant explore its cognitive learning abilities through "playful conversation". "The more you chat with Tay the smarter she gets," said Microsoft. The stunt however, took an unexpected turn when Tay's verified Twitter account began issuing a series of inflammatory statements after being targeted by Twitter trolls. The conversational learning curve saw the bot tweet posts from her verified account mentioning Hitler, 9/11 and feminism, some of which (including the below) have now been deleted.

Artificial Intelligence seems to have just taken a disruptive leap forward. As reported in Wired this month, over 500M US have been invested in AI-related startups, with VIV Labs making to the feature story of this month Wired. In the meantime, IBM made the headlines just a week ago by commercialising Watson as a business-friendly AI machine, and the buzz keeps going. Now, for somebody like me, who is passionate about advancing faster towards personalised medicine, somebody who has been fighting, along with colleagues to piece together clinical data, genomics data, environmental data, you name it, to start painting a stratified, personalised view of patients, diagnosis, and treatments, what will this mean? Will AI help us make a leap forward?

In an earlier post, "What Type of Artificial Intelligence Do You Need?", two paradigms of artificial intelligence are described; deterministic and statistical. Deterministic is similar to speaking with an expert. In this blog post, we will consider how Deterministic Artificial Intelligence can be applied to Client Suitability, which is a well-known regulatory challenge in financial services. Deterministic A.I. can help financial institutions process high volumes of Client Suitability analysis. By automating large amounts of decision-making and data analysis, financial institutions can limit their regulatory risk and increase efficiency.

Clearly, the idea of Artificial Intelligence (AI) has been with us in pop culture for a while. One of their breakthroughs involves the use of a technique called deep learning. Though it takes many forms, one example of deep learning is the creation of electronic "neural networks" that can mimic different basic operations occurring in real webs of neurons. This advancement enables the machine to take direct inputs from the world without human involvement and create its own internal representation for further processing."