Many predict artificial intelligence (AI) will someday cure cancer, clean up our environment, drastically improve our cities, and send us to Mars, among a host of other accomplishments. However, with the potential benefits that inspire oohs and aahs also comes risk. Before there was Siri, there was HAL. And in the epic science fiction movie "2001: A Space Odyssey," when the poor astronaut Dave Bowman asks the supercomputer HAL 9000 to let him back inside the spacecraft, HAL merely responds, "I'm sorry Dave, I'm afraid I can't do that." Granted, this was a movie--albeit a brilliant one--but there are still real life concerns about what exactly are artificial intelligence best practices and a need for guidelines.

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For a while (at least several months since many people began to implement it with Python and/or Theano, PyLearn2 or something like that), nearly I've given up practicing Deep Learning with R and I've felt I was left alone much further away from advanced technology… But now we have a great masterpiece: {h2o}, an implementation of H2O framework in R. I believe {h2o} is the easiest way of applying Deep Learning technique to our own datasets because we don't have to even write any code scripts but only to specify some of its parameters. That is, using {h2o} we are free from complicated codes; we can only focus on its underlying essences and theories. With using {h2o} on R, in principle we can implement "Deep Belief Net", that is the original version of Deep Learning*1. I know it's already not the state-of-the-art style of Deep Learning, but it must be helpful for understanding how Deep Learning works on actual datasets. Please remember a previous post of this blog that argues about how decision boundaries tell us how each classifier works in terms of overfitting or generalization, if you already read this blog.

"Machine Learning (ML)" and "Traditional Statistics(TS)" have different philosophies in their approaches. With "Data Science" in the forefront getting lots of attention and interest, I like to dedicate this blog to discuss the differentiation between the two. I often see discussions and arguments between statisticians and data miners/machine learning practitioners on the definition of "data science" and its coverage and the required skill sets. All is needed, is just paying attention to the evolution of these fields. However, there is a significant difference in approach, applications, and philosophies of the two camps that is often overlooked.

Artificial intelligence has become more of a philosophy than a programming tool at Facebook. Laced across the company's products, it holds the power to analyze data at the massive scale required by a social network connecting a quarter of the world's population. In a speech today at Web Summit, Facebook CTO Mike Schroepfer laid out a vision for the role artificial intelligence and machine learning will play in the company's ambitions to improve global connectivity, technology accessibility, and human computer interaction. "People want to stay connected and close to other people, so whatever is the best current technology to deploy that is the business we want to be in," said Schroepfer. Large companies like Facebook play an incredibly important role in the artificial intelligence and machine learning ecosystem.

It may be Tesla's Elon Musk who most often invites comparison to Marvel's superhero Iron Man – the alter ego of billionaire inventor Tony Stark – but it is Mark Zuckerberg who might be the first to bring Stark's technology to life. Memorably, the Facebook CEO sets himself annual goals such as learning Mandarin in 2010, eating only meat from animals he killed himself in 2011, or reading two books a month in 2015. In January, the Facebook founder said that his 2016 challenge would be to build an artificial intelligence-based personal assistant for his home. In his Facebook post announcing his aim, Zuckerberg said that "You can think of it kind of like Jarvis in Iron Man." In a Facebook conversation on Thursday, Zuckerberg invited suggestions for who should voice his real-life Jarvis (which, in the Iron Man and Avengers movies, stands for Just A Rather Very Intelligent System). Suggestions included actors Morgan Freeman, Benedict Cumberbatch and Paul Bettany – who voices Jarvis in the movies – as well as astrophysicist Neil deGrasse Tyson.

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Can you spot the lone dugong in the image above? Now do that with 45,000 more, and you'll have a general idea of the population of these endangered critters. If that sounds tedious, then perhaps you, like researchers at Murdoch University, would prefer to delegate the duty to a specially-trained computer. Amanda Hodgson, of the school's Cetacean Research Unit, has been using UAVs to capture images of marine animals for years, but the data piles up fast, and there are only so many grad students. Hodgson worked with computer scientist Frederic Maire, of the Queensland University of Technology, to automate the process.

Today, consumers have access to modern wearables and smartphones as well as AI and machine learning-powered platforms like BioBeats Hear and Now. Consumers can take it upon themselves to reduce their stress levels and improve their overall quality of life. To be clear, stress management and other types of healthcare platforms/applications are not intended to be replacements for regular visits to a primary care physician (PCP). There are often cases where high blood pressure, obesity, heart disease, and other stress-related health issues not only require regular visits to a PCP, but also specialized treatments and medications. With that said, consumers can use AI and machine learning-powered stress management platforms to be proactive about stress; changing behaviors and reducing stress levels on their own using focused techniques such as clinically validated breathing exercises, biometric feedback, mindfulness exercises, and meditation.

Some of the smartest minds in the UK are being lured away from their research positions at Oxford and Cambridge by DeepMind -- a London-based AI lab that was acquired by Google for £400 million in 2014. More than a dozen AI researchers have left the academic powerhouses over the last couple of years for what are likely to be better-paid roles at DeepMind, according to LinkedIn. Steven Cave, the director of Cambridge University's new Centre for the Future of Intelligence, believes that the exodus of talent from academia to corporates is something of a problem. "The best people are being offered huge sums of money to go and work at these tech companies," Cave told Business Insider in Cambridge last week. "You find that you're talking to someone and they're expressing a great deal of interest in a research project and then they're snapped up. We understand that ambitious young people want to work at these big name companies and earn lots of money and that's fine. But at the same time we hope that there will be enough bright young things who are motivated by the intellectual challenge of the issues we're working on and by the sense of wanting to do something good that makes a difference for the world."