Sure, the autonomous era will wipe out a lot of jobs. Automakers, tech titans, and startups are racing to essentially put four million truckers, cabbies and other drivers out of work. But like all radical technological shifts, self-driving cars will provide opportunities, too--for those with the right skills. Working in the most compelling part of this field requires an understanding of deep learning, the branch of artificial intelligence that trains computers to do things like discern pedestrians from lamp posts. Universities can't crank out graduates fast enough.

We have all seen the marketing around Microsoft's Azure ML offering. For those of us that have actually tried it we know it's a train wreck: On the blog post there is a quote from a supposed data scientist: "Look at the amazing choice of datasets from many sources and so many data processing/feature engineering options. Oh, oh… and don't forget the vast array of powerful ML algorithms at my fingertips, and then web service modules so that I can work in harmony with the app developers" I'm sorry, but reading this my first reaction was: "Said no data scientist ever....". If you were what I would consider a real data scientist you would be able to pump out kickass solutions using sklearn/pandas/numpy or R faster than anything Microsoft has to offer on this WYSIWYG platform. The idea sounds cool: a Labview/Simulink type environment for building models (see below).

SAN FRANCISCO – Over the last 30 years, consumers have reaped the benefits of dramatic technological advances. In many countries, most people now have in their pockets a personal computer more powerful than the mainframes of the 1980s. The Atari 800XL computer that I developed games on when I was in high school was powered by a microprocessor with 3,500 transistors; the computer running on my iPhone today has two billion transistors. Back then, a gigabyte of storage cost 100,000 and was the size of a refrigerator; today it's basically free and is measured in millimeters. Even with these massive gains, we can expect still faster progress as the entire planet – people and things – becomes connected.

After learning so much from Kaggle's collaborative community over the past eight months since I first joined, I wanted to share some of my favorite data science resources including suggestions from my fellow Kagglers. Like many others who have a seemingly endless queue of languages and techniques we hope to learn, I had tried MOOCs like Udacity and coding platforms like HackerRank. Right before joining Kaggle earlier this year, I was working through Andrew Ng's famed machine learning Coursera. Following the blogs, newsletters, and podcasts I'm sharing here is another way I try to stay (or become) savvy about topics in machine learning, data visualization, and industry trends. This list is far from exhaustive, so if you have any favs that are tragically missing, please add them to the comments!

Want to understand how Amazon recommends items we might be interested to buy? Want to predict the selling price of your house? It is all possible because of Machine Learning technology – it is about the science of getting computers to act without being explicitly programmed. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level artificial intelligence. Come join us for a session on'Introduction to Machine Learning,' complete with snacks, drinks, and networking.

Blurring or pixelating information to obscure it may not work anymore thanks to machine learning researchers from the University of Texas at Austin and Cornell University. The researchers developed an algorithm that could identify faces and numbers even after they were blurred out. The researchers developed the algorithm using open-source machine-learning software. Just take a bunch of training data, throw some neural networks on it, throw standard image recognition algorithms on it, and even with this approach…we can obtain pretty good results." The algorithm is built using a very simple process.

In one example, Classic Car Liquidators used Ascend to find the best design for its affiliate revenue program, testing copy, layout, format and image changes of multiple items. The total number of potential designs was 28,800. Ascend determined the best option in only three weeks, and 40,000 visitors, lifting performance by 557%. "Sentient is bringing to market a disruptive product that can easily be described as a modern marketing director's dream," said Courtney Connell, marketing director for Cosabella, another Ascend customer. "Their CRO platform is allowing us to test all variables at once without human intervention, fear of contamination or traditional timelines. I see this type of solution sending tremors throughout the entire industry and ultimately shifting all conversion related benchmarks into hyper-drive."

Data center workloads are changing. Not long ago these systems were primarily used to handle storage and serve up web pages, but now they're increasingly tasked with AI workloads like understanding speech, text, images and video or analyzing big data for insights. Billions of consumers want instant answers to a multitude of questions, while enterprise companies want to analyze mountains of data to better serve their customers' needs. Where do those answers come from? As a leader in server systems, IBM saw this trend coming several years ago, and partnered with us to accelerate new data center workloads.

A recent post highlighted how artificial intelligence (AI) is already playing important roles in health care, and concluded that expanded use of AI may be ready for us before we are ready for it. One example of the kind of problem we'll face is: who would we sue if care that an AI recommended or performed went wrong? Last week Stanford's One Hundred Year Study On Artificial Intelligence released its 2016 report, looking at the progress and potential of AI, as well as some recommendations for public policy. The report urged that we be cautious about both too little regulation and too much, as the former could lead to undesirable consequences and the latter could stifle innovation. One of the key points is that there is no clear definition of AI, because "it isn't any one thing."

Autonomous cars learn to drive with driving data using machine learning. The machine learning component of autonomous vehicles require millions of miles of actual driving data. A robust and enabling EV supply chain that makes diverse components and manufacturing supply chains that serve the mobile and consumer business are still developing in the EV sector. Apple doesn't have the manufacturing expertise to build an EV without a mature supply chain like the mobile supply chain behind it.