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) …
We talk about artificial intelligence (AI), robots, and machine learning as if they're coming soon, or are just some tech pipe dream. That's not a century from now; it's not even a decade. It's just three short years away. That can either terrify you if you've seen too many sci-fi films, or excite you if you consider the upside and benefits it could yield. The reality probably lies somewhere in the middle.
Unless you've been living under a rock, ignoring every big tech advance in the past decade, you've probably heard of machine learning. Whether it's better fraud detection and prevention, the handy online recommendations made by Netflix and Amazon, revolutionary facial recognition technology, or futuristic self-driving cars, machine learning is powering the current artificial intelligence revolution. But what is it exactly? Here's a handy beginner's guide. Machine learning is an approach to artificial intelligence that's focused on making machines which can learn without being explicitly programmed.
Summary: This is the third in our series on chatbots. In this installment we'll look at the best practice dos and don'ts as described by a number of successful chatbot developers. In our first article we covered the chatbot basics including their brief technological history, uses, basic design choices, and where deep learning comes into play. The second article focused on the universal NLU front ends for all chatbots and some of the technical definitions and programming particulars necessary to understand how these really function. In this article, we've scoured the internet for advice from successful chatbot developers to provide some useful best practices, or at least some valuable dos and don'ts.
If you're not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean at the end of his keynote earlier this year at a conference on web search and data mining. Dean was referring to the rapid increase in machine learning algorithms' accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build. But breakthroughs in deep learning aren't the only reason this is a big moment for machine learning. Just as important is that over the last five years, machine learning has become far more accessible to nonexperts, opening up access to a vast group of people.
I want to answer some questions that I'm commonly asked: What kind of computer do I need to do deep learning? What deep learning library do you recommend for beginners? How do you put deep learning into production? I think these questions all fall under a general theme of What do you need (in terms of hardware, software, background, and data) to do deep learning? This post is geared towards those new to the field and curious about getting started.
SoftBank Group Corp. said it has upgraded the customer service capabilities of its Pepper humanoid robot, aiming to create demand in the business sector as Japan struggles with manpower shortages. The upgraded Pepper can take orders in English and Chinese, a new feature mainly designed to attract the restaurant industry, which is expected to see more foreign customers thanks to an increase in visitors from abroad, SoftBank said Monday. Customers can select a language on a screen located on Pepper's chest. The robot can recommend the day's special in the customer's preferred language. Orders can be placed via Pepper's chest screen, the company said.
In the context of machine learning, tensor refers to the multidimensional array used in the mathematical models that describe neural networks. In other words, a tensor is usually a higher-dimension generalization of a matrix or a vector. Through a simple notation that uses a rank to show the number of dimensions, tensors allow the representation of complex n-dimensional vectors and hyper-shapes as n-dimensional arrays. Tensors have two properties: a datatype and a shape. TensorFlow is an open source deep learning framework that was released in late 2015 under the Apache 2.0 license.
Strata Data Conference, NEW YORK––September 26, 2017––Anaconda, Inc., the most popular Python data science platform provider, today announced it is partnering with Microsoft to embed Anaconda into Azure Machine Learning, Visual Studio and SQL Server to deliver data insights in real time. Microsoft and Anaconda will partner to deliver Anaconda for Microsoft, a subset of the Anaconda distribution available on Windows, MacOS and Linux. Anaconda, Inc. will also offer a range of support options for Anaconda for Microsoft. Python is the leading data science language and Anaconda is the most popular Python data science distribution with over 4.5 million active users. Anaconda for Microsoft will initially be included in Microsoft Azure Machine Learning, Machine Learning Server, Visual Studio and SQL Server.
In 2013 Bagnall wrote a Gstreamer plug-in that used a recurrent neural network (RNN) to generate video in imitation of a program it was watching. Pretty soon the same RNN library was being used in another Gstreamer plug-in to classify speech on the radio according to language, and to detect birds by listening for their calls (the language classification is quite accurate and runs at 1500 faster than real time on an old laptop, which is at least a data-point for those wondering about spying capabilities). The RNN has also been used to generate text and code, and to classify text by language and author at a fine-grained level. He shows how the RNN is trained, and how it might be adapted for other forms of time-series data. He demonstrates the various plug-ins and text utilities and, for excitement, execute RNN-generated code on the fly.
ODSC East 2018 is one of the largest applied data science conferences in the world. Our speakers include some of the core contributors to many open source tools, libraries, and languages. Attend ODSC West 2017 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field. See schedule for many more.. Core Contributor of scikit learn.