Chief Operating Officer Rachel Cooke Rachel is responsible for business operations including overseeing client services, research events and project management. Prior to joining Brandon Hall Group, Rachel was the Chief Operating Officer Co-founder of AC Growth. Rachel has over 15 years of experience in sales, marketing, business development, and sales performance management. Prior to AC Growth, she held several senior management roles and was on the leadership team at Bersin & Associates, a pioneer analyst firm in e-learning and now industry leading HR and talent Research Company. In her Senior Director role, Rachel developed the strategy and led the commercial execution of the solution provider vertical, and grew the vertical into the company's largest market segment.

When Tesla Motors CEO Elon Musk proclaims that artificial intelligence is "our biggest existential threat," it makes headlines worldwide. But what goes unreported is that the very search engines people used to find Musk's comments are themselves an example of how AI has subtly but forcefully become a part of everyday, real-world life. When it comes to a discussion of AI, it helps to have a sense of history--as well as a sense of humor. Thanks to premonitory proclamations by Musk, Microsoft's Bill Gates, Cambridge's Stephen Hawking and other prominent technologists, AI has become a popular topic again, after a 20-year cooling-off period. It's tempting to assume that the "dire warnings" about AI being a threat to mankind were mostly tongue-in-cheek, but the end result is that just as it happened in the 1980s and '90s, the hype over AI is again outpacing the reality (virtual and otherwise). The first question that needs to be answered though is: Whatever happened to AI and why did it go underground for so many years?

BERLIN (Reuters) - Germany plans new legislation to require manufacturers of cars equipped with an autopilot function to install a black box to help determine responsibility in the event of an accident, transport ministry sources told Reuters on Monday. The fatal crash of a Tesla Motors Inc Model S car in its Autopilot mode has increased the pressure on industry executives and regulators to ensure that automated driving technology can be deployed safely. Under the proposal from Transport Minister Alexander Dobrindt, drivers will not have to pay attention to traffic or concentrate on steering, but must remain seated at the wheel so they can intervene in the event of an emergency. Manufacturers will also be required to install a black box that records when the autopilot system was active, when the driver drove and when the system requested that the driver take over, according to the proposals. The draft is due to be sent to other ministries for approval this summer, a transport ministry spokesman said.

Deep Learning Methods Within Video An End Game Application – We'll explore the use cases of using deep learning to drive higher video views. The coming Valhalla of Deep Learning is being realized in visual object recognition and image classification within video. Mobile video has and continues to transform the way video is being distributed and consumed. Mobile video advertising is the fastest growing segment projected to account for 25 billion worth of ad spend by 2021. Deep Learning and artificial intelligence are also growing within the very same companies who are jockeying for your cognitive attention.

On 21 June, the Law Society hosted a conference on the topic of artificial intelligence (AI) and machine learning in law. Artificial intelligence and machine learning technology have made significant progress in recent years. Arguably, legal and ethical thinking about the societal implications could now be lagging behind this technical progress. We explored how machine learning is currently being used, how it may be used in the future and what the legal implications could be. The discussion will also examine the potential liability and regulatory considerations in relation to machine learning, and the role of the law and lawyers in this rapidly evolving area.

Technology created by an Australian startup is going to bring the conservation of orang-utans in a Sumatran jungle into the digital age. Adelaide-based Jemsoft's Monocular API is a computer vision machine learning technology, which can be taught to search for items in the real world. Under a new agreement the company will licence the software to conservationists to be applied to footage taken via drone above the canopies to identify nests of the endangered mammal. Rather than conservationists sifting through photos and video manually, Jemsoft's intelligent software can be trained to identify the orang-utan nests. Jemsoft, founded in 2013, is already being used by a number of large enterprise clients, including Fortune 500 companies, for other purposes.

The purpose of this blog post is to demonstrate how to install the Keras library for deep learning. Let me start by saying that Keras is my favorite deep learning Python library. It's a minimalist, modular neural network library that can use either Theano or TensorFlow as a backend. Furthermore, the primary motivation behind Keras really resonates with me: you should be able to experiment super quickly -- going from idea to result, as fast as possible. Coming from a world that mixes both academia and entrepreneurship, the ability to iterate quickly is extremely valuable, especially in the deep learning world where it can take days to weeks to train just a single model.

It's not easy to be healthy. And it's even harder to be healthy at work, where chances are--despite the vogue for standing desks and the like--you're parked in a chair for most of the day, focused on a screen. The average workweek, by one recent measure, is now 47 hours and counting. By and large, more time spent at the office means more time hunched over a computer, probably eating lunch at a desk. Stress--which has been linked to increased risk of stroke, heart disease, and other health issues--is just about inevitable as a result.

Turing test or no Turing test, Intelligent Virtual Assistants are here to stay. As more and more of our daily interactions are between people and artificial intelligence systems, the demands for meaningful and effective interactions is increasing. Expectations from users of intelligent agents need to be the source of great attention, with the appropriate planning, modeling and measurement in place to guarantee the desired performance and results. Join us as we explore the topic of "Training the Robots" where our experts will discuss the elements needed to build virtual assistant capabilities including identification of target processes for automation/AI enhancement, capturing and organizing process knowledge, and contextualizing virtual assistant /human interactions.

Artificial intelligence (AI) and a world in which machines threaten humanity's status quo has been the preserve of science fiction for decades. In the Eighties, Terminator was set in a post-apocalyptic world in which cyborgs rule, RoboCop's protagonist was part-man, part-machine and Short Circuit toyed with the idea of robots developing human-like minds, with rather more endearing results. The reality is the bot has bolted. AI is walking and talking among us. In 2016, we use voice-recognition systems, driverless cars are being trialled and robotic hotel receptionists work in Japan.