The implementation of AI in ecommerce should come as no surprise. Online businesses have always been quick to adopt new technologies, and this is how the industry thrives; enhancing the customer experience, discovering new markets, and driving further sales. And with the continued development of AI technology like chatbots, visual search, and personalized recommendations, the world of ecommerce is transforming again. But just how effective and useful is AI-powered tech? Where is it being used? In this article, we'll look at the most popular implementations of AI in ecommerce to determine what they do, how they are being used, and first steps for practical application.

For better user experience and business effectiveness, Click-Through Rate (CTR) prediction has been one of the most important tasks in E-commerce. Although extensive CTR prediction models have been proposed, learning good representation of items from multimodal features is still less investigated, considering an item in E-commerce usually contains multiple heterogeneous modalities. Previous works either concatenate the multiple modality features, that is equivalent to giving a fixed importance weight to each modality; or learn dynamic weights of different modalities for different items through technique like attention mechanism. However, a problem is that there usually exists common redundant information across multiple modalities. The dynamic weights of different modalities computed by using the redundant information may not correctly reflect the different importance of each modality. To address this, we explore the complementarity and redundancy of modalities by considering modality-specific and modality-invariant features differently. We propose a novel Multimodal Adversarial Representation Network (MARN) for the CTR prediction task. A multimodal attention network first calculates the weights of multiple modalities for each item according to its modality-specific features. Then a multimodal adversarial network learns modality-invariant representations where a double-discriminators strategy is introduced. Finally, we achieve the multimodal item representations by combining both modality-specific and modality-invariant representations. We conduct extensive experiments on both public and industrial datasets, and the proposed method consistently achieves remarkable improvements to the state-of-the-art methods. Moreover, the approach has been deployed in an operational E-commerce system and online A/B testing further demonstrates the effectiveness.

Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.

There's nothing worse than waking up on a Sunday morning, already dealing with the Sunday scaries, and realizing that your home is a mess. But before you pull out the old vacuum cleaner, broom, and dust pan, maybe it's time to consider a tool that does the work for you, and no, I'm not talking about hiring a maid. Robot vacuums are a major trend when it comes to keeping your home clean and tidy. Simply unpack the robot vacuum, plug it in, and away it goes, sucking up dust and crumbs the house over. But let's be real, in 2020 robot technology has made some serious advances, and automated vacuums are better than ever.

Google Assistant is honoring prominent women in March with the help of its latest feature launched for Women's History Month. Anyone with Google's smart speaker can wish it a "Happy International Women's Day" and count on their smart device to read out loud information related to a trailblazer. On devices with screens, such as a smartphone or Google Home smart display, you can also see an image of the woman with a written summary. International Women's Day is celebrated on Sunday, March 8. Google has chosen to highlight 12 women from diverse nationalities and disciplines, including labor rights activist Dolores Huerta, architect Zaha Hadidand environmental scientist Rachel Carson, among others. "Our goal is to showcase some examples of the far-ranging impact women have had on all aspects of culture, and inspire women and girls to be their own trailblazers," said Google Assistant's senior director of product management, Lilian Rincon.

To many, Machine Learning may be a new word, but it was first coined by Arthur Samuel in 1952, and since then, the constant evolution of Machine Learning has made it the go-to technology for many sectors. Right from robotic process automation to technical expertise, Machine Learning technology is extensively used to make predictions and get valuable insight into business operations. It's considered as the subset of Artificial Intelligence (intelligence demonstrated by machines). If we go by the books, Machine Learning can be defined as a scientific study of statistical models and complex algorithms that primarily rely on patterns and inference. The technology works independently of any explicit instruction, and that's its strength.

For doctors that are exhausted from long hours typing up patient medical records, it could be game-changing, says Punit Soni, founder and CEO of Suki AI, a virtual assistant app for clinicians. The startup just raised a $20 million Series B round from Flare Capital Partners, First Round Capital, and Venrock, doubling its total funding to $40 million since its 2017 launch. The premise of Suki AI is simple: It's Alexa for doctors. Similar to how people can order Amazon's voice-enabled digital assistant to set a reminder or tell them their schedule, doctors can use Suki to take notes during patient appointments and those notes will automatically fill out electronic health records (EHRs). That's increasingly important as doctors spend more time logging data and less face time with patients.

"Alexa, turn on the 30C." (Photo: Eufy) Purchases you make through our links may earn us a commission. Back in the day, robot vacuums were the kind of investment that might have struck some people as a little excessive. But nowadays with smart tech making everything we hate about daily life--like cooking dinner and finishing chores--so much easier, it makes sense that robot vacuums would become more ubiquitous, too. A smart robot vacuum like the Eufy Robovac 30C can make vacuuming suck a lot less, and right now you can get this top-rated device for its second-lowest price of all-time on Amazon, at just $190. At full price, the 30C is advertised as being $299.99,

Coronavirus concerns have now spread to dating apps. Tinder is urging its users to stay safe amid the COVID-19 outbreak with pop-up alerts. The notification randomly appears when swiping for matches with the headline, "your wellbeing is our #1 priority." The popular dating app also features a series of safety tips within the alert before linking users back to the World Health Organization (WHO) to learn more. The safety tips, aligned with WHO's recommendations, include avoiding touching your face and carrying hand sanitizer.

How do we know the profile of each of our products? A product's profile should represent the key underlying characteristics of the product, especially the ones that users care about. A very simple product profile would just be the category. For example for books, the profiles would be history, fantasy, science fiction, nonfiction, biography, romance, humor, etc. Quantitatively, books would just be represented something like this: And we would try to find out what categories (genres) our customers liked to read. Let's say we go with approach 2 -- Jeremy gives us a list of 7 fantasy books and 3 romance books.