The frustration and anguish of trying and failing to piece together Ikea furniture may seem like an exercise in humiliation for you, but know this: The particleboard nightmare may one day lead to robots that aren't so stupid. In recent years, roboticists have been finding that building Ikea furniture is actually a great way to teach robots how to handle the chaos of the real world. One group of researchers coded a simulator in which virtual robot arms used trial and error to put chairs together. Others managed to get a different set of robot arms to construct Ikea chairs in the real world, though it took them 20 minutes. And now, a helpful robot can assist a human in assembling an Ikea bookcase by predicting what part they'll want next and handing it over.

The global health crisis accelerated the adoption of omnichannel shopping and fulfillment. Consumers spent $861.12 billion online with US merchants in 2020, up an incredible 44% compared to the previous year, which marks the highest annual growth in U.S. e-commerce in at least two decades. To keep up pace with this shift and more effectively sell, businesses have substantially moved investments to online infrastructures, such as e-commerce platforms, inventory management, product recommendations and chatbots and delivery. On one hand, setting up e-commerce sites and/or optimizing online stores means increased sales and market penetration; on the other, these benefits are potentially outweighed by the increased costs as retailers essentially shift a part of their business to logistics and fulfillment. As businesses make the transition to online retailers, they will have to focus on these four key customer areas to ensure profitability: fraud, delivery theft, returns and customer service.

Hugging Face is the technology startup, with an active open-source community, that drove the worldwide adoption of transformer-based models thanks to its eponymous Transformers library. Earlier this year, Hugging Face and AWS collaborated to enable you to train and deploy over 10,000 pre-trained models on Amazon SageMaker. For more information on training Hugging Face models at scale on SageMaker, refer to AWS and Hugging Face collaborate to simplify and accelerate adoption of Natural Language Processing models and the sample notebooks. In this post, we discuss different methods to create a SageMaker endpoint for a Hugging Face model. If you're unfamiliar with transformer-based models and their place in the natural language processing (NLP) landscape, here is an overview.

Proper inventory management is a top concern for retailers. Out-of-stock items and inefficient replacement strategies can result in lost sales, reduced customer satisfaction, and lower loyalty levels. In response to these challenges, companies like Walmart and Kellogg's are harnessing the power of artificial intelligence to improve real-time product substitutions and predict shortages weeks in advance. Artificial intelligence in the food and beverage market is expected to reach $29.94 billion by 2026, growing at a CAGR of over 45.77% during the forecast period, reported Research and Markets. This growth is largely attributed to consumer's increasing demand for fast, affordable, and easily accessible food options.

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.