P-values for coefficients of cylinders, horsepower and acceleration are all greater than 0.05. This means that the relationship between the dependent and these independent variables is not significant at the 95% certainty level. I'll drop 2 of these variables and try again. High p-values for these independent variables do not mean that they definitely should not be used in the model. It could be that some other variables are correlated with these variables and making these variables less useful for prediction (check Multicollinearity).

One in three U.S. consumers are interested in buying Apple's new HomePod speaker, a new survey found. Morning Consult polled 2,200 adults in the U.S. between June 8-12, a few days after the HomePod was revealed at Apple's Worldwide Developers Conference. The iPhone company pitched the device as a hybrid high-end music speaker and home assistant, which can control smart home devices with Apple's HomeKit. Users will also be able to use the HomePod for basic reminders, ask questions about the news and will also control music and audio playback. The survey shows people are interested in buying the iPhone maker's high-end speaker, with 33 percent of respondents saying they were interested in purchasing the device.

Welcome to part four of Learning AI if You Suck at Math. (Be sure to check out the other articles in this series: 1, 2, 3, 4, 5, 6, and 7.) Maybe you've downloaded TensorFlow and you're ready to get started with some deep learning? But then you wonder: What the hell is a tensor? Perhaps you looked it up on Wikipedia and now you're more confused than ever. Maybe you found this NASA tutorial and still have no idea what it's talking about? The problem is most guides talk about tensors as if you already understand all the terms they're using to describe the math. I hated math as a kid, so if I can figure it out, you can too!

After my first post on Anomaly Detection for Time Series post, I would like to continue presenting what I did during the course at for the Data Science for IoT Course at Department of Continued Education of the University of Oxford with Ajit Jaokar. In line with what I wrote previously, this second post will be about predictive maintenance. The post will conclude the initial exploration of the topics I covered at Oxford. When researching materials to cover this course, I had a general idea of what to look for. Having worked already in industrial environments, I had a good idea of what predictive maintenance should be and how it could be used.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

Machine learning may be a buzzword now, but what could it actually mean for business and sales down the line? Will salespeople be replaced by Rosie from The Jetsons, or a more frightening HAL? Doubtful. But the ability to automate sales tasks could mean big changes on the horizon. It's all about how you could leverage it. With the right, high-quality data, machine learning could have an undeniable impact on how business is done.

When it launched, the Udacity Artificial Intelligence Nanodegree program became a kind of landmark in this history of AI. There was no precedent for the program's groundbreaking combination of content, platform, partners, and services. That simply did not exist before. The first term of students to enroll and start the program got to experience something genuinely new. They were risk-takers in every sense of the term.

Artificial intelligence can predict Supreme Court decisions better than some experts. Decision outcomes included whether the court reversed a lower court's decision and how each justice voted. The model then looked at the features of each case for that year and predicted decision outcomes. "Every time we've kept score, it hasn't been a terribly pretty picture for humans," says the study's lead author, Daniel Katz, a law professor at Illinois Institute of Technology in Chicago.

While it might sound like an elusive dream for most, the perfect score for arcade classic Ms. Pac-Man has been achieved – albeit by a computer. Researchers have created an artificial intelligence-based system that learned how to get the maximum score of 999,990 on the addictive 1980s video game. And the innovative method used could help to make advances in other areas of AI research, such as natural language processing. Researchers have created an artificial intelligence-based system that learned how to get the maximum score of 999,990 on the addictive 1980s video game, Ms. Pac-Man The technique, which the team has named'Hybrid Reward Architecture', used 150 agents, which worked in parallel with one another. For example, some agents were rewarded for successfully finding one specific pellet, while others were tasked with staying out of the way of ghosts.

Beginning in fall 2017, some students and educators at the University of Michigan may be getting help on writing assignments from computers. Campus Technology reports that a team of educators developed a writing-to-learn tool called M-Write, which uses automated text analysis (ATA) to identify the strengths of a writing submission. Developed by two professors, the tool was initially meant to help students grow their conceptual learning skills in large courses and to help streamline the grading process, reports a UMich article. ATA works by "using a variety of text analysis techniques, such as vocabulary matching or topic matching, which the algorithm detects." Using M-Write also lets educators identify the students who are going to need help.