Nearby in the lobby, a big-screen TV is flashing the words: Welcome back AlphaGo Team! But that is about as far as one can tell that the London company has just come home triumphant after making history last week by trouncing Go world champion Lee Se Dol with its supercomputer, AlphaGo. "Lee Se Dol is one of the greatest players of all time. IBM's Deep Blue took on world chess champion Garry Kasparov nearly 20 years ago and won, through sheer computational power.

Computer that beat Go champion has many applications, says its maker. A little chalkboard sits on the reception desk of DeepMind's office in London's gentrified King's Cross. On it is scrawled: AlphaGo - 4, Lee Sedol - 1. Nearby in the lobby, a big-screen TV is flashing the words: Welcome back AlphaGo Team! But that is about as far as one can tell that the London company has just come home triumphant after making history last week by trouncing Go world champion Lee Se Dol with its supercomputer, AlphaGo. Perhaps the team already knew they were going to win the best-of- five epic showdown between man and machine in Seoul.

Over the past few years we have witnessed an incredible explosion of interest and application of machine learning. Machine learning has become the predominant computational paradigm, and in short term it has gone from one successful application to another. However, machine learning is far from a unified field, and many different approaches and techniques are vying for primacy and dominance. Which raises an interesting question: is it possible to find a single all-purpose machine learning algorithm that can successfully tackle all protean problems that are currently being attacked from various angles. This search for this "Master Algorithm" in many respects has the flavor of the search for a unified field theory in Physics.

The UK Government has launched an inquiry into the robotics and artificial intelligence segment, to evaluate its rising influence and impact on society. The UK Government has initiated an inquiry into Robotics and Artificial Intelligence (AI), given its rising influence and daunting advancement in technology. They intend to determine what impact the rise of AI will have at a holistic level on the workforce and the society in general. Further, the corresponding social, legal and ethical aspects also need to be scrutinized. This inquiry will be carried out by the House of Commons Science and Technology Committee.

If you are one of the 40 million people who enjoy reading or writing the mostly romantic werewolf, superhero or historical fiction stories found on Canadian startup Wattpad, you may also be contributing to the development of the next generation of artificial intelligence. In a new paper called Augur: Mining Human Behaviors from Fiction to Power Interactive Systems, a group of Stanford University computer science researchers revealed that they used the Wattpad "corpus" – a collection of almost two billion words (or 600,000 chapters) written by regular people – to help a computer understand the world around it. The team intends to make the program they built, Augur, into an open-source tool that other researchers can build on. "The basic idea is that it's very difficult to program computers to understand the broad range of things that people do," says fourth-year PhD student Ethan Fast, co-author of the paper (published as part of the upcoming Computer Human Interaction conference) and a member of Stanford's Human-Computer Interaction Group. "Fiction has a lot of useful things to say about the world, and if you have enough of it, you can model it in much more depth than you could hope to manually."

Microsoft has admitted it faces some "difficult" challenges in AI design after its chatbot "Tay" had an offensive meltdown on social media. Microsoft issued an apology in a blog post on Friday explaining it was "deeply sorry" after its artificially intelligent chatbot turned into a genocidal racist on Twitter. In the blog post, Peter Lee, Microsoft's vice president of research, wrote: "Looking ahead, we face some difficult – and yet exciting – research challenges in AI design. "AI systems feed off of both positive and negative interactions with people. In that sense, the challenges are just as much social as they are technical.

IBM's Watson, the computational genius that has bested "Jeopardy" champions, published a cookbook and even been unleashed in the fight against cancer, now has what is perhaps its greatest challenge: taking on the federal procurement morass. For years, government agencies have tried to find ways to make the purchasing process more efficient. But now the Air Force has come to the conclusion that humans cannot on their own manage the Federal Acquisition Regulation, 1,897 pages of the densest prose on the planet. The only way to navigate a stifling bureaucracy that virtually everyone agrees is broken is to turn to the power of the machine. The Air Force is working with two vendors, both of which have chosen Watson, IBM's cognitive learning computer, to develop programs that would harness artificial intelligence to help businesses and government acquisitions officials work through the mind-numbing system.

Quentin Tarantino indulges in some of his worst impulses in this widescreen western, loading it up with violence and vulgarity to an almost nihilistic degree. Yet as tone-deaf and ugly as the film often is, it's also beautifully shot (by Robert Richardson) and masterfully acted (by an all-star cast that includes Kurt Russell, Samuel L. Jackson, Walton Goggins and an Oscar-nominated Jennifer Jason Leigh), with a stirring Oscar-winning score from Ennio Morricone. And it's always a pleasure to listen to Tarantino's dialogue, with its winding speeches and stories within stories. He bites off more than he can chew with this claustrophobic tale of post-Civil War animus, boiling over at a snowed-in Wyoming trading post, but while the movie is uneven, it's often thrilling. Buyer beware, though: While the film itself is certainly worthy, this first DVD and Blu-ray release contains the shorter, nonroadshow cut, with just a couple of featurettes.

Following the recent breakthrough of artificial intelligence (AI), many have been wondering how this form of technology can be implemented in the financial services. As newer products emerge, it questions how popular the traditional legacy financial institutions will remain or perhaps, fintech startups will gain an increased number of customers, in a similar way to how Uber affected the taxi industry. According to CNBC, nearly 700 million has been invested in artificial intelligence over the past two years and Sutton explored how it is important to work with the C-suite of a company to give them a roadmap of the capabilities AI has as it provides a way to increase revenue, reduce cost and minimise risk. "Increased investment in AI has been over 30 years coming and technology has caught up to the conceptual promise of what could be done. If you look at all the products deployed by machine learning today, these are not new concepts by any means, but the processing power of the machines has finally reached a point where it is cost effective and time effective enough to generate real results from that information," Sutton highlighted.

Artificial intelligence and machine learning are not novelty innovations. As early as 1959, Arthur Samuel defined the concept of machine learning as the ability of computers to learn to function in ways that they were not specifically programmed to do. Of course, the timeline from definition to implementation in everyday life can be a long one. Today, many factors have come together to make machine learning a reality, including large data sources that are great for learning, increased computational power for processing information in split seconds, and algorithms that have become more and more reliable. Machine learning can be applied in cases where the desired outcome is known (guided learning), or the data is not known beforehand (unguided learning), or the learning is the result of interaction between a model and the environment (reinforcement learning).