It was hailed as the most significant test of machine intelligence since Deep Blue defeated Garry Kasparov in chess nearly 20 years ago. Google's AlphaGo has won two of the first three games against grandmaster Lee Sedol in a Go tournament, showing the dramatic extent to which AI has improved over the years. That fateful day when machines finally become smarter than humans has never appeared closer--yet we seem no closer in grasping the implications of this epochal event. Late last year, SpaceX co-founder Elon Musk warned that AI could take over the world, sparking a flurry of commentary both in condemnation and support. For such a monumental future event, there's a startling amount of disagreement about whether or not it'll even happen, or what form it will take.

Social networking giant Facebook is using its artificial intelligence (AI) technology and resources to map the entire Earth and launch the world's most detailed population maps that will help it beam cheap internet to remote areas. To begin with, the Facebook AI team crunched 14.6 billion images of maps from across 20 countries, including India, covering 21.6 million sq kms to come up with the first detailed map of human settlement for these countries. "This is an impressive project from our team developing solar-powered planes for beaming down internet connectivity and our AI research team. Many people live in remote communities and accurate data on where people live doesn't always exist," wrote Facebook CEO Mark Zuckerberg in a latest post. The 20 countries mapped were Algeria, Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, India, Ivory Coast, Kenya, Madagascar, Mexico, Mozambique, Nigeria, South Africa, Sri Lanka, Tanzania, Turkey, Uganda, Ukraine and Uzbekistan.

Each are British artificial intelligence and machine learning startups bought by US tech giants--HP, Google, Microsoft, and Apple, respectively. Alongside growing VC funding in AI, US tech firms are snapping up British-founded startups, leading to concerns that the UK is losing the best of its artificial intelligence to Silicon Valley just as it becomes a key technology. Simon Walker, partner in corporate technology at law firm Taylor Wessing, said the sale of AI startups to US firms isn't new and doesn't look like it'll stop soon. "It is obviously disappointing that the AI cannot be retained in the UK," he said. "However, top-of-the-market AI, such as that developed by companies such as SwiftKey and DeepMind, requires huge investment and a significant platform for its use and it is only very large tech companies which have the necessary resources and platforms."

Artificial Intelligence is the buzzword these days. It has become the most popular component of many innovative software startups that are seeking to redefine their markets. But whenever we hear this word, a plethora of voices can be heard in the background raising concerns about long-term consequences of AI uses. Among these skeptics is one Elon Musk, Co-founder, Tesla Motors, who sees AI "more dangerous than nukes". Stephen Hawking has also echoed similar fears, predicting that "the development of full artificial intelligence could spell the end of the human race." Though these concerns carry weight and need to be addressed responsibly, the changes and the advancement that the world is witnessing due to AI and machine learning techniques suggest that technology is in safe hands.

The move didn't make sense to the humans packed into the sixth floor of Seoul's Four Seasons hotel. But the Google machine saw it quite differently. The machine knew the move wouldn't make sense to all those humans. And yet it played the move anyway, because this machine has seen so many moves that no human ever has. In the second game of this week's historic Go match between Lee Sedol, one of the world's top players, and AlphaGo, an artificially intelligent computing system built by a small team of Google researchers, this surprisingly skillful machine made a move that flummoxed everyone from the throngs of reporters and photographers to the match commentators to, yes, Lee Sedol himself.

"I believe that AliveCor's approach to empowering people to be proactive with their heart health data is going to significantly impact the way we think about healthcare," said Simon Prakash, vice president of products and design of AliveCor. "I look forward to expanding upon what the team has already created, and working to get our technology into the hands of more people." "We are excited to welcome both Frank and Simon to the AliveCor leadership team. Frank is one of the most renowned experts in visualization engineering and Simon is a leader in product integrity and design. Their unique skills in both software and hardware engineering and machine learning are in line with our company goals and will help further our vision of saving more lives by producing the most innovative Wearable MedTech devices and services," said Vic Gundotra, chief executive officer of AliveCor.

Data can't be racist or sexist, but the way it is used can help reinforce discrimination. The internet means more data is collected about us than ever before and it is used to make automatic decisions that can hugely affect our lives, from our credit scores to our employment opportunities. If that data reflects unfair social biases against sensitive attributes, such as our race or gender, the conclusions drawn from that data might also be based on those biases. But this era of "big data" doesn't need to to entrench inequality in this way. If we build smarter algorithms to analyse our information and ensure we're aware of how discrimination and injustice may be at work, we can actually use big data to counter our human prejudices. This kind of problem can arise when computer models are used to make predictions in areas such as insurance, financial loans and policing.

In the first part of our interview with Sean Owen, Cloudera's Director of Data Science, we discussed the relationship between machine learning and Hadoop, the future of Apache Mahout and why machine learning has become such hot property. In this part of our discussion, we delved into the future of deep learning and neural networks, and how Owen foresees the relationship between machine learning and enterprise evolving. What do you think are some of the main trends in machine learning right now? To be honest, I think machine learning is still an advanced topic for enterprises. The infrastrcutres of most enterprises are built around reporting and retroactive analytics, and predictive analytics is still considered difficult and expensive.

Welcome to the fifth and final chapter in a five-part series about machine learning. In this final chapter, we will revisit unsupervised learning in greater depth, briefly discuss other fields related to machine learning, and finish the series with some examples of real-world machine learning applications. Recall that unsupervised learning involves learning from data, but without the goal of prediction. This is because the data is either not given with a target response variable (label), or one chooses not to designate a response. It can also be used as a pre-processing step for supervised learning.

I make a lot of dumb mistakes when I write, and I've always dreamed of having a smart computer that could point out the errors that escape my notice. Building such a grammar checker is hard. You can't just write down the rules of English grammar and check that they are followed like you can when building a compiler for a programming language. Natural languages such as English have some syntactic regularity, but they are squishy, and a grammar checker needs to have some understanding of the content to see that underlying regularity. This means that a computer must understand what you intended to write to know if you have written it correctly.