We develop pre-trained estimators for structural econometric models. The estimator uses a neural net to recognize the structural model's parameter from data patterns. Once trained, the estimator can be shared and applied to different datasets at negligible cost and effort. Under sufficient training, the estimator converges to the Bayesian posterior given the data patterns. As an illustration, we construct a pretrained estimator for a sequential search model (available at pnnehome.github.io). Estimation takes only seconds and achieves high accuracy on 12 real datasets. More broadly, pretrained estimators can make structural models much easier to use and more accessible.

Building machine learning models can be a time-consuming process that often takes several months to implement in typical business scenarios. To ensure consistent model performance and account for variations in data distribution, regular retraining is necessary. This paper introduces a solution for improving online customer service in e-commerce by presenting a universal model for predict-ing labels based on customer questions, without requiring training. Our novel approach involves using machine learning techniques to tag customer questions in transcripts and create a repository of questions and corresponding labels. When a customer requests assistance, an information retrieval model searches the repository for similar questions, and statistical analysis is used to predict the corresponding label. By eliminating the need for individual model training and maintenance, our approach reduces both the model development cycle and costs. The repository only requires periodic updating to maintain accuracy.

In the dynamic field of eCommerce, the quality and comprehensiveness of product descriptions are pivotal for enhancing search visibility and customer engagement. Effective product descriptions can address the 'cold start' problem, align with market trends, and ultimately lead to increased click-through rates. Traditional methods for crafting these descriptions often involve significant human effort and may lack both consistency and scalability. This paper introduces a novel methodology for automating product description generation using the LLAMA 2.0 7B language model. We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms. The model is then fine-tuned for domain-specific language features and eCommerce nuances to enhance its utility in sales and user engagement. We employ multiple evaluation metrics, including NDCG, customer click-through rates, and human assessments, to validate the effectiveness of our approach. Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions. This study underscores the considerable potential of large language models like LLAMA 2.0 7B in automating and optimizing various facets of eCommerce platforms, offering significant business impact, including improved search functionality and increased sales.

Product embedding serves as a cornerstone for a wide range of applications in eCommerce. The product embedding learned from multiple modalities shows significant improvement over that from a single modality, since different modalities provide complementary information. However, some modalities are more informatively dominant than others. How to teach a model to learn embedding from different modalities without neglecting information from the less dominant modality is challenging. We present an image-text embedding model (ITEm), an unsupervised learning method that is designed to better attend to image and text modalities. We extend BERT by (1) learning an embedding from text and image without knowing the regions of interest; (2) training a global representation to predict masked words and to construct masked image patches without their individual representations. We evaluate the pre-trained ITEm on two tasks: the search for extremely similar products and the prediction of product categories, showing substantial gains compared to strong baseline models.

With its breakthrough generative AI technology, Nextech3D.ai is poised to revolutionize 3D modeling applications, particularly in the fast-growing e-commerce industry, and emerge as a leader. The recent advent of ChatGPT, a sophisticated chatbot and trained language model, revolutionized the world of AI, bringing its vast potential and attention to the collective forefront of users and investors. AI-powered product offerings explicitly focused on 3D modeling for e-commerce serve a massive Total Addressable Market (TAM) and Serviceable Addressable Market (SAM). The estimated market size of the 3D modeling for e-commerce space is around $100 billion within the $5.5 trillion global e-commerce industry. With its suite of innovative products, Nextech3D.ai is already a preferred 3D model supplier for the e-commerce behemoth Amazon's private label products.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Artificial Intelligence (AI) is transforming nearly all industries, and ecommerce is no exception. One of the areas where savvy online businesses are using AI to streamline operations is fraud detection. Where merchants once employed legions of employees dedicated to reviewing transactions, algorithms can now analyze millions of data points to flag irregularities and fraudulent behavior. Successful fraud detection requires a delicate balance and extreme precision.

Shopper AI, an Israel-based tech company that specializes in shopper behavior recognition, is making its official American debut at the ShopTalk retail convention. Via computer vision and AI, they are able to collect anonymous data and offer actionable insights and personalized recommendations, using existing in-store cameras. The data this provides amounts to an exciting development in retail technology, closing a persistent gap with ecommerce, which has been able to leverage data analytics for years. "In ecommerce, you can analyze your clicks and abandon-cart rates, and run A/B tests to see what's working and what's not. It's time to empower retailers and brands to make data-driven decisions in-store as well, and we are excited to start partnering with companies in America," said Lanor Daniel, co-founder and CEO of Shopper AI.

The rise of AI (Artificial Intelligence) is transforming the whole world, mainly how businesses operate. AI has played a pivotal role in the success of online businesses. It has revolutionized the way people sell and purchase different goods. AI has made online shopping more convenient, secure, and fast. Moreover, it has also automated business operations that were comparatively harder for business owners to manage.

AI Bots Already are Writing Stories. You've heard about AI-written articles, but now there are entire AI-written books. "Shelley," for example, is a bot developed by researchers at MIT, and it's already written more than 140 stories. The bot draws from more than 100 public domain horror stories, and while its work isn't up to the quality of a Stephen King novel, it's getting better all the time. The team behind Shelley has a Twitter account where it posts new stories every hour or so. The most recent one is called "The Last Monster," and it's about a man who finds a way to kill monsters.

Artificial intelligence is a technology that many people still liken to something from a futuristic movie from days gone by. While to the uninitiated, the words artificial intelligence may conjure up images of humanoid robots, the reality is that AI is already all around and widely adopted throughout society. Almost everyone has some kind of contact with artificial intelligence on a daily basis. From using the face recognition function to unlock your cell phone to asking Alexa to play your favorite song. So, whether they know it or not, most consumers are already interacting with artificial intelligence and enjoying its benefits every day.