Google has made one its largest European acquisitions to date with a deal to buy DeepMind technologies, a London-based artificial intelligence firm which specialises in machine learning, advanced algorithms and systems neuroscience. The Guardian understands that Google paid 400m ( 650m) for DeepMind, which develops technologies for e-commerce and games, and has demonstrated computer systems capable of playing computer games. It aims, it says, to develop computers that think like humans. The two-year-old artificial intelligence startup was founded by former child chess prodigy and neuroscientist Demis Hassabis alongside Shane Legg and Mustafa Suleyman. DeepMind has reportedly competed with Google and other major artificial intelligence companies for talent and Google's chief executive Larry Page is said to have led the deal himself.

I strongly believe we've only scratched the surface of what's possible with machine learning... so there is no shortage of good research topics. Here are four I think are particularly important today (and I'm only working on two of them):

Computer science focused educational research institute International Institute of Information Technology (IIIT)-Hyderabad is starting a new accelerator focused on technologies such as artificial intelligence, natural language processing, machine learning, augmented reality and virtual reality. These technologies, incidentally, are focused around research areas of the premier educational institute. "The ecosystem around IIIT-Hyderabad should be able to leverage the goldmine of the institute," said Vasudev Varma, dean (research and development) at IIIT-Hyderabad. The model is similar to one in the US where research universities spawn startups centered around technologies they develop. IIIT's accelerator, named Avishkar, will be a six-month program.

I have been trying to use ML for sentiment analysis of sentences, I have been successful with Naive Bayes and SVM but I would like to implement Neural Networks for Sentiment Analysis but couldn't find a way to convert words as input for neural networks. I know that representing word as a numerical is not efficient. How is nlpnet implemented, I tried to understand that but that flew over my head.

If there's one thing software company Ebsta understands, it's customer relationship management. The San Diego- and London-based company sells a 10 per month per user Chrome browser extension that syncs customers' email accounts to the Salesforce database to streamline the onerous task of updating a CRM system. But finding new prospects for Ebsta is difficult and, as vice president of sales Bernhard Peters points out, expensive -- especially for a company with roughly 1 million in annual revenue. "We're still a tiny company," Peters says. "We have to be careful who we chase; we don't have a lot of money or manpower to spare. Buying lists never works; they're out of date. And data-mining companies charge at least 25,000 upfront, with no guarantee of ROI." Peters was in a San Diego restaurant when he overheard Olin Hyde, co-founder and CEO of Englue, explaining how his artificial intelligence product LeadCrunch could mine the web to uncover leads based on names of a company's best customers.

Designing games that support knowledge and skill acquisition has become a promising frontier of education techniques, since games are able to capture the user concentration for long periods and can present users with realistic and compelling challenges. In this scenario, there is a need for scientific and engineering methods to build games not only as more realistic simulations of the physical world but as means to provide effective learning experiences. Abstracting state of the art serious games' (SGs) features, we propose a new design methodology for the sand box serious games (SBSGs) class, decoupling content from the delivery strategy during the gameplay. This methodology aims at making design more efficient and standardized in order to meet the growing demand for interactive learning. The methodology consists in modeling an SBSG as a hierarchy of tasks (e.g., missions) and specifies the requirements for a runtime scheduling policy that maximizes learning objectives in a full entertainment context.

AI has mastered one of the most complex board games on Earth, beating the world's best at Go. Just weeks after its overwhelming victory over world-class Go player Lee Sedol, Google's AI operation AlphaGo is set to be challenged. Not given much time to toast its convincing victory, reports have emerged that the team behind China Computer Go team has Google in its crosshairs, with an AI showdown in the works. Shanghai Securities News reported that the Chinese team will set a challenge before the year is out, although it's unclear exactly what that challenge will be. However, it marks a quick deviation away from regular old humans playing Go at the very pinnacle of the game. After winning 99pc of its games against other Go programmes, last year AlphaGo became the first AI system to beat a pro player when it won out 5-0 against European Go champion Fan Hui.

Last week, Microsoft inadvertently revealed the difficulty of creating moral robots. Chatbot Tay, designed to speak like a teenage girl, turned into a Nazi-loving racist after less than 24 hours on Twitter. "Repeat after me, Hitler did nothing wrong," she said, after interacting with various trolls. "Bush did 9/11 and Hitler would have done a better job than the monkey we have got now." Of course, Tay wasn't designed to be explicitly moral.

Machine learning algorithms are worked into a variety of popular products on the market today and used by the biggest technology companies, such as Microsoft, Amazon, Google, Oracle, and IBM, to name a few. But while machine learning algorithms are fairly routine and practical, there is the primary subject from which it branches: artificial intelligence (AI). After decades of popular films and books, what has been achieved from AI? First, the benefits of AI research have yielded various applications, from Apple's Siri to IBM's prototype diagnostic app, Watson. "AI has attracted more than 17 billion in investments since 2009. Last year alone more than 2 billion was invested in 322 companies with AI-like technology" (Kelly).

It used to be the case that you had to program a computer so that it knew how to do things. Now computers can learn from experience. The breakthrough is called "machine learning." It's unimaginably important for understanding where technology is going, and where society is going with it. Netflix's movie recommendations, Amazon's product recommendations, Facebook's ability to spot your friends faces, dating app's matching you with potential dates - these are all early examples of machine learning. And Google's self-driving car is becoming the classic case study.