How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound. The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, technical fellow and a managing director at Microsoft Research. Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city.

Starred articles are candidates for the picture of the week. A comprehensive list of all past resources is found here. We are in the process of automatically categorizing them using indexation and automated tagging algorithms. Combining the Strengths of MLlib, scikit-learn, and R What is Machine Learning and Predictive Analytics? A Real World Exa... How William Cleveland Turned Data Visualization Into a Science * OpenText Data Visualization – Red Carpet Edition ** Sharing & Preserving Beautiful Graphs With Your Data A Complete Tutorial to learn Data Science in R from Scratch How to create confounders with regression: a lesson from causal inf... What is Machine Learning and Predictive Analytics?

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree.

From MOOCs (Massive open online courses) to the use of iPads in schools, there's no doubt that technology is changing the way that we learn. Chatbots could have a similar effect, too -- by offering a means by which children can better interact with the subjects they're studying. That was the conclusion reached by UK-based tech company rehabstudio. During a recent hackathon event, they came up with the idea of creating an "edubot" that would enable kids to ask questions to a tyrannosaurus rex (called Tina) using Facebook Messenger. The finished product was a collaboration between rehabstudio (providing the tech) and National Geographic Kids (providing the data.)

Developing machine learning solutions that give a lift from your existing prediction algorithms is not an easy task. They require a multitude of activities to get it right including cleaning up the data, setting up the infrastructure, testing & re-testing the model & finally deploying the algorithm. Here are five machine learning services that can help reduce the pain of deploying your machine learning solution. Based on Microsoft's Azure Cloud Platform, Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments. Machine Learning Studio features a library of time-saving sample experiments, R and Python packages and best-in-class algorithms from Microsoft businesses like Xbox and Bing. Azure ML also supports R and Python custom code, which can be dropped directly into your workspace.

When Amazon first coined the strapline "Ask Alexa" for its virtual assistant, it couldn't have predicted the X-rated nature of some of the requests. "She" may boast an encyclopaedic knowledge, but research by consumer behaviour analysts Canvas8 reveals that some users are more interested in a virtual hook-up than fact finding. And she's not the only target: the equally smooth voice of Microsoft's Cortana is getting customers just as hot under the collar apparently. From perma-smiling avatars in traditionally female support roles, to hyper-sexualised "fembots" pandering to male fantasies, the female form is everywhere in techno-world - attractive, servile and at your command. A little more conservative, but just as eager to please, is virtual personal assistant Amy Ingram, the brainchild of New York start-up X.ai.

Hey, you know that I'm big supporter of women in tech. I'm actually partnering with Women Who Tech for the 4th Women Startup Challenge at Google and focused on women-led ventures in artificial intelligence (AI), augmented reality (AR), and virtual reality (VR). Note: if you haven't applied you have until December 12th to get your applications in. The winner will receive $50,000 as a cash grant, and $15,000 in probono legal services by global law firm Paul Hastings, LLP. All finalists will join join Samsung for a special startup showcase and reception featuring the 10 finalists.

This course is designed to make you an expert in AWS Machine Learning and it teaches you how to convert your cool ideas into highly scalable products in a matter of days. Biggest challenge for a Data Science professional is how to convert the proof-of-concept models into actual products that your customers can use. Traditional approaches take too much effort and time to convert the model into a scalable product. With AWS Machine Learning service, you can very easily conduct experiments and test your concepts. Once you are happy, you can instantly scale to support millions of requests.

Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation. No overlap with other fields such as statistics or machine learning. Other words for DDS (deep data science) could be pure data science or core data science.

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