Tech moguls Elon Musk and Mark Zuckerberg butted heads Tuesday over the ostensible threat that artificial intelligence, aka AI, poses on the human race. The Facebook CEO has been praised for his extensive and ever-growing knowledge of AI, but his SpaceX CEO rival considers his beliefs to be restrained. The SpaceX CEO believes that the world will meet its inevitable end because of AI, whereas Zuckerberg believes the use of AI is harmless. So who is on the right side of the AI argument? Truthfully, the answer is complicated.

This team's solution write-up was originally published here by Daniel Hammack and cross-posted on No Free Hunch with their permission. Julian and I independently wrote summaries of our solution to the 2017 Data Science Bowl. What is below is my (Daniel's) summary. For the other half of the story, see Julian's post here. Julian is a freelance software/machine learning engineer so check out his site and work if you are looking to apply machine intelligence to your work. He won 3rd in last year's Data Science Bowl too! This blog post describes the story behind my contribution to the 2nd place solution to the 2017 Data Science Bowl. I will try to describe here why and when I did certain things but avoid the deep details on exactly how everything works. For those details see my technical report which has more of an academic flavor.

More than 70% of Australians play video games, and I'm one of them. With its spread from computers to TVs to mobile phones, the global games industry is now worth over $100bn, with an Australian market alone worth $3bn. But the value of games goes beyond just money. Interactive entertainment and the "serious games" that share lessons and skills have psychological and social benefits. We play to bond with other players, to build communities, to learn, to experience worlds beyond our imagination, and – as in the Dionysian theatres of old – to enjoy a temporary catharsis and channel feelings that otherwise preoccupy us into something vicarious.

Do you believe AI and robots taking over jobs is, as Elon Musk recently put it, the "biggest risk that we face as a civilization?" There's probably a research-backed prediction that supports your view. We've ranked them here, in order from "certain doom" to "possible utopia," for your convenience. How they got there: Carl Benedikt Frey and Michael A. Osborne, researchers at Oxford University, asked machine-learning experts to assess whether a sample set of occupations were "automatable" or "not automatable," which helped to inform how the presence of "engineering bottlenecks" that couldn't be easily automated, such as creativity and social engineers, factored into whether an occupation could be automated. They created a machine-learning algorithm to estimate a probability of automation across each US occupation.

It's only natural, albeit rather unfortunate, that technology improves weapons. In the end, however, it's not AI directly that poses a threat to humanity -- it's people using AI." Of course it's people using AI -- we are the parents. But it's the AI that's going to destroy the human race, because the brainless humans let it, over ridiculous military AI concerns that shouldn't have been, but are. Whether it's the enemy or our own bloody selves we do not know, but I guarantee that in the next thirty years there will be a war to end all wars, and we will go poof -- extinction time -- specifically because of AI.

Ford's automotive revenue of $37 billion was in line with Wall Street's expectations. Total revenue rose 1 percent to $39.85 billion. The elevated performance in the second quarter was due mostly to a lowering of the company's corporate tax rate, from 30 percent down to 10 percent, Chief Financial Officer Bob Shanks acknowledged. Ford has put some overseas losses back on its books in anticipation of changes in the U.S. corporate tax code, Shanks said. The company expects to have a 15 percent rate this year, but that will return to 30 percent next year.

While data science has emerged as an ambitious new scientific field, related debates and discussions have sought to address why science in general needs data science and what even makes data science a science. Following a comprehensive literature review,5,6,10,11,12,15,18 I offer a number of observations concerning big data and the data science debate. For example, discussion has covered not only data-related disciplines and domains like statistics, computing, and informatics but traditionally less data-related fields and areas like social science and business management as well. Data science has thus emerged as a new inter- and cross-disciplinary field. Although many publications are available, most (likely over 95%) concern existing concepts and topics in statistics, data mining, machine learning, and broad data analytics. This limited view demonstrates how data science has emerged from existing core disciplines, particularly statistics, computing, and informatics. The abuse, misuse, and overuse of the term "data science" is ubiquitous, contributing to the hype, and myths and pitfalls are common.4 While specific challenges have been covered,13,16 few scholars have addressed the low-level complexities and problematic nature of data science or contributed deep insight about the intrinsic challenges, directions, and opportunities of data science as an emerging field. Data science promises new opportunities for scientific research, addressing, say, "What can I do now but could not do before, as when processing large-scale data?"; "What did I do before that does not work now, as in methods that view data objects as independent and identically distributed variables (IID)?"; "What problems not solved well previously are becoming even more complex, as when quantifying complex behavioral data?"; and "What could I not do better before, as in deep analytics and learning?"

At the University of Washington (UW) Medical Center, a radiotherapy system shoots high-powered radiation beams into the heads of patients, to treat cancers of the tongue and esophagus. Any software errors in the system could prove fatal, so engineers at the medical center have teamed with a group of computer scientists from the university to ensure the system will not fail, and that the beam will shut off if prescribed settings go out of tolerance. This is made possible by a process known as software verification, and verifying implementations of critical systems like that radiotherapy setup is one of the things about which Zachary Tatlock is passionate. Over three years ago, Tatlock was a Ph.D. candidate giving a talk at the university on his thesis research in program verification. The lead engineer for the medical center's radiotherapy team was in the audience, and asked Tatlock how they could apply verification to that system.

What will the car look like by the time the petrol and diesel combustion engine is banned from sale? Will we still be driving, or will we be living in a utopia filled with gleaming white pods silently whizzing about the streets ferrying people to and fro? The car of the future is still in flux. While many agree that it will be autonomous, will hopefully be accident-free, probably battery powered and will likely be radically different on the inside, if still familiar on the outside, there are broadly two routes down which the car can wind to 2040. The first is the model we are familiar with: car ownership.

Studies have shown we like happy faces because positive emotions in others immediately boosts our own mental state. But what are the emotional consequences of trying to seem happy in order to please others? Researchers have found that we can adjust facial expressions and body gestures without actually changing our emotional state – for example putting on a smile without being happy. People performing surface acting'put on a mask', which creates an unhealthy inner conflict between expressed and felt emotions, researchers found. In an article for The Conversation, Milda Perminiene, a senior lecturer in occupational psychology at the University of East London, shows that this can cause emotional exhaustion, strain and reduced job satisfaction.