Lee Sedol, the South Korean Go champion who lost to a computer earlier this month, has said he would like a rematch. Lee, a top-ranking Go player who has 18 international titles under his belt, lost a five-game match to AlphaGo, a computer program developed by British artificial intelligence company DeepMind. However, Lee is ready to take on the machine once again. Speaking to Yonhap News, he said: "I will have to consider it carefully, but if AlphaGo wants a rematch, I'd like to face it again, on the condition it will take place in the near future." He also hinted that he may have discovered the program's secrets, saying: "I figured out AlphaGo to some degree during our last meeting."

I'd like to build a self-driving, self-learning RC car that can move around my apartment at top speed without running into anything--especially my cats. But before busting out the soldering iron and scaring the crap out of Echo and Bear, I figured it best to start in a virtual environment. I've learned a lot going from "what's reinforcement learning?" to watching my Robocar skillfully traverse the environment, so I decided to share those learnings with the world. Update, Feb 24, 2016: Part 2 is now available. Update, March 7, 2016: Part 3 is now available.

Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.

Machine learning is increasingly enabling companies to gain strategic advantage by turning big data into insightful and actionable information. But how do they access this new world of artificial intelligence? Offering easy access to machine learning, predictive APIs [application programming interfaces] are emerging as a key driving factor for machine learning (ML) and the overall world of artificial intelligence (AI). Predictive APIs offer the flexibility of deciding to host machine learning in the cloud, in-house or both, giving developers the freedom to work in the language and tools they want. "A predictive API exposes machine learning capability so it can either be an API that gives access to a predictive model, and it can also expose access to an ability to learn new models and to create new models from data," said Louis Dorard, who has a PhD in machine learning and is the author of Bootstrapping Machine Learning and is founder of the series of predictive API conferences, including PAPIs.io "There's machine learning APIs where you can send a data set -- for example, one column for the tweet and another column for positive or negative or neutral -- and then they send it back [through] the API and then you can reuse the model for data," Dorard said.

Being an analyst working on statistical analysis, more often than none, there is almost always a need to "explain" how did you come up with the conclusion with the techniques applied? The most demanding part of all this is: " you must explain this like I am 5 years old." Oki, so let me try to explain one machine learning algorithm called "randomforest" for a classification problem that is recognised as a black-box algorithm! Let's get started with a metaphor that hopefully is as close to everyday life as possible... In a congress, say that there are 100 members in total, they need to vote to decide whether they are going to pass a new law or not.

Riverview Law provides large corporations with a high quality, fixed priced and proven alternative to using traditional law firms and/or growing the size of their in-house legal function. Using client dedicated teams which combine lawyers, client managers, data analysts and other professionals, Riverview Law helps free the internal legal team so that it can evolve its legal operating mode to focus on higher value added strategic and tactical requirements. Riverview Law has three core offerings. It provides managed service solutions via its Legal Advisory Outsourcing services. It licenses to in-house teams its service delivery and workflow platform, which manages instructions into the function, triage, case management and reporting. It provides barrister-led litigation, risk and compliance advice and support.

Until now, medicine has been a prestigious and often extremely lucrative career choice. But with intelligent machines now used to diagnose diseases, in the near future, will we need as many doctors as we have now? Are we going to see significant medical unemployment in the coming decade? Dr Saxon Smith, president of the Australian Medical Association NSW branch, said in a report late last year that the most common concerns he hears from doctors-in-training and medical students are, "what is the future of medicine?" The answers, he said, continue to elude him.

What is the time lag from Science Fiction to reality? Arthur C. Clarke wrote about geostationary satellites in 1945 and it became reality 20 years later, but Clarke's work was more grounded in science than the Kubrick classic 2001 featuring the Hal AI computer. That was released in 1968 – 48 years ago. It is such an easy fiction trick to imagine a machine as smart as a human, but the reality of AI has been much harder. It is now getting a lot easier.

Geoffrey Hinton, an artificial intelligence (AI) expert who splits his time between Google and the University of Toronto, believes machines could match human abilities in "more than five years." Hinton, known as the godfather of "deep learning," said the most powerful machines are still about a million times smaller than the human brain. They only have the equivalent of around a billion synapses (the connections between the neurons in the brain), compared to 1,000 trillion in the human brain. But machines are becoming more sophisticated every year. When asked to predict how long it will take before machines possess human-level abilities, Hinton said: "More than five years. I refuse to say anything beyond five years because I don't think we can see much beyond five years."

This is a guest post by Ola Lidmark Eriksson, CTO at Wide Ideas. Two years ago, I asked myself if it would be possible to use machine learning to better predict the outcome of soccer games. I decided to give it a serious try and today, two years and contextual data from 30,000 soccer games later, I've gained lots of interesting insights. To begin with, I harvested as many data points as possible. I mined old game data from every different source and API I could find.