In case you hadn't noticed, the amount of data in the world is increasing at an exponential rate. For example, every minute there are nearly 4.2 million posts uploaded to Facebook, nearly 3 million tweets, and thousands of responses to open-ended survey questions. I'm often asked about all this unstructured data and how companies can make sense of it. Inevitably, the follow up question is, "What's the best approach to implementing categorization using text analytics?". In this post I'm going to dive deeper into these two questions and their answers.

We constantly talk about next-gen manufacturing, Industrie 4.0, IIoT etc, but I think the underlying nervous system supporting all these is connectivity and convergence. Increased connectivity and computational power have converted hype to reality. Previously Cloud, IoT, Artificial Intelligence (AI), Big Data and analytics were like nebulous concepts at boardroom discussions; but now they are mature enough and ready to be fully integrated into the design, engineering, and operational environment. Digitalization is impacting every aspect of our lives because three forces are reinforcing one another: consumer pull; technology push; and economic benefits. This was the theme of the two-day NASSCOM 2017 edition of the Design & Engineering Summit for which ARC Advisory Group was invited.

FICO's chief analytics officer writes that as criminals start to learn our AI systems we must use defensive AI to thwart the bad guys. I'm convinced we are entering the Golden Age of artificial intelligence (AI). With so much promise and potential in front of us, I am feeling a little like Neo in The Matrix as he swallows the red pill. However, rather than science fiction, my recent work at FICO to make AI better has drawn upon my background in theoretical physics to create what we call defensive AI. I'm talking about the Heisenberg Uncertainty Principle -- from the German physicist Werner Heisenberg, not Walter White -- which states that "the more precisely the position of a particle is determined, the less precisely its momentum can be known, and vice versa."

Analysis "OK, the error rate is terrible, but it's Artificial Intelligence – so it can only improve!" AI is always "improving" – as much is implied by the cleverly anthropomorphic phrase, "machine learning". Learning systems don't get dumber. But what if they don't actually improve? The caveat accompanies almost any mainstream story on machine learning or AI today. But it was actually being expressed with great confidence forty years ago, the last time AI was going to "revolutionise medicine". IBM's ambitious Watson Health initiative will unlock "$2 trillion of value," according to Deborah DiSanzo, general manager of Watson Health at IBM.

Large data and fast computers mean that new and different things can be discovered from large datasets of text by writing and running software. In the 1990s, statistical methods and statistical machine learning began to and eventually replaced the classical top-down rule-based approaches to language, primarily because of their better results, speed, and robustness. The statistical approach to studying natural language now dominates the field; it may define the field. Data-Drive methods for natural language processing have now become so popular that they must be considered mainstream approaches to computational linguistics.

Machine learning and artificial intelligence have become buzzwords in the financial services industry. Daniel Fernandez helps to break down the difference between the two terms and explains how these technologies are being used by compliance departments today. By delving into how these technologies work, Daniel sheds light on the issues they can help to solve, the challenges facing their increased adoption and what financial institutions should be doing right now to take advantage. Like many industry buzzwords, artificial intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is this: AI has become an overloaded and misused term, often mistaken for machine learning (ML).

For years, video game developers have used artificial intelligence to animate those characters encountered by a player, but non-playable characters, or NPCs, have been based on sets of rules coded by humans. Using the AI technology du jour, machine learning, future NPCs will program and reprogram their own rules, based on the experiences they encounter in games, in the process getting smarter the longer they play. So says Danny Lange, the VP of AI and machine learning at Unity Technologies, a major maker of game "engine" software that handles the underlying mechanics of titles like Firewatch and ChronoBlade. Today the company announced Unity Machine Learning Agents--open-source software linking its game engine to machine learning programs such as Google's TensorFlow. It will allow non-playable characters, through trial and error, to develop better, more creative strategies than a human could program, says Lange, using a branch of machine learning called deep reinforcement learning.

NEW YORK – The foreign ministers of Japan, the United States and India agreed Monday in New York to work together to develop strategically important ports and other infrastructure in the Indo-Pacific region, apparently seeking to balance China's bid to strengthen its regional influence. Foreign Minister Taro Kono said he, U.S. Secretary of State Rex Tillerson and Indian External Affairs Minister Sushma Swaraj "completely agreed to coordinate with each other toward the realization of a free and open Indo-Pacific." They agreed to work to spread and establish their shared basic values of the rule of law and the freedom of navigation and overflight in the region, Foreign Ministry officials said. The ministers affirmed that they will strengthen connectivity in the region through investment in infrastructure and work together to assist strategically important coastal nations in the region with maritime capacity-building, centering on key ports. According to the U.S. State Department, the ministers "discussed the importance of a free and open Indo-Pacific region underpinned by a resilient, rules-based architecture that enables every nation to prosper."

Machine learning is coming to auto finance, bringing with it the potential to revamp traditional lending approaches and stimulate sales volumes. It remains early days but some industry players see considerable potential…. This content is available only to members of Automotive World with a valid subscription.

Since the introduction of 3-D Secure in 2007, adoption rates have been slow, and have varied between countries. Concerns about lower conversions and rising customer friction are preventing higher penetration rates, but new fraud prevention software solutions offer ways to balance customer satisfaction and security requirements. Roger Lester, Payments Expert at Featurespace, explores how advanced machine learning and adaptive behavioral analytics technology help balance 3-D Secure checks against futureproofing an organization's fraud defenses. Why has adoption of 3-D Secure been mixed? Designed to be an additional layer of security for online payments, the 3-D ("3 Domain") Secure protocol has been adopted by all major card networks--more familiar to many of us under commercial names, such as "Verified by Visa" and "MasterCard SecureCode."