In the short term, data scientists are unlikely to be replaced. Kevin Murphy, a Senior Research Scientist at Google notes that: 'The first problem is that current Machine Learning methods still require considerable human expertise in devising appropriate features and models. The second problem is that the output of current methods, while accurate, is often hard to understand, which makes it hard to trust.' Murphy cites the'automatic statistician' project from Cambridge, which'aims to address both problems, by using Bayesian model selection strategies to automatically choose good models/ features, and to interpret the resulting fit in easy-to-understand ways, in terms of human readable, automatically generated reports.' Their project won a 750,000 Google Focused Research Award, but it still has a number of challenges to overcome if it going to be a success. What Murphy says initially still stands true, and Machine Learning methods require considerable expertise at the point of origination.

Artificial intelligence and thinking computers are a prominent plot device for Hollywood. Movies such as 2001: A Space Odyssey, Alien, and Terminator come to mind, as well as current day examples such as I, Robot, Ex Machina, and Transcendence. These pop culture examples underscore the fears and challenges we face as we attempt to program natural behavior and interact with intelligent technology. I think about this whenever I read the growing list of examples of how bots are becoming more pervasive, especially after seeing an article that proclaimed "the bots are taking over." In reality, bots do not carry such hyperbolic doomsday results.

Sad, mad, glad, confused about the presidential race so far? Well, if you are really lost on who to vote for, IBM may have a solution, depending on your mood. Watson Elections, one of the surprisingly few political applications for IBM's supercomputer, debuted on stage this morning at TechCrunch's Disrupt Hackathon in Brooklyn, New York to help us decided the best candidate fitting our feelings. If you are angry and disgusted right now, you may want to go for Donald Trump. A little less angry and just sadder fits a vote for Bernie Sanders.

Stephen Hawking warns artificial intelligence could end mankind. He is one of Britain's pre-eminent scientists and has said that efforts to create thinking machines pose a threat to our very existence. But till the time it happens, lets play games and kill AI. Going in the same direction, while casually talking to my friend Vishal Shukla about general purpose AI, a thought came up. Which gets boring after you figure-out the pattern in which AI play against player.

Digital personal assistants have been with us for quite a few years already, and this is valid for Android smartphone users as well as fans of iOS and/or Windows Phone. Needless to say, the biggest players in this segment are Google Now, Siri, and Cortana. These personal assistants are present on their respective operating systems out-of-the-box, and a relatively large number of third-party digital assistants are also available for download, some being more powerful and successful than others. However, as clever as the majority of personal assistants may have become, due to a variety of limitations they have yet to completely change the way we interact with our smartphones. But with the passage of time, personal assistants could become more relevant to smartphone users, and to prove this during a recent tech event in New Your City, Dag Kittlaus – one of the developers behind Apple's Siri – took the veil off "Viv", or what looks to be the most advanced personal assistant created to date.

Our next chapter meeting will be Tuesday, May 17 from 11:30-12:30 at the Louisiana Technology Park, 7117 Florida Blvd. This month we will be taking a look at recent developments in Artificial Intelligence. Is AI a technology to be feared or embraced? Will AI be a boon to humanity or, as Elon Musk has warned, "our biggest existential threat"? Our meetings are open to the public.

IBM's Watson supercomputer hardly needs any more resumé-padding. It's already won Jeopardy, written a cookbook, and dabbled in revolutionizing healthcare. Today, IBM announced that Watson is taking its cognitive learning chops to the cloud, where it'll apply them to analyzing, identifying, and (hopefully) preventing cybersecurity threats. But first, it's going to have to learn. There are already plenty of computer-enhanced approaches to combating cybercrime, most of which involve identifying outliers or abnormalities--like when a user logs a few too many failed password attempts--and determining whether those constitute some sort of threat.

Computing science researcher Siamak Ravanbakhsh (middle), with his co-supervisors Russell Greiner (left) and David Wishart. Ravanbakhsh has developed a computer system called Bayesil that could dramatically improve diagnosis and treatment of a wide spectrum of diseases. Siamak Ravanbakhsh, who recently completed his PhD in computing science at the University of Alberta and whose research was recently published in the scientific journal PLOS ONE, said Bayesil, the computer application resulting from this breakthrough, is pretty easy to explain in basic terms. "There is this technology called NMR spectrometry, which uses some of the same physical principles as MRI. This technology is very cool, because it can determine the concentration of certain compounds in your body," he said.

Over the last few blog posts, I've discussed some of the basics of what machine learning is and why it's important: Throughout those posts, I've been using the following definition of machine learning: creating computational systems that learn from data in order to make predictions and inferences. However, machine learning isn't the only subject in which we use data for prediction and inference. Anyone who's taken an introductory statistics class has heard a similar definition about statistics itself. And if you talk to someone who works in data-mining, you'll hear the same thing: data mining is about using data to make predictions and draw conclusions from data. This raises the question: what is the difference between machine learning, statistics, and data mining? The long answer has a bit of nuance (which we'll discuss soon), but the short answer answer is very simple: machine learning, statistical learning, and data mining are almost exactly the same.

OpenAI and Shocking AI Salaries This week, Elon Musk and Sam Altman, head of the famed incubator Y Combinator, announced the launch of the OpenAI project, which is a company that's building AI products and services and giving away their tech for free. Just after the announcement, OpenAI's team of researchers were reportedly mobbed with huge employment offers from some of the tech world's biggest firms. Microsoft's Vice President of Research said that the cost of a top artificial intelligence researcher has eclipsed the cost of a top quarterback prospect in the National Football League. CLICK TO TWEET THIS: The cost of a top artificial intelligence researcher has eclipsed the cost of a top quarterback prospect in the National Football League. Bill Gates' 2b Clean Energy Fund Recently, the MIT Technology Review sat down for a Q&A session with Bill Gates and they discussed the Breakthrough Energy Coalition.