Recent advances in neural forecasting have produced major improvements in accuracy for probabilistic demand prediction. In this work, we propose novel improvements to the current state of the art by incorporating changes inspired by recent advances in Transformer architectures for Natural Language Processing. We develop a novel decoder-encoder attention for context-alignment, improving forecasting accuracy by allowing the network to study its own history based on the context for which it is producing a forecast. We also present a novel positional encoding that allows the neural network to learn context-dependent seasonality functions as well as arbitrary holiday distances. Finally we show that the current state of the art MQ-Forecaster (Wen et al., 2017) models display excess variability by failing to leverage previous errors in the forecast to improve accuracy. We propose a novel decoder-self attention scheme for forecasting that produces significant improvements in the excess variation of the forecast.

AWS DeepComposer provides a creative and hands-on experience for learning generative AI and machine learning (ML). We recently launched the Edit melody feature, which allows you to add, remove, or edit specific notes, giving you full control of the pitch, length, and timing for each note. In this post, you can learn to use the Edit melody feature to collaborate with the autoregressive convolutional neural network (AR-CNN) algorithm and create interesting Bach-style compositions. Through human-AI collaboration, we can surpass what humans and AI systems can create independently. For example, you can seek inspiration from AI to create art or music outside their area of expertise or offload the more routine tasks, like creating variations on a melody, and focus on the more interesting and creative tasks.

For decades humans have been cultivating the concept that "teams" are more powerful and effective than the sole individual. Professor Leigh Thompson of the Kellogg School of Management at Northwestern University defined a team as "a group of people who are interdependent with respect to information, resources, knowledge, and skills and who seek to combine their efforts to achieve a common goal." Robert E. Cole of the Haas School of Business at the University of California, Berkeley studied the evolution of the team concept in industrialized countries from the 1960s to the 1980s. Whether we call them "autonomous workgroups" or just "teams," the result is often higher productivity with more superb quality. From my experience managing engineering teams, my preferred high-tech strategy has been to form "small teams that move fast."

The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Note to the reader: This article assumes some familiarity with data science and analytics concepts. The finance team of a major Australian retailer wanted to know the answer to the question of "How much does the weather affects the weekly sales?" However, nobody in the business has a scientific method of solving it. As pointed above the business value of solving this problem is tremendous, as this could help better understand the impact of certain weather events on the top and bottom line. Not just that, the business can better monitor and report weekly transactional habits of the customer, which are influenced by the weather.

As retail and e-commerce demands continue to intensify, more businesses are considering autonomous vehicles. Since its founding in 2017, the mission of Gatik has been to deliver goods safely and efficiently using self-driving vehicles. The startup said it focuses on business-to-business, short-haul logistics for the retail industry. In this presentation, Apeksha Kumavat, co-founder and chief engineer at Gatik, will discuss how constraining the operational design domain (ODD) has brought middle-mile autonomous delivery to market quickly and effectively. Gatik's fleet of Class 1-6 autonomous vehicles moves goods from micro-fulfillment centers and "dark" stores to pick-up points – retail stores, distribution centers and offices – known as the middle mile.

Intermittency is a common and challenging problem in demand forecasting. We introduce a new, unified framework for building intermittent demand forecasting models, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but also for an natural inclusion of neural network based models---by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios that compares favorably to the state of the art.

This week brought a handful of good tech deals along with the announcement of Amazon Prime Day 2020. While Amazon's annual shopping event will take place on October 13 and 14, there are still some deals worth considering right now (not to mention, a couple of early Prime Day deals for members). Here are the best deals from this week that you can still get today. Prime members can get two 3rd-generation Echo Dots for $20 each when they use the code DOTPRIME2PK at checkout. This brings the price per Echo Dot down to its lowest ever, making this a great deal if you don't absolutely need the redesigned Echo Dot Amazon announced last week.

Where our mission is to create a legacy of wisdom, to seek out discernment and insights, to boldly grow where few have chosen to grow before. Hello, my friend, I am Guthrie Chamberlain, your captain on our journey to increase Wisdom and Create a Living Legacy. Thank you for joining us today as we explore wisdom on our 2nd millennium of podcasts. Today is Day 1488 of our Trek, and our focus on Fridays is the future technological and societal advances, so we call it Futuristic Fridays. My personality is one that has always been very future-oriented.

Founded in 2019, San Francisco-based Synthesis AI has developed technology that generates vast quantities of photorealistic images and pixel-perfect labels to optimize computer vision training. "The world is exploding with cameras," says Synthesis AI CEO Yashar Behzadi. This is great news for AI startups that specialize in computer vision, a field of AI that trains computers to interpret elements from digital images and videos. Up to now, computer vision has relied heavily on supervised learning, in which humans label key attributes in an image and then teach computers to do the same. But to Behzadi, this method has some pretty major setbacks.