Bloomberg Television's show "That's Debatable" had an unusual participant on its show broadcasted on October 9. In a debate on the topic "Is it time to redistribute the world's wealth?", IBM Watson synthesised thousands of responses and opinions received from the public to incorporate into the debate. IBM Watson used a new natural language processing feature called key point analysis which categorises and summarises thousands of public opinions to a handful of concrete key points. Key point analysis is basically the next generation of'extractive summarisation' which processes statements in a given text document to summarise the most significant points.

During a livestreamed event this afternoon, Google detailed the ways it's applying AI and machine learning to improve the Google Search experience. Soon, Google says users will be able to see how busy places are directly in Google Maps without having to search for a specific business, an expansion of the existing busyness metrics. The company also said it's adding COVID- 19 safety information to business profiles across Search and Maps, revealing whether they're using safety precautions like temperature checks and more. An algorithmic improvement to "Did you mean," Google's spell-checking feature for Search, will enable more accurate and precise spelling suggestions. Google says the new model contains 680 million parameters and runs in less than three milliseconds.

Build Facebook Messenger Chatbot with IBM Watson Assistant - Facebook messenger chatbot Created by Tushar SKumarPreview this course Udemy GET COUPON CODE Chatbots are software agents capable of having interaction with human. The demand for chatbots are increasing everyday and the reason behind this is not implausible. They can also greatly build your brand so it is not surprise that being able to create a chatbot is a very lucrative skill. IBM Watson Assistant is the platform which allows user to utilize Artificial Intelligence without the coding background. After this course you will be able to build chatbot, will can learn by itself by leveraging on Watson's Natural Language Processing (NLP) capabilities.

Google showed how it's using artificial intelligence to provide users with better search results. During the company's "Search On" event on Thursday, Google announced new algorithms that not only parse through videos and articles to pull specific results, but can decipher your query through bad spelling and even what song you're interested in based on your humming the tune.. Explore this storyboard about Search Engines, Google Lens, Google by Tech on Flipboard.

What remains is a fingerprint Google compares with thousands of songs from around the world to identify potential matches in real time, much like the Pixel's Now Playing feature. "From new technologies to new opportunities, I'm really excited about the future of search and all of the ways that it can help us make sense of the world," Raghavan said. Last month, Google announced it will begin showing quick facts related to photos in Google Images, enabled by AI. Starting in the U.S. in English, users who search for images on mobile might see information from Google's Knowledge Graph -- Google's database of billions of facts -- including people, places, or things germane to specific pictures. Google also recently revealed it is using AI and machine learning techniques to more quickly detect breaking news around natural disasters and other crises.

What is Customer Effort, and how can it be measured from chatbot conversations? And, how can Disambiguation improve Customer Effort? Aslo, can Automatic Learning be employed to improve Customer Effort over time? Below you will find an explanation of what customer effort is. And a complete how to guide on extracting Customer Effort from your IBM Watson Assistant chatbot. Customer effort is an extremely convenient metric to measure your chatbots performance.

From cancelled conferences to disrupted supply chains, not a corner of the global economy is immune to the spread of COVID-19. IBM Watson Health has launched a digital blockchain app so individuals can prevent verifiable health status such as COVID-19 test results to gain access to various public locations. The app, IBM Digital Health Pass, allows individuals to store, manage and share health status from their mobile devices. The app uses IBM Blockchain and can use multiple data sources and will be available on Apple and Google platforms later this year. IBM launched IBM Digital Health Pass at the HLTH VRTL 2020 conference.

CARMA is an advisory system for rangeland grasshopper infestations that demonstrates how AI technology can deliver expert advice to compensate for cutbacks in public services. CARMA uses two knowledge sources for the key task of predicting forage consumption by grasshoppers: (1) cases obtained by asking a group of experts to solve representative hypothetical problems and (2) a numeric model of rangeland ecosystems. These knowledge sources are integrated through the technique of model-based adaptation, in which case-based reasoning is used to find an approximate solution, and the model is used to adapt this approximate solution into a more precise solution. CARMA has been used in Wyoming counties since 1996. The combination of a simple interface, flexible control strategy, and integration of multiple knowledge sources makes CARMA accessible to inexperienced users and capable of producing advice comparable to that produced by human experts.

In this article, we first survey the three major types of computer music systems based on AI techniques: (1) compositional, (2) improvisational, and (3) performance systems. Representative examples of each type are briefly described. Then, we look in more detail at the problem of endowing the resulting performances with the expressiveness that characterizes human-generated music. This is one of the most challenging aspects of computer music that has been addressed just recently. The main problem in modeling expressiveness is to grasp the performer's "touch," that is, the knowledge applied when performing a score.

Following a brief overview discussing why we prefer listening to expressive music instead of lifeless synthesized music, we examine a representative selection of well-known approaches to expressive computer music performance with an emphasis on AI-related approaches. In the main part of the paper we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on TempoExpress, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting on complementing audio information with information of the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This paper is based on the "2011 Robert S. Engelmore Memorial Lecture" given by the first author at AAAI/IAAI 2011.