The less constrained model indeed performs better (judging by AIC, which drops from to 3696 to 3278). Autocorrelation of errors also is reduced overall. Now, with the improved models, let's finally get forecasting!

Amazon's voice assistant Alexa has become a hugely popular and growing business. In fact, David Limp, an Amazon senior vice president who oversees Alexa and all of its Amazon devices, says that Alexa is rapidly adding "skills," with more than 1,000 people working on it. On Tuesday, at Fortune's Brainstorm Tech conference, Limp spoke to Fortune's Adam Lashinsky about the inspiration for Alexa (hint: Think Star Trek) and the origin of the name to where the business is heading. Here is the lightly-edited transcript. Dave Limp: The device business is less about building hardware for customers and more about building services behind that hardware. So the original vision of Kindle was to deliver any book ever written in less than 60 seconds, and that was all about creating a cloud-based service that had a great catalogue of books, great selection, and great prices. And as we've rolled out devices since then, everything from Fire TV to, as you mentioned, Echo and Alexa and everything in between, it's about creating that backend service that constantly improves and adds value for customers, and isn't just a gadget but instead a full end to end service that can benefit what customers want.

In this course, intended to expand upon your knowledge of neural networks and deep learning, you'll harness these concepts for computer vision using convolutional neural networks. Going in-depth on the concept of convolution, you'll discover its wide range of applications, from generating image effects to modeling artificial organs. Explore the StreetView House Number (SVHN) dataset using convolutional neural networks (CNNs) Build convolutional filters that can be applied to audio or imaging Extend deep neural networks w/ just a few functions Note: we strongly recommend taking The Deep Learning & Artificial Intelligence Introductory Bundle before this course. The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning.

A few weeks ago I was emailing Tom Blomfield, the CEO of startup bank Monzo, to arrange lunch. He passed me over to his assistant, Amy Ingrams, by CCing her into an email. Amy and I exchanged eight emails fixing up a date and then another five when Blomfield had to rearrange. Only then did I spot something odd in Amy's email signature: "Artificial intelligence for scheduling meetings." It turns out that I had been talking to an algorithm the whole time.

Vertical markets such as the automotive, agriculture and healthcare have long been impervious, if not indifferent to many of the new technology trends sweeping the corporate business world. The absence of early adopters, the dependence on proprietary technology systems, and uncomfortably low product margins made these markets unappealing to cutting edge technology providers that could have transformed them. While each of these vertical markets is distinct, the rise of autonomous machines and services, which perform activities much more efficiently than humans ever could, will disrupt the current way of business in all markets whether on the road, in the field or in the hospital. Autonomous software based on advanced neural networks and deep learning will rapidly find its way into existing business and consumer products, but its potential to catapult traditional vertical industries into the 21 century is what has us most excited. Rather than resist the inevitable, previously conservative market players in non-tech verticals are beginning to engage with startups to be among the first to leverage the promise of autonomous technology.

Japan's major cancer research institution has announced plans to use artificial intelligence to diagnose cancer and decide the best courses of treatment for patients. The National Cancer Center said Tuesday that it will work with other organizations, including the National Institute of Advanced Industrial Science and Technology, to make a system for cancer diagnosis and treatment for trial use within five years. The system will use a database created from massive amounts of data accumulated by the center that includes genetic information and results of blood tests and diagnostic imaging, and also check relevant medical research papers, it said. The center plans to use the system for finding new markers in detecting cancer at an early stage and developing drugs to fight the disease. Similar moves to utilize AI in the medical field are already underway at various institutions nationwide.

Behind the scenes, AI engines in the form of smart algorithms "work" on stock exchanges, offer up suggestions for books and films on Amazon and Netflix and even write the odd article. But AI does not have the greatest public image - often due to sci-fi films that display dystopian visions of robots taking over the world. Hal is perhaps the most famous AI turned bad. Created by Arthur C Clarke for the book and film 2001: A Space Odyssey, Hal stands for Heuristically Programmed Algorithmic Computer. A recent survey of business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems.

Since 2014, $6B USD has been raised by 900 startups globally in AI, making it one of the hottest technology themes. From entrepreneurs like Elon Musk to tech giants like Facebook and Google, enormous resources and focus is being directed towards pushing the boundaries of AI capabilities and applications. Much like the Internet re-defined technology industry in the last century, will AI revolutionize the technology landscape and Man-Machine equation forever? What role can AI play in shaping India's economy and innovation culture? With over 200 startups leading the way, there is a real opportunity for every entrepreneur to make a mark in this sunrise sector.

For years, those of us in the technology sectors have been building integrations between disparate systems. In fact, enterprise organizations often have specific resources dedicated toward building and maintaining integrations between mission critical systems. According to dictionary.com, the word integrate is defined as a verb meaning "to bring together or incorporate (parts) into a whole." For decades, technologists have been manually creating integrations between systems whose interfaces have been continuously changing. Those of us who were lucky got the opportunity to create an integration between a mainframe and a front-line database, thus ensuring that fewer updates to the integration were necessary.

A few weeks ago I was emailing Tom Blomfield, the CEO of startup bank Monzo, to arrange lunch. He passed me over to his assistant, Amy Ingrams, by CCing her into an email. Amy and I exchanged eight emails fixing up a date and then another five when Blomfield had to rearrange. Only then did I spot something odd in Amy's email signature: "Artificial intelligence for scheduling meetings." It turns out that I had been talking to an algorithm the whole time.