I did a webcast earlier today about Bayesian statistics. Some time in the next week, the video should be available from O'Reilly. In the meantime, you can see my slides here: And here's a transcript of what I said: Thanks everyone for joining me for this webcast. At the bottom of this slide you can see the URL for my slides, so you can follow along at home. I'm Allen Downey and I'm a professor at Olin College, which is a new engineering college right outside Boston. Our mission is to fix engineering education, and one of the ways I'm working on that is by teaching Bayesian statistics. Bayesian methods have been the victim of a 200 year smear campaign. If you are interested in the history and the people involved, I recommend this book, The Theory That Would Not Die.

The Home Depot Product Search Relevance competition challenged Kagglers to predict the relevance of product search results. Over 2000 teams with 2553 players flexed their natural language processing skills in attempts to feature engineer a path to the top of the leaderboard. In this interview, the second place winners, Thomas (Justfor), Sean (sjv), Qingchen, and Nima, describe their approach and how diversity in features brought incremental improvements to their solution. Thomas is a pharmacist, with his PhD in Informatics and Pharmaceutical Analytics and works in Quality in the pharmaceutical industry. At Kaggle he joined earlier competitions and got the Script of the Week award.

The idea of having sex with a robot might seem more like something out of a science fiction film, but one in five of us are now open to the idea, according to new research. A recent survey found 21 per cent of British people would have sex with a droid, and one in three would go on a date. It comes as a leading expert on future technology claims human-on-robot sex will be more common than human-on-human sex by 2050. The survey was done VoucherCodesPro who asked 2,816 sexually active Brits aged 18 to describe which activities they would then carry out with a cyborg. Researchers asked those participants who said they would have sex with a robot why they would do it.

The Sunway TiahuLight machine is the fastest supercomputer in the world running on the 10 million-core with a peak of 125 petaflops. The TaihuLight supercomputer is being harnessed for some interesting work on deep neural networks. What is fascinating here is that currently, the inference side of such workloads can scale to many processors, but the training side is often scale-limited hardware and software-wise. Fu described an ongoing project on the Sunway TaihuLight machine to develop an open source deep neural network library and make the appropriate architectural optimization for both high performance and efficiency on both the training and inference parts of deep learning workloads. "Based on this architecture, we can provide support for both single and double precision as well as fixed point," he explains.

I trained an LSTM Recurrent Neural Network (a deep learning algorithm) on the first four Harry Potter books. I then asked it to produce a chapter based on what it learned. He looked like Madame Maxime. When she strode up the wrong staircase to visit himself. "I'm afraid I've definitely been suspended from power, no chance -- indeed?" said Snape.

Drone technology is developing so quickly--and morphing into commercial uses never before contemplated--that aviation regulators are having trouble keeping pace. Air-safety authorities on both sides of the Atlantic have acknowledged that traditional rule making is too slow and rigid to cope with the rapidly expanding applications of the flying machines, from bridge inspections to land surveys to news photography. And the pressure to spell out exactly what's allowed and what isn't is growing as the industry booms. Millions of hobbyists already operate drones, and over the next few years businesses are projected to begin flying millions more in the U.S. alone. Now regulators are scrambling to draft new, more-nimble rules and procedures.

During his company's first-quarter earnings call, Google CEO Sundar Pichai said delivering artificial intelligence and machine learning as cloud services to enterprise customers "is going be a huge source of differentiation for us." "We are at an exceptionally interesting tipping point where these technologies are really taking off," Pichai told financially grounded Wall Street investors. Google, Mountain View, Calif., is engineering solutions that can enable services partners to help customers use machine learning to understand their data, he said. A couple of weeks later, at Google's I/O developers' conference, Pichai revealed a new custom chip, the Tensor Processing Unit, or TPU, that will power machine-learning workloads. One notable customer, Snapchat, already is using Google's platform to learn more about its users by studying the content of the photos they post on the social media site. And in March, Google enjoyed its own board game coup.

Artificial intelligence, which has been around since the 1950s, has seen ebbs and flows in popularity over the last 60 years. But today, with the recent explosion of big data, high-powered parallel processing, and advanced neural algorithms, we are seeing a renaissance in AI--and companies from Amazon to Facebook to Google are scrambling to take the lead. According to AI expert Roman Yampolskiy, 2016 is the year of "AI on steroids." While there are different forms of AI, machine learning represents today's most widely valued mechanism for reaching intelligence. Machine learning is a branch of AI.

DESCRIPTION Amazon.com is ranked as one of the most admired companies in the US, #1 most innovative, and # 1 in Customer Service. Amazons technology business has a history and tradition of leading the world in Web-related technologies and services. Now, with Amazon Web Services (AWS) you have the chance to help individuals and businesses take their computing infrastructures and applications into the Cloud. Amazon leaders have said publicly that AWS can become as big as our retail business and includes a fast paced release cycle that saw 400 features launched in 2014. The AWS Support team is both a self-standing P&L and a critical operational function with the ability to drive Free Cash Flow and a world-class customer experience.

Data is rarely clean and often you can have corrupt or missing values. It is important to identify, mark and handle missing data when developing machine learning models in order to get the very best performance. In this post you will discover how to handle missing values in your machine learning data using Weka. How To Handle Missing Data For Machine Learning in Weka Photo by Peter Sitte, some rights reserved. The problem used for this example is the Pima Indians onset of diabetes dataset.