What is IBM Watson?

by Stephen M. Walker II, Co-Founder / CEO

What is Watson?

IBM Watson is a computer system capable of answering questions posed in natural language. It was developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's founder and first CEO, industrialist Thomas J. Watson.

Watson was initially developed to answer questions on the quiz show Jeopardy! and in 2011, it competed against champions Brad Rutter and Ken Jennings, winning the first place prize of 1 million USD.

Watson uses advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to answer questions. It uses more than 100 different techniques to analyze natural language, identify sources, find and generate hypotheses, find and score evidence, and merge and rank hypotheses.

Watson's capabilities have been applied in various fields such as medical diagnosis, business analytics, and tech support. IBM sees a future in which these fields are automated by question-answering software like Watson.

Watson is based on commercially available IBM Power 750 servers. Its architecture consists of 2,880 POWER7 processor threads, 16 terabytes of RAM, and it operates at a speed of 80 TeraFLOPS.

Watson has evolved from a single computer to a suite of products that enable organizations to infuse AI and automation into business workflows. The latest product in the Watson suite is Watsonx, an enterprise-ready AI and data platform that organizations can use to train and deploy machine learning models for a range of applications and use cases.

The Watson suite includes products like Watsonx Assistant, a conversational AI platform; Watsonx Code Assistant, which provides AI-generated recommendations for software developers; IBM Watson Discovery, an AI-powered platform for document understanding and content analysis; and Watson Studio, a set of tools to prepare and analyze data and to develop sophisticated machine learning models.

Watson has been used in various sectors including healthcare, finance, legal, retail, and even fantasy football. In healthcare, it has been used for cancer research and patient care, speeding up DNA analysis in cancer patients to help make their treatment more effective. In finance, Watson's question and answer capabilities have been used to give financial guidance and manage financial risk. In the legal sector, Watson has been used to answer legal questions, and in retail, it has been used to customize preferences for individual consumers.

What is Watson's history?

IBM Watson is a computer system capable of answering questions posed in natural language. It was developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's founder and first CEO, industrialist Thomas J. Watson. The system was initially developed to answer questions on the quiz show Jeopardy! and in 2011, the Watson computer system competed on Jeopardy! against champions Brad Rutter and Ken Jennings, winning the first place prize of 1 million USD.

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. Watson uses IBM's DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework implementation. The system was written in various languages, including Java, C++, and Prolog, and runs on the SUSE Linux Enterprise Server 11 operating system using the Apache Hadoop framework to provide distributed computing.

In February 2013, IBM announced that Watson's first commercial application would be for utilization management decisions in lung cancer treatment at Memorial Sloan Kettering Cancer Center, New York City, in conjunction with WellPoint (now Elevance Health) . However, some of Watson's healthcare initiatives, such as Watson for Genomics and Watson for Oncology, were discontinued due to their lack of flexibility and usefulness.

Despite these setbacks, Watson has found success in other areas. For instance, Watson's natural language processing technology powers IBM’s popular Watson Assistant, used by businesses to automate customer service inquiries. As of 2023, IBM reported having 40,000 Watson customers across 20 industries worldwide, more than double the number four years ago.

In recent years, IBM has shifted Watson's focus towards generative AI. In 2023, IBM announced a new AI platform called WatsonX that aims to give business clients a toolkit to build their own generative AI models, including services for data storage, model training, and documentation. This move marks a new chapter in Watson's history, as IBM seeks to leverage foundational models to help businesses consume and leverage AI.

Watson's history is marked by significant achievements in natural language processing and question answering, as well as some setbacks in healthcare applications. Today, Watson continues to evolve, with a focus on generative AI and providing businesses with the tools to leverage AI in their operations.

What is Watson's purpose?

IBM Watson's purpose is to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to a range of applications and processes, enabling computers to interact in natural human terms and provide answers that humans can understand and justify. Watson was initially designed as a question-answering system, but its applications have expanded to various industries, including healthcare, finance, law, and academia.

Today, Watson is not a single computer but a suite of products that help organizations infuse AI and automation into their business workflows. Some of the products in the Watson suite include Watson Discovery, Watson Assistant, and Watson Studio. These products enable organizations to build AI-based applications, automate customer service inquiries, and develop machine learning models, among other tasks. Overall, Watson's purpose is to enhance decision-making, improve efficiency, and provide valuable insights across a wide range of industries and use cases.

How does Watson work?

IBM Watson is a question-answering computer system capable of interpreting and responding to questions posed in natural language. It was developed by IBM's DeepQA project and is named after IBM's founder and first CEO, Thomas J. Watson.

Watson uses more than 100 different techniques to analyze natural language, identify sources, generate hypotheses, find and score evidence, and merge and rank hypotheses. It applies advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.

Watson's software, IBM's DeepQA, and the Apache UIMA (Unstructured Information Management Architecture) framework implementation are written in various languages, including Java, C++, and Prolog. It runs on the SUSE Linux Enterprise Server 11 operating system using the Apache Hadoop framework for distributed computing.

The system's information sources include encyclopedias, dictionaries, thesauri, newswire articles, and literary works. Watson also uses databases, taxonomies, and ontologies, including DBPedia, WordNet, and Yago. The IBM team provided Watson with millions of documents, including dictionaries, encyclopedias, and other reference material, to build its knowledge.

Watson's capabilities have been extended in recent years to take advantage of new deployment models, evolved machine learning capabilities, and optimized hardware available to developers and researchers. It can now 'see', 'hear', 'read', 'talk', 'taste', 'interpret', 'learn', and 'recommend'.

Watson Natural Language Understanding, an API, uses machine learning to extract meaning and metadata from unstructured text data. Watson Machine Learning, a service on IBM Cloud, has features for training and deploying machine learning models and neural networks.

IBM embraces five foundational pillars of trustworthy AI: Explainability, Fairness, Robustness, Transparency, and Privacy. These pillars underpin the development, deployment, and use of AI systems like Watson.

Watson works by applying a combination of natural language processing, machine learning, and other AI technologies to analyze and interpret natural language, generate and evaluate hypotheses, and provide answers or recommendations. It uses a vast array of information sources and has been continually improved and extended to take advantage of new technologies and deployment models.

What are some applications of Watson?

IBM Watson, a question-answering computer system capable of processing natural language, has a wide range of applications across various industries. Here are some key applications:

  1. Healthcare — Watson is used extensively in the medical field. It assists with cancer research and patient care at top cancer hospitals like Memorial Sloan Kettering Cancer Center, University of Texas MD Anderson Cancer Center, and the Mayo Clinic. Watson speeds up DNA analysis in cancer patients to make their treatment more effective. It also aids physicians with diagnoses. For instance, a dermatology app called schEMA allows doctors to input patient data, and Watson, using natural-language processing (NLP), helps identify potential symptoms and treatments. Watson also uses vision recognition to help doctors read scans such as X-rays and MRIs.

  2. Finance — In the financial sector, Watson's question and answer capabilities are used for financial guidance and risk management. Companies like ANZ Global Wealth use the Watson Engagement Advisor Tool to observe and field customer questions. DBS bank in Singapore uses Watson to ensure proper advice and experience for customers of its wealth management business.

  3. Legal — Startups like ROSS Intelligence use Watson to answer legal questions. Users can ask questions in plain English, and the app uses NLP to understand the questions and sift through a database to return a cited answer with relevant legislation.

  4. Retail — Watson is used in online travel purchases. Travel company WayBlazer has created a Discovery Engine that uses Watson to analyze data to better link additional offers and customize preferences for individual consumers.

  5. Sports — Watson is used in fantasy sports. Edge Up Sports has a Watson-powered platform that analyzes NFL data to help fantasy football fans make better choices during the season.

  6. Machine Learning — Watson Machine Learning helps businesses simplify and harness AI at scale across any cloud. It helps data scientists and developers accelerate AI and machine learning deployments. It also allows for the deployment of models built with IBM Watson Studio and other open-source tools.

  7. Virtual Assistance — Watson Assistant provides customers with fast, consistent, and accurate answers across any application, device, or channel.

  8. Weather Forecasting — IBM uses Watson for weather forecasting. It analyzes data from over 200,000 Weather Underground personal weather stations, as well as data from other sources, as a part of Project Deep Thunder.

These are just a few examples of how Watson is applied across different sectors. Its capabilities in natural language processing, machine learning, and data analysis make it a versatile tool for many industries.

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