Hephaestus, the Greek god of blacksmiths, metalworking and carpenters, was said to have fashioned artificial beings in the form of golden robots. Myth finally moved toward truth in the 20th century, as AI developed in series of fits and starts, finally gaining major momentum--and reaching a tipping point--by the turn of the millennium. Here's how the modern history of AI and ML unfolded, starting in the years just following World War II. In 1950, while working at the University of Manchester, legendary code breaker Alan Turing (subject of the 2014 movie The Imitation Game) released a paper titled "Computing Machinery and Intelligence." It became famous for positing what became known as the "Turing test."

Bruce Newsome reviews the recently published book: "Strategy, Evolution, and War: From Apes to Artificial Intelligence," authored by Kenneth Payne and published by Georgetown University Press. Artificial intelligence (AI) has been explicit in the practices and policies of defence since at least the 1970s, at least in high-capacity countries, given the exponential growth in the power of electronic computing per unit cost. It was already specified in training and forecasting simulations, decision-making aids, targeting aids, robotics, adaptive navigation systems (as in the Tomahawk Cruise Missile), and ballistic missile defence. Any child with a video game could experience AI. AI raced up Western governmental priorities in the 2000s by application to countering terrorism; in 2009, the US escalated its cyber capabilities and authorities, partly on the promise of AI; in 2014, the Russians seemed to know first what the defenders of Ukraine were doing, in part because of integration of AI; and in 2016, Western governments consensually blamed Russia for unprecedented interference in American and other elections, partly aided by AI.

The majority of the deep learning applications that we see in the community are usually geared towards fields like marketing, sales, finance, etc. We hardly ever read articles or find resources about deep learning being used to protect these products, and the business, from malware and hacker attacks. While the big technology companies like Google, Facebook, Microsoft, and Salesforce have already embedded deep learning into their products, the cybersecurity industry is still playing catch up. It's a challenging field but one that needs our full attention. In this article, we briefly introduce Deep Learning (DL) along with a few existing Information Security (hereby referred to as InfoSec) applications it enables. We then deep dive into the interesting problem of anonymous tor traffic detection and also present a DL-based solution to detect TOR traffic.

Subcommittee on Research and Technology and Subcommittee on Energy Hearing - Artificial Intelligence - June 26th, 2018 Dr. Tim Persons, chief scientist, GAO Mr. Greg Brockman, co-founder and chief technology officer, OpenAI Dr. Fei-Fei Li, chairperson of the board and co-founder, AI4ALL OpenAI was founded by Elon Musk and Sam Altman

That's the rough amount of money that counterfeiters displaced last year by selling phony products. Some 2.5% of all trade is for fake goods. The United States is hit hardest by the scourge of counterfeit products -- U.S. brands accounted in 2013 for 20% of the world's infringed intellectual property. When most people think about counterfeiting, they think of knock-off Louis Vuitton handbags sold on the sidewalk. But fake products also include business and enterprise products, as well as everyday consumer goods.

Thousands of artificial intelligence developers and researchers -- including Elon Musk, Google DeepMind co-founder Demis Hassabis, and Google Machine Intelligence head Jeffrey Dean -- just signed a "Lethal Autonomous Weapons Pledge," vowing to resist delegating the decision to murder in a military context to a machine. On its face, this pledge seems like a step in the right direction, a recognition of the concerns of tech employees. But here's the main problem with this pledge: the top drone manufacturers for the U.S. military -- including but not limited to Northrop Grumman, Boeing, General Atomics, and Textron, which make up 66 percent of the U.S. drone military market -- did not sign on to the contract. "We will neither participate in nor support the development, manufacture, trade, or use of lethal autonomous weapons," the pledge reads. "We ask that technology companies and organizations, as well as leaders, policymakers, and other individuals, join us in this pledge."

Fifty years ago this month, in the midst of the Cold War, nations began signing an international treaty to stop the spread of nuclear weapons. Today, as artificial intelligence and machine learning reshape every aspect of our lives, the world confronts a challenge of similar magnitude and it needs a similar response. There is a danger in pushing the parallel between nuclear weapons and AI too far. But the greater risk lies in ignoring the consequences of unleashing technologies whose goals are neither predictable nor aligned with our values. The immediate prelude to the Treaty on Non-Proliferation of Nuclear Weapons was the Cuban missile crisis in 1962.

Facebook Inc. suspended another company that harvested data from its site and said it was investigating whether the analytics firm's contracts with the U.S. government and a Russian nonprofit tied to the Kremlin violate the platform's policies. Crimson Hexagon, based in Boston, has had contracts in recent years to analyze public Facebook data for those and other clients, according to people familiar with the matter and federal procurement data. Crimson Hexagon says it has the largest repository of public social media posts, totaling more than one trillion, from sites that also include Twitter Inc. TWTR -0.05% and Instagram. Crimson Hexagon operates with little oversight from Facebook once it pulls public data from the social-media platform, according to more than a dozen people familiar with the business. The government contracts weren't approved by Facebook in advance, for example, the people said.

That's the scenario proposed by the Defense Advanced Research Projects Agency (DARPA), representatives said in a statement. The group is seeking innovative designs for robots that measure just a fraction of an inch, and the tiny bots will compete against each other in a series of contests of strength, speed and agility -- similar to those that try the limits of human achievement in the Olympics. The robots would be developed for a new DARPA program called Short-Range Independent Microrobotic Platforms (SHRIMP). Under SHRIMP, the bug-sized bots will be tested for deploying in locations that are difficult for people to navigate, or are dangerous or inaccessible to humans, according to the statement. SHRIMP will research and develop novel solutions for powering small robots, and will investigate new materials that could improve the robots' performance without significantly increasing their size or heft.

The management of digital and online data continues to be on the rise as emerging technologies, like AI and Analytics, makes it more convenient for businesses to gather vital details and insights on their customers. In fact, by 2020, the world will have accumulated 44 zettabytes of information, according to market research firm International Data Corp. The solution, according to Dan Baird, Founder & CEO of Wrench.ai, is to use "private" machine learning approach that allows the technology to only pull insights from data sets, while not having to access peoples' personally identifiable information. He explains how in an interview with Digital Journal. Digital Journal: How important is digital transformation for business?