Rule-Based Reasoning

As networks become more software-driven, they generate vastly greater amounts of data, which provides some challenges: adhering to compliance and customer privacy guidelines, while harvesting the massive amounts of data--it is physically impossible for humans to tackle the sheer volume that is created. But the vast amounts of data also provide an opportunity for businesses: leveraging analytics and machine learning to gather insights that can help network management move from reactive to proactive to assurance. This doesn't just mean a massive shift in technology because the human element won't simply go away. Instead, by combining human intellect and creativity with the computing power AI offers, innovative design and management techniques will be developed to build self-improving intelligent algorithms. The algorithms allow networks to operate in a way that far outweighs networks of the past.

It's also been notoriously difficult to combat – until now. Learn about how artificial intelligence can keep your game and players safe from increasingly aggressive online criminals, when you join this VB Live event! There are over 2 billion gamers in the world. Almost half of them are shelling out cold, hard cash in those games – rounding up somewhere around $108.8 billion in revenue across platforms, devices, and game types. And all of them – from players to platforms – are incredibly vulnerable to the insidious types of fraud that infest every online game out there, which includes account takeovers, game hacks, credential ripoffs, and bots.

The government has published its long-awaited Brexit White Paper. The document is 104 pages long and follows last week's Chequers agreement which set out the sort of relationship the UK wants with the EU after Brexit. The White Paper is split into four chapters: economic partnership, security, cooperation and institutional arrangements. So here are the key excerpts from the chapter on "economic partnership" and what they mean. This is a line that emerged in the Chequers statement last Friday, and it is one of the most important in this White Paper.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.

You wouldn't think a 9th century Persian mathematician would be relevant to modern business. But the term algorithm stems from his name, Muhammed Al Khwarizmi (along with the Greek word arithmos), and the algorithmic business is sweeping across the business landscape with its autonomous, rules-based, lightning-fast operations--augmenting, and in some cases supplanting, human decision making. An algorithm is a step-by-step process or set of rules for calculating and solving problems. "Algorithmic business is the industrialized use of complex mathematical algorithms pivotal to driving improved business decisions or process automation for competitive differentiation," Gartner explains. The algorithmic business is based upon capturing knowledge in software, which then takes automated actions that speed business processes and perform decision making.

The effective use and adoption of Machine Learning requires algorithms that are not only accurate, but also understandable. To address this need, BigML now includes functionality that allows for Prediction Explanation, model-independent explanations of classification and regression predictions. In this post, we will summarize what it means for a prediction to be "explainable," why this is important, and share a use case in which prediction explanation plays a key role. Rather than being hard-programmed with an exhaustive set of "if-then" rules, Machine Learning algorithms "learn" rules based on large datasets of examples. Understanding what these rules are, and how they are applied to new data, is generally referred to as the interpretability or explanation of the model.

GENEVA – Major U.S. trading partners including the European Union, China and Japan voiced deep concern at the World Trade Organization (WTO) on Tuesday about possible U.S. measures imposing additional duties on imported autos and parts. Japan, which along with Russia had initiated the discussion at the WTO Council on Trade in Goods, warned that such measures could trigger a spiral of countermeasures and result in the collapse of the rules-based multilateral trading system, an official who attended the meeting said. Over 40 WTO members, including the 28 countries of the European Union -- warned that the U.S. action could seriously disrupt the world market and threaten the WTO system, given the importance of cars to world trade. The United States has imposed tariffs on European steel and aluminum imports and is conducting another national security study that could lead to tariffs on imports of cars and car parts. Both sets of tariffs would be based on concerns about U.S. national security.

Your own business is likely living in what I'll call Scenario 1: Every customer inquiry and request has to be handled personally by your capable, well-trained human agents–even when the floodgates have opened due to a product glitch or similar event. Frankly, some of these inquiries aren't worthy of your agents and, anyway, in the floodgate situation, personnel constraints will make a timely human response quite literally impossible. So imagine a Scenario 2: In this scenario, the simple stuff, the 20% of inquiries that are straightforward, known issues or conform to fully learnable business rules or policies ("customer has requested a refund; customer deserves the refund because X conditions have been met") can be handled completely and nearly instantly (in less than a minute) and at a cost of under a dollar a ticket. Your well-trained, accomplished human beings agents never have to be troubled with these irritants, freeing them to work on more interesting issues, challenges more worthy of their time, attention and training. Enter Seattle-based AnswerIQ, an AI solution for customer support that promises to reduce ticket volume by 20% while significantly increasing first contact resolutions (FCR).

Monitoring transactions for suspicious ones can be more efficient. Up to $2 trillion dollars representing 5% of global GDP – that's the estimated amount of money laundered worldwide each year according to the United Nations Office on Drugs and Crime. The fight against money laundering is one of top priorities of financial institutions – but it also poses a significant challenge for them. To combat the phenomenon, one needs to have a large number of human and technology resources at hand. And even then, the good guys have a hard time winning.