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) …
This book is for intermediate level developers who want to become a master of PHP. Basic knowledge of PHP is required across areas such as basic syntax, types, variables, constants, expressions, operators, control structures, and functions. PHP is a server-side scripting language that is widely used for web development. The book starts by unveiling the new features of PHP 7 and walks you through several important standards set by PHP Framework Interop Group (PHP-FIG). You'll see, in detail, the working of all magic methods, and the importance of effective PHP OOP concepts, which will enable you to write effective PHP code.
How to predict stock prices with neural networks and sentiment with neural networks. Let's dive into data science with python and predict stock prices and customer sentiment. How to learn machine learning in python? And what is transfer learning? How to create a sentiment classification algorithm in python?
Transportation Security Administration agents help passengers through a security checkpoint at Newark Liberty International Airport in Newark. New figures released Sunday reveal a record number of agents are not showing up to work. The Transportation Security Administration has reported that the number of airport security agents not showing up to work reached an all-time high over the holiday weekend, according to the Washington Post, a side-effect of the government shutdown that the Department of Homeland Security previously stated was non a concern. TSA agents are among the estimated 800,000 federal workers who are furloughed or working without pay during a government shutdown that is reaching its 30th day. The Washington Post reported that the number of unscheduled absences hit 8 percent nationally this weekend, up from a 3 percent a year ago.
Uber is only gradually resuming its self-driving car program, but it's already thinking about expanding that technology to its two-wheeled services. The Telegraph has discovered that Uber is hiring for a "micromobility robotics" team that would bring "sensing and robotics technologies" to shared bikes and scooters. While the exact plans aren't clear, the newspaper believed this would lead to rides that park themselves -- important when carelessly parked scooters are a plague in some cities. Given that the company is only just starting to hire for the new team, it's going to be a while before you see the fruits of whatever Uber is planning. It wouldn't be shocking if self-parking bikes and scooters are in the cards, mind you, and not just for tidiness reasons.
In Tensorflow 2.0 Keras will be the default high-level API for building and training machine learning models, hence complete compatibility between a model defined using the old tf.layers and the new tf.Keras.layers is expected. In version 2 of the popular machine learning framework the eager execution will be enabled by default although the static graph definition session execution will be still supported (but hidden a little bit). In this post, you'll see that the compatibility between a model defined using tf.layers and tf.keras.layers is not always guaranteed when using the graph definition session execution, but it works as expected if the eager execution is enabled (at least from my tests). The post is organized as follows: definition of the common data input pipeline, definition of the same model using both tf.layers and tf.keras.layers, The model we're going to use to highlight the differences between the 2 versions is a simple binary classifier.
Several years ago, you may recall a publication describing our growing dependency upon machines, devices, and "AI." On several occasions, I've attempted to bring awareness to this phenomenon of artificial intelligence's abilities in creating and/or re-creating itself... over and over again. What once was a'science-fiction' story has been brought to bear, in living color, a scientific fact. The article, "Device Machine Dependent," has described instances and descriptions where robots or robotics have been designed to emulate the actions, abilities, and appearance(s) of mankind... "Human-Like"; "The Image of Its' Creator!" How many times have you been in your car and engaged in a shouting match or argumentative interaction with your'GPS' or "onboard interface?" Aw, c'mon now... haven't you gotten angry and screamed at the device when the voice behind it gives you screwed up or wrong directions?
This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Check back to The New Stack for future installments. For the background and context, we strongly recommend you to read the previous article on the rise of ML PaaS followed by the article on the overview of Azure ML service. In this tutorial, we will build and deploy a machine model to predict the salary from the Stackoverflow dataset. By the end of this, you will be able to invoke a RESTful web service to get the predictions.
Artificial intelligence (AI) will have a major impact on the digital workplace in the very near future. IBM studies revealed that within the next three years about 120 million workers in the world's 10 largest economies may need to be retrained or reskilled because of AI and intelligent automation. IBM officials also found that 67 percent of CEOs believe AI will drive significant value in HR. But, how is AI impacting the digital workplace now? Gartner reported that AI is currently being applied to areas that include collaboration (fee required), content services, intranets, human capital management/recruiting, IoT in the digital workplace, help desk/IT service monitoring, knowledge management, meeting solutions, search and insight engines, and virtual employee assistants.
Equian, a provider of P&C payment integrity solutions announced that its Data Analytics Division created the first NLP based AI platform called EquianAI built exclusively for the P&C subrogation market. EquianAI's greatest impact is improving operational performance and recovery cycle times, and it easily integrates into existing workflows with minimal technology resources required. Equian's Subject Matter Experts worked alongside the Analytics Division to develop the model. The Analytics Division utilized Equian's data repository of P&C subrogation data to formulate a unique set of algorithms to predict outcomes and improve the existing process. "P&C organizations have unique challenges when it comes to subrogation. Most carriers rely on manual evaluation of free text with some predictive analytics on a limited amount of structured data to identify losses with subrogation opportunity. EquianAI provides automation that removes tedious human intervention by analyzing the free text, the structured data, and the unstructured data simultaneously. The technology performs in-depth analyses that result in the ability to prioritize claims by scoring recoverable files as High, Medium or Low based on recoverability. Rules based technology then directs the scored cases to appropriate investigation and recovery experts."
There are over 2.6 billion active social media users. Among them are your customers and potential customer. The question is, how to reach them! In today's fast-paced digital landscape, artificial intelligence can help your business create more effective marketing and social media strategies. AI can help you improve the consumer journey and change the way you attract, capture and nurture leads.