Computer vision and image recognition are integral parts of artificial intelligence (AI), which has quickly gone from niche to mainstream in the past few years. And nowhere was this more evident than at CES 2017 earlier this month. From a few days of wandering the floor, here are some of the coolest new uses of computer vision. The biggest displays of computer vision are coming from the automotive industry, because computer vision, after all, is one of the central enabling technologies of semi- and fully-autonomous cars. NVIDIA, which already helped supercharge the deep learning revolution with its deep learning GPU tools, is powering many of the autonomous car innovations with the NVIDIA Drive PX 2, a self-driving car reference platform that Tesla, Volvo, Audi, BMW, and Mercedes-Benz are already using for semi- and fully-autonomous functions.

Prof. Tom Griffiths is the director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences at UC Berkeley. He studies human cognition and is involved with the Center for Human Compatible Artificial Intelligence. I asked him for insight into the intersection of cognitive science and AI. He offers his thoughts on the historical interaction of the fields and what aspects of human cognition might be relevant to developing AI in the future.

If you think Americans have a love affair with cars now, just wait until people begin to treat them not merely as a means of transportation but as smartphones on wheels -- or robot servants. Automobiles are more wired than ever. But experts say the late-model cars on the road today are just a precursor to truly connected cars. In the near future this next generation of cars -- loaded with sensors and screens, feeding back to third parties huge amounts of data on vehicle performance and even driver behavior -- will communicate with other devices, be loaded with apps and even make their own decisions. While for now the smart car is lagging behind the smart home, automobile manufacturers, technology firms, ride-hailing companies and financial institutions are on course to integrate automobiles into the burgeoning Internet of Things. In the process they intend to revolutionize transportation and the fabric of people's daily lives. "The car is going to be an extension of who you are, just like the phone is," said Suresh Ramamurthi, the chairman and chief technology officer of CBW Bank, a small bank in Weir, Kan., that has attracted a national client base of fintech startups. In the more distant future, the car may become something greater still: an autonomous agent that can carry out tasks and authorize payments without requiring its owner's input.

Oxford philosopher and author Nick Bostrom (left) and DeepMind CEO Demis Hassabis (right). Machines will quickly become significantly smarter than humans when they achieve human level intelligence, according to a high-profile panel of artificial intelligence (AI) leaders. A YouTube video released by the Future of Humanity Institute this week shows Elon Musk, the billionaire cofounder of Tesla, SpaceX and PayPal, talking on a panel earlier this month alongside the likes of DeepMind CEO Demis Hassabis, who sold his company to Google for £400 million in 2014, and Oxford philosopher Nick Bostrom. "Once we get to human level-AI, how long before we get to where things start taking off?" asked MIT professor and panel moderator Max Tegmark, citing an "intelligence explosion." Tegmark added: "Some people say days or hours. Others envision it will happen but it might take thousands of years or decades."

Artificial Intelligence (AI) revenues will reach $47 billion in 2020, from $8.0 billion last year, According to International Data Corporation (IDC). Artificial Intelligence is complex technologies and algorithms that enable machines, software, and systems to make human-like decisions. End user organizations and software developers are in the process of deploying artificial intelligence into almost every type of enterprise process or application. Since AI represents an important opportunity, today we will highlight 10 contemporary technology trends in artificial intelligence. Nowadays, hardware is getting a lot of attention in the AI industry.

Neural networks are all the rage in Silicon Valley, infusing so many internet services with so many forms of artificial intelligence. But as good as they may be at recognizing cats in your online photos, AI researchers know that neural networks are still quite flawed, so much so that some wonder whether these pattern recognition systems are a viable path to more advanced--and more reliable--forms of AI. Able to learn tasks by analyzing vast amounts of data, neural networks power everything from face recognition at Facebook to translation at Microsoft to internet search at Google. They're beginning to help chatbots learn the art of conversation. But because they can't make sense of the world without help from such large amounts of carefully labelled data, they aren't suited to everything.

Almost every day, there's news about a massive data leak--a breach at Yahoo that reveals millions of user accounts, a compromise involving Gmail phishing scams. Security professionals are constantly moving the chess pieces around, but it can be a losing battle. Yet, there is one ally that has emerged in recent years. Artificial intelligence can stay vigilant at all times, looking for patterns in behavior and alerting you to a new threat. While AI is not anywhere close to being perfect, experts tell CSO that machine learning, adaptive intelligence, and massive data models that can spot hacking much faster than any human are here to help.

Two AI researchers are behind a daring open challenge to fight the spread of outrageous headlines that are completely detached from reality. The Fake News Challenge (FNC) is organized by Dean Pomerleau, an entrepreneur and adjunct professor at Carnegie Mellon University, and Delip Rao, an employee at Joostware. The aim is to explore how AI – particularly machine learning and natural language processing – might be used to combat the negative effects of false information. The problem of fake news has been bubbling away for some time, but reached a climax as Donald Trump was sworn in as the 45th President of the United States. People were quick to blame dodgy websites for pushing lies – such as the Pope backing Donald – that potentially skewed the election results in the telly celebrity's favor.

We at AIBusiness are officially launching our new series'AI Innovators' this January. This is a series dedicated to featuring interviews with relevant spokespersons across a range of businesses that have entered the world of artificial intelligence. AI Innovators aims to provide our readers with a broad insight into how artificial intelligence is implemented within different industries, anything from finance to medical research. The fourth company to feature in these series is EY, a multinational professional services firm headquartered in London, United Kingdom. EY is one of the largest professional services firm in the world and is one of the "Big Four" accounting firms.

In vogue for several years, analysis of Big Data enables companies to process huge volumes of unstructured data and derive strategic business intelligence. The classic analysis of Big Data is partly based on Machine Learning, but it should be noted that the statistical modelling and manual processing steps are essential to its success. Recent advancements in Machine Learning, especially with AlphaGo from Google and Watson from IBM, are setting cases and are gradually taking their place in the corporate world. Machine learning refers to the methods and technologies allowing machines to "learn" without using dozens of complex statistical models and rules. Take the example of AlphaGo, to beat the World Go champion, the algorithm was not based on rules indicating how to react to each situation during the game; it learned to play by processing a large dataset of games played by human players.