In this post, we look at how we can automate the detection of anomalies in a manufactured product using Amazon Lookout for Vision. Using Amazon Lookout for Vision, you can notify operators in real time when defects are detected, provide dashboards for monitoring the workload, and get visual insights from the process for business users. Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Defect and anomaly detection during manufacturing processes is a vital step to ensure the quality of the products. The timely detection of faults or defects and taking appropriate actions is important to reduce operational and quality-related costs. According to Aberdeen's research, "Many organizations will have true quality-related costs as high as 15 to 20 percent of sales revenue, in extreme cases some going as high as 40 percent." Manual inspection, either in-line or end-of-line, is a time-consuming and expensive task.

As the holiday weekend approaches, deals on the latest gadgets have been popping up across the web. Apple's 10.2-inch iPad is $30 off right now and Solo Stove, the maker of compact, stainless steel fire pits, has knocked $120 off most of its devices. We even have a few holdouts from Amazon Prime Day still available, like deals on Anker's Eufy RoboVac 11S and a two-pack Nest WiFi system. Here are the best July 4th tech deals we could find. The 10.2-inch iPad remains on sale for $299, or $30 off its normal price.

Sweigart focuses on three major subjects: common difficulties in getting started (seeking help, setting up a work environment); best practices, tools, and techniques; and using object-oriented Python. The second section is the largest in the book . . . The book is all the more useful for collecting together between one pair of covers material that you would typically dig up from multiple resources." Al Sweigart is a professional software developer who teaches programming to kids and adults. Sweigart has written several bestselling programming books for beginners, including Automate the Boring Stuff with Python, Invent Your Own Computer Games with Python, Coding with Minecraft, and Cracking Codes with Python (all from No Starch Press).

Stephen Normandin spent almost four years racing around Phoenix delivering packages as a contract driver for Amazon.com Then one day, he received an automated email. The algorithms tracking him had decided he wasn't doing his job properly. The 63-year-old Army veteran was stunned. He'd been fired by a machine. Normandin says Amazon punished him for things beyond his control that prevented him from completing his deliveries, such as locked apartment complexes. He said he took the termination hard and, priding himself on a strong work ethic, recalled that during his military career he helped cook for 250,000 Vietnamese refugees at Fort Chaffee in Arkansas.

We study online convex optimization in the random order model, recently proposed by \citet{garber2020online}, where the loss functions may be chosen by an adversary, but are then presented to the online algorithm in a uniformly random order. Focusing on the scenario where the cumulative loss function is (strongly) convex, yet individual loss functions are smooth but might be non-convex, we give algorithms that achieve the optimal bounds and significantly outperform the results of \citet{garber2020online}, completely removing the dimension dependence and improving their scaling with respect to the strong convexity parameter. Our analysis relies on novel connections between algorithmic stability and generalization for sampling without-replacement analogous to those studied in the with-replacement i.i.d.~setting, as well as on a refined average stability analysis of stochastic gradient descent.

This post is co-authored by Lezgin Bakircioglu, Innovation and Security Manager at Daniel Wellington. In their own words, "Daniel Wellington (DW) is a Swedish fashion brand founded in 2011. Since its inception, it has sold over 11 million watches and established itself as one of the fastest-growing and most coveted brands in the industry." In this post, we share how DW saved 99% on translation costs with Amazon Translate and other AWS services. At DW, having the ability to respond to customers in their local language is critical to the customer journey.

The application of AI is very interesting and fantasizing in the domain of healthcare, defense, and security. However, we do not see a lot of commercial applications reaching end-users. The papers are promising but very difficult to implement on the scale, also because of the regulations. However, in the Retail domain, the stakes are not that high as healthcare, hence, can be experimented with. Retail encompasses shopping malls to online purchases and from transportation to stacking of items in warehouses.

Big-box retailer Walmart is using artificial intelligence (AI) to aid customers and personal shoppers and better handle still-surging online demand for groceries amidst the COVID-19 pandemic. In a blog post Thursday, Srini Venkatesan, Walmart's global tech executive vice president, noted that as Americans increasingly turned to the internet to shop for essentials, stores like Walmart were presented with a "unique challenge." The alternate shopping method combined with the volume of in-store shoppers – especially in the months of March and April – resulted in popular items quickly selling out. Last July, Walmart corporate affairs said the company had hired more than 400,000 new associates to mitigate the sudden "customer rush on essentials as lockdowns spread across the U.S." "Walmart's solution was to use artificial intelligence to help both customers and Personal Shoppers choose the best substitute for an out-of-stock item," said Venkatesan. An illustrated video included in the blog post shows a Walmart personal shopper who needs to make a substitution for an online order.

There is an escalating need for methods to identify latent patterns in text data from many domains. We introduce a new method to identify topics in a corpus and represent documents as topic sequences. Discourse Atom Topic Modeling draws on advances in theoretical machine learning to integrate topic modeling and word embedding, capitalizing on the distinct capabilities of each. We first identify a set of vectors ("discourse atoms") that provide a sparse representation of an embedding space. Atom vectors can be interpreted as latent topics: Through a generative model, atoms map onto distributions over words; one can also infer the topic that generated a sequence of words. We illustrate our method with a prominent example of underutilized text: the U.S. National Violent Death Reporting System (NVDRS). The NVDRS summarizes violent death incidents with structured variables and unstructured narratives. We identify 225 latent topics in the narratives (e.g., preparation for death and physical aggression); many of these topics are not captured by existing structured variables. Motivated by known patterns in suicide and homicide by gender, and recent research on gender biases in semantic space, we identify the gender bias of our topics (e.g., a topic about pain medication is feminine). We then compare the gender bias of topics to their prevalence in narratives of female versus male victims. Results provide a detailed quantitative picture of reporting about lethal violence and its gendered nature. Our method offers a flexible and broadly applicable approach to model topics in text data.

At many firms, the marketing function is rapidly embracing artificial intelligence. But in order to fully realize the technology's enormous potential, chief marketing officers must understand the various types of applications--and how they might evolve. Classifying AI by its intelligence level (whether it is simple task automation or uses advanced machine learning) and structure (whether it is a stand-alone application or is integrated into larger platforms) can help firms plan which technologies to pursue and when. Companies should take a stepped approach, starting with rule-based, stand-alone applications that help employees make better decisions, and over time deploying more-sophisticated and integrated AI systems in customer-facing situations. Of all a company's functions, marketing has perhaps the most to gain from artificial intelligence.