DANKO NIKOLIC has spent his life studying human intelligence. Lately, however, he's been thinking about the artificial kind. Just how smart can AI get? Are we really headed towards the so-called technological singularity? That was the topic of a debate in Berlin last month. Nikolic, a neuroscientist at the Max Planck Institute for Brain Research in Frankfurt, stood up in front of an audience of artificial intelligence researchers and made a bold claim: we will never make a machine that is smarter than we are. "You cannot exceed human intelligence, ever," says Nikolic. "You can asymptotically approach it, but you cannot exceed it."

Nowadays because of the internet we are flooded with waves of data to such an extent that we actually don't need more data, more spread-sheets, or even more beautiful visualizations. Instead, what we need are the key takeaways with surgical deep drill-downs. At best, risk data and analytics technologies aggregate, analyze, and visualize data. Even so, an important gap remains: the story of what that risk data means, and how to interpret this data. The problem is that companies don't have a way to efficiently bridge this gap and convey the most important and interesting information within the risk data to benefit people all across the business.

When Google's AlphaGo program beat grandmaster Lee Se-Dol four games to one, both programmers and professional Go players were surprised. The general consensus was that it would be years before a computer could defeat a human at the complex board game, which players describe as requiring elegance and imagination. Director of the MARCS Institute for Brain, Behaviour and Development and electrical engineering professor, Jon Tapson, said AlphaGo's victory was cause for a re-evaluation of how we use artificial intelligence (AI). "They could find ways of manipulating the stock market -- maybe by buying and selling shares in rapid succession to create the illusion of a change in market sentiment," he said. He said unless there was reason to go looking, it was unlikely humans would notice that kind of behaviour, and that it would be difficult to program or regulate the actions of an AI if we do not know how it makes decisions.

Jose Luis Cordeiro is a futurist thinker, the director of the Venezuelan node of the Millennium Project as well as energy advisor and part of the founding faculty of Singularity University (SU). In his speech at this year's START Summit he passionately argued that things like human-level artificial intelligence or physical immortality aren't nearly as far away in the future as most people would think, due to the power of exponential growth patterns observed in Moore's Law and other key areas, and he shared his vision of how technology will change almost every aspect of our lives, including ourselves. His conception of the future is very similar to that of his friend, the author, inventor, director of engineering at Google and co-founder of Singularity University, Ray Kurzweil. Both are radical optimists, both believe in a merger of humans and machines and both don't shy away from controversy. After Cordeiro's keynote I had the chance to do a short interview with him.

What makes the UK such a breeding ground for businesses working with Artificial Intelligence? We asked Alexandre Flamant and John Henderson, Co-founders of the LondonAI meetups, for their thoughts. Last week, a prototype programme from Google DeepMind achieved what many commentators thought would take at least another decade: it won a five match series of the ancient Chinese board game Go against reigning world champion Lee Sedol. In doing so, AlphaGo made a number of'creative' moves that flummoxed Go experts – no human would have ever played in such a way. This is true intelligence, even if currently confined to a specific board game.

Humans have been taking a beating from computers lately. The 4-1 defeat of Go grandmaster Lee Se-Dol by Google's AlphaGo artificial intelligence (AI) is only the latest in a string of pursuits in which technology has triumphed over humanity. Self-driving cars are already less accident-prone than human drivers, the TV quiz show Jeopardy! is a lost cause, and in chess humans have fallen so woefully behind computers that a recent international tournament was won by a mobile phone. There is a real sense that this month's human vs AI Go match marks a turning point. Go has long been held up as requiring levels of human intuition and pattern recognition that should be beyond the powers of number-crunching computers.

As if Minecraft wasn't already popular enough, it is now being used to help artificial intelligence. Well, at least, a mod of the game by Microsoft is going to. Katja Hofmann and her team of colleagues at Microsoft have created a platform called AIX, which is a mod of Minecraft. The company intends for this system to help artificial intelligence agents have a better understanding of general intelligence. For those of you who are not aware, artificial intelligence is rapidly growing and has improved leaps and bounds of the years.

Machine learning is already a very big deal. It's here, and it's in use in far more businesses than you might suspect. A few months back, I decided to take a deep dive into this topic to learn more about it. In today's post, I'll dive into a certain amount of technical detail about how it works, but I also plan to discuss its practical impact on SEO and digital marketing. For reference, check out Rand Fishkin's presentation about how we've entered into a two-algorithm world.

In January, the hedge fund Aidyia started trading on the stock market using an artificially intelligent algorithm. Hedge funds are increasingly turning to artificial intelligence in order to spot trends to try to make money for their customers. BBC Click spoke to Ben Goertzel about his hopes for the future of the company.

This long article with a lot of source code was posted by Suraj V Vidyadaran. Suraj is pursuing a Master in Computer Science at Temple university primarily focused in Data Science specialization. His areas of interests are in sentiment analysis, data visualization, big data and machine learning. This data is obtained from UCI Machine learning repository. The purpose of the analysis is to evaluate the safety standard of the cars based on certain parameters and classify them.