This is just one exercise that we could do to imagine a future in which we are irrelevant bystanders. A world in which we kneel at the outer wall of a kingdom we're locked out of. This would be the world in which artificial superintelligence, or ASI, has emerged. ASI is an intellect that exceeds all the smartest, most capable human beings in every field, in abstract reasoning and social manoeuvring and creative experimentation, by unfathomable degrees. This intelligence could take form as a seed AI, a few cognitive steps above a person, or it can be a mature superintelligence that soars miles above, beyond, the blip, the dot of us, collected.

When we imagine a future where humans and robots coexist, it doesn't take long for us to arrive at a conclusion where the human race tragically ends. A robot takeover usually occurs, followed by the inevitable enslavement of all humankind. But when it comes to the future and what will actually unfold, Palmer Luckey, founder of Oculus VR (which Facebook now owns) and inventor of the Oculus Rift virtual reality headset, isn't sweating it. "The reason I'm not creeped out is pretty simple," said Luckey, who sat down with Apple co-founder Steve Wozniak and Re/code journalist Kara Swisher on Saturday at the Silicon Valley Comic Con in San Jose, California. "A lot of people look to science-fiction for representations of technology. It can also be flawed."

Small and medium sized businesses are being targeted by IBM and MasterCard as they look to bring big data analytics insights to better understand their markets and consumers. A partnership has been formed by the two companies that sees MasterCard integrate IBM Watson Analytics into its platform, along with its own anonymised transaction data that is gathered through the payment company's Local Market Intelligence. This combination will bring artificial intelligence to its payments platform. The aim is to be able to offer SMBs insights on revenue, market share, customer demographics and competitors in a particular location and across multiple locations. The problem being tackled is that smaller merchants often don't have the resources to maximise data insights.

Machine learning has taken a significant role in many data initiatives today. Facebook, for instance, is using machine learning to offer personalized ads, whilst Google uses it to learn about its users, and other technology companies are now able to crunch data in a fraction of the time. Organizations have been looking at machine learning as something that has the most use in looking at optimizing its current markets, but this may not be the case for too much longer. Several companies are now using machine learning combined with predictive analytics to help expand into new markets and exploit opportunities as soon as they come up. We heard about this from Wolf Rendall, Data Scientist at Auction.com, at last year's Social Media & Web Analytics Innovation Summit.

"Soon, our habitat will be invaded by unreal humans. Not only they'll influence our way of living, but also intervene in our modus operandi." I'm not the only one who thinks this way. Last week I released a list of must watch movies on Machine Learning and Data Science. I've watched 8 of them till now.

There is no dearth of articles around the web comparing and contrasting data science terminology. There are all sorts of articles written by all types of people relaying their opinions to anyone who will listen. So let me set the record straight, for those wondering if this is one of those types of posts. I think that, while there may be an awful lot of opinion pieces defining and comparing these related terms, the fact is that much of this terminology is fluid, is not entirely agreed-upon, and, frankly, being exposed to other peoples' views is one of the best ways to test and refine your own. So, while one may not agree entirely (or even minimally) with my opinion on much of this terminology, there may still be something one can get out of this.

Iris Capital is a pan-European venture capital fund manager specializing in digital economy. In such a world, many information platforms are available, but there is no software tool that applies the latest in machine and deep learning. Come to us to present us the next generation software tool or platform that automatically detects the right innovative teams/companies depending on who's looking for it and how innovation is defined. Our pitching competition is aimed at international early-stage start-ups between 1 and 5 years of existence. The 5 to 10 best start-ups will be evaluated on stage by a jury made of Iris Capital investors, large corporate innovation VP, leading start-up CEOs and media agencies.

With the massive growth of big data and the value of those data, machine learning is quickly becoming a technology that every organization should tap into. It has the ability to positively affect everything from analytics to customer relationship management (CRM). Many organizations are interested in these CRM improvements--as they should be. Machine learning in CRM can allow organizations a much more intimate look at their customer than has ever been possible in the past. Here are three ways in which machine learning can improve CRM.

Developing robust quantitative trading strategies is an intensive, rigorous, time-consuming process with no guarantee for success. In this webinar, you will learn how to apply techniques from the Artificial Intelligence and machine learning fields to improve the quantitative strategy development process and maximize your chances of success with every strategy. Attendees will learn practical applications that they can apply to their own trading and will come away with a strategy they can actually trade live. Attendees should have a basic understanding of quantitative and algorithmic trading. No programming experience is required.

What an utterly interesting time to be alive. We all remember how Deep Blue defeated Kasparov. Chess became a'solved problem' pretty soon after that historic event. And in the past week, we have witnessed an even more amazing feat: AlphaGo beating world-class Go player Lee Sedol. The game that knows more positions than there are atoms in the universe was no match for Google's DeepMind team. It's fascinating to see how a game with relatively simple rules can lead to such complex and strategic gameplay.