In this AI Podcast, Doina Precup describes why their doesn't need to be a gender gap in computer science education. An associate professor at McGill University and research team lead at DeepMind, Precup shares her personal experiences, along with the AI4Good Lab she co-founded to give women more access to machine learning training. Growing up in Romania, Precup attended a high school that specialized in computer science and a technical university. "If anything, programming was considered a very good job for women, because you did not need to be working in the fields," she explained. It made the gap in Canadian universities and companies even more noticeable.

Keeping up with artificial intelligence (AI) and data privacy can be overwhelming. While there's loads of promise and opportunity, there are also concerns about data misuse and personal privacy being at risk. As we evaluate these topics and as the Fourth Industrial Revolution unfolds, questions arise about the promise and peril of AI, and how can organizations put steps in place to better realize the value of it. Integrating "ethics" into technology products can feel abstract for engineers and developers. While many technology companies are independently working on initiatives to do this in concrete and tangible ways, it is imperative that we break out of those silos and share best practices.

AIC is a Montreal based company involved in software development. The major business of AIC comes from the acquisition of a commercial Warehouse Management System that is currently used by a number of 3PL warehouses that operate for clients like Unilever and Coca Cola. We begin by focusing on a known 1.5M square foot 3PL warehouse in Burlington (NJ) operated for a number of different clients and product groups. Different product groups pose significant constraints on the pick route given that apparel, electronics, food and hazardous materials have very different constraints on how they are to be received, stored and then picked, packed and shipped. We first examine the optimization of picking routes for a single picker given a specific simulated warehouse layout.

The American Council for Technology and Industry Advisory Council (ACT-IAC), the premier public-private partnership dedicated to advancing government through the application of information technology, today officially announced the release of the "Artificial Intelligence (AI) Playbook for the U.S. Federal Government." It was produced through a collaborative, volunteer effort by a working group of 133 leaders from government and industry plus academia and associations, hosted by the ACT-IAC Emerging Technology Community of Interest (COI). "The AI Playbook is designed to help the United States Federal Government achieve successful outcomes and reduce risk in its understanding and application of AI technologies," said David Wennergren, CEO of ACT-IAC, "and this important work directly supports the President's Management Agenda (PMA), Cross Agency Priority (CAP) Goal 6 - Shifting from Low-Value to High-Value Work." The Playbook also follows the General Service Administration's Office of Government-wide Policy Modernization and Migration Management (M3) framework used for Shared Services. AI has the power to accelerate government services in fields as diverse as medical research and disaster recovery to help save lives and improve quality of service in impactful ways.

Governments across the world are increasingly promoting AI technology through investments in R&D and by developing education programs to train the workforce with AI skills, which can support businesses across industries. Retail, BFSI, and manufacturing are a few of the major industries that are increasing their investments in AI to automate business functions. Many key countries have initiated AI development plans to drive economic and technological growth. Some instances include the launch of Germany's digital strategy on AI called "AI Made in Germany" in November 2018, and China's announcement of its "Next Generation Artificial Intelligence Development Plan", in 2017. These strategies focus on the development of talent and education, government investments, and research and collaborative partnerships in AI.

PNNL scientists working with Stanford researchers have put forth a new approach to incorporate medical knowledge into AI systems, improving the accuracy of patient diagnosis dramatically. Artificial intelligence will never replace a doctor. However, researchers at the Department of Energy's Pacific Northwest National Laboratory have taken a big step toward the day when AI can help physicians predict medical events. A new approach developed by PNNL scientists improves the accuracy of patient diagnosis up to 20 percent when compared to other embedding approaches. The PNNL approach seeks to capture and recreate the types of connections physicians do naturally when they apply a lifetime of learning and knowledge to the patient standing in front of them in the exam room.

Supervised Learning is the crown jewel of Machine Learning. A couple years ago, Harvard Business Review released an article with the following title "Data Scientist: The Sexiest Job of the 21st Century." Ever since its release, Data Science or Statistics Departments become widely pursued by college students and, and Data Scientists (Nerds), for the first time, is referred to as being sexy. For some industries, Data Scientists have reshaped the corporation structure and reallocated a lot of decision-makings to the "front-line" workers. Being able to generate useful business insights from data has never been so easy.

Supervised Learning is the crown jewel of Machine Learning. A couple years ago, Harvard Business Review released an article with the following title "Data Scientist: The Sexiest Job of the 21st Century." Ever since its release, Data Science or Statistics Departments become widely pursued by college students and, and Data Scientists (Nerds), for the first time, is referred to as being sexy. For some industries, Data Scientists have reshaped the corporation structure and reallocated a lot of decision-makings to the "front-line" workers. Being able to generate useful business insights from data has never been so easy.

ABOUT A CENTURY ago, engineers created a new sort of space: the control room. Before then, things that needed control were controlled by people on the spot. But as district heating systems, railway networks, electric grids and the like grew more complex, it began to make sense to put the controls all in one place. Dials and light bulbs brought the way the world was working into the room. Levers, stopcocks, switches and buttons sent decisions back out. By the 1960s control rooms had become a powerful icon of the modern. At Mission Control in Houston, young men in horn rimmed glasses and crewcuts sent commands to spacecraft heading for the Moon. In the space seen through television sets, travellers exploring strange new worlds did so within an iconic control room of their own: the bridge of Star Trek's USS Enterprise. A hexagonal room built in Santiago de Chile a decade later fitted right into the same philosophy--and aesthetic. It had an array of screens full of numbers and arrows. It was linked to a powerful computer. It had futuristic swivel chairs, complete with geometric buttons in the armrests to control the displays. Unlike the Johnson Space Centre and the Enterprise, it even had a small bar where occupants could serve themselves drinks after a hard day's controlling.

With an aim to help warship crews make quick decisions and process data efficiently, the U.K.'s Ministry of Defense recently announced contracts to use AI-based (artificial intelligence) technology in warships. According to a source, Defense and Security Accelerator (DASA) will be funding £1 million (around US$1.3 million) for AI contracts as part of its "Intelligent Ship – The Next Generation" competition, which is aimed at using innovative approaches for Human-AI and AI-AI teaming for various defense platforms like warships, aircraft, and land vehicles. James Heappey, U.K.'s Defense Minister, said, "The astonishing pace at which global threats are evolving requires new approaches and fresh thinking to the way we develop our ideas and technology. The funding will research pioneering projects into how AI and automation can support our armed forces in their essential day-to-day work." DASA's warship competition, in alliance with the Defense Science and Technology Laboratory (Dstl), is intended to enhance the designs of future defense platforms by using advances in automation, autonomy, machine learning, and AI.