About this course: By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area The Internet of Things (IoT) is an area of rapid growth and opportunity. Technical innovations in networks, sensors and applications, coupled with the advent of'smart machines' have resulted in a huge diversity of devices generating all kinds of structured and unstructured data that needs to be processed somewhere. Collecting and understanding that data, combining it with other sources of information and putting it to good use can be achieved by using connectivity, analytical and cognitive services now available on the cloud, allowing development and deployment of solutions to be achieved faster and more efficiently than ever before. This course is an entry level introduction to developing and deploying solutions for the Internet of Things.

In the 1950s, scientists began working to build machines capable of imitating intelligent human behaviour. Progress accelerated recently when we entered a new phase of machine learning--one that has led to a dramatic decrease in the cost of prediction. Moore's Law (the 18-month doubling of transistor intensity on microprocessors) was the driver of the previous phase--a revolution in technology hardware that advanced mobile innovation and smartphone adoption. Increased connectivity and scalable cloud-based storage catalysed a step change in the amount of data we collected and consumed. Information taken from sensors, images, videos, and other digital sources is being used to generate a more accurate view of real-time context.

We've all heard the warning cries: automation will disrupt entire industries and put millions of people out of jobs. In fact, up to 45 percent of existing jobs can be automated using current technology. However, this may not necessarily apply to the education sector. After a detailed analysis of more than 2,000-plus work activities for more than 800 occupations, a report by McKinsey & Co states that of all the sectors examined, "…the technical feasibility of automation is lowest in education." There is no doubt that technological trends will have a powerful impact on global education, both by improving the overall learning experience and by increasing global access to education.

By Cecile Alper-Leroux Smart technologies powered by machine learning, natural language processing, augmented intelligence and distributed data collection interfaces are poised to transform the workplace and the world of HR leaders. In this blog post, you will learn about the potential virtues of ambient HR and virtual reality experiences for the future of work. Read on to decide if HR is ready. Note that, regardless of the technology, putting people first is a must. Employees today want it all… and so do their employers!

Lecture 1 of Tim Roughgarden's Algorithmic Game Theory class at Stanford (Autumn 2013) Class description: Topics at the interface of computer science and game theory such as: algorithmic mechanism design; combinatorial auctions; computation of Nash equilibria and relevant complexity theory; congestion and potential games; cost sharing; game theory and the Internet; matching markets; network formation; online learning algorithms; price of anarchy; prior-free auctions; selfish routing; sponsored search.

Last Friday, we mentioned how Google's artificial intelligence software DeepMind has the ability to teach itself many things. It can teach itself how to walk, jump and run. Or defeat the world's best player of the Chinese strategy game, Go. The science of teaching computers how to do things is called Deep Learning. Offered through Udacity, the course is taught by Vincent Vanhoucke, the technical lead in Google's Brain team.

This spring, as part of their coursework, four Stanford University students found themselves in Coronado, California, doing pushups on the beach and charging into a 61-degree surf while overseen by Navy SEAL trainers. They performed this extraordinary homework to better understand the process of inculcating recruits into the elite corps of military frogmen and women. The end result of their (literal) immersion was a solution to an inefficiency in evaluating prospective SEALS: the time-consuming process of analyzing the mountains of comments made about each candidate. Tackling the problem like the internet entrepreneurs they hoped to become, the students created a mobile app to streamline the process. Their reward was thanks from a grateful military establishment--and college credit. Dan Raile is a freelance journalist based in San Francisco.

The plug-in – loved and hated across the world – won't actually be put out of its misery until 2020. But the company that makes it has signalled it will come to an end. Flash was once the technology powering the many games and videos of the early internet. As an animation platform it allowed for the creation of clickable games and videos on places like YouTube, and in so doing helped create the web as we know it today. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.

Digital technologies like the internet and smartphones are transforming our lives and society. They are proving to be powerful tools for liberating individuals' creative and entrepreneurial potential, as well as providing new educational opportunities and higher wages for marginalized people, both in the U.S. and around the globe. Unfortunately, in the U.S., outdated government regulations and weak consumer protections are undermining these opportunities. What's more, the Trump administration has not yet made significant moves to address this growing crisis: As of this writing, five key White House positions are vacant, without even acting directors or interim leaders to help the executive branch formulate U.S. science and technology policy. As the founder of both the Open Technology Institute and the X-Lab policy and innovation organization, I have spent years at the heart of many Washington, D.C. battles over technology policy, fighting for ideas that would best serve American workers and the general public.