After roaming the planet for 165 million years, it is thought the dinosaurs were wiped off the face of the planet when an asteroid crashed into Earth 66 million years ago. The impact of the asteroid would have thrown up huge amounts of debris, and new computer simulations have revealed the extent to which this affected our planet. The findings suggest that Earth would have been plunged into darkness for as long as two years. The impact of the asteroid would have thrown up huge amounts of debris, and new computer simulations have revealed the extent to which this affected our planet (artist's impression) The team used computers to model what Earth might have looked at the end of the Cretaceous Period, after a large asteroid struck what is now the Yucatán Peninsula, beneath the gulf of Mexico. The simulations found 15,000 million tons of soot would have been sent into the atmosphere after the asteroid hit, forming a barrier between the Earth and sunlight.

In this video course, we start by focusing on R's similarities with programming languages such as basic/C/C# with loops and conditional tests like if, so that you can feel at home and be productive straight away. We begin by introducing R and setting things up so that you are ready to go using Rstudio, the associated IDE. Then we look at R as a programming language and see how the standard things are done in it, so you can see that it's not that different from other programming languages. Next, we introduce some R commands, which are very useful and not as common in traditional languages since manipulating data is more important in R. Moving on, we look at an example in the Titanic dataset, which is the kind of thing you'll come across in R, a multidimensional collection of variables of different types. Using the tools that we cover we can form a picture, a story behind the data.

I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation.

Last fall, University of Virginia computer-science professor Vicente Ordóñez noticed a pattern in some of the guesses made by image-recognition software he was building. "It would see a picture of a kitchen and more often than not associate it with women, not men," he says. That got Ordóñez wondering whether he and other researchers were unconsciously injecting biases into their software. So he teamed up with colleagues to test two large collections of labeled photos used to "train" image-recognition software. Two prominent research-image collections--including one supported by Microsoft and Facebook--display a predictable gender bias in their depiction of activities such as cooking and sports.

You can now purchase an Xbox One X. Microsoft has opened pre-orders for the upcoming 4K console, which the company describes as "the world's most powerful console". The Xbox One X was officially unveiled in June, and will come out on 7 November. It costs £449, and is claimed to be 40 per cent more powerful than any other console on the market. Microsoft has also launched a special version of the Xbox One X, called the Project Scorpio Edition. Project Scorpio was the One X's codename, and the company wants it to "live on" and enable fans "to celebrate both our heritage and the power and precision that we are bringing into our future". It's exactly the same as the standard Xbox One X, but the words "Project Scorpio" are printed on both the console and the controller, and it comes with an exclusive vertical stand.

Methods 2127 observations of competition best performances and mass spectrometry-measured serum androgen concentrations, obtained during the 2011 and 2013 International Association of Athletics Federations World Championships, were analysed in male and female elite track and field athletes. To test the influence of serum androgen levels on performance, male and female athletes were classified in tertiles according to their free testosterone (fT) concentration and the best competition results achieved in the highest and lowest fT tertiles were then compared. Results The type of athletic event did not influence fT concentration among elite women, whereas male sprinters showed higher values for fT than male athletes in other events. Men involved in all throwing events showed significantly (p 0.05) lower testosterone and sex hormone binding globulin than men in other events. When compared with the lowest female fT tertile, women with the highest fT tertile performed significantly (p 0.05) better in 400 m, 400 m hurdles, 800 m, hammer throw, and pole vault with margins of 2.73%, 2.78%, 1.78%, 4.53%, and 2.94%, respectively. Such a pattern was not found in any of the male athletic events. I'm sure you wouldn't be surprised to see these kinds of mistakes in published work. What is more distressing is that this evidence is said to be a key submission in the IAAF's upcoming case against CAS [the Court of Arbitration for Sport], since the CAS has argued that sex classification on the basis of T levels are only justified if high T confers a "significant competitive advantage".

A new milestone in human speech recognition has been reached by Microsoft, matching the accuracy of trained human transcribers. The firm's software, used in its Cortana voice assistant, has achieved a 5.1 per cent margin of error, putting it on a par with professionals. One of the big frustrations of voice recognition has been getting machines to accept commands, a process which often involves repetition and exaggerated speech. The development means the company's products will soon accept orders with super-human precision. A new milestone in human speech recognition has been reached by Microsoft, matching the accuracy of trained human transcribers.

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you'll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We're approaching a new era where only apps and games that are considered "smart" will survive.

From personal experience, read as many of these as you can, maybe more than once. Yes, some will be better than others but the more you read different explanations and see different diagrams the more you will "get it". Also, it's not either or, read these and do the coursera course.

This episode of Fast Forward was recorded in the IBM Watson Experience Center here in New York City. My guest was Rob High, the Vice President and Chief Technology Officer of IBM Watson. High works across multiple teams within IBM, including engineering, development, and strategy. He is one of the most lucid thinkers in the space of artificial intelligence, and our conversation covered many of the way that technology is reshaping our jobs, our society and our lives. Read and watch our conversation below. Dan Costa: What is the dominant misconception that people have about artificial intelligence? Rob High: I think the most common problem that we're running into with people talking about AI is they still live in the world where I think Hollywood has amplified this idea that cognitive computing, AI, is about replicating the human mind, and it's really not. Things like the Turing test tend to reinforce that what we're measuring is the idea of AI being able to compete with fooling people into believing that what you're dealing with is another human being, but that's really not been where we have found the greatest utility. This even goes back to, if you look at almost every other tool that has ever been created, our tools tend to be most valuable when they're amplifying us, when they're extending our reach, when they're increasing our strength, when they're allowing us to do things that we can't do by ourselves as human beings. That's really the way that we need to be thinking about AI as well, and to the extent that we actually call it augmented intelligence, not artificial intelligence.