In a recent article, we demystified some of the technical jargon that's being thrown around these days like "artificial intelligence", "SaaS, "the cloud", and "deep learning". While the techies can debate among themselves the difference between "machine learning" and "deep learning", we're going to consider the two terms synonymous and henceforth just talk about "deep learning". So just what is "deep learning"? We wanted to understand more, so we came across this excellent TED talk given by Jeremy Howard which finally explains in layman's terms just what deep learning is. If you have 20 minutes, watch the video now and no need to read any further. If you don't have time to watch the video, here's what we learned. When you use Google Images to search for a "grey cat", Google Images shows you grey cats. Is this because Google can recognize what a grey cat looks like? This is simply because Google searches text to find grey cat images. So how can we train Google to identify grey cats by only looking at images? Here's how we do it. Let's start with a sample of 10 million random pictures from Facebook and teach Google how to learn. The first part entails scanning this massive set of pictures using an algorithm developed by a software developer at Google. What does this algorithm do? It looks at the relationships of pixels in a digital photo and tries to find objects of a similar shape. Let's try this with a simple example. Let's say the pictures were black and white and composed of circles, triangles and squares. You could quite easily imagine an algorithm that could first identify the differences in color (every color is actually a unique code in software) and then start to map sharp differences in color that would denote shapes. The shapes could then be described by the direction of the lines as either circles, triangles, or squares. You could even go ahead and make them color pictures. The computer can now point out a "red triangle" or even a "beige circle". Without even having to do much coding, the computer now has the intelligence of a small child when it comes to identifying shapes. Now let's take this to the next level. Let's take a sophisticated deep learning algorithm and feed it 100 million pictures from Facebook. Let's tell the algorithm to try and find similar objects in this "big data" set and then group them. These groups are displayed to a developer who can then label them. Humans would perhaps be the most obvious and frequent object that the computer would identify. The developer would then be shown 50 humans the computer identified and could start to label sets within the group like "old person", "baby, "Chinese person" or "freckled person".

Artificial intelligence (AI) is real, albiet maturing slowly. You experience it when you talk to Alexa, when you see a creepily-targeted online ad, and when Netxflix turns you on to Stranger Things. Oh yea, and that self-driving car over there is AI super-powered! AI is indeed cool, but many are scared about how it ultimatley may impact society. Stephen Hawking, Elon Musk, and even the Woz warned that "...artificial intelligence can potentially be more dangerous than nuclear war."

Artificial Intelligence (AI) has captured the attention of C-suite executives across all industries and is poised to transform businesses in ways we've never seen since the impact of computer technology in the late 20th century. We are already seeing venture capitalists funding AI start-ups at a rapid pace. Technology companies are also moving swiftly to create and capture value in this emerging area. High-profile acquisitions by Google, Apple and Facebook are piquing interest in Artificial Intelligence technologies such as robotics, expert systems, computer vision, speech, gesture and facial recognition. Companies are creating new research labs devoted to innovating with these technologies. In Africa, AI has a strong role to play.

If you're an average retail investor who just saw a 60-minute special on artificial intelligence (AI), you probably thought to yourself "why can't we use AI for stock trading"? Then maybe you plugged the phrase "artificial intelligence stock trading" into Google and this article popped up. We're going to answer your question and hopefully we'll all learn something along the way. Yes, we've written before about at least three hedge funds using AI to generate superior returns for their investors. Take as an example the hedge fund Renaissance Technologies which is said to have "the best physics and mathematics department in the world".

Much of the current machine learning revolution originated around applications like computer vision that have nothing to do with finance. Financial data modelling is beset by a low signal to noise ratio, whereas data used to teach a computer to identify a picture of a cat, for example, is unambiguous. The financial universe is a non-stationary environment with variable patterns of correlation between stocks, bonds and other instruments. Not least, the task in hand is essentially about predicting things that haven't happened yet. For nearly 30 years now, UK hedge fund manager Man AHL has been trawling through enormous historical datasets trying to understand what is predictable and what's just noise.

Security technology company Radware (NASDAQ:RDWR) has acquired machine learning security company Seculert for an undisclosed amount. The team and technology acquisition provides Radware with important new threat analytics capabilities that combine its big data platform with Seculert's cloud-based machine learning and behavioral analysis. Although the deal will have a negligible impact on short-term EPS, I view it as a necessary longer term investment in next-generation technologies that will reap rewards to Radware as it sells its enhanced offerings to enterprises and data centers. Israel-based Seculert was founded in 2010 by Alex Milstein, Aviv Raff and Dudi Matot to utilize machine learning to improve perimeter cyber security defenses for enterprises. The company uses a combination of data analytics, machine learning and behavioral analysis to create visibility without requiring hardware.

Will A.I. make our government smarter and more responsive – or is that the last step towards the end of privacy? As chief scientist of U.S. Government Accountability Office, Tim Persons conceives its vision for advanced data analytics. Learn about the promise and challenges around government A.I. and what those portend for private sector companies. Dr. David A. Bray began work in public service at age 15, later serving in the private sector before returning as IT Chief for the CDC's Bioterrorism Preparedness and Response Program during 9/11; volunteering to deploy to Afghanistan to "think differently" on military and humanitarian issues; and serving as a Senior Executive advocating for increased information interoperability, cybersecurity, and civil liberty protections. He completed a PhD in from Emory University's business school and two post-docs at MIT and Harvard. He serves as a Visiting Executive In-Residence at Harvard University, a member of the Council on Foreign Relations, and a Visiting Associate at the University of Oxford. He has received both the Arthur S, Flemming Award and Roger W. Jones Award for Executive Leadership. In 2016, Business Insider named him one of the top "24 Americans Who Are Changing the World". Dr. Timothy M. Persons is a member of the Senior Executive Service of the U.S. federal government and was appointed the Chief Scientist of the United States Government Accountability Office (GAO) in 2008. In addition to establishing the vision for advanced data analytic activities at GAO, he also serves to direct GAO's Center for Science, Technology, and Engineering (CSTE), a group of highly specialized scientists, engineers, and operations research staff. In these roles he directs science and technology (S&T) studies and is an expert advisor and chief consultant to the GAO, Congress, and other federal agencies and government programs on cutting-edge S&T, key highly-specialized complex systems, engineering policies and best practices, and original research studies in the fields of engineering, computer, and the physical and biological sciences to ensure strategic and effective use of S&T in the federal sector. Michael Krigsman: Welcome to Episode #216 of CxOTalk. I'm Michael Krigsman, I'm an industry analyst and the host of CxOTalk, where we bring truly amazing people together to talk about issues like the one we're talking about today, which is the role of AI and the impact on public policy; or maybe I should say, the impact of public policy on AI. Our guest today, we have two guests actually, are Tim Persons, who is the Chief Scientist of the General Accountability Office of the United States Government, and David Bray, who has been on CxOTalk many times, the Chief Information Officer of the Federal Communications Commission. And David, let's start with you. Maybe, just introduce yourself briefly.

Will this be the year that artificial intelligence (AI) becomes so seamlessly connected in our lives as to trigger action at the speed of thought? Will you and everyone you know have a "digital twin"? And if so, which twin would you prefer to talk to? Experts warn that one blurry-eyed morning, your coffee maker might even turn on you. Those were some of the extraordinary and unexpected predictions shared by the experts who participated in the five-part Coffee Break with Game-Changers series 2017 Predictions Special.

It will allow developers to write deep learning applications as standard Spark programmes that run on top of existing Spark or Hadoop clusters. Intel has launched a deep learning library – the open-source BigDL for Apache Spark cluster-computing framework. BigDL, which is already running in the Databricks Spark Platform, allows users to write their deep learning applications as standard Spark programmes that can directly run on top of existing Spark or Hadoop clusters. It allows the exporting of artificial intelligence expertise to data scientists that currently work across several applications in various fields. BigDL is modeled after Torch, an open source deep learning framework used in scientific computing.

"Artificial intelligence is one of the hottest subjects these days, and recent advances in technology make AI even closer to reality than most of us can imagine. Robots are no longer limited to traditional blue-collar jobs, fully automated assembly lines and high-frequency trading algorithms. White-collar jobs are ripe for automation, and robots are replacing bank tellers, mortgage brokers and loan officers in the financial industry." And I can speak from personal experience on the matter: my background is in financial planning, private banking, and portfolio strategy. I saw the Robos coming, saw my skills quickly losing relevancy, and got out of the industry as soon as I had researched where I could have a future impact.