Last year saw the rise of the threat of deepfakes – a technique used to combine and superimpose images and videos onto others using a machine learning algorithm, creating hyper-realistic but fake content. AI buffs have split into two major groups – one working to make such images and video more realistic, and another developing tools that would tell users whether a video has been manipulated or not. A team of researchers from the University of California at Riverside and the R&D firm Mayachitra have developed a novel deep-learning architecture that can detect content-changing manipulation. This is not the first study on the problem, but this neural network appears to have gone further in recognising deepfakes than its predecessors. Different manipulation techniques may create a convincing video for human eyes, but the algorithm is able to see minor distortions, such as shearing and compression. It exploits resampling features, a long short-term memory (LSTM) based network, and encoder-decoder architectures in order to analyse videos pixel by pixel, and is said to be capable of spotting whole patches of the footage that have been doctored.

"Across the board, AI will make predictive analysis markedly easier," Coleman adds. "AI greatly enhances the ability to detect and identify threats. This helps security teams stay ahead of attacks. Experts agree that this new era in tech and cybersecurity is driven by prediction, detection, and response." Anthony Ferrante, senior managing director and global head of cybersecurity at FTI Consulting, says the use of AI and machine learning to work smarter, advance business objectives, and protect against cyber threats is becoming more and more prevalent.

Why it matters: AI is a major growth force for American companies and "of paramount importance to maintaining the economic and national security of the United States" President Trump said in an executive order signed in February dubbed the "American AI Initiative." The big picture from Axios emerging tech reporter Kaveh Waddell: The U.S. had a head start in commercializing AI, thanks to unmatched talent and eager VC money. What's happening: In addition to the Chinese government allocating significant spending to AI, more new companies are being started in a raft of different countries and raising equity, analysts from CB Insights tell Axios in an email. Yes, but: The total amount of funding is still tilted heavily toward the U.S. -- with the exception of a handful of Chinese mega-companies like TikTok owner ByteDance, which is the top-funded AI startup in the world. The U.S. funding lead is likely to continue because of its concentration of AI experts.

Artificial intelligence, quantum computing, 5G and the rise of the Internet of Things are just some of the emerging technologies that could aid cybercriminals in ways that could make them more dangerous than ever – and law enforcement must innovate quickly in order to help keep citizens safe, a new report has warned. IBM launches its Watson for Cyber Security beta program to test how cognitive computing can boost cybersecurity. Published by Europol, the'Do criminals dream of electric sheep: how technology shapes the future of crime and law enforcement' report – the title of which references the work of science fiction writer Philip K. Dick – explores the consequences that emerging technology could have for cybercrime. It's also suggested that law enforcement itself could take advantage of some of the emerging technologies to help in the fight against cybercrime. For example, AI is detailed as a technology that could benefit law enforcement by helping to improve the security of systems and devices.

Former secretary of state Henry Kissinger, former Google CEO Eric Schmidt, and Cornell University professor Daniel Huttenlocher worked together for three years to write a single article for the August 2019 issue of The Atlantic. In it, they set out to solve the "riddles" of artificial intelligence, which they call an "unstoppable revolution," but they only managed to mimic other AI sign-twirlers. Their conclusions read like an encyclopedia entry for "hyperbole": "The challenge of absorbing this new technology into the values and practices of the existing culture has no precedent. The most comparable event was the transition from the medieval to the modern period. AI software is more than technology, the authors contend. Its advent will change the meaning of truth as we know it: "The Enlightenment philosopher Immanuel Kant ascribed truth to the impact of the structure of the human mind on observed reality.

Facial recognition technology has spread prodigiously. Google, Microsoft, Apple and others have built it into apps to compile albums of people who hang out together. It verifies who you are at airports and is the latest biometric to unlock your mobile phone where facial recognition apps abound. Need to confirm your identity for a £1,000 bank transfer? Just look into the camera.

At a recent panel on using marketing data to grow your business, Visier CMO Christy Marble noted that every piece of technology we use today should incorporate some degree of machine learning (ML). In enterprise cybersecurity, given the well-documented concerns around skills shortages and tool sprawl, it certainly seems wise to take advantage of any technology that can increase efficiency, make individual employees more effective, and ultimately scale the business. The cloud may be the factor that finally takes ML from being an overhyped pipe dream of a technology to an integral part of every successful security practice. Not all ML is created equal, however, and now that this tech has entered the realm of table stakes, it's time to raise our collective standards and understand exactly what differentiates genuinely powerful, effective ML from systems that are capitalizing on the hype to sell subpar products to under-informed customers. Machine learning systems need a whole lot of data in order to actually work.

As reported by PwC and CB Insights' MoneyTree Report, the overall growth of Venture Capital investments activity in the United States reached $100 billion in 2018 - a truly watershed moment. And as U.S. Senator, Everett Dirksen once said: "A billion here, a billion there, pretty soon, you're talking real money"... So, since we are talking about so many billions, it's more important than ever for the VC community to make smarter investments. Instead of relying on startup founder's charisma, or the quality of his/her pitch deck, well-trained AI can help to predict investment's success probability much faster than going through manual analysis, and with far fewer emotions while at it. All business plans should meet the same rigorous and unbiased investment criteria - and this is where AI comes handy. It can be used all across the board: to discover, evaluate and support VC's investments! For example blogs, posts and other social media sources can be successfully scanned using Natural Language Processing - to identify early-stage companies searching for CVC/IVC investors. And since AI can be constantly fed with new data sets and new information, it can follow the emerging trends - accurately.

For decades, Hava Siegelmann has explored the outer reaches of computing with great curiosity and great conviction. The conviction shows up in a belief that there are forms of computing that go beyond the one that has dominated for seventy years, the so-called von Neumann machine, based on the principles laid down by Alan Turing in the 1930s. She has long championed the notion of "Super-Turing" computers with novel capabilities. And curiosity shows up in various forms, including her most recent work, on "neuromorphic computing," a form of computing that may more closely approximate the way that the brain functions. Siegelmann, who holds two appointments, one with the University of Massachusetts at Amherst as professor of computer science, and one as a program manager at the Defense Advanced Research Projects Agency, DARPA, sat down with ZDNet to discuss where neuromorphic computing goes next, and the insights it can bring about artificial intelligence, especially why AI succeeds and fails.

Washington, DC - These days, the latest developments in artificial intelligence (AI) research always get plenty of attention, but an AI researcher at the U.S. Naval Research Laboratory believes one AI technique might be getting a little too much. Ranjeev Mittu heads NRL's Information Management and Decision Architectures Branch and has been working in the AI field for more than two decades. "I think people have focused on an area of machine learning--deep learning (aka deep networks) -- and less so on the variety of other artificial intelligence techniques," Mittu said. "The biggest limitation of deep networks is that a complete understanding of how these networks arrive at a solution is still far from reality." Deep learning is a machine learning technique that can be used to recognize patterns, such as identifying a collection of pixels as an image of a dog.