Google DeepMind AI program AlphaGo defeated human Go world champion, Lee Sedol, in a five game series. The victory draws the week-long Challenge Series tournament in South Korea to a conclusion. This win marks a major milestone for artificial intelligence research, as Go is a fairly simple game, but has proven to be infamously difficult for computers to master due to the vast number of potential moves. Many Go players say the game primarily relies on intuition as a strategy. The victory is also significant, as it marks the first time in history that an AI program beat one of the best Go players in the world.

Natural Language Processing, Artificial Intelligence and Machine Learning are changing how content is discovered, analyzed and shared online. More recently, there has been a push to harness the power of Text Analytics to help understand and distribute content at scale. This is particularly evident with the popularity of recommendation engines and intelligent content analysis technologies like Outbrain and Taboola, who now have a presence on most content focused sites. Intelligent software and technological advancements allow machines to understand content as a human would. When we read a piece of text, we make certain observations about it.

One of the biggest problems with Apple TV is that in order to log into applications, users have to enter usernames and passwords into their set-top box one letter at a time using a remote control. Apple is aiming to fix that with a forthcoming update to tvOS, the operating system powering the Apple TV. Users will soon be able to dictate text to Siri, including usernames and passwords, so they don't have to hunt and peck out long strings of text. What's not clear is how Apple will secure users' spoken password data; Apple TV will have to record and process people literally speaking out their passwords. The company will release tvOS 9.2 (which was previously in beta) late Monday, the company said.

Image recognition is a hot area of research using artificial intelligence, and now IBM offers an online demo to let anyone test out the capabilities offered by its Watson cognitive computing system. Six sample photos are provided for illustration, or you can upload your own and ask Watson to analyze them. Either way, the cognitive system will produce a series of "classifiers" offering descriptions of the image's contents along with confidence scores for each of them. You can also create custom classifiers tailored for specific purposes. Watson gained worldwide fame when it won on the quiz show Jeopardy back in 2011, and IBM has been developing commercial applications ever since.

The fundamental assumption in Machine Learning is that analytical solutions can be built by studying past data models. Machine Learning supports that kind of data analysis that learns from previous data models, trends, patterns, and builds automated, algorithmic systems based on that study. This article takes a realistic look at where that data technology is headed into the future. As Machine Learning relies solely on pre-built algorithms for making data-driven analysis and predictions, it claims to replace data analytics and prediction tasks carried out by humans. In Machine Learning, the algorithms have the capability to study and learn from past data, and then simulate the human decision-making process by using predictive analysis and decision trees.

Drone images accumulate much faster than they can be analyzed. Researchers have developed a new approach that combines crowdsourcing and machine learning to speed up the process. Who would win in a real-life game of "Where's Waldo," humans or computers? A recent study suggests that when speed and accuracy are critical, an approach combing both human and machine intelligence would take the prize. With drones being used to monitor everything natural disaster sites, pollution, or wildlife populations, analyzing drone images in real-time has become a critically important big data challenge. Publishing in the journal Big Data, researchers, including Stéphane Joost from EPFL, present a new approach to rapidly interpret aerial images taken by camera drones that combines human crowdsourcing and machine learning.

Computers are getting pretty good at predicting the future. In many cases they do it better than people. That's why Amazon uses them to figure out what you're likely to buy, how Netflix knows what you might want to watch, the way meteorologists come up with accurate 10-day forecasts. Now a team of scientists has demonstrated that a computer can outperform human judges in predicting who will commit a violent crime. In a paper published last month, they described how they built a system that started with people already arrested for domestic violence, then figured out which of them would be most likely to commit the same crime again.

Moving on, let's look at artificial intelligence (AI). For AI to work well, it needs information. Up until recently, AI systems could acquire information and learning through machine-based learning. Show something like a picture of a car to an AI system, provide commentary and off it goes to find more cars. The problem with that system is it doesn't scale well.

While not widely understood, machine learning has been easily accessible since Google Prediction API was released in 2011. With many applications in a wide variety of fields, this tutorial by Alex Casalboni on the Cloud Academy blog is a useful place to start learning how to build a machine learning model using Google Prediction API. The API offers a RESTful interface as a means to train a machine learning model, and is considered a "black box" due to the restricted access users have to internal configuration. This leaves users with only the "classification" vs "regression" configuration, or the applying of a PMML (Predictive Model Markup Language) file with weighting parameters for categorical models. This tutorial begins with some brief definitions before beginning on how to upload your dataset to Google Cloud Storage, as required by Google Prediction API.

Deep in the heart of Imperial College, London, a computer is learning how to play Pac-Man. Like many humans, it struggles to get the hang of the classic 1980s video game at first. With time though, experience helps it decide which manoeuvres will allow it to evade the clutches of a relentless gang of animated ghosts. This is just one of dozens of artificial intelligence (AI) projects slowly transforming the UK into the global hub for a technology that elicits fascination and fear in equal measure. The point of teaching a computer to master Pac-Man is to help it "think" and learn like a human.