What do Halloween and Big Data have in common? Both can be scary, as the following KDnuggets cartoon shows. Robot: "My Costume is Scary Big Data" ... we recommend these socks because you bought Ace shoes and based on your health record ... Here is KDnuggets Big Data, Data Mining, and Data Science Cartoon page Recent KDnuggets Cartoons: Cartoon: Labor Day in the era of Robotics Cartoon: Data Scientist - the sexiest job of the 21st century until ... Cartoon: Make Data Great Again Cartoon: Facebook data science experiments and Cats Cartoon: When Automation Goes Too Far The Secret to a Perfect Data Science Interview Cartoon: Citizen Data Scientist At Work Data Scientist Valentine's Day Collection Cartoon: Deeper Deep Learning More Data Science Humor and Cartoons Cartoon: Surprise Data Science Recommendations Cartoon: 2nd place in a Data Science contest Cartoon: It all started with the iPhone answering my email Cartoon: KDnuggets Addiction Cartoon: Big Data in Retirement Cartoon: Big Data and the dog question Cartoon: Where humans are still ahead of Deep Learning Cartoon: Data Scientist Mother Cartoon: A solution for Data Scientists allergies caused by Big Data Cartoon: Labor Day in the era of Robotics Cartoon: Data Scientist - the sexiest job of the 21st century until ... Cartoon: Make Data Great Again Cartoon: Facebook data science experiments and Cats Cartoon: When Automation Goes Too Far The Secret to a Perfect Data Science Interview Cartoon: Citizen Data Scientist At Work Data Scientist Valentine's Day Collection Cartoon: Deeper Deep Learning More Data Science Humor and Cartoons Cartoon: Surprise Data Science Recommendations Cartoon: 2nd place in a Data Science contest Cartoon: It all started with the iPhone answering my email Cartoon: KDnuggets Addiction Cartoon: Big Data in Retirement Cartoon: Big Data and the dog question Cartoon: Where humans are still ahead of Deep Learning Cartoon: Data Scientist Mother Cartoon: A solution for Data Scientists allergies caused by Big Data

Artificial intelligence is rapidly making its way into industries from cybersecurity to manufacturing, bringing with it a growing need for data scientists and developers with a proficiency in deep learning. California-based AI chipmaker Nvidia, one of our 50 Smartest Companies of 2017, today announced an expansion of its Deep Learning Institute (DLI) aimed at curbing this issue. Founded last year, the DLI aims to address the AI skills gap internationally by training up students and today's workforce in the ways of AI--and specifically deep learning, the technique that powers today's powerful speech and image recognition algorithms, among others. Deep learning is complex, and working in the field has traditionally required great technical knowledge and expertise (see "10 Breakthrough Technologies 2013: Deep Learning"). By involving numerous AI experts, Nvidia is attempting to make the technology more accessible.

Machine learning and its younger sibling deep learning are continuing their acceleration in terms of increasing the value of enterprise data assets across a variety of problem domains. A recent talk by Dr. Amitai Armon, Chief Data-Scientist of Intel's Advanced Analytics department, at the O'reilly Artificial Intelligence conference, New-York, September 27 2016, focused on the usage of Intel's new server processors for various machine learning tasks as well as considerations in choosing and matching processors for specific machine learning tasks. Intel formed a machine learning task force with a mission to determine how the company can advance the machine learning domain. The vast majority of machine learning code today runs on Intel servers but the company wanted to do even better for the present and the future use cases. We need to understand the needs for these domains and prepare processors for those needs," said Dr. Amitai Armon. "This is not a simple challenge because in machine learning you have many algorithms, many data types and the field is constantly evolving.

Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now. The term "AI" is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don't understand what AI is.

Artificial intelligence (AI) gives machines the ability to "think" and accomplish tasks. AI already is a big part of our lives in areas such as banking, shopping, security and healthcare. Soon it will help us get around in automated vehicles. By 2025, the global enterprise AI market is predicted to be worth more than $30 billion. Israeli industry can expect a nice piece of that pie due to its world-class capabilities in AI and its subsets: big-data analysis, natural-language processing, computer vision, machine learning and deep learning.

In this special guest feature, Arvin Hsu, Senior Director of Data Science and Machine Learning for GoodData, discusses that despite the two terms being used interchangeably, deep learning and machine learning are very different in terms of the business problems they solve and the outcomes they enable. Arvin has over 15 years of experience in the field of Data Science and Data Modeling, including 6 years building Machine Learning based data products with both enterprise companies like Disney and startups. He's passionate about the innovations being created at the intersection of Big Data, Machine Learning, and Enterprise Data. He's also fascinated by how new technology will merge with ancient wisdoms to shift the way the world works. As artificial intelligence (AI) works its way into mainstream business practices, various different applications are coming up in conversations about how to best leverage the technology.

As the computers that operate driverless cars digest the rules of the road, some engineers think it might be nice if they can learn from mistakes made in virtual reality rather than on real streets. SAN FRANCISCO -- As the computers that operate driverless cars digest the rules of the road, some engineers think it might be nice if they can learn from mistakes made in virtual reality rather than on real streets. Companies like Toyota, Uber and Waymo have discussed at length how they are testing autonomous vehicles on the streets of Mountain View, California, Phoenix and other cities. What is not as well known is that they are also testing vehicles inside computer simulations of these same cities. Virtual cars, equipped with the same software as the real thing, spend thousands of hours driving their digital worlds.

As you plan your agenda, machine learning is undoubtedly a hot topic on your list. This year we have a lot of great technical content in the Machine Learning track, with over 50 breakout sessions, hands-on workshops, labs, and deep-dive chalk talks. You'll hear first-hand from customers and partners about their success with machine learning including Facebook, NVIDIA, TuSimple, Visteon, Matroid, Butterfleye, Open Infuence, Whooshkaa, Infor and Toyota Racing Development. This year we're hosting our inaugural Deep Learning Summit where thought leaders, researchers, and venture capitalists share their perspectives on the direction in which deep learning is headed. In addition you can take part in our deep-learning-powered Robocar Rally. Join the rally to get first-hand experience building your own autonomous vehicle and competing in an AI-powered race.

Recently Google DeepMind announced AlphaGo Zero -- an extraordinary achievement that has shown how it is possible to train an agent to a superhuman level in the highly complex and challenging domain of Go, 'tabula rasa' -- that is, from a blank slate, with no human expert play used as training data. It thrashed the previous reincarnation 100–0, using only 4TPUs instead of 48TPUs and a single neural network instead of two. Want to quickly learn how it works?