Young hackers and data miners gunning for a job in the intelligence community don't need to worry about robots taking their jobs, the director of national intelligence said Tuesday. Artificial intelligence and machine learning systems will increasingly help intelligence agencies parse the behemoth troves of data they collect, but humans will still need to analyze that data for insights and ensure it isn't manipulated by foreign hackers, Director Dan Coats told students at The Citadel military college in Charleston, S.C. "Our problem now is we have more demand for these types of capabilities, including cybersecurity and so forth, than we have supply," Coats said in response to a student question about the effects of artificial intelligence on hiring. "Those of you who are in curriculum that fits into those categories, Uncle Sam needs you," Coats continued. The military's broad goal with artificial intelligence, Coats said is to gather insights from satellites and other signals intelligence sources where the data is now too voluminous to be sufficiently analyzed. The National Geospatial-Intelligence Agency, for example, is only able to analyze about 20 percent of the data it collects right now, Coats said.

Fei Fang has saved lives. At MIT Technology Review's EmTech conference on Wednesday, Fang outlined recent work across academia that applies AI to protect critical national infrastructure, reduce homelessness, and even prevent suicides. Fang explained how a system she developed in 2013, while doing her PhD at the University of Southern California, is used every day to protect 60,000 passengers on the Staten Island Ferry in New York City. There are more ferries traveling between Staten Island and Manhattan than US Coast Guard patrol boats in the same territory. Previously, one patrol boat would follow one ferry for the whole journey, leaving the other ferries unprotected.

In this e-guide we explore how artificial intelligence is going to play a key role in the future of the Nordics. We discuss whether AI is truly a threat to humans in terms of the future of work, how AI can benefit humans in the future and how the Finnish government is backing a national AI development strategy. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

Machine learning security is an emerging concern for companies, as recent research by teams from Google Brain, OpenAI, US Army Research Laboratory and top universities has shown how machine learning models can be manipulated to return results fitting the attacker's desire. One area of significant finding has been in image recognition models. Image recognition is one of the stalwarts of machine learning and deep learning systems, allowing for superhuman performance on classification tasks and enabling proofs of concept in autonomous vehicles. Recent highly successful research showing the exploitation of image recognition models, specifically convolutional neural networks, is especially troubling for autonomous vehicles as attackers could theoretically take control of vehicles, or at least cause them to lose control. Advancements by Geoffrey Hinton and team address a few of the key problems plaguing convolutional neural networks, or CNNs, (more on that below), however, definitive research has not yet been performed to check if they fix the security problems. I'll outline several security issues that exist in current algorithmic deployments and then walk through some steps to take in order to provide assurance over algorithmic integrity.

The widespread disruption of AI is summed up best by Andrew Ng, the former chief scientist of Baidu, who views AI as the new electricity: "Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years." While most large corporations are undergoing technological transformations to use, support or offer AI technologies, the real magic takes place in the AI startup arena. According to CB Insights, the top 100 AI startups of 2017 have raised $11.7 billion in aggregate funding across 367 deals, thereby making the market rich with innovations plus financial backings. The most surprising AI startups and applications are those that are paving the way towards more unconventional verticals, such as insurance, background checks, real estate, health and retail. The unique application of AI in areas that aren't traditionally high-tech is particularly fascinating for multiple reasons.

Automation and artificial intelligence (AI) are often cited as delivering efficiencies in business. Now a quarter of surveyed Australians think that technology should be used to replace our politicians too. Just a fortnight after Australians were handed a new Prime Minister – the sixth in the last decade – tech management firm OpenText released the findings of a survey on the role of AI in government. It found that 27 per cent believe AI would make better decisions than elected politicians. However, they still want humans kept in the loop to make final decisions.

Digital commerce channels are presenting new opportunities for bad actors, making cybercrime a colossal problem for companies of all sizes. All told, U.S. businesses and consumers lost more than $1.4 billion in 300,000-plus reported cyberattacks last year, according to the Federal Bureau of Investigation (FBI). Perhaps more troubling, however, is digital fraud's projected future. Recently published research noted global fraud losses could top $6 billion by 2021, more than doubling the $3 billion lost worldwide in 2015. There is seemingly no rule, rhyme or reason as to when a cyberattack will strike, or whom or what it will target, which can be particularly frightening.

Executives from Amazon, Apple, AT&T, Charter Communications, Google, and Twitter are heading to Washington Wednesday to testify before the Senate Commerce Committee on the topic of privacy. As ever, the main question will be: Are these companies doing enough to protect consumer privacy, and if not, what should Congress do about it? It has been the backdrop to just about every hearing with tech leaders over the last year--and there have been many. And yet, the threat of regulation carries new weight this time around. Over the summer, California passed the country's first data privacy bill, giving residents unprecedented control over their data.

Abstract--This paper explores using a Long short-term memory (LSTM) based sequence autoencoder to learn interesting features for detecting surveillance aircraft using ADS-B flight data. An aircraft periodically broadcasts ADS-B (Automatic Dependent Surveillance - Broadcast) data to ground receivers. The ability of LSTM networks to model varying length time series data and remember dependencies that span across events makes it an ideal candidate for implementing a sequence autoencoder for ADS-B data because of its possible variable length time series, irregular sampling and dependencies that span across events. The motivation for this research was inspired by the original research presented by Richards, MacDonald-Evoy, and Hernandez in their "Tracking Spies In The Skies" talk at DEF CON 25 [1]. The goal of their research is to leverage ADS-B (Automatic Dependent Surveillance - Broadcast) data that is broadcast by commercial and private aircraft to detect surveillance aircraft.

Artificial Intelligence and Machine Learning have become transformative to a number of industries, and as such many industries need for AI talent is increasing the demand for individuals with these skills. This continues to exacerbate the difficulty of acquiring and retaining talent for the United States Federal Government, both for its direct employees as well as the companies that support it. We take the position that by focusing on growing and retaining current talent through a number of cultural changes, the government can work to remediate this problem today.