TechRepublic's Karen Roby talked with Will Hayes, CEO of Lucidworks, about how artificial intelligence can better help retailers understand customer intent when shopping online. The following is an edited transcript of their conversation. Karen Roby: We all have a tendency, I think, from time-to-time to abandon our carts. We put something in, we take it out, or we just leave it there and we go onto the next site. What typically happens with shoppers, Will?

Amazon's latest set of crowdfunded Echo devices aim for luxury over eccentricity. The retailer has unveiled three new trippy Echo Dot concepts from Belgian fashion designer Diane von Furstenberg (DVF) that you can pre-order today for $60 each. Well, as long as they hit their sales target. Like the trio of weird products Amazon unveiled in February (cuckoo clock anyone?) these dinky speakers are part of the Built It program that borrows from Kickstarter and Indiegogo. Basically, Amazon will only ship out this second round of gadgets if they generate enough consumer interest within 30 days.

The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data faces strong overdispersion and sporadic demand, especially zero demand. We discuss resulting modeling issues concerning adequate probabilistic forecasting of such count data processes. Unfortunately, the majority of popular prediction methods used in the M5 competition (e.g. lightgbm and xgboost GBMs) fails to address the data characteristics due to the considered objective functions. The distributional forecasting provides a suitable modeling approach for to the overcome those problems. The GAMLSS framework allows flexible probabilistic forecasting using low dimensional distributions. We illustrate, how the GAMLSS approach can be applied for the M5 competition data by modeling the location and scale parameter of various distributions, e.g. the negative binomial distribution. Finally, we discuss software packages for distributional modeling and their drawback, like the R package gamlss with its package extensions, and (deep) distributional forecasting libraries such as TensorFlow Probability.

Last year, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart hosts 196 computer vision models, 64 natural language processing (NLP) models, 18 pre-built end-to-end solutions, and 19 example notebooks to help you get started with using SageMaker. These models can be quickly deployed and are pre-trained open-source models from PyTorch Hub and TensorFlow Hub. These models solve common ML tasks such as image classification, object detection, text classification, sentence pair classification, and question answering. The example notebooks show you how to use the 17 SageMaker built-in algorithms and other features of SageMaker.

Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for machine learning (ML), is now integrated with SageMaker's automatic model tuning capability. Customers can add a model tuning step (TuningStep) in their SageMaker Pipelines which will automatically invoke a hyperparameter tuning job. The hyperparameter tuning finds the best version of a model by running many training jobs on the dataset using the algorithm and the ranges of hyperparameters specified by the customer. They can then register the best version of the model into the model registry using the RegisterModel step.

In less than a year, the American grocery store has gone from an age-old, in-person shopping institution to a destination at the forefront of a technological transformation. Grocery giant Kroger, for instance, covered 98 percent of households in its delivery areas in 2020 by investing in a large digital and delivery presence. Furthermore, according to a recent global study, over 50 percent of respondents are not planning to re-integrate in-store shopping into their routine for "a long time"--underlining the need for innovative solutions. As the world traverses a slow path to recovery amid COVID-19, how we purchase food and staple goods may never look quite the same, thanks to new technologies and consumer habits. Though automats and self-service technologies have existed since the 1930s, the first proper self-checkout platform was introduced in 1992 by Dr.

Artificial Intelligence (AI) has been redefining society in ways we have never anticipated. Technology is clinging to us in every walk of our lives, right from unlocking our smartphones to our day-to-day activities, online shopping, intelligent car dashboards, autonomous robots and so on. Though the concept of AI was first talked about in the early 1950s, forming a basis for many computer learning and complex decision-making processes, it is only of late, where processing huge amounts of data is required, that this field of technology is picking up pace. What is in the AI basket? AI is not a technology, rather it is a science or field of study.

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

In this post, you learn how to create a MLOps project to automate the deployment of an Amazon SageMaker endpoint with multiple production variants for A/B testing. You also deploy a general purpose API and testing infrastructure that includes a multi-armed bandit experiment framework. This testing infrastructure will automatically optimize traffic to the best-performing model over time based on user feedback. Amazon SageMaker MLOps projects are a new capability recently released with Amazon SageMaker Pipelines, the first purpose-built, easy-to-use, continuous integration and continuous delivery (CI/CD) service for ML. The MLOps project template provisions the initial setup required for a complete end-to-end MLOps system, including model building, training, and deployment, and can be customized to support your own organizations requirements.

Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, AutoML, training, tuning, hosting, explainability, monitoring, and workflow automation. You can use a variety of techniques to deploy new ML models to production, so choosing the right strategy is an important decision. You must weigh the options in terms of the impact of change on the system and on the end users. In this post, we show you how to deploy using a shadow deployment strategy.