The Apple HomePod is slated to come out later this year, but it might be difficult to find on store shelves. Limited shipments at launch may give the Siri-powered home speaker a gradual rollout, according to the Nikkei Asian Review. During an earnings conference call Monday, HomePod supplier Inventec Appliances gave further details about the new Apple device's launch schedule. Inventec president David Ho said via the Review that the company is expecting to have a smaller amount of units when the HomePod launches this fall. "We will finally ship the smart home device this year, but its contribution will be fairly limited and hopefully that will improve next year."

According to the new market research report "Artificial Intelligence in Manufacturing Market by Offering (Hardware, and Software), Technology (Deep Learning, Computer Vision, Context Awareness, and Natural Language Processing), Application, Industry, and Geography - Global Forecast to 2023", published by MarketsandMarkets, the artificial intelligence (AI) in manufacturing market is expected to grow from USD 272.5 Million in 2016 to USD 4,882.9 The growing usage big data technology, Industrial IoT in manufacturing, extensive usage of robotics in manufacturing, computer vision technology in manufacturing, cross-industry partnerships and collaborations, and significant increase in venture capital investments would propel the growth of the AI in manufacturing market. Browse 61 Market Data Tables and 58 Figures spread through 189 Pages and in-depth TOC on "Artificial Intelligence in Manufacturing Market - Global Forecast to 2023" In recent years, major software vendors such as IBM Corporation (US), Microsoft Corporation (US), and Alphabet Inc. (US) have started offering cognitive applications and cognitive software platforms. Moreover, many start-ups such as DataRPM (US), Clearpath Robotics Inc. (Canada), Sight Machine (US), Preferred Networks Inc. (Japan), SkyMind Inc. (US), Kespry Inc. (US), and AIBrain, Inc. (US) are bringing intelligent software solutions in manufacturing. The booming venture capital market for AI software-based start-ups and usage of big data technology are driving the growth of the AI in manufacturing market for the software segment.

These two papers have nice overview of recent works. First one explain recent architectures and introduce a state of the art method for video recognition problem and the second one analyze datasets, algorithms and evaluation metrics in a different perspective.

The Hugo Awards, widely considered the most prestigious science fiction and fantasy prizes, were announced Friday, with female authors dominating and N.K. Jemisin winning the award for novel for the second year in a row. Jemisin, who became the first black author to win the Hugo's novel award last year (for "The Fifth Season"), won again with the book's sequel, "The Obelisk Gate." The third and final book in Jemisin's trilogy, "The Stone Sky," will be released Tuesday. The awards were announced at a ceremony at Worldcon 75, a science fiction festival held this year in Helsinki, Finland. Female authors also took home the awards for novella ("Every Heart a Doorway" by Seanan McGuire), novelette ("The Tomato Thief" by Ursula Vernon) and short story ("Seasons of Glass and Iron" by Amal El-Mohtar).

Claims currently are touched by multiple employees. However, a new process of "touchless" claims doesn't require any human intervention. That's because the insurance industry is one of the least innovative areas for customer experience. Customers typically come away from their interactions dissatisfied. However, things are definitely changing, and artificial intelligence is playing a large role.

DL practitioners and ML engineers typically spend most days working at an abstract Keras or TensorFlow level. But it's nice to take a break once in a while to get down to the nuts and bolts of learning algorithms and actually do back-propagation by hand. It is both fun and incredibly useful! Andrew Ng's new adventure is a bottom-up approach to teaching neural networks -- powerful non-linearity learning algorithms, at a beginner-mid level. In classic Ng style, the course is delivered through a carefully chosen curriculum, neatly timed videos and precisely positioned information nuggets.

A smart cognitive hearing aid could make life significantly easier for people who are deaf or hard of hearing. Whether it's Apple's smart cochlear implant collaboration or tools designed to make sign language communication easier, there is no shortage of cutting-edge gadgetry available to make life easier for people who are deaf or hard of hearing. A new piece of technology coming out of Columbia University School of Engineering and Applied Science could make things even better, however -- courtesy of a hearing aid that is designed to read brain activity to determine which voice a hearing aid user is most interested in listening to and then focusing in on it. The resulting "cognitive hearing aid" could be transformative in settings like crowded rooms in which multiple people are speaking at the same time. "My research has been focused on understanding how speech is processed in the brain, and to create models of it that can be used in automatic speech-recognition technologies," Nima Mesgarani, an associate professor of electrical engineering, told Digital Trends. "Working at the intersection of brain science and engineering, I saw a unique opportunity to combine the latest advances from both fields, to create a solution for decoding the attention of a listener to a specific speaker in a crowded scene which can be used to amplify that speaker relative to others."

CHENNAI, India, Aug 9 (Thomson Reuters Foundation) - With more than 20 million humans working as modern slaves, a technology developer is hoping artificial intelligence will help clean up the world's supply chains and root out worker abuse. Developer Padmini Ranganathan said mobile phones, media reports and surveillance cameras can all be mined for real-time data, which can in turn be fed into machines to create artificial intelligence (AI) that helps companies see more clearly what is happening down the line. "The time to do this now is better than ever before, with so many countries and companies focusing on modern slavery," she said in an interview with the Thomson Reuters Foundation. "At the start of the decade, the driving force for compliance was fear of being penalised. Now companies are looking at social impact and saying they want to do this."

Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. In this tutorial, you will discover how you can develop an LSTM model for multivariate time series forecasting in the Keras deep learning library. This tutorial assumes you have a Python SciPy environment installed. You can use either Python 2 or 3 with this tutorial.

Schooling – MS Applied Math, Illinois State – MBA, Illinois State How to get ahold of me: – @rawkintrevo – trevor.grant@ibm.com Hopefully after this you – Won't keep using words wrong – Will know when someone else is be pretentious or don't Bonus material: – We build a fairly cool, yet super simple online recommender – Apache Flink Apache Spark Apache Mahout Why does any of this matter?