From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.

You may know Watson as IBM's Jeopardy-winning, cookbook-writing, dress-designing, weather-predicting supercomputer-of-all trades. Starting today, 40 organizations will rely upon the clever computers cognitive power to help spot cybercrime. The Watson for Cybersecurity beta program helps IBM too, because Watson's real-world experience will help it hone its skills and work within specific industries. After all, the threats that keep security experts at Sun Life Financial up at night differ from those that spook the cybersleuths at University of New Brunswick. IBM researchers started training Watson in the fundamentals of cybersecurity last spring so the computer could begin to analysize and prevent threats.

Predictive analytics and machine learning are seen as the pair of tools to save the day for most organizations currently. We try to de-mystify both, taking a look at what they are, how they work, and what they are good for. Predictive analytics and machine learning working separately or together can be just what a company needs to succeed. But understanding how they work is key to figuring out how they can help businesses thrive. So, what is predictive analytics?

Machine learning can help retailers address the challenges of offering fresh foods, which account for up to 40% of a grocers' revenue and one-third of the cost of goods sold, according to a report by McKinsey and Company. The increasing demand of these products have led to new offerings, like exotic and hard-to-find items as well as "ultrafresh" items with a shelf life of no more than one or two days. Old processes can make it difficult to order the correct amount of food: order too much, and the food goes to waste; order too little, and you lose sales. Most traditional supply chain planning systems take a fixed, rule-based approach to forecasting and replenishment, but because local demand and conditions vary from day to day, planners have to manually enter different types of data into their replenishment systems. These manual processes are time consuming, error prone and reliant on individual planners' experience and instincts.

In the world of connected devices, where the IoT ecosystem is moving towards maturity, the maturity of IoT Analytics will play a key role in coming years. The investment being made in the area of IoT will be unlocked by the adoption of IoT analytics. At the layer where analytics are applied: At a broad level, there are various physical layers in an IoT ecosystem, which can be broadly classified into the following: As per the complexity of the use case and implementation: IoT analytics ranges from rule-based implementations to complex event processing implementation using advanced analytics techniques. This is the area which we will focus our attention in this article. As per the complexity of the use case and implementation: IoT analytics ranges from rule-based implementations to complex event processing implementation using advanced analytics techniques. This is the area which we will focus our attention in this article.

Bots are a growing segment of software that acts as an agent on a human's behalf. These tasks range from ordering online, to making dinner reservations, to handling customer service requests, to helping employees be more productive in the workplace. Historically, most bots have used simple rules-based approaches to present an output for a given input (such as presenting the weather). But today, with advances in server-side processing power and improvements in implementing artificial intelligence (AI) and machine learning (ML), bots are starting to provide real value to consumers. The tide has finally turned and bots are entering the mainstream consciousness, especially after the recent announcements at Facebook's annual conference F8.

In an industry known for its love of buzzwords and hype, artificial intelligence (AI) has become marketing's new'big data'. But where big data ultimately led to new layers of complexity, AI promises the opposite. Big data forced marketers to become data scientists (or hire them, if they could be found), but AI holds out the hope that marketers may get to go back to doing what they signed up for the in the first place. Recent months have seen technology providers such as Salesforce, Oracle and Microsoft bring new AI-based technologies to market, promising to derive insights and improve conversions by mimicking the processes of the human brain in software. Salesforce, for example, is rolling out its Einstein AI technology to provide functions such as product recommendations within the Commerce Cloud, email content recommendations within its Marketing Cloud, and predictive forecasting tools for sales managers with its Sales Cloud.

In an industry known for its love of buzzwords and hype, artificial intelligence (AI) has become marketing's new'big data'. But where big data ultimately led to new layers of complexity, AI promises the opposite. Big data forced marketers to become data scientists (or hire them, if they could be found), but AI holds out the hope that marketers may get to go back to doing what they signed up for the in the first place. Recent months have seen technology providers such as Salesforce, Oracle and Microsoft bring new AI-based technologies to market, promising to derive insights and improve conversions by mimicking the processes of the human brain in software. Salesforce, for example, is rolling out its Einstein AI technology to provide functions such as product recommendations within the Commerce Cloud, email content recommendations within its Marketing Cloud, and predictive forecasting tools for sales managers with its Sales Cloud.

Artificial intelligence celebrates its 50th year this year and it is only advancing at a more rapid pace than ever before. From its very first task, which was to represent reasoning by a rule-based system, it has evolved into the professional field, the corporate environment, and even in the homes of many people. In the past it could never be compared to the reasoning of a human being, but even that has been improved during the course of the past half century. Descartes' logical belief that man is not machine may be a rewritten as the machine is now man. Some of the important artificial intelligence advancements have shown that intelligence can be created out of nothing. A machine is becoming more and more likely to think like a human being.

In a way, AI is about understanding, and then mimicking how we think, learn and process information. The science and applications of AI have evolved since the early years: 1950's 1980's 2010's Generation 1 (From 1950's): Rule Based Systems (No Learning) In the early days, most applications of AI were rule-based computer programs (commonly known as Expert Systems) designed to solve problems that human brains performed easily. Such AI programs required experts to develop rules and combine with programs to solve problems. It required a programmer to write a program to capture the knowledge of a subject matter expert. The program then asked a series of questions to a user (usually not an expert in that subject) and then based on the answers/input provided, the computer would suggest a "solution" to the problem.