If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
For example, for personalized recommendations, we have been working with learning to rank methods that learn individual rankings over item sets. Figure 1: Typical data science workflow, starting with raw data that is turned into features and fed into learning algorithms, resulting in a model that is applied on future data. This means that this pipeline is iterated and improved many times, trying out different features, different forms of preprocessing, different learning methods, or maybe even going back to the source and trying to add more data sources. Probably the main difference between production systems and data science systems is that production systems are real-time systems that are continuously running.
Machine Learning is most often considered a branch of the broad pursuit of Artificial Intelligence in which it is used to process unstructured data, such as text. But there is an even greater potential for its application in enhancing analytics of structured numerical data. In this domain, we predict Machine Learning capabilities will continue to offer further insights by discovering patterns in our extensive data set of more than 4.2 billion observations of software development revisions. Machine Learning offers an extension of the sophistication of data analytics, from automating analyses that our statisticians carry out, to discovering patterns that humans cannot. For example, our data scientists recognise that a software application that is no longer being worked on is likely to be no longer in use and can be retired.
The promise of artificial intelligence has captured our cultural imagination since at least the 1950s--inspiring computer scientists to create new and increasingly complex technologies, while also building excitement about the future among regular everyday consumers. What if we could explore the bottom of the ocean without taking any physical risks? While our understanding of AI--and what's possible--has changed over the the past few decades, we have reason to believe that the age of artificial intelligence may finally be here. So, as a developer, what can you do to get started? While there are a lot of different ways to think about AI and a lot of different techniques to approach it, the key to machine intelligence is that it must be able to sense, reason, and act, then adapt based on experience.
Job Title: Senior Software Engineer - NLP & Machine Learning Artigen Corporation is seeking a Senior Software Engineer/Team Lead NOTE: This is a Hands on position, Software Development, Design, Framework, Programming etc. NO OPT, NO Sponsorship, No relocation assistance, Local applicants ONLY, Face-to-face interview required. Artigen is a software development company that intends to specialize in enabling Artificial Intelligence software integration with e-commerce site and back end network operations. We are looking for Senior Team Lead/Software Engineer specifically in the development of software encompassing Artificial Intelligence, Machine Learning, Natural Language Processing to develop a virtual agent for Artigen's platform of software and services for Global B2B/B2C clients/customers. The role will develop and mentor a software team dedicated to Ai and cognitive technologies, utilizing existing technologies (such as IBM Watson API, Google's Nuance, Apple's Siri, other open source upcoming platforms such as Viv) and platforms to develop and customize a platform for Artigen's own Ai platform. This role will also require hands-on complex programming in various languages, platforms and must understand and develop machine learning algorithms, data integration and manipulation.
Vincent Granville *** (DSC) - Dr. Vincent Granville is a visiory data scientist with 15 years of big data, predictive modeling, digital and business alytics experience. Vincent is widely recognized as the leading expert in scoring technology, fraud detection and web traffic optimization and growth. Over the last ten years, he has worked in real-time credit card fraud detection with Visa, advertising mix optimization with CNET, change point detection with Microsoft, online user experience with Wells Fargo, search intelligence with InfoSpace, automated bidding with eBay, click fraud detection with major search engines, ad networks and large advertising clients. Most recently, Vincent launched Data Science Central, the leading social network for big data, business alytics and data science practitioners. Vincent is a former post-doctorate of Cambridge University and the tiol Institute of Statistical Sciences.
I am really excited to announce that the general availability of the Azure N-Series will be December 1st, 2016. Azure N-Series virtual machines are powered by NVIDIA GPUs and provide customers and developers access to industry-leading accelerated computing and visualization experiences. I am also excited to announce global access to the sizes, with N-series available in South Central US, East US, West Europe and South East Asia, all available on December 1st. We've had thousands of customers participate in the N-Series preview since we launched it back in August. We've heard positive feedback on the enhanced performance and the work we have down with NVIDIA to make this a completely turnkey experience for you.
In this post, I'll introduce you to the Julia programming language and a couple long-term projects of mine: Plots for easily building complex data visualizations, and JuliaML for machine learning and AI. Easily create strongly-typed custom data manipulators. "User recipes" and "type recipes" can be defined on custom types to enable them to be "plotted" just like anything else. We believe that Julia has the potential to change the way researchers approach science, enabling algorithm designers to truly "think outside the box" (because of the difficulty of implementing non-conventional approaches in other languages).
Take a sneak peek at the awesome innovative technologies built on Intel architecture featured at this year's Intel Developer Forum. Build Caffe* optimized for Intel architecture, train deep network models using one or more compute nodes, and deploy networks. Find out how the Intel Xeon processor E5 v4 family helped improve the performance of the Chinese search engine Baidu's* deep neural networks Read about Bob Duffy's experiences getting his Microsoft* Surfacebook* set up to best maximize virtual reality (VR) applications. Intel Developer Zone experts, Intel Software Innovators, and Intel Black Belt Software Developers contribute hundreds of helpful articles and blog posts every month.
The convergence of location awareness and data analysis has led to the creation of applications that are contextually aware. An example of that is the navigation application Waze. Drivers using Waze can share information with other "Wazers" such as a crash ahead, or heavy traffic, or a police car on the scene. Now, add to that the ability for the application to learn and make recommendations, and to push that information to the user instead of sitting idly until the user requests, and you've got the latest class of application: the smart app. The problem with smart apps, according to Derek Roos, CEO and cofounder of low-code solution provider Mendix, is that it makes sense from the user experience perspective, but it is very hard to do.
You could think of a hospital as a big cruise liner, afloat on a sea of data, charting and correcting course as the captain and crew read the shifting wave patterns, monitor their instruments, pump the bilge ... Put more prosaically, hospital executives have an awful lot of numbers to navigate while checking their dashboards and generating feedback internally and externally. Artificial intelligence technologies like machine discovery, machine learning and natural language generation are revolutionizing the performance of those tasks. It wasn't until the mid-1990s that the notion of comparing an organization's performance with results achieved by other organizations in the same business even entered the health care mindset. Today, the search term "hospital benchmarking" turns up 5,636 PubMed entries. Type those words into a search engine and you'll be deluged with URLs.