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How can I become a data scientist? A quick search yields a plethora of possible resources that could help -- MOOCs, blogs, Quora answers to this exact question, books, Master's programs, bootcamps, self-directed curricula, articles, forums and podcasts. Their quality is highly variable; some are excellent resources and programs, some are click-bait laundry lists. Since this is a relatively new role and there's no universal agreement on what a data scientist does, it's difficult for a beginner to know where to start, and it's easy to get overwhelmed. Many of these resources follow a common pattern: 1) Here are the skills you need and 2) Here is where you learn each of these.

The Knoxville R Users Group is presenting a workshop on text analysis using R by Bob Muenchen. The workshop is free and open to the public. A description of the workshop follows. When analyzing text using R, it's hard to know where to begin. There are 37 packages available and there is quite a lot of overlap in what they can do.

Yesterday, during DLD Conference, Demis Hassabis, founder and CEO of DeepMind, discussed AlphaGo beating Go-champion with machine learning, and what the collaboration between humans and machines will be bringing in the future. He highlighted the importance of staying creative and follow our own intuitions when working with AI, forecasting 10 exciting years ahead of us. We also hosted our own panel on "Fixing Education for the A.I. age" where our panelists discussed the shifts in education that will push towards a computational knowledge economy. Conrad Wolfram introduced us to the need to teach students the skills to solve problems with the support of computers, instead of learning how to do specific things (particularly in maths) ourselves. Jurgen Schmidhuber instead observed how we are only thinking of short-term improvements in the education system, while we should be looking decades ahead.

There are some truly fascinating jobs out there. What Do You Do? aims to highlight unique jobs and the interesting men and women behind them. To encourage the next generation to pursue work that inspires them through relatable role models. Can you describe your job? What do you do for a living?

Teacher tested and approved by educators across the United States, Quantum's AI software is proven in research studies to improve comprehension, problem solving skills and test scores by as much as 50%. Quantum's intelligent tutoring engines are integrated with existing web-based learning products, providing a strong competitive edge for distributing partners. A "technology think tank," Quantum is funded and supported by the U.S. Department of Education, the National Science Foundation and the National Institutes of Health.

Interpreting deep learning and machine learning models is not just another regulatory burden to be overcome. Scientists, physicians, researchers, and analyst that use these technologies for their important work have the right to trust and understand their models and the answers they generate. This talk is an overview of several techniques for interpreting deep learning and machine learning models and telling stories from their results. Speaker: Patrick Hall is a Data Scientist and Product Engineer at H2O.ai. Prior to joining H2O, Patrick spent many years as a Senior Data Scientist SAS and has worked with many Fortune 500 companies on their data science and machine learning problems.

In the final months of the Obama White House, I was honored to be invited by the President's National Economic Council to discuss the recent report, Artificial Intelligence, Automation, and the Economy. I was joined by several other venture capitalists and entrepreneurs to comment on how the tech community sees AI -- its potential for positive impact as well as the implications for our workforce. As a VC at NextWorld Capital, a big area of my investment focus is on the companies that are digitizing and automating the physical world, including drones, the Internet of Things, and artificial intelligence. As an undergraduate and graduate student at MIT studying AI in the late 90's, I saw the commercial potential of technologies such as computer vision and robotics, but now I am convinced that AI is ready to drive systemic changes to businesses and services of all kinds. This new wave of technology will have an outsized impact on what I call the "field office," operated by deskless workers are building and servicing physical goods.

We consider the problem of learning a policy for a Markov decision process consistent with data captured on the state-actions pairs followed by the policy. We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs. The features are known a priori, however, only an unknown subset of them could be relevant. The policy parameters that correspond to an observed target policy are recovered using $\ell_1$-regularized logistic regression that best fits the observed state-action samples. We establish bounds on the difference between the average reward of the estimated and the original policy (regret) in terms of the generalization error and the ergodic coefficient of the underlying Markov chain. To that end, we combine sample complexity theory and sensitivity analysis of the stationary distribution of Markov chains. Our analysis suggests that to achieve regret within order $O(\sqrt{\epsilon})$, it suffices to use training sample size on the order of $\Omega(\log n \cdot poly(1/\epsilon))$, where $n$ is the number of the features. We demonstrate the effectiveness of our method on a synthetic robot navigation example.

Artificial intelligence can now outperform humans on a standard intelligence test. A new computational model scores within the 75th percentile, better than the average person, on a test known as Raven's Progressive Matrices. Researchers say this demonstrates that it can take on abstract visual reasoning tasks, and is a major step toward AI that can see and understand the world the way we do. Using Raven's Progressive Matrices, a nonverbal standardized test that measures abstract reasoning, the team found that their model is not only on par with humans, but performs better than many. In this example, participants choose which shape should come next in the sequence.