Zegami is a visual data exploration tool for viewing large collections of images and other visual data. By presenting vast quantities of images within a single field of view, Zegami allows users to quickly see correlations, outliers and relationships by leveraging the innate pattern recognition capabilities of the human brain. This combined with image processing and advanced machine learning techniques like Deep Learning presents a formidable combination of the best aspects of man and machine. In addition, by including associated metadata (structured and unstructured) users can search, sort and filter the images to find exactly what is required. In the above example researchers at the Weatherall Institute of Molecular Medicine, University of Oxford were able to quickly observe the effects of various concentrations of a particular drug against a control group.

Without question, Github is the biggest code sharing platform on the planet. With more than 14 millions users and 35 million repositories, the insights you can discover by analyzing the data available through its API are surprising and revealing. I've been very passionate about this data for a long time, as much as I am about Neo4j. Importing this data into a graph database can reveal interesting new information. Two years ago I created a Github Gist with some interesting queries on that dataset.

Artificial intelligence, or A.I., has been around since the start of computing and has had many false starts. The reality did not live up to the expectations set by science fiction. Accordingly, for many years, the majority of people's understanding of A.I. was confined to university laboratories, corporate skunk works, research parks, and that movie with Haley Joel Osment and Jude Law. Attempts to introduce A.I. products and services into the marketplace and for the broader benefits of society were ill-fated. Computing power was insufficient, and the abundance of structured data -- let alone a knowledge of what to do with said data -- was not yet upon us.

Michio Kaku, a noted theoretical physicist and futurist, predicts, "The job market of the future will consist of those jobs that robots cannot perform." Robots have recently entered the legal workplace, performing several tasks once assigned to newly minted law grads. What does this mean for current and future lawyers? Simple answer: robots will not replace lawyers but they will work with them. Technology has already produced a new class of support professionals that work with lawyers -- just as techs work with doctors in healthcare delivery. Lawyers, like physicians and other professionals supported by technology, will be freed to leverage their time and expertise to interpret data, render professional judgment, and perform functions that require their professional training.

Barking voice commands at a phone, car, computer, or a dedicated voice assistant like Alexa, is pretty commonplace these days, but these systems are usually designed with an adult manner of speaking in mind. Kids have very different speech patterns, and Disney Research has developed a system that caters to a younger crowd, picking out key words from excited chatter and overlapping speech to let kids play a video game with their voice. Mole Madness is the name of the game, and kids control the character with just two simple voice commands. Playing in pairs (either with another child or a robot named Sammy), one player says "go" to get the mole moving across the screen, while their partner steers it upwards by saying "jump". As simple as that seems for a speech recognition system to identify, the kids threw a few spanners in the works with a tendency to chitchat and talk over each other.

If Japan, home to the world's largest public debt, wanted to save a bundle, it would close the Bank of Japan. Auctioning off its giant neo-baroque headquarter buildings around the nation and pink-slipping roughly 4,900 full-time employees would cheer Moody's and Standard & Poor's and plug holes in the national balance sheet. That's not going to happen, of course. But imagine if the BOJ had closed shop 17 years ago, right after it first cut interest rates to zero, and turned its function over to a computer program. Would the artificial-intelligence version of the BOJ be any closer to 2 percent inflation than the well-compensated humans occupying its buildings?

In this age of modern technology, there is one resource that we have in abundance: a large amount of structured and unstructured data. In the second half of the twentieth century, machine learning evolved as a subfield of artificial intelligence that involved the development of self-learning algorithms to gain knowledge from that data in order to make predictions. Instead of requiring humans to manually derive rules and build models from analysing large amounts of data, machine learning offers a more efficient alternative for capturing the knowledge in data to gradually improve the performance of predictive models, and make data-driven decisions. Not only is machine learning becoming increasingly important in computer science research but it also plays an ever greater role in our everyday life. Thanks to machine learning, we enjoy robust e-mail spam filters, convenient text and voice recognition software, reliable Web search engines, challenging chess players, and, hopefully soon, safe and efficient self-driving cars. The main goal in supervised learning is to learn a model from labeled training data that allows us to make predictions about unseen or future data.

Calico, a company focused on aging research and therapeutics, today announced that Daphne Koller, Ph.D., is joining the company as Chief Computing Officer. In this newly created position, Dr. Koller will lead the company's computational biology efforts. She will build a team focused on developing powerful computational and machine learning tools for analyzing biological and medical data sets. She and her team will work closely with the biological scientists at Calico to design experiments and construct data sets that could provide a deeper understanding into the science of longevity and support the development of new interventions to extend healthy lifespan. Calico will try to use machine learning to understand the complex biological processes involved in aging.

No longer simply the subject of science-fiction movies, artificial intelligence is making its way into the insurance enterprise. According to Accenture, four in five insurers are planning to or have deployed some sort of artificial intelligence technology in their enterprises. Allstate Business Insurance deployed ABie -- the Allstate Business Insurance Expert -- virtual assistant to help walk agents through the quoting process for complex products. The context-aware technology understands agents inputs and is able to direct them through the process without using the call center. AIG has partnered with Human Condition Safety to deploy devices that "couples wearable technology with artificial intelligence (AI) and building information modeling."

Artificial Intelligence (AI) has captured the human imagination since its inception. With that, there's a tendency for that imagination to veer towards machines "taking over" in some way. Often, the predictions are apocalyptic (think Terminator, I, Robot, or practically any other movie involving AI). Other times, we're warned of AI rendering one profession or another redundant. Basically, we're all going to either die, or be unemployed.