Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents" – any device that perceives its environment and takes actions that maximize its chance of success at some goal. Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and futurism. Machine learning is a type of AI that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. Deep learning is a specific machine learning technique. Most deep learning methods involve artificial neural networks, modeling how our brain works. At the moment deep learning forms the basis for most of the incredible advances in machine learning (and in turn AI). Big data is a term for extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
"Today is the slowest rate of technological change you will ever experience in your lifetime," wrote Shelly Palmer in his e-book Data-Driven Thinking (Digital Living Press, 2016). As one of the world's premier voices on the accelerating pace of digital technology, he is increasingly preoccupied with helping companies and individuals prepare for the dramatic changes he sees coming, particularly in entertainment and media.
Google's TensorFlow has been a hot topic in deep learning recently. The open source software, designed to allow efficient computation of data flow graphs, is especially suited to deep learning tasks. It is designed to be executed on single or multiple CPUs and GPUs, making it a good option for complex deep learning tasks. In it's most recent incarnation – version 1.0 – it can even be run on certain mobile operating systems. This introductory tutorial to TensorFlow will give an overview of some of the basic concepts of TensorFlow in Python. These will be a good stepping stone to building more complex deep learning networks, such as Convolution Neural Networks and Recurrent Neural Networks, in the package. We'll be creating a simple three-layer neural network to classify the MNIST dataset. This tutorial assumes that you are familiar with the basics of neural networks, which you can get up to scratch with in the neural networks tutorial if required. To install TensorFlow, follow the instructions here. The code for this tutorial can be found in this site's GitHub repository. First, let's have a look at the main ideas of TensorFlow.
Since 2009, Chris has been building and leveraging artificial intelligence systems to cognify a wide range of business functions -- marketing, sales, customer support and decision automation to name a few. In the context of the hiring process, we created an AI system -- specifically a natural language processing platform -- that analyzes outbound job descriptions and makes recommendations around how to structure the job description to solicit their ideal responses. So instead of trying to dive into an open source AI platform right out of the gate -- hiring a development team, bringing in data scientists, assigning executives and all that jazz -- you can just use IBM Watson or Google Cloud or Microsoft Azure or nearly any other cloud provider. For example, if I need a natural language processing engine -- I might compare IBM Watson against Google Cloud -- testing their performance, checking accuracy, etc.
Element Data, Inc., a decision support software platform that harnesses artificial intelligence and machine learning has acquired the technology assets and team of PV Cube, a Seattle area start-up. The acquisition expands the size of the team of existing engineers building the world's first cognitive decision engine.