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.

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.

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.

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.

Artificial intelligence (AI) and advanced machine learning (ML) are made up of technologies and processes like deep learning and neural networks. What began as algorithms to automate manual tasks, borrowing from advanced statistical techniques has developed into a broader framework and architecture that learns like a human might, and can use historical data to predict the future. These systems will become more adaptable and potentially operate autonomously. Cearley noted, "Applied AI and advanced machine learning give rise to a spectrum of intelligent implementations, including physical devices (robots, autonomous vehicles, consumer electronics) as well as apps and services (virtual personal assistants, smart advisors). These implementations will be delivered as a new class of obviously intelligent apps and things as well as provide embedded intelligence for a wide range of mesh devices and existing software and service solutions."

Artificial intelligence is an amazing technology that's changing the world in fantastic ways, but anybody who has ever seen the movie Terminator knows that there are some dangers associated with advanced A.I. That's why Elon Musk, Stephen Hawking, and hundreds of other researchers, tech leaders, and scientists have endorsed a list of 23 guiding principles that should steer A.I. development in a productive, ethical, and safe direction. The Asilomar A.I. Principles were developed after the Future of Life Institute brought dozens of experts together for their Beneficial A.I. 2017 conference. The experts, whose ranks consisted of roboticists, physicists, economists, philosophers, and more had fierce debates about A.I. safety, economic impact on human workers, and programming ethics, to name a few. In order to make the final list, 90 percent of the experts had to agree on its inclusion. "What remained was a list of 23 principles ranging from research strategies to data rights to future issues including potential super-intelligence, which was signed by those wishing to associate their name with the list," Future of Life's website explains.

The insurance industry is one that is just beginning to tap into the potential of artificial intelligence. If you've been monitoring the ImageNet challenge over the years, you know that AI's image classification surpassed human accuracy about 18 months ago, indicating the technology is reaching a stable and mature state. Once a technology is customer-ready, it's important that it's also customer-centric -- in that it solves an inherent problem. Using AI technology to automate a visual task, such as inspecting damage to a car, is a nearly instant way to provide insurance customers with crucial information about the extent of the damage. Image classification AI within the app compares the customer's photos with thousands of other anonymized crash photos to generate a cost estimate for their repair.

FusionOps was ahead of its time. Founded in 2005, the company has only recently found its groove with the AI wave. Focused on the supply chain area, they are doing some very cool stuff with Cloud, Big Data, and AI. Sramana Mitra: Tell us about the company and yourself. At that time, we had a vision and we still have the same vision.

Artificial intelligence is a massive opportunity, but triggers some risks which cannot be sorted through over-regulations that might damage the market. One of the main topics of the World Economic Forum 2017 was artificial intelligence (AI). I found extremely interesting the interview to Ginni Rometty, the Chairwoman, President and CEO of IBM. Because of these 3 revolutions, there is a huge amount of information that cannot be dealt by humans, but we need systems that can deal with such data, reason around it and learn. This led to the rise of artificial intelligence.