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.

Artificial Intelligence (AI) has gained considerable prominence over the last decade fueled by a number of high profile applications in Autonomous Vehicles (AV), intelligent robots, image and speech recognition, automatic translations, medical and law usage as well as beating champions in games like chess, Jeopardy, GO and poker [1]. The successes of AI are based on the utilization of algorithms capable of learning by trial and error and improving their performance over time, not just by step-by-step coding instructions based on logic, if-then rules and decision trees, which is the sphere of traditional programming. In light of the above, AI found applications in the field of forecasting and a considerable amount of research has been conducted on how a special class of it, utilizing Machine Learning methods (ML) and especially Neural Networks (NNs), can be exploited to improve time series predictions. Literally hundreds of papers propose new ML algorithms, suggesting methodological advances and accuracy improvements [2–8]. Yet, limited objective evidence is available regarding their relative performance as a standard forecasting tool [9–12].

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. It is the UK government's answer to the concerns expressed by businesses that rely on "just-in-time" manufacturing supply chains (such as car manufacturers), and to the need to avoid the reimposition of a hard border in Ireland.

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.

Most of you will have interacted with several algorithms already today. Algorithms are of course simply sets of rules for solving problems, and existed long before computers. But algorithms are now everywhere in digital services. An algorithm decided the results of your internet searches today. If you used Google Maps to get here, an algorithm proposed your route. Algorithms decided the news you read on your news feed and the ads you saw.

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. Supply chain uses algorithms to forecast demand, optimize inventory, schedule production, and route transportation.

In the previous article, we presented two ways of categorizing conversational agents -- more widely known as chatbots -- along with their advantages, limitations, and use case scenarios. In this article, we are focused on emphasizing how chatbots infer knowledge from human conversations through a basic rule-based system, machine learning, and natural language processing, which all play a crucial role in facilitating the automation of a request-handling process. A few months ago, we took up the challenge to build a chatbot that could seamlessly integrate into a customer support platform, applicable to various industries. At Tremend, we developed a chatbot prototype for a major telecom client. The primary scope of the project was to create a quality-consistent chatbot that could minimize human labor with respect to tedious or repetitive tasks, usually time-consuming and hard to scale across brief periods of time and on an ad hoc basis.

Developers know a lot about the machine learning (ML) systems they create and manage, that's a given. However, there is a need for non-developers to have a high level understanding of the types of systems. Artificial neural networks and expert systems are the classical two key classes. With the advanced in computing performance, software capabilities and algorithm complexity, analytical algorithm can arguably be said to have joined the other two. This article is an overview of the three types.