After the success of Deep Blue, the chess-playing computer that beat grandmaster Garry Kasparov in 1997, IBM turned to the challenge of trying to beat the best Jeopardy! players. The result of this undertaking was a computer system named Watson, which managed to beat the two most successful participants to date in two exhibition matches in 2011.
Watson is an IBM supercomputer that combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a “question answering” machine. The supercomputer is named for IBM’s founder, Thomas J. Watson.
IBM Watson is an example of a cognitive system. It can tease apart the human language to identify inferences between text passages with human-like accuracy, and at speeds and scale that are far faster and far bigger than any person can do on their own. It can manage a high level of accuracy when it comes to understanding the correct answer to a question. However, Watson does not really understand the individual words in the language. Rather it understands the features of language that are used by people. From those features, it can determine whether one text passage (which we call a question) infers another text passage (which we call an answer), with a high level of accuracy under changing circumstances.
Watson teases apart the question and potential responses in the corpus, and then examines it and the context of the statement in hundreds of ways. Watson then uses the results to gain a degree of confidence in its interpretation of the question and potential answers. But we must back up a bit. How does Watson derive its responses to questions?
It goes through the following process:
- When a question is first presented to Watson, it parses the question to extract the major features of the question.
- It generates a set of hypotheses by looking across the corpus for passages that have some potential for containing a valuable response.
- It performs a deep comparison of the language of the question and the language of each potential response by using various reasoning algorithms. This step is challenging. There are hundreds of reasoning algorithms, each of which does a different comparison. For example, some look at the matching of terms and synonyms, some look at the temporal and spatial features, and some look at relevant sources of contextual information.
- Each reasoning algorithm produces one or more scores, indicating the extent to which the potential response is inferred by the question based on the specific area of focus of that algorithm.
- Each resulting score is then weighted against a statistical model that captures how well that algorithm did at establishing the inferences between two similar passages for that domain during the “training period” for Watson. That statistical model can then be used to summarize a level of confidence that Watson has about the evidence that the candidate answer is inferred by the question.
- Watson repeats this process for each of the candidate answers until it can find responses that surface as being stronger candidates than the others of paramount importance to the operation of Watson is a knowledge corpus. This corpus consists of all kinds of unstructured knowledge, such as textbooks, guidelines, how-to manuals, FAQs, benefit plans, and news. Watson ingests the corpus, going through the entire body of content to get it into a form that is easier to work with. The ingestion process also curates the content. That is, it focuses on whether the corpus contains appropriate content, sifting out the articles or pages that are out of date, that are irrelevant, or that come from potentially unreliable sources.
Internet of Things is changing the way that businesses operate and people interact with the physical world. A cognitive IoT can make sense of all types of data. It can choose its own data sources and decide which patterns and relationships to pay attention to. It uses machine learning and advanced processing to organize the data and generate insights. A cognitive IoT can also evolve and improve on its own through learned self-correction and adaptation.
Watson is currently in pilots with leading healthcare and financial services organizations and will be expanding to production-level deployments in new use cases and industries going forward. Getting ready to deploy a Watson solution takes thoughtful planning and selection of where and how to apply its power. Laying the groundwork often involves building strong big data and analytics capabilities which are complementary to Watson itself. Doing so on project-by-project basis ensures that each step is justifiable on its own from a financial and business value perspective and also brings you closer to readiness to apply the transformative power of Watson to your business.
Watson Analytics is a smart data analysis and visualization service you can use to quickly discover patterns and meaning in your data – all on your own. With guided data discovery, automated predictive analytics and cognitive capabilities such as natural language dialogue, you can interact with data conversationally to get answers you understand. Whether you need to quickly spot a trend or you have a team that needs to visualize report data in a dashboard, Watson Analytics has you covered.
IBM Watson’s key components include:
- Apache UIMA (Unstructured Information Management Architecture) frameworks, infrastructure and other elements required for the analysis of unstructured data.
- Apache’s Hadoop, a free, Java-based programming framework that supports the processing of large data sets in a distributed computing
- SUSE Enterprise Linux Server 11, the fastest available Power7 processor operating system.
- 2,880 processor cores.
- 15 terabytes of RAM.
- 500 gigabytes of preprocessed information.
- IBM’s DeepQA software, which is designed for information retrieval that incorporates natural language processing and machine learning.
IBM Watson IoT Platform can help you get a quick start on your next Internet of Things project. It is a fully managed, cloud-hosted service designed to make it simple to derive value from your Internet of Things devices. It provides capabilities such as device registration, connectivity, control, rapid visualization and storage of Internet of Things data.
Watson is a project to use natural language processing to analyse data. The goal is to search through data and draw meaning from it. Made famous on Jeopardy in 2011 where it successfully defeated top opponents. As an adaptive system, it learns when it is wrong and corrects itself . A cognitive AI system for understanding complex data and information. Multiple Incarnations in IBM research facilities . Involved Universities: RPI, MIT, Carnegie Mellon, UT Austin,..
Watson Health is aimed to provide support for physicians by offering treatment and analyzing patient’s symptoms. Natural language processing – the ability for software to understand the intent and the meaning of the question asked by a human Tradeoff analytics – providing optimized solutions to conflicting objectives.
On the way towards Cognitive Computing, Watson adopts key elements of cognitive computing :
- Expanding the boundaries of human cognition : Watson extends a capability of a human to reason, think deeply, manipulate and manage huge amount of data (not only to search in big volume, but make decisions on top of it).
- More natural human-computer interaction : Watson applies more natural interaction and engagement with computers via more general speech and natural language communication with the system, as well as, use of infographics and visual data representation techniques.
- Use of Learning : Watson helps to design personalized and adaptable systems able to constantly learn and evolve based on feedback from used interaction applying machine learning, statistics, etc.
IBM Watson Applications:
- In business environment Watson Analytics can be fed with unstructured data and asked in natural language to find connections.
- Watson can talk with children, answering the typical questions with the level adjusted to comprehensive level of a child.
- Watson for Cyber Security project is aimed to create a cognitive system able to respond to the security threats.
IBM Watson Architecture overview:
Illustrates the five types of technologies that augment and support Watson’s cognitive capabilities :
Watson Advisors. These IBM-designed solutions target a multi-industry or industry-specific task. Examples include answering customer or agent questions (Engagement Advisor), conducting document research (Discovery Advisor), or processing and advising on compliance requests (Policy Advisor). Industry-specific offerings include Watson for Oncology, Watson for Wealth Management, and Chef Advisor. Other one-of-akind advisor solutions are likely to be developed in the future.
Watson Platform Components. Watson Explorer, now available, and Watson Analytics, currently in beta, expand Watson’s capabilities to more effectively mine structured data, including the integration of existing databases. IBM intends to move toward a hybrid environment, which leverages insights from both unstructured and structured data—all calibrated to the specific business situation.
Watson Platform Services. Watson was introduced with only the Question Answer application program interface (API). One year later, Watson now has a total of 13 APIs and SDKs, with more coming soon and throughout the year.
Watson Data Services. In October 2014, the Watson Curator was introduced to help clients efficiently assess and gather relevant information across multiple sources. This service helps subject matter experts create higher-quality information collections. • Watson Foundations. IBM is branding many of its existing information management and analytics software products under the Watson umbrella as a bridge to cognitive computing.
Here is the list of Top 35 IoT Platforms in 2018.
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