Luis Vera believes the third time is the charm. The self-proclaimed serial entrepreneur admits that his vision for digitizing retail was a decade or two early when he started his journey in the 90s. Through a pair of startups -- Prospect and SCOPIX -- he tried a variety of methods to help capture store inventory, including placings cameras on shelves and a ceiling-based system where one ran on tracks. He was, effectively, attempting to compete with Amazon well before Amazon was, well, Amazon -- at least in any meaningful sense. Computer vision, machine learning and the like have caught up a lot since then, of course.

Wayfair held its first annual Way Day sale back in 2018 on the heels of Amazon's Prime Day event, and it's back yet again. Similarly to Prime Day, Way Day deals are exclusively available for a very limited time. Unlike Prime Day, no membership is required to shop Way Day deals. It's important to check prices before placing an order. We've done that work and listed some of the best Way Day deals below, but you can check out the entire event here.

Rajiv Malhotra was trained initially as a Physicist, and then as a Computer Scientist specializing in AI in the 1970s. After a successful corporate career in the US, he became an entrepreneur and founded and ran several IT companies in 20 countries. Since the early 1990s, as the founder of his non-profit Infinity Foundation (Princeton, USA), he has been researching civilizations and their engagement with technology from a historical, social sciences and mind sciences perspective. He has authored several best-selling books. Infinity Foundation has also published a 14-volume series on the History of Indian Science & Technology.

Identifying paraphrased text has business value in many use cases. For example, by identifying sentence paraphrases, a text summarization system could remove redundant information. Another application is to identify plagiarized documents. In this post, we fine-tune a Hugging Face transformer on Amazon SageMaker to identify paraphrased sentence pairs in a few steps. A truly robust model can identify paraphrased text when the language used may be completely different, and also identify differences when the language used has high lexical overlap.

You could think of it as a rebirth. Or maybe it's one of those COVID-era glow-ups that had people emerging from isolation with straighter teeth and cuter clothes. Whatever you want to call it, Edible Arrangements is in the middle of a major transformation. A few years ago, the company that introduced the world to bouquets of skewered fruit was in freefall. Now, after a bunch of new product launches, one hired-and-fired CEO, and a pandemic, the company is boasting record-setting sales numbers and a renewed sense of self. It's even changed its name: The new Edible sells desserts and doodads of all kinds--not just fruit--and aims to be, as the CEO put it, "the Domino's of gifting." But like most extreme makeovers, Edible's has its detractors, specifically within its own ranks.

In the current era of digitization and globalization, when technology is undoubtedly causing disruption to almost every other industry sector, the online commerce segment is, of course, no exception. The pandemic-led dramatic shift in consumer behavior and market dynamics and thus the accelerated focus towards'online-first' and'digital-first' shopping experiences have added fuel to usher in a better future for the new-age commerce ecosystem, wherein multiple latest and emerging technologies are already playing, or are bound to play a pivotal role, in the times to come. Probably one of the biggest technological frontiers that have been contributing majorly in the recent past vis-à-vis positively reshaping e-commerce and online shopping is Artificial Intelligence AI. In particular, AI has been proven to be a boon in creating best-in-class, tailored, and customized shopping experiences on a plethora of online shopping, e-commerce, and m-commerce marketplaces for today's consumers. With the optimal use of AI and allied technologies, e-tailers or digital sellers are nowadays able to regularly and consistently learn, track and analyze their customers' online behaviors, and parallelly able to use the predictive analytics powered by AI to near-accurately forecast their future purchase decisions – which in turn is leading to increased repeat buys and customer loyalty over a period of time.

This blog highlights different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. The potential of blockchain to solve the retail supply chain manifests in three areas. Provenance: Both the retailer and the customer can track the entire product life cycle along the supply chain. Smart contracts: Transactions among disparate partners that are prone to lag can be automated for more efficiency. IoT backbone: Supports low powered mesh networks for IoT devices reducing the needs for a central server and enhancing the reliability of sensor data.

AI is just about everywhere these days. It simplifies and expedites processes that would otherwise be done manually. Though once an exotic term of science fiction, it's now what greets you the moment you interact with the customer service page of any major retailer. It should be no surprise that AI has entered the cannabis sphere. Artificial intelligence has the capacity to boost production, improve efficiency, and even make the entire process more environmentally friendly.

I have a background in IT, marketing, and business development with over ten years of experience in startups and multinational companies. I live by the philosophy that "entrepreneurship isn't about starting your own company or making money, it's all about solving problems." I m the co-founder of Expert Project from 2010 to 2019, where he oversaw day-to-day operations as well as international expansion. I also founded People Marketing (2012 to 2014) which was a startup focused on creating scalable customer acquisition campaigns for small businesses. When I m not working on my own projects, I can be found reading books about new cultures and innovation or traveling to places for inspiration.

Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know which query will provide the best retrieval performance on the current information retrieval system, meaning that any query autocompletion methods trained to mimic user behavior can lead to suboptimal query suggestions. To overcome this limitation, we propose a new approach that explicitly optimizes the query suggestions for downstream retrieval performance. We formulate this as a problem of ranking a set of rankings, where each query suggestion is represented by the downstream item ranking it produces. We then present a learning method that ranks query suggestions by the quality of their item rankings. The algorithm is based on a counterfactual learning approach that is able to leverage feedback on the items (e.g., clicks, purchases) to evaluate query suggestions through an unbiased estimator, thus avoiding the assumption that users write or select optimal queries. We establish theoretical support for the proposed approach and provide learning-theoretic guarantees. We also present empirical results on publicly available datasets, and demonstrate real-world applicability using data from an online shopping store.