This guide is a sample of what will be in Joe's upcoming book: The Ultimate Guide to Chatbots. If you enjoy this article and want to supercharge your bot, consider signing up for the book's waitlist. The incredible potential of chatbots lies in the ability to individually and contextually communicate one-to-many. One-to-many communication: There are plenty of tools out there that allow us to communicate one-to-many at this point in history. These tools, including everything from basic mailers to email marketing to social media, make it easy to reach a large audience and target groups within that audience all at the same.

And see also this great piece from Mashable on what manufacturers are up to next. In the near future, sensing algorithms will achieve super-human performance for capabilities required for driving. Automated perception, including vision, is already near or at human-performance level for well-defined tasks such as recognition and tracking. Advances in perception will be followed by algorithmic improvements in higher level reasoning capabilities such as planning. Beyond self-driving cars, we'll have a variety of autonomous vehicles including robots and drones.

Dutch carrier KLM has begun using artificial intelligence (AI) to better serve the many customers who communicate with the airline via social media. "Applying AI, KLM can handle a greater volume of questions while still maintaining its personal approach and speed," Tjalling Smit, senior vice president, digital at Air France-KLM, said recently when announcing the technology. The carrier's move comes as flyers around the world increasingly use services such as Twitter, Facebook Messenger, WeChat and Instagram to submit comments, complaints and questions to airlines. Carriers have had to adapt, employing teams of customer service agents to respond to postings both public and private. KLM has been a leader in that area.

BellHawk Systems Corporation announces the availability of a new white paper "Artificial Intelligence and the Smart Industrial Warehouse." This white paper is available for download from the front page News section of www.BellHawk.com. In the popular press, there is much ado made about Artificial Intelligence (AI) being used in robots that buzz about high volume retail warehouses, automatically picking consumer products that are being shipped overnight in response to orders made over the Internet. But this misses all the ways that AI can be used to inexpensively improve the operation of industrial warehouses without a major investment in robots or other expensive materials handling equipment by providing the information and advice that managers, supervisors, and material handlers need to do their jobs efficiently. This white paper examines how AI based operations tracking and management systems, such as BellHawk, can be used to improve the efficiency of industrial warehouses, prevent mistakes, and enable customer orders to be shipped on time.

From customer service bots, self-driving cars and playing ancient Chinese board games, AI seems to be making breakthroughs in all industries. However despite the huge amount of media interest in this technology, the phrase AI is all too often used as a catch-all term for many different technologies. In order to provide a useful critique of the developments in the healthcare sector, it is important that we take a look at what AI means when referring to different aspects of the industry. Across healthcare and pharmaceutical development, AI brings with it the possibilities of significant improvements through marginal gains. The key to generating the maximum value of the technology is to identify where real problems lie and where real business opportunity exists.

View full lesson: http://ed.ted.com/lessons/the-ethical... Self-driving cars are already cruising the streets today. And while these cars will ultimately be safer and cleaner than their manual counterparts, they can't completely avoid accidents altogether. How should the car be programmed if it encounters an unavoidable accident?

You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. In this mega Ebook written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch. I live in Australia with my wife and son and love to write and code.

When technologists think of "supercomputer" they usually imagine government-owned computers like China's TaihuLight or public-private partnerships like the DOE-IBM's planned Summit. Private in-house supercomputers are much rarer. That makes Nvidia's new supercomputer, the DGX SaturnV, even more surprising. Not only has Nvidia revealed its own supercomputer, but the machine cracked TOP500's list of the 500 most powerful computers and took the number one spot as the world's most energy-efficient supercomputer. It relies on numerous Nvidia's 125 DGX-1s, the "AI supercomputer in a box" units built for deep learning.

DeepMind is a'vacuum for very, very clever people', says Ocado Technology's head of data

Abstract: The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem – unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) a'Watch, Listen, Attend and Spell' (WLAS) network that learns to transcribe videos of mouth motion to characters; (2) a curriculum learning strategy to accelerate training and to reduce overfitting; (3) a'Lip Reading Sentences' (LRS) dataset for visual speech recognition, consisting of over 100,000 natural sentences from British television. The WLAS model trained on the LRS dataset surpasses the performance of all previous work on standard lip reading benchmark datasets, often by a significant margin. This lip reading performance beats a professional lip reader on videos from BBC television, and we also demonstrate that visual information helps to improve speech recognition performance even when the audio is available.