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2. IBM Watson - Wikipedia

   en.wikipedia.org/wiki/IBM_Watson

   The high-level architecture of IBM's DeepQA used in Watson [9]. Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.

3. IBM Watsonx - Wikipedia

   en.wikipedia.org/wiki/IBM_Watsonx

   Watsonx.ai is a platform that allows AI developers to leverage a wide range of LLMs under IBM's own Granite series and others such as Facebook's LLaMA-2, free and open-source model Mistral and many others present in Hugging Face community for a diverse set of AI development tasks.

4. Question answering - Wikipedia

   en.wikipedia.org/wiki/Question_answering

   In 2011, Watson, a question answering computer system developed by IBM, competed in two exhibition matches of Jeopardy! against Brad Rutter and Ken Jennings, winning by a significant margin. [32] Facebook Research made their DrQA system [33] available under an open source license. This system uses Wikipedia as knowledge source. [2]

5. IBM's Watson-powered voice assistant is built for security pros

   www.aol.com/news/2017-02-13-ibm-watson-voice...

   IBM is not only launching Watson for Cybersecurity, a cognitive computing service that parses legions of security reports to extract relevant info, but is unveiling an experimental voice helper to ...

6. Chatbot - Wikipedia

   en.wikipedia.org/wiki/Chatbot

   A chatbot (originally chatterbot) [1] is a software application or web interface designed to have textual or spoken conversations. [2] [3] [4] Modern chatbots are typically online and use generative artificial intelligence systems that are capable of maintaining a conversation with a user in natural language and simulating the way a human would behave as a conversational partner.

7. Virtual assistant - Wikipedia

   en.wikipedia.org/wiki/Virtual_assistant

   Virtual assistants spur the filter bubble: As for social media, virtual assistants' algorithms are trained to show pertinent data and discard others based on previous activities of the consumer: The pertinent data is the one which will interest or please the consumer. As a result, they become isolated from data that disagrees with their ...