Articles

Build a RingCentral Virtual Voicemail Assistant for Your Business   — Part 1

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RingCentral Virtual Voicemail Assistant

Nowadays, consumers have a variety of options for obtaining services and getting the help they need. They can use webchat, email, the Internet, and face-to-face contact, yet telephone customer service is still the first choice for most customers when they have questions or a problem that needs to be resolved.

In order to ensure your customers are happy with the customer service they receive, it’s even more important for you to provide exceptional customer service, including outstanding telephone service. Consumers expect better service than ever before, and the capabilities of modern telephone communications allow you to offer them the satisfaction and resolution they demand.

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Deep Learning for Manufacturing: Overview and Applications

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Deep learning

Introduction to Deep Learning for Manufacturing

Before getting into the details of deep learning for manufacturing, it’s good to step back and view a brief history. Concepts, original thinking, and physical inventions have been shaping the world economy and manufacturing industry since the beginning of the modern era, i.e. early 18th century.

Ideas of economies-of-scale by the likes of Adam Smith and John Stuart Mill, the first industrial revolution and steam-powered machines, electrification of factories and the second industrial revolution, and the introduction of the assembly line method by Henry Ford are just some of the prime examples of how the search for high efficiency and enhanced productivity have always been at the heart of manufacturing.

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How to Do Deep Learning for Java

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Deep in thought studying deep learning for Java.

Introduction

Some time ago, I came across this life-cycle management tool (or cloud service) called Valohai, and I was quite impressed by its user-interface and simplicity of design and layout. I had a good chat about the service at that time with one of the members of Valohai and was given a demo. Previous to that, I had written a simple pipeline using GNU Parallel, JavaScript, Python, and Bash — and another one purely using GNU Parallel and Bash.

I also thought about replacing the moving parts with ready-to-use task/workflow management tools like Jenkins X, Jenkins Pipeline, Concourse or Airflow, but due to various reasons, I did not proceed with the idea.

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AI Movie Restoration: Scarlett O’Hara HD

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With convolutional neural networks and state-of-the-art image recognition techniques it is possible to make old movie classics shine again. Neural networks polish the image, reduce the noise, and apply colors to the aged images.

The first movies were created in the late nineteenth century with celluloid photographic film used in conjunction with motion picture cameras.

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TensorFlow vs. PyTorch vs. Keras for NLP

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Before beginning a feature comparison between TensorFlow, PyTorch, and Keras, let’s cover some soft, non-competitive differences between them.

Non-competitive facts:

Below, we present some differences between the 3 that should serve as an introduction to TensorFlow, PyTorch, and Keras. These differences aren’t written in the spirit of comparing one with the other but with a spirit of introducing the subject of our discussion in this article.

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The Impact of Machine Learning in Agriculture

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Machine learning is now a big part of every single one of our lives. When you use Netflix, recommended shows are presented based on an AI algorithm. Your order history on Amazon is run through a program to create a list of potential products that are uniquely suited to your tastes. Marketers use automation as a way to reach potential prospects and keep current customers engaged with their company. Believe it or not, 79 percent of the top businesses are currently using AI in their business model in some way.

At a glance, it seems like everything related to machine learning involves relatively new products and technology. A new study by the Journal of Field Robotics at the University of Cambridge is challenging our preconceptions about AI and how it ought to impact our lives. Their research and experiments resulted in a tremendous breakthrough in agriculture. We are going to take a look at the challenge, their new robot, and what this means for the future.

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Light Switch With Natural Language Interface

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In this article, I’ll show how to build a natural language interface for a typical home light switch so that you could turn the lights on and off with simple commands like Turn off all the lights, please, or Get the lights on in the kids bedroom.

We’ll concentrate on Natural Language Interface (NLI) part, and I’ll leave speech-to-text and the actual light controlling outside of the scope of this short blog. You can easily add speech-to-text with WebSpeech, if necessary, while Arduino/HomeKit can provide simple API to control the lights in your house.

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What You Must Know Before You Dive Into Machine Learning

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Machine learning refers to the process of enabling computer systems to learn with data using statistical techniques without being explicitly programmed. It is the process of active engagement with algorithms in order to enable them to learn from and make predictions on data. Machine learning is closely associated with computational statistics, mathematical optimization, and data learning. It is associated with predictive analysis, which allows producing reliable and fast results by learning from historical trends. There are basically two kinds of machine learning tasks:

  1. Supervised learning: The computer is presented with some example inputs, based on which the desired outputs are to be formed. The computer is made to learn general rules of converting inputs to outputs.

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Microsoft Launches Latest Bid to One Up AI Competition

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Photo credit by Unsplash/Hermes Rivera

It’s no secret these days that AI is a big deal in the world of business. According to Gartner, the percentage of enterprises using the technology has jumped astronomically over the past several years, tripling in the last year alone.

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Using AI To Gauge The Accuracy Of The News

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As the impact of fake news has grown, so too have attempts to detect and remove it. I wrote recently about an AI-driven approach developed by the University of Michigan, which is able to accurately spot fake news stories around 76% of the time.

A second system, developed by a team from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI), are attempting to do likewise. Their approach focuses less on the reliability of individual claims and more on the general reliability of the news sources themselves.

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