With Arc enabled machine learning, customers can mix and match CPU hosts and GPU hosts of a Kubernetes cluster to run distributed training jobs.

The choice between traditional machine vision and deep learning depends upon the type of application being solved, the amount of data being …

March 15, 2021 — Machine-learning models can predict whether a CT-guided transthoracic biopsy will have complications and if so, how severe they …

… their country and Western economies, they are poised to become worldwide leaders in artificial intelligence, big data, and machine learning.

sponsored. In the era of the Internet of Things, machine learning and technology that drives innovation and growth for industries, there’s no dispute that …

Most conversational recommendation approaches are either not explainable, or they require external user's knowledge for explaining or their explanations cannot be applied in real time due to computational limitations. In this work, we present a real time category based conversational recommendation approach, which can provide concise explanations without prior user knowledge being required. We first perform an explainable user model in the form of preferences over the items' categories, and then use the category preferences to recommend items. The user model is performed by applying a BERT-based neural architecture on the conversation. Then, we translate the user model into item recommendation scores using a Feed Forward Network. User preferences during the conversation in our approach are represented by category vectors which are directly interpretable. The experimental results on the real conversational recommendation dataset ReDial [12] demonstrate comparable performance to the state-of-the-art, while our approach is explainable. We also show the potential power of our framework by involving an oracle setting of category preference prediction. Keywords: Conversational Recommendation · Category Preference Based Recommendation · Explainable Conversational Recommendation · Cold Start Explainable Recommendation.

Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features -- informed by the needs of our participants -- that could improve user understanding of and trust in fairness-aware recommender systems.

Christoph Molnar is one of the main people to know in the space of interpretable ML. In 2018 he released the first version of his incredible online book, interpretable machine learning. Interpretability is often a deciding factor when a machine learning (ML) model is used in a product, a decision process, or in research. Interpretability methods can be used to discover knowledge, to debug or justify the model and its predictions, and to control and improve the model, reason about potential bias in models as well as increase the social acceptance of models. But Interpretability methods can also be quite esoteric, add an additional layer of complexity and potential pitfalls and requires expert knowledge to understand.

A new intelligent system relying on artificial intelligence technology has been launched. Its collects data on calls, and can analyze residents' …

It estimated that AI will contribute $150 trillion to the global economy in the coming years.