Top 3 free online courses for Artificial Intelligence and Machine Learning

By Shaheryar Ehsan  There is no doubt that Artificial Intelligence is one of the most sophisticated of all the emerging technologies. Artificial Intelligence deals with the understanding of machines and programming them to do tasks autonomously as well as helping themContinue reading… Top 3 free online courses for Artificial Intelligence and Machine Learning

Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models and Adaptive Boosting

by Jake Drew, PhD Introduction This article describes methods for machine learning using bootstrap samples and parallel processing to model very large volumes of data in short periods of time. The R programming language includes many packages for machine learningContinue reading… Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models and Adaptive Boosting

Microsoft presents The Explanimators – Episode 1: An Animated Guide to Artificial Intelligence

An Introduction to Machine Learning with AI Artificial intelligence is helping make the world a more sustainable environment. Microsoft recently released a video covering the topic of AI and machine learning. The video points out that AI has helped increasedContinue reading… Microsoft presents The Explanimators – Episode 1: An Animated Guide to Artificial Intelligence

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Abstract Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervisedContinue reading… Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Building meaningful machine learning models for disease prediction

Dr Shirin Glander Setup All analyses are done in R using RStudio. For detailed session information including R version, operating system and package versions, see the sessionInfo() output at the end of this document. All figures are produced with ggplot2.Continue reading… Building meaningful machine learning models for disease prediction

Leveraging Data Analytics and Internet of Things in Your Digital Transformation

Increasingly, connected systems and devices are generating data that produce insights for improving business processes and consumer experiences. IDC predicts that by the year 2020 there will be 44 zettabytes (that’s 44 x 10) of information, spawned partly by consumerContinue reading… Leveraging Data Analytics and Internet of Things in Your Digital Transformation

How a Japanese cucumber farmer is using deep learning and TensorFlow

It’s not hyperbole to say that use cases for machine learning and deep learning are only limited by our imaginations. About one year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike started helping outContinue reading… How a Japanese cucumber farmer is using deep learning and TensorFlow

Facebook’s advice to students interested in artificial intelligence

Math. Math. Oh and perhaps some more math.

That’s the gist of the advice to students interested in AI from Facebook’s Yann LeCun and Joaquin Quiñonero Candela

 who run the company’s Artificial Intelligence Lab and Applied Machine Learning group respectively.

Tech companies often advocate STEM (science, technology, engineering and math), but today’s tips are particularly pointed. The pair specifically note that students should eat their vegetables take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics as early as possible.

From this list, probability and statistics are perhaps the most interesting. From what I remember about high-school, those two subjects are regularly dismissed as too-obvious strategies for skirting the informal AP Calculus preference of top colleges and universities (AP Statistics is often thought of as a cop-out by students).

If differential equations represents the electricity that powers machine learning, statistics represents the gears of the machine itself — as the company touches on in a series of AI explainer videos we linked to at the bottom of this post.

To be fair, LeCun and Candela are most likely addressing the college crowd, though its important to consider incentives across all levels of education. Simply, we all could probably use some more statistics in our lives. Beyond math, the two say more math engineering, computer science, economics and neuroscience are also important subjects in today’s economy. How else would a fledgling machine learning student learn to leverage neuroeconomics and cognitive bias to target ads?

The pair also point to philosophy as a necessary prerequisite to understanding knowledge and learning. Amidst all the talk of News Feed bias, it’s important to remember that there is a human behind every application of machine learning. We don’t yet know how to escape the black box problem, but we do know that it will be humans working to figure it out and it would sure help if those humans understood how learning works before they start manipulating data.

Lastly, Facebook turns its attention to the actual mechanics of getting a job in the field of machine learning. Most of these tips are self-explanatory: find a professor to work with, consider working with PhD students who have more time on their hands and try to secure an industry-focused internship regardless of your future aspirations to understand how AI works in the real world.

When applying to PhD programs the two note that being able to identify a professor you want to work with is far more important than program ranking. Once there, students should work to address a specific problem and try to release a piece of open source code before all is said and done.

For more, click here.

How to become an Alexa developer: The smart person’s guide

Alexa has evolved beyond the Amazon Echo into one of the hottest new platforms in tech. Learn how developers and businesses can leverage the technology.

The launch of Amazon Echo and its voice service, Alexa, brought virtual assistants out of our smartphones and into our homes and offices. While the Echo is a solid product, Alexa as a voice platform is where the real value is.

After starting off with 100 things the Echo could do, the number of available Alexa Skills now tops 7,000. CES 2017 showed how eager tech companies are to integrate Alexa, as the Amazon virtual assistant was everywhere at CES, despite the fact that neither the Echo or Alexa had booth space on the show floor.

As such, the interest in developing tools for the platform has skyrocketed, with many developers eager to jump into the ecosystem. To help developers and companies better understand how to get started working with Alexa and its related services, we’ve pulled together the most important details and resource

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