Pretty much every company, to some extent, has to power their algorithms with data and machine learning if they're going to be competitive with anyone else in their space. The challenge there, though, is that not everyone can have the massive computational power and data of a company like Google -- especially not startups. Siddharth Mall and his co-founders started Playment in India based on that bet. Playment allows companies to send over sets of training data that need some kind of light analysis, and then divvies it up across a large on-demand workforce that can resolve those tasks on their mobile devices. That, in turn, feeds back additional data to the companies to help refine their algorithms -- whether that's visual search, quality checks of recommendation engines.

Can artificial intelligence, or machine learning, be deployed to predict earthquakes, potentially saving thousands of lives around the world? Some seismologists are working to find out. But they know such efforts are eyed with suspicion in the field. "You're viewed as a nutcase if you say you think you're going to make progress on predicting earthquakes," Paul Johnson, a geophysicist at Los Alamos National Laboratory, told Scientific American. In the past, scientists have used various criteria to try to predict earthquakes, including foreshocks, electromagnetic disturbances, changes in groundwater chemistry.

Under the watchful eyes of the public and lawmakers, several traditional automakers and tech companies have been racing to the finish line of "fully autonomous driving." Companies are already testing their cars in real-world conditions on public streets, and to date, some have driven millions of miles. Though opinions vary, it is hypothesized that we will be safer in autonomous vehicles (AKA self-driving cars). And while we would really need significantly more data to determine this as a statistic, I can imagine the experience of riding in full autopilot will be somewhat like flying: though much scarier than driving (for most), you are still safer, statistically-speaking. Regardless, it will be some time before the majority of average consumers have replaced their conventional vehicles.

The focus on financial crime, and the money laundering that funds terrorist attacks and other criminal activities, has forced the industry to look for smarter approaches. In the previous posts in this mini-series, TJ Horan noted that AI is the newest hope for compliance, and Frank Holzenthal explored the benefits that AI can bring to compliance officers. Now it's my turn, and I'm going to explore the AI and machine learning technologies my team has integrated into the FICO TONBELLER Anti-Financial Crime Solutions. We have built on top of the FICO TONBELLER solutions using FICO's battle-proven and patented artificial intelligence and machine-learning algorithms, which are used in FICO Falcon Fraud Manager to protect about two-thirds of the world's payment card transactions. Industry experts have begun to realize the significance of analytics in combatting anti-money laundering.

The Defense Department is likely within 18 months of introducing autonomous cybersecurity tools that will be capable of augmenting human analysts by predicting threats, including insider activity, and dynamically isolating parts of the network that may come under attack, the department's outgoing chief information officer said Thursday. Terry Halvorsen, speaking to reporters during his last media roundtable before retiring at the end of the month, said as the department continues deployment of its 12 Joint Regional Security Stacks--a $1.6 billion effort to eliminate hundreds of disparate firewalls with centrally managed commercial security appliances and network monitoring tools--the next major step will be the deployment and testing of AI-based security applications. "Given the volume [of attacks] and where I see the threat moving it will be impossible for humans by themselves to keep pace. We can and we're very close to being able to put more autonomy into the security tools, and we will get to the point within the next 18 months where AI is becoming a key factor in augmenting the human analyst in making those decisions about what to do," Halvorsen said. Halvorsen recently hosted a special meeting of the Five Eyes alliance (Australia, Canada, New Zealand, the United Kingdom, and the United States), plus Germany, Japan, and NATO, at which AI-based cybersecurity was discussed in detail. "This is very real inside the department.

As advancements in Quantum Computing and Artificial Intelligence are always popular media topics, I thought it'd be interesting to take a look at how the two are converging and what such a convergence might mean for cybersecurity. Today's traditional computers operate by processing information as "bits." A bit can be either a "1" or a "0." Even massive data-crunching super computers such as the US Department of Energy's Titan Cray XK7 operate in this fashion. These computers explore potential solutions to problems in a methodical, sequential manner.

Security is a broad term, and in industry and government there are a myriad of "security" contexts on a variety of levels – from the individual to nation-wide. Artificial intelligence and machine learning technologies are being applied and developed across this spectrum. While many of these technologies have the potential and have greatly benefited society (helping reduce credit card fraud, for example), the evolving social contexts and applications of these technologies often leave more questions than answers – in terms of rules, regulations and moral judgments – in their wake. Artificial intelligence and security were – in many ways – made for each other, and the modern approaches of machine learning seem to be arriving just in time to fill in the gaps of previous rule-based data security systems. The purpose of this article is to shed light on current trends and applications, in industry and government, at the intersection of artificial intelligence and the security field.

On Jan. 20, the drone war entered its third Administration. Over the inaugural weekend, American drones fired missiles at suspected Al Qaeda fighters in Yemen, killing five people. The drone war, that is, the popular, unmanned-vehicle term for America's strategy of targeted killing, is an outgrowth of President George W. Bush's war on terror, a vestigial organ that became the centerpiece for the Obama administration's eight years of low-intensity warfare. With much of American national security strategy poised to change under the new Trump administration, it's worth taking a step back to examine what, exactly, the United States hoped to do with its drones. The United States is, it's worth noting, at war.

Fan Hui is speaking to a chatty audience at the 2016 European Go Congress in St. Petersburg, Russia, gushing over a game of Go played by one of his mentors. Hui's enthusiasm is infectious--the room's chatter subsides as he pulls up slides of the complex Chinese game, whose players battle to dominate a 19 19 board with black and white tiles called stones. Hui's mentor, AlphaGo, has studied strategies built over the game's 4,000-year history, and has played thousands of practice matches. But a training regiment that took Hui years to perfect, AlphaGo did in about four weeks. The game Hui showed to the Saint Petersburg audience is one that the machine played against itself, and to Hui, is an example of the beauty of AlphaGo's strategic mind Even the smartest humans miss patterns that computers see instantly. Problems like file-compression, translation, and custom drug fabrication have millions of variables and data points whose limits exceed human understanding dozens of times over.

Eric Schmidt on Wednesday shared some of his top concerns regarding the future of the internet -- namely, preserving its interconnectedness and ensuring that artificial intelligence isn't used to militarize it. "Machine learning capabilities... need to be done in the open and not in military research labs," the Alphabet executive chairman said on stage at the RSA cybersecurity conference in San Francisco. Had the internet developed within the confines of military research labs, "We'd spend all day worrying if they were in our networks, the internet would begin being shut down," he said. "One of the questions the industry should be asking is can we come up with a way where countries can agree not to use machine learning technologies in a way that militarizes the internet." He noted that at the end of the Obama administration, President Obama and President Xi of China inked a deal to bring down the number of cyberattacks against each other.