NOTE: THIS IS AN EARLY TIME SLOT SESSION - repeated on Thu Jan 26 at 3:30-4:30 pm ET. This a follow-up to the "Pre-Trained Language Models: A New Standard for NLU Tasks" sessions held on Jan 18 & 19, 2023, where we introduced pre-trained language models and explained how they have become a new standard for solving various NLP tasks. In this session, we will start with a quick recap of the previous session and then do a deep dive into the tools and frameworks for loading and fine-tuning pre-trained language models. We will load models from the HuggingFace Model Hub https://huggingface.co/models and fine-tune them on a public dataset to solve a downstream NLP task using a small dataset and a few training steps. Reza Fazeli is a conversational AI engineer for Watson Assistant, working closely with IBM Research teams to develop and deploy algorithms for improving our virtual assistant products.

The connector leverages eGain's unique BYOB (Bring Your Own Bot) architecture, allowing business users to easily plug in the Watson Assistant into the eGain platform with no coding. Per Gartner, less than 10% of customer service journeys are fulfilled using self-service, which is why it is critical to integrate chatbots with human-assisted service channels such as live chat. The eGain Connector for Watson Assistant improves customer, agent, and business experiences at once. When customers escalate from Watson to human-assisted chat, their context is passed to the contact center agent so that they do not need to repeat information to the agent. Agents get to see interactions that customers have already had with Watson before they start their conversation with the customer.

"We shape our tools and, thereafter, our tools shape us." Making the right technology decisions at the start of your chatbot journey has a significant influence on what your chatbot's trajectory will be. Choose and shape your tools wisely. As later in the process those tools will shape and influence the way you plan, develop, scale your chatbot. Chatbot development tools and frameworks can be divided into three categories, roughly.

Chatbots have revolutionized the way humans interact with computer systems and they have substituted the use of service agents, call-center representatives etc. Fitness industry has always been a growing industry although it has not adapted to the latest technologies like AI, ML and cloud computing. In this paper, we propose an idea to develop a chatbot for fitness management using IBM Watson and integrate it with a web application. We proposed using Natural Language Processing (NLP) and Natural Language Understanding (NLU) along with frameworks of IBM Cloud Watson provided for the Chatbot Assistant. This software uses a serverless architecture to combine the services of a professional by offering diet plans, home exercises, interactive counseling sessions, fitness recommendations.

IBM Watson Assistant is a chatbot that employs artificial intelligence. It comprehends customers queries and responds quickly, consistently, and accurately across any application, device, or channel. And mainly Watson Assistant is a service that allows you to integrate conversational interfaces into any website or app. In this tutorial, I will show how to use Kommunicate to link a Watson Assistant chatbot to WhatsApp, extending its capabilities. Assuming you're familiar with Watson Assistant and how it works.

Note: This code pattern uses the classic Watson Assistant experience. After October 8, 2021, all instances (except the standard plan) can switch between the classic and new Watson Assistant experiences by going to the upper-right corner of the Watson Assistant screen and clicking the Manage icon. In this code pattern, build a framework that lets users send voice queries using the WhatsApp application and get a response from IBM Watson Assistant. The query from the user is sent to the Watson Speech to Text Service through a custom application. The output from the Watson Speech to Text Service is then fed to Watson Assistant.

Currently all actions created in the bot are included in the deployment version. I would like to be able, to select specific Actions, and only deploy selected Actions and not all actions in the bot. An orchestration layer managing or combining different bots might also be helpful. Within a bot, there will be various actions. You will get to a situation where you do not want to duplicate actions across bots, and use multiple bots simultaneously in one implementation.

For investors looking to invest in booming fields in the tech trade, artificial intelligence (AI) stocks are a viable play. Namely, the stock market today is home to various AI firms that employ the tech in a vast array of industries. This would be the case as the use cases for the tech continues to grow day by day. After all, not only does AI help organizations with computational issues, but the tech is constantly improving via machine learning. As a result, investors would be keeping an eye on emerging names in the field.

Gartner predicts that "by 2022, 70 percent of white-collar workers will interact with conversational platforms on a daily basis." As a result, the research group found that more organizations are investing in chatbot development and deployment. IBM Business Partners like Sopra Steria are making chatbot and virtual assistant technology available to businesses. Sopra Steria, a European leader in digital transformation, has developed an intelligent virtual assistant for organizations across several industries who want to use an AI conversational interface to answer recurrent customer service questions. In developing our solution, we at Sopra Steria were looking for AI technology that was easy to configure and could support multiple languages and complex dialogs.

This article is part of the Get started with natural language processing learning path. As shown in the previous demo, with IBM Watson natural language processing features, you can efficiently analyze and parse large amounts of text input to produce actionable insights. Give Watson a URL or a popular news site, and Watson is able to ingest text from the site and analyze it within seconds, much faster than a human. The text is analyzed by categories, concepts, emotions, entities, relations, sentiments, and more, all of which you can customize. The information extracted from this service enables you to find more meaning in text, understand trends, and recommend similar content from large amounts of data.