Earlier this week, I found myself answering a question from a new colleague at Finning International that relates both to the research I do in the iSchool at the University of British Columbia, as well as the analytics, engineering & technology work that I lead at Finning. The questions were simple: 1) What is artificial intelligence? As I sat to reflect last evening, it dawned on me that taking time to craft a clear answer to these questions might be extremely beneficial for many. Analytics, data science, and predictive intelligence are hot topics in many communities and business areas. And yet, despite this interest, few folks I have talked to have a clear understanding of the history of the discipline; one, that frames much of the work currently going on within the space.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

A more technical application, is computer vision to analyze rapid diagnostic tests. A pregnancy test is the most popular example of a rapid diagnostic test. Pharma companies now make these tests for all types of targets like malaria and cancer to get quick "yes" or "no" results in the field for low cost, usually pennies on the dollar. Mobalysis tech can quantify, or apply a measured number, to these tests. Instead of telling you you have cholesterol in your blood for instance, Mobalysis can tell you "how much".

There is also the issue of safety around using such AI-driven robots in customer facing roles. Google's recent paper examining the likely issues that will arise from AI technology, 'Concrete Problems in AI Safety,' noting among them the fairly basic'Avoiding Negative Side Effects'. This asked how tech companies could ensure that AI system not disturb its environment in negative ways while pursuing its goals, for example a cleaning robot knocking over a vase because it can clean faster by doing so. This problem may seem petty, but if it were to happen in a hotel it could proof fatal to public trust.

We are a team of scientists, engineers, designers, and developers with offices in London and Boston and together we've developed the DESKGEN platform for CRISPR genome editing. Founded in 2012, Desktop Genetics is venture backed by some of the UK's leading life science thought leaders and supports customers in the UK, USA, Japan, and beyond. As a Machine Learning (AI) Scientist, you'll work to translate the latest CRISPR research into software that powers the experiments of thousands of labs. The application of artificial intelligence to genomics is a rapidly evolving field currently at the forefront of research. The work is highly interdisciplinary and successful candidates will have experience or advanced training in aspects of computer science, data science, engineering, biology, statistics, mathematics, or machine learning.

This article is an interview with computational linguist Jason Baldridge. It's for anyone who's interested in, or needs to know about, natural language processing (NLP). Jason and NLP go way back. He joined the University of Texas linguistics faculty in 2005 and, a few years back, helped build a text-analytics system for social-media agency Converseon. Jason's Austin start-up, People Pattern, applies NLP and machine learning for social-audience insights; he co-founded the company in 2013 and serves as chief scientist.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

Gideon Mann is Head of Data Science at Bloomberg, guiding the strategic direction for machine learning, natural language processing and search on the core terminal for the past two years. He joined Bloomberg after a notable role with Google Research. In addition to academic research, his team at Google built the core middle-ware libraries for small-to-medium sized machine learning and publicly released the Google Prediction API, Smart Auto-Fill for Sheets and coLaboratory (open-sourced as part of iPython/Project Jupyter). In our conversation with Gideon, he shares key skills for success in data science, the latest at Bloomberg and growing up with current peers. In order to be an effective data scientist, you must have 1) the skills to apply techniques to utilize data, 2) a deep knowledge of mathematics and 3) the ability to listen. The third skill is most important since the solution to problems is often deeply rooted in the question itself.

The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is "the most aggressive, responsive, dynamic and credible AI I've seen to date." Details on ALPHA -- a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment. In its earliest iterations, ALPHA consistently outperformed a baseline computer program previously used by the Air Force Research Lab for research.

ARTIFICIAL INTELLIGENCE: Artificial Intelligence's White Guy Problem Kate Crawford The New York Times "Like all technologies before it, artificial intelligence will reflect the values of its creators. So inclusivity matters--from who designs it to who sits on the company boards and which ethical perspectives are included. Otherwise, we risk constructing machine intelligence that mirrors a narrow and privileged vision of society, with its old, familiar biases and stereotypes." ROBOTICS: How Amazon Triggered a Robot Arms Race Kim Bhasin and Patrick Clark Bloomberg "For the new breed of robot makers, the potential market is wide open. Logistics companies that run their own warehouses started designing automatons while ambitious engineers saw the hole Bezos blew in the market and jumped in...As promising as all this technology may be, robots aren't going to do away with human-run warehouses entirely--not yet, anyway."