Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Anyline, a company that builds mobile data capture and scanning technologies for multiple industries, has raised $20 million. Founded out of Vienna, Austria, in 2013, Anyline has developed a range of data capture products such as barcode scanning, optical character recognition (OCR)-powered document scanning, biometric face authentication, serial number scanning, and even driving licensing scanning which enables retailers to easily verify a person's age and identity at the point-of-sale or curbside pickup. Elsewhere, police forces can integrate Anyline's technology to scan all manner of IDs and vehicle license plates to verify drivers instantly, which not only speeds things up but also reduces the chances of errors through traditional manual processes such as typing or broadcasting data across radio. This, according to Anyline CEO and cofounder Lukas Kinigadner, is perhaps the number one benefit Anyline brings to organizations across the spectrum.

We introduce the problem of sleeping dueling bandits with stochastic preferences and adversarial availabilities (DB-SPAA). In almost all dueling bandit applications, the decision space often changes over time; eg, retail store management, online shopping, restaurant recommendation, search engine optimization, etc. Surprisingly, this `sleeping aspect' of dueling bandits has never been studied in the literature. Like dueling bandits, the goal is to compete with the best arm by sequentially querying the preference feedback of item pairs. The non-triviality however results due to the non-stationary item spaces that allow any arbitrary subsets items to go unavailable every round. The goal is to find an optimal `no-regret' policy that can identify the best available item at each round, as opposed to the standard `fixed best-arm regret objective' of dueling bandits. We first derive an instance-specific lower bound for DB-SPAA $\Omega( \sum_{i =1}^{K-1}\sum_{j=i+1}^K \frac{\log T}{\Delta(i,j)})$, where $K$ is the number of items and $\Delta(i,j)$ is the gap between items $i$ and $j$. This indicates that the sleeping problem with preference feedback is inherently more difficult than that for classical multi-armed bandits (MAB). We then propose two algorithms, with near optimal regret guarantees. Our results are corroborated empirically.

The common belief about the growing medium of livestreaming is that its value lies in its "live" component. In this paper, we leverage data from a large livestreaming platform to examine this belief. We are able to do this as this platform also allows viewers to purchase the recorded version of the livestream. We summarize the value of livestreaming content by estimating how demand responds to price before, on the day of, and after the livestream. We do this by proposing a generalized Orthogonal Random Forest framework. This framework allows us to estimate heterogeneous treatment effects in the presence of high-dimensional confounders whose relationships with the treatment policy (i.e., price) are complex but partially known. We find significant dynamics in the price elasticity of demand over the temporal distance to the scheduled livestreaming day and after. Specifically, demand gradually becomes less price sensitive over time to the livestreaming day and is inelastic on the livestreaming day. Over the post-livestream period, demand is still sensitive to price, but much less than the pre-livestream period. This indicates that the vlaue of livestreaming persists beyond the live component. Finally, we provide suggestive evidence for the likely mechanisms driving our results. These are quality uncertainty reduction for the patterns pre- and post-livestream and the potential of real-time interaction with the creator on the day of the livestream.

We hope you're able to celebrate the long weekend with your family and friends. Maybe stay indoors this year to take your mind off the heat wave currently working its way through the US. If you're outside, we have lots of advice on how to stay cool. The good news is there's money to be saved on some of our favorite home and outdoor products this weekend thanks to a bevy of July 4th sales. Special offer for Gear readers: Get a 1-Year Subscription to WIRED for $5 ($25 off).

Dusty Phillips is a Canadian software developer and author currently living in New Brunswick. He has been active in the open source community for two decades and programming in Python for nearly as long. He holds a master's degree in computer science and has worked for Facebook, the United Nations, and several startups. Python 3 Object Oriented Programming was his first book. He has also written Creating Apps In Kivy, and self-published Hacking Happy, a journey to mental wellness for the technically inclined.

The Jetsons suggested a life of automation and ease that we haven't quite mastered yet, but the use of artificial intelligence and machine learning has the potential to get the future of online shopping – especially retail – closer to the ideal.

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.