Project Controls Cubed is banking on InEight Schedule's Iris AI to automatically generate new schedules for massively complex projects they've already …

If we need to learn one thing about the numerous AI applications around us today, it is that they are examples of "artificial specific intelligence." In other words, they rely on algorithms that are great at very particular tasks, such as selecting a movie based on our watching history or keeping our car in the proper lane on the highway. Because it is so highly specialized, AI greatly outperforms human intelligence in those narrowly defined tasks. Take it from a person who recently spent 50 minutes picking a movie that itself lasted 77 minutes. However, AI's effectiveness at specialized jobs comes at the price of severe context blindness and a general inability to develop meaningful feedback loops: The typical algorithm does not and cannot consider the wider implications of the decisions it makes and hardly affords us users any control over its inner workings.

One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models on how to resolve the dependency, and thus these models are limited in understanding human dialogues. In this paper, we propose a novel framework, ExCorD (Explicit guidance on how to resolve Conversational Dependency) to enhance the abilities of QA models in comprehending conversational context. ExCorD first generates self-contained questions that can be understood without the conversation history, then trains a QA model with the pairs of original and self-contained questions using a consistency-based regularizer. In our experiments, we demonstrate that ExCorD significantly improves the QA models' performance by up to 1.2 F1 on QuAC, and 5.2 F1 on CANARD, while addressing the limitations of the existing approaches.

Over the past two decades, recommender systems have attracted a lot of interest due to the explosion in the amount of data in online applications. A particular attention has been paid to collaborative filtering, which is the most widely used in applications that involve information recommendations. Collaborative filtering (CF) uses the known preference of a group of users to make predictions and recommendations about the unknown preferences of other users (recommendations are made based on the past behavior of users). First introduced in the 1990s, a wide variety of increasingly successful models have been proposed. Due to the success of machine learning techniques in many areas, there has been a growing emphasis on the application of such algorithms in recommendation systems. In this article, we present an overview of the CF approaches for recommender systems, their two main categories, and their evaluation metrics. We focus on the application of classical Machine Learning algorithms to CF recommender systems by presenting their evolution from their first use-cases to advanced Machine Learning models. We attempt to provide a comprehensive and comparative overview of CF systems (with python implementations) that can serve as a guideline for research and practice in this area.

Artificial intelligence is an intuitive principle that integrates machine learning in creating the shift from the traditional ways to a new paradigm.

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