Since the 2013 Target breach, it's been clear that companies need to respond better to security alerts even as volumes have gone up. With this year's fast-spreading ransomware attacks and ever-tightening compliance requirements, response must be much faster. Adding staff is tough with the cybersecurity hiring crunch, so companies are turning to machine learning and artificial intelligence (AI) to automate tasks and better detect bad behavior. In a cybersecurity context, AI is software that perceives its environment well enough to identify events and take action against a predefined purpose. AI is particularly good at recognizing patterns and anomalies within them, which makes it an excellent tool to detect threats.

We're working with Booz Allen Hamilton to help the U.S. federal government apply deep learning techniques to key challenges in healthcare, defense and cybersecurity. Certified Deep Learning Institute instructors from NVIDIA and Booz Allen will provide hands-on training to federal customers across a variety of government agencies to build deep learning and data-driven solutions that are needed in the field. "The Deep Learning Institute has developed industry-leading curricula with the world's leading AI experts, and we deliver that in hands-on classes taught by certified instructors," said Greg Estes, vice president of Developer Programs at NVIDIA. "By working together with Booz Allen Hamilton, we will train specialists and data scientists to help tackle complex challenges that confront the federal government in healthcare, cybersecurity and other important areas." "Deep learning and AI-first approaches are critical to every federal agency. Booz Allen and NVIDIA working together to meet the demand of the federal government for training and applying deep learning techniques will further innovation," said Dr. Josh Sullivan, a senior vice president who leads Booz Allen's data science capabilities.

Preserving the privacy of individuals by protecting their sensitive attributes is an important consideration during microdata release. However, it is equally important to preserve the quality or utility of the data for at least some targeted workloads. We propose a novel framework for privacy preservation based on the k-anonymity model that is ideally suited for workloads that require preserving the probability distribution of the quasi-identifier variables in the data. Our framework combines the principles of distribution-preserving quantization and k-member clustering, and we specialize it to two variants that respectively use intra-cluster and Gaussian dithering of cluster centers to achieve distribution preservation. We perform theoretical analysis of the proposed schemes in terms of distribution preservation, and describe their utility in workloads such as covariate shift and transfer learning where such a property is necessary. Using extensive experiments on real-world Medical Expenditure Panel Survey data, we demonstrate the merits of our algorithms over standard k-anonymization for a hallmark health care application where an insurance company wishes to understand the risk in entering a new market. Furthermore, by empirically quantifying the reidentification risk, we also show that the proposed approaches indeed maintain k-anonymity.

Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of an approximate inference algorithm on a specific data set. This paper introduces the auxiliary inference divergence estimator (AIDE), an algorithm for measuring the accuracy of approximate inference algorithms. AIDE is based on the observation that inference algorithms can be treated as probabilistic models and the random variables used within the inference algorithm can be viewed as auxiliary variables. This view leads to a new estimator for the symmetric KL divergence between the approximating distributions of two inference algorithms. The paper illustrates application of AIDE to algorithms for inference in regression, hidden Markov, and Dirichlet process mixture models. The experiments show that AIDE captures the qualitative behavior of a broad class of inference algorithms and can detect failure modes of inference algorithms that are missed by standard heuristics.

Voters have a right to keep their political beliefs private. But according to some researchers, it won't be long before a computer program can accurately guess whether people are liberal or conservative in an instant. All that will be needed are photos of their faces. Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Using photos, AI will be able to identify people's political views, whether they have high IQs, whether they are predisposed to criminal behavior, whether they have specific personality traits and many other private, personal details that could carry huge social consequences, he said.

Despite my week-long Twitter consumption sabbatical (helped -- in part -- by the nigh week-long internet and power outage here in Maine), I still catch useful snippets from folks. My cow-orker @dabdine shunted a tweet by @terrencehart into a Slack channel this morning, and said tweet contained a link to this little gem. Said gem is the text of a very recent ruling from a District Court in Texas and deals with a favourite subject of mine: robots.txt. The background of the case is that there were two parties who both ran websites for oil and gas professionals that include job postings. One party filed a lawsuit against the other asserting that the they hacked into their system and accessed and used various information in violation of the Computer Fraud and Abuse Act (CFAA), the Stored Wire and Electronic Communications and Transactional Records Access Act (SWECTRA), the Racketeer Influenced and Corrupt Organizations Act (RICO), the Texas Harmful Access by Computer Act (THACA), the Texas Theft Liability Act (TTLA), and the Texas Uniform Trade Secrets Act (TUTS).

Alphabet boss Eric Schmidt has warned the Chinese are poised to erase a key American advantage -- and says the Trump administration is helping them. 'I'm assuming our [U.S.] lead will continue over the next five years and then that China will catch up extremely quickly,' the Google leader told the Center for New American Security's Paul Scharre at the Artificial Intelligence & Global Security Summit on Wednesday, according to Defense One. Schmidt, who also chairs the Defense Innovation Advisory Board, said the key difference was the importance the Chinese government put on AI - and slammed Donald Trump's administration for falling behind. Schmidt said the key difference was the importance the Chinese government put on AI - as slammed Donald Trump's administration for slashing funds for basic science and research. 'We need to get our act together, as a country…This is the moment when the [U.S.] government collectively, and private industry, needs to say, 'these technologies are important.'

At Google's Go North event today in Toronto, which features a slate of speakers focused primarily on artificial intelligence, Alphabet chairman Eric Schmidt spoke to Prime Minister Justin Trudeau (and actually asked him some tough questions on NAFTA negotiations and his feelings about Trump, surprisingly). Trudeau talked a lot about the Canadian perspective on innovation, and about why Canada is doing so well with regards to acting as a hub for research and development around artificial intelligence in general. "I just think Canadians realize better than most that there is an opportunity here," Trudeau began, also nothing that this extends not only to the innovation side, but also to the "consequences of AI, the consequences of automation," and the "economic imbalance of those who own the robots and those who are displaced by them." Trudeau explained that while he has no specific foresight in terms of where technological progress with artificial intelligence is taking us, he believes it's not up to the Canadian government to "pick winners," but that instead that it is their role to say that they're going to "invest in quantum, we're gonna invest in AI, we're going to invest in robotics, we're going to invest in high-value, innovative, creative, groundbreaking areas" that match the Canadian education system and the country's entrepreneurial values. He added that Canada has a drive to search for a way to "be relevant in a positive way on the world stage," and that AI fits with that goal, as does investment in other high-tech areas.

In April, as Eric Schmidt watched a computer program defeat China's top go player in a ground-breaking match in the Chinese city of Wuzhen, the executive chairman of Google's parent company was struck less by the considerable innovations displayed by human and machine than by the audience: "To me the more interesting thing [was that] all the top computer science people in China had shown up." It showed, Schmidt said, the importance placed on AI development by both the Chinese government and its people, and was a postcard from the future competition for AI dominance. "I'm assuming our [U.S.] lead will continue over the next five years and then that China will catch up extremely quickly," the Google leader told the Center for New American Security's Paul Scharre at the Artificial Intelligence & Global Security Summit on Wednesday. Schmidt doesn't like the term "arms race" to describe the U.S.-Chinese rivalry in artificial intelligence, in part because defining AI as a weapon is limiting at best and flatly inaccurate at worst. But it is a tool that can make one military, company, economy, and even nation much more effective than another. And China, he says, is positioning itself to devour the current U.S. advantage in just a few years.

With artificial intelligence set to revolutionize how the military runs surveillance missions around the world, one top Defense Department official hopes to bring intelligent systems to the Pentagon's efforts both on and off the battlefield. As director of defense intelligence for warfighter support, Air Force Lt. Gen Jack Shanahan spearheaded Project Maven, a Pentagon initiative to rapidly turn drone surveillance footage into useful intelligence through machine learning. The tool is scheduled to launch by the end of the year. The Pentagon has long used drones over the Middle East to inform the fight against groups like ISIS. Though drone and camera technology have advanced significantly, the back end looks much the same as it did decades ago, with analysts still spending countless hours manually scrolling through video for points of interest.