Markov chains provide an easy way to generate plausible-sounding nonsense in a chatbot. They aren't particularly useful for serious purposes, but they can be used for an entertainment bot or to lighten the conversation with occasional silliness. A Markov chain is a statistical process built on a state machine. In computational theory, a state machine is a physical or virtual machine that has some number of discrete states. In a Markov process, the rules are based on probabilities. Given the machine's current state, there's a specified probability for one or more states that it will go there next.

"Alexa, what was the score of the Manchester United game yesterday?" If you've ever asked a question like that of your personal assistant device (such as Amazon Echo or Google Home), you are already enjoying the fruits of the deep learning revolution. As Ralf Herbrich, Director of Machine Learning & Managing Director of Amazon Development Germany explains, when you speak to your device, "features from a deep neural network are used to describe the audio stream in order to detect the wake-words… Once the wake-word is detected, neural networks are used to predict the sequence of phonetic states from the audio sequence of the whole microphone array." This same cutting-edge deep learning/neural network technology is already being used for a wide range of tasks, such as enabling the navigation of self-driving cars, colorizing old black-and-white films, assisting physicians in diagnosing illnesses, and beating the best human competitors at complex strategy games such as Go. But what, exactly, are deep learning and neural networks, and how do they work?

People often talk about artificial intelligence as if it were all one thing. It's more accurate to think of AI as a collection of approaches to problems. An AI system usually combines several approaches, doing whatever produces the best results. The design of a piece of software needs to decide what problem domain it will cover. In other words, what class of problems will it deal with? The narrower the domain is, the easier the job.

Deep learning is hot right now. Applications such as voice recognition, facial recognition, language translation, medical diagnostics, self-driving vehicles, and even the detection of credit fraud, are becoming more and more woven into the fabric of modern life. Because of such successes, and the opportunities they open up for further extensions of the technology, deep learning is currently one of the most active fields in computer science research, and progress has been rapid. In this article we'll take a brief look at several of the latest trends in deep learning research. Perhaps the area of deep learning research that has received the most public notice in recent years relates to the advent of driverless cars and trucks.

The world has witnessed significant advancements in human-computer dialogue. Today, conversational interfaces are slowly taking the place of rigid GUI dialogue boxes and web forms. The evident evolution of UI design is all about leveraging artificial intelligence to enable end-users to get answers and perform routine tasks quickly without strain. However, what's the key to designing and building AI-powered and user-friendly chat interfaces? Chatbots are programs that facilitate text-based conversations between computers and people in a natural language.

The idea of chatbots has been around since the mid 1960s. ELIZA was the first of its kind, created in the MIT Artificial Intelligence Laboratory. Since then, though, there has been a proliferation of chatbots. When they first began to become more widespread and available to the general public, in the late 1990s, they mainly served in customer service roles. It is for this reason that chatbot hackathons have grown in popularity.