Artificial Intelligence (AI) and its impact and applications in the legal profession is examined in a new white paper, entitled Artificial Intelligence in Law The State of Play 2016, by Michael Mills, Co-Founder & Chief Strategy Officer of Neota Logic, a provider of intelligent software. In the paper, published by Thomson Reuters' Legal Executive Institute, Mills analyzes AI -- what the author calls a "big forest of academic and commercial work around'the science and engineering of making intelligent machines'" -- and how AI is being implemented in legal areas such as ediscovery, legal research, compliance, contract analysis, case prediction and document automation. Lawyers do not need robots or machine vision, but other branches of AI are indeed useful. Practical use of cognitive technologies in legal services is by no means new, nor did it begin when IBM's general counsel predicted that Watson could pass the bar exam by 2016. Artificial intelligence is hard at work in the law -- for example, in legal research, ediscovery, compliance, contract analysis, case prediction and document automation -- though often there is no "AI Inside" label on the box.

We will be discussing this interesting topic and others just like it at Boston's top Data Science Conference, ODSC East. Neural networks (NN) are powerful statistical models that can integrate lots of different kinds of data in complex ways. They are at their most powerful when their structure is optimally suited to the data you have available. NNs are composed of an input layer, hidden layers, and an output layer. 'Network architecture' refers to how many hidden layers a NN has, the number of units on each layer, and the patterns though which units are connected.

"Nothing can have value without being an object of utility." Karl Marx Marx's words have unexpected application in the world of commercial technology – essentially the takeaway is that the success of any technology trend or movement can only be proven by how "useful" it proves over time. Let's examine one of the technology trends dominating the airwaves today, Machine Learning, through the lens of "utility". BCC research has predicted that the market for Machine Learning will grow at a compounded annual rate of close to 20% and cross 15 Billion by 2019. Presumably with numbers like that, there's no denying the prevalence of the technology trend – just what kind of utility is it really delivering, though?

There are so many precious moments in a newborn's life that parents love to capture on film: The first time their child sits up on her own, the first time she stands, her first cautious steps. Igor Mordatch, a robotics post-doctorate student at the University of California, Berkeley, has been doing similarly for a humanoid robot, called Darwin, which he programed to learn just like a human child might. Mordatch and his team at Berkeley's Robotics lab started out by working for two years on a computer system that simulates how a robot might act in certain situations. The system is a group of neural networks--computer algorithms modeled after the structure of a human brain. In the last few months, his team has been transferring that system over into Darwin itself.

Artificial intelligence* is developing much faster than we thought. Just last month, Google's DeepMind AI beat Lee Sedol, a legendary Go player, at his own game in a defining moment for the industry. What enabled this win is a relatively new AI technique called deep learning, which is transforming AI. Until deep learning was introduced, even the best AI systems were always highly tuned for specific problems and required many rules to operate successfully. But deep learning has changed that, causing many researchers to abandon classical AI approaches.

The topic of Machine Learning has increasingly gained popularity over the last few years. Although it's not a new science, it continues to gain momentum. Allowing computers to find hidden insights that can guide better and faster decisions in real time without the use of human intervention allows this science to continue to grow. However, what does the future hold for Machine Learning? Let's assume analytical solutions can be built by studying past data models; where does that kind of data technology play its cards in the future?

A conference dedicated to a versatile computer chip is expected to draw thousands of researchers and hundreds of tech companies to San Jose next week for a look at advances in some of Silicon Valley's hottest technologies. Now in its seventh year, the Nvidia GPU Technology Conference opening Tuesday at the San Jose Convention Center celebrates the graphics processing unit, or GPU, a chip that has become the Swiss Army knife of computing. Some industry observers credit the annual conference for helping spark the research that has led to recent leaps forward in artificial intelligence. Patrick Moorhead, a semiconductor analyst with Moor Insight and Solutions, said that the San Jose Convention Center conference -- now in its seventh year -- became a meeting ground for scientists, academics and developers. "What happened is that once you bring these researchers together in one place and get them focused on this whole notion of using graphics to do a compute engine, they find these new ways to use it. That's exactly what happened," Moorhead said.

XGBoost is a library that is designed for boosted (tree) algorithms. It has become a popular machine learning framework among data science practitioners, especially on Kaggle, which is a platform for data prediction competitions where researchers post their data and statisticians and data miners compete to produce the best models. For structured learning problems on Kaggle, it can be difficult to get into the top 10 without including XGBoost. Typically, data scientists use multi-thread single machines to train XGBoost models. Very few people have deployed XGBoost on a distributed environment and achieved good performance.

Joining the conversation today is Jakob Foerster; an artificial intelligence expert who recently helped develop a machine that solved the notoriously difficult '100 hat riddle' used by Google and Goldman Sachs to weed out the highest calibre candidates during interviews . The neural network had to first figure out a way of communicating with other AIs before going on to solve the problem –Jakob explains that this is'basically a first step toward having machines that can communicate and collaborate'. Despite these achievements Jakob maintains a level head about the near future believing humans will continue to rule the machines for a while yet. This is a refreshing position given the recent warning issued by Stephen Hawking and Elon Musk that we may be near to creating something so powerful it cannot be controlled. Regardless of which future awaits us Jakob provides a fascinating insight into how artificial intelligence is already here and controlling everything from vehicles to the stock market and may very soon be creating music more emotional and beautiful than anything we can conceive of today.

In the very same week that Artificial Intelligence lost one of its greatest pioneers, Marvin Minsky, it saw major progress on a decades-old challenge of playing human-level Go. There is much to shout about, but also a lot of hype and confusion about what we just saw. With so much at stake as people try to handicap the future of AI, and what it means for the future of employment and possibly even the human race, it's important to understand what was and was not yet accomplished. Fact: The paper published yesterday in Nature by DeepMind represents major progress in getting AI to play Go, a game that has been notoriously difficult for machines. Confusion: The European champion of Go is not the world champion, or even close.