IBM recently announced several new Watson technologies designed to help organizations identify, understand, and analyze some of the most challenging aspects of the English language with greater clarity and insights. These new features are considered the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater. There is a new advanced sentiment analysis feature defined to identify and analyze idioms and colloquialisms for the first time. So it can recognize phrases such as "hardly helpful" or "hot under the collar." Phrases like those have been challenging for artificial intelligence systems since they are difficult for algorithms to spot.

In previous posts we explored what analysts want to discover about their virtual assistant and some building blocks for building analytics. In this post I will demonstrate some common recipes tailored to Watson Assistant logs. First we extract raw log events and store on the file system. This requires the apikey and URL for your skill. For a single-skill assistant you will also need the workspace ID (extractable from the "Legacy v1 Workspace URL"), for a multi-skill assistant there are other IDs you can use to filter on (described in the Watson Assistant list log events API).

Artificial intelligence researchers at IBM have introduced a major upgrade to the famed Watson computer, allowing it to understand idioms and colloquialisms for the first time. IBM says the update makes it the first commercial AI system capable of identifying, understanding and analysing some of the most challenging aspects of the English language. Phrases like "hardly helpful" and "hot under the collar" are tricky for algorithms to spot, meaning AI is unable to debate complex topics or have nuanced conversations with humans. "Language is a tool for expressing thought and opinion, as much as it is a tool for information," said Rob Thomas, a general manager at IBM Data and AI. "This is why we believe that advancing our ability to capture, analyse, and understand more from language with NLP will help transform how businesses utilise their intellectual capital that is codified in data." Russia has launched a humanoid robot into space on a rocket bound for the International Space Station (ISS).

IBM is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights. The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyze idioms and colloquialisms for the first time. Phrases, like'hardly helpful,' or'hot under the collar,' have been challenging for AI systems because they are difficult for algorithms to spot. With advanced sentiment analysis, businesses can begin analyzing such language data with Watson APIs for a more holistic understanding of their operation.

The travel sector has arguably been slower than other industries to take up machine learning – a subset of the larger field of Artificial Intelligence – focusing on automation methods to learn and predict, from past data. Is it a cultural phenomenon? The travel industry is among most traditional of all in terms of its main selling point – the personalised, human-facing customer experience – and has struggled to come to terms with machines replacing human recommendation and action. Today's customer is seeking more answers, more quickly, from companies before and after buying products and services – and the modern traveller is no exception. Traditional travel firms need to move with the times and respond to customer expectations.

The supply chain industry is in a state of transition and transformation. New technology such as AI, Big Data and machine learning is making life easier for industry executives as an ever-increasing number of companies begin to digitise their offerings. In order to stay ahead in a dynamic and continuously evolving industry, businesses must trial technology to increase efficiency. The technology giants, IBM Watson, understands the challenge that supply chains face. The company has announced Watson Supply Chain Insights, an AI-based solution that enables supply chain professionals to get through a data overload for enhanced visibility throughout the entire supply chain.

NEWPORT NEWS, Va., Jan. 30, 2020 – More than 1,600 nuclear physicists worldwide depend on the Continuous Electron Beam Accelerator Facility for their research. Located at the Department of Energy's Thomas Jefferson National Accelerator Facility in Newport News, Va., CEBAF is a DOE User Facility that is scheduled to conduct research for limited periods each year, so it must perform at its best during each scheduled run. But glitches in any one of CEBAF's tens of thousands of components can cause the particle accelerator to temporarily fault and interrupt beam delivery, sometimes by mere seconds but other times by many hours. Now, accelerator scientists are turning to machine learning in hopes that they can more quickly recover CEBAF from faults and one day even prevent them. Anna Shabalina is a Jefferson Lab staff member and principal investigator on the project, which has been funded by the Laboratory Directed Research & Development program for the fiscal year 2020.

Learning from Observation (LfO), also known as Behavioral Cloning, is an approach for building software agents by recording the behavior of an expert (human or artificial) and using the recorded data to generate the required behavior. jLOAF is a platform that uses Case-Based Reasoning to achieve LfO. In this paper we interface jLOAF with the popular OpenAI Gym environment. Our experimental results show how our approach can be used to provide a baseline for comparison in this domain, as well as identify the strengths and weaknesses when dealing with environmental complexity.

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The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence (AI) in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations. While distance functions for propositional representations have been thoroughly studied, work on distance functions for structured representations, such as graphs, frames or logical clauses, has been carried out in different communities and is much less understood. Specifically, a significant amount of work that requires the use of a distance or similarity function for structured representations of data usually employs ad-hoc functions for specific applications. Therefore, the goal of this paper is to provide an overview of this work to identify connections between the work carried out in different areas and point out directions for future work.