Watson - A new era of computing.

Editor's Notes

• #3: Watson Business Unit Launch video can be downloaded here: http://cattail.boulder.ibm.com/cattail/#[email protected]/files/31441EF04E293DDE8309DEEB093F23B6 Preview on YouTube here http://www.youtube.com/watch?feature=player_embedded&v=Y_cqBP08yuA To link the videos to the images, put the MOV files on the computer you will be using to present (or if you're bringing a memory stick and presenting from someone else's machine, put the vids on the memory stick), right click the screen capture image in the PPT, select "action settings" and hit the "hyperlink to" radio button. In the drop down box, scroll to the "other file" option and then browse to select the right video file. It helps if you have the video files already launched and sized to fit the screen BEFORE you start presenting so that there won't be a lag as the files open or a chance that they are not full screen. After you link the files, you can't change their location on the computer or the link will break.
• #6: Main Point: Watson represents a whole new class of industry specific solutions called cognitive systems. It builds on the current paradigm of Programmatic Systems and is not meant to be a replacement; programmatic systems will be with us for the foreseeable future. But in many cases, keeping pace with the demands of an increasingly complex business environment and challenges requires a paradigm shift in what we should expect from IT. We need an approach that recognizes today’s realities and treats them as opportunities rather than challenges. Further speaking points: For example, most digitized information of the past was structured. It was organized into tables, stored in easily identified cells in databases, and easily searched and accessed. Unstructured information was largely ignored as too difficult to utilize…and therefore it lay fallow. Similarly, traditional IT has largely limited itself to deterministic applications. 2+2=4. 100cm in a meter. Situations where there is only one answer to a question But this rules out a whole world of real world situations that have a more probabilistic outcome. It is very likely that the car will not start because of a dead battery but there is a chance there is a clog in the fuel line. It is very likely to be sunny tomorrow but it may rain. Traditional IT relies on search to find the location of a key phrase. Emerging IT gathers information and combines it for true discovery. Traditional IT can handle only small sets of focused data while IT today must live with big data. And traditional IT interacts with machine language while what we as users really need is interaction the way we ourselves communicate – in natural language.
• #7: Main Point: It also helps to briefly summarize what makes Watson unique and set apart from conventional software systems as well as what that means for outcomes for users. At the core of what makes Watson different are three powerful technologies - natural language, hypothesis generation, and evidence based learning. But Watson is more than the sum of its individual parts. Watson is about bringing these capabilities together in a way that’s never been done before resulting in a fundamental change in the way businesses look at quickly solving problems What does this mean for users? <shift focus to right hand side> All of this means new and different possibilities for users that have never been possible in the past. <hit a few highlights from the list depending on the priories of the customer>
• #14: DeepQA generates and scores many hypotheses using an extensible collection of Natural Language Processing, Machine Learning and Reasoning Algorithms. These gather and weigh evidence over both unstructured and structured content to determine the answer with the best confidence. DeepQA generates and scores many hypotheses using an extensible collection of Natural Language Processing, Machine Learning and Reasoning Algorithms. These gather and weigh evidence over both unstructured and structured content to determine the answer with the best confidence. Watson – the computer system we developed to play Jeopardy! is based on the DeepQA softate archtiecture.Here is a look at the DeepQA architecture. This is like looking inside the brain of the Watson system from about 30,000 feet high. Remember, the intended meaning of natural language is ambiguous, tacit and highly contextual. The computer needs to consider many possible meanings, attempting to find the evidence and inference paths that are most confidently supported by the data. So, the primary computational principle supported by the DeepQA architecture is to assume and pursue multiple interpretations of the question, to generate many plausible answers or hypotheses and to collect and evaluate many different competing evidence paths that might support or refute those hypotheses. Each component in the system adds assumptions about what the question might means or what the content means or what the answer might be or why it might be correct. DeepQA is implemented as an extensible architecture and was designed at the outset to support interoperability. <UIMA Mention> For this reason it was implemented using UIMA, a framework and OASIS standard for interoperable text and multi-modal analysis contributed by IBM to the open-source community. Over 100 different algorithms, implemented as UIMA components, were integrated into this architecture to build Watson. In the first step, Question and Category analysis, parsing algorithms decompose the question into its grammatical components. Other algorithms here will identify and tag specific semantic entities like names, places or dates. In particular the type of thing being asked for, if is indicated at all, will be identified. We call this the LAT or Lexical Answer Type, like this “FISH”, this “CHARACTER” or “COUNTRY”. In Query Decomposition, different assumptions are made about if and how the question might be decomposed into sub questions. The original and each identified sub part follow parallel paths through the system. In Hypothesis Generation, DeepQA does a variety of very broad searches for each of several interpretations of the question. Note that Watson, to compete on Jeopardy! is not connected to the internet. These searches are performed over a combination of unstructured data, natural language documents, and structured data, available data bases and knowledge bases fed to Watson during training. The goal of this step is to generate possible answers to the question and/or its sub parts. At this point there is very little confidence in these possible answers since little intelligence has been applied to understanding the content that might relate to the question. The focus at this point on generating a broad set of hypotheses, – or for this application what we call them “Candidate Answers”. To implement this step for Watson we integrated and advanced multiple open-source text and KB search components. After candidate generation DeepQA also performs Soft Filtering where it makes parameterized judgments about which and how many candidate answers are most likely worth investing more computation given specific constrains on time and available hardware. Based on a trained threshold for optimizing the tradeoff between accuracy and speed, Soft Filtering uses different light-weight algorithms to judge which candidates are worth gathering evidence for and which should get less attention and continue through the computation as-is. In contrast, if this were a hard-filter those candidates falling below the threshold would be eliminated from consideration entirely at this point. In Hypothesis & Evidence Scoring the candidate answers are first scored independently of any additional evidence by deeper analysis algorithms. This may for example include Typing Algorithms. These are algorithms that produce a score indicating how likely it is that a candidate answer is an instance of the Lexical Answer Type determined in the first step – for example Country, Agent, Character, City, Slogan, Book etc. Many of these algorithms may fire using different resources and techniques to come up with a score. What is the likelihood that “Washington” for example, refers to a “General” or a “Capital” or a “State” or a “Mountain” or a “Father” or a “Founder”? For each candidate answer many pieces of additional Evidence are search for. Each of these pieces of evidence are subjected to more algorithms that deeply analyze the evidentiary passages and score the likelihood that the passage supports or refutes the correctness of the candidate answer. These algorithms may consider variations in grammatical structure, word usage, and meaning. In the Synthesis step, if the question had been decomposed into sub-parts, one or more synthesis algorithms will fire. They will apply methods for inferring a coherent final answer from the constituent elements derived from the questions sub-parts. Finally, arriving at the last step, Final Merging and Ranking, are many possible answers, each paired with many pieces of evidence and each of these scored by many algorithms to produce hundreds of feature scores. All giving some evidence for the correctness of each candidate answer. Trained models are applied to weigh the relative importance of these feature scores. These models are trained with ML methods to predict, based on past performance, how best to combine all this scores to produce final, single confidence numbers for each candidate answer and to produce the final ranking of all candidates. The answer with the strongest confidence would be Watson’s final answer. And Watson would try to buzz-in provided that top answer’s confidence was above a certain threshold. ---- The DeepQA system defers commitments and carries possibilities through the entire process while searching for increasing broader contextual evidence and more credible inferences to support the most likely candidate answers. All the algorithms used to interpret questions, generate candidate answers, score answers, collection evidence and score evidence are loosely coupled but work holistically by virtue of DeepQA’s pervasive machine learning infrastructure. No one component could realize its impact on end-to-end performance without being integrated and trained with the other components AND they are all evolving simultaneously. In fact what had 10% impact on some metric one day, might 1 month later, only contribute 2% to overall performance due to evolving component algorithms and interactions. This is why the system as it develops in regularly trained and retrained. DeepQA is a complex system architecture designed to extensibly deal with the challenges of natural language processing applications and to adapt to new domains of knowledge. The Jeopardy! Challenge has greatly inspired its design and implementation for the Watson system.
• #17: Main Point: To put the announcements in context, it’s helpful to see some of the highpoints of this past year. IBM Watson announced its first two commercial offerings in February (both in healthcare) Announced the Watson Engagement Advisor in May to help lower barriers to discussions and interaction between brands and their customers In October we announced the work we’ve been doing with MD Anderson and their Expert Oncology Advisor powered by Watson to bridge clinical and medical research And then in November, we announced the Watson Ecosystem program which opened Watson as a platform for development by third party software developers. All of this sets the stage for the next wave of innovation
• #19: Main Point: The Watson Group has several inter-related components each of which contribute to a new generation of cognitive apps. After the Jeopardy championship, IBM began developing one-of-a-kind solutions to meet extremely challenging use cases. Examples include our work in partnership with Memorial Sloan Kettering, WellPoint, and MD Anderson. We’ve also created scalable, repeatable solutions called Watson Advisors such as the Watson Engagement Advisor. We’ve created the Watson Developer Cloud, Content Store and Talent Hub to help launch the Watson Ecosystem to expand access to ISVs The Ecosystem and other ‘Powered by Watson’ undertakings is supported by infrastructure, APIs, tooling and development kits of the Watson Cognitive Fabric Underlying all of these outcroppings of Watson’s various use cases is the same Watson core foundational capabilities
• #24: Watson and MSK video can be downloaded here: https://w3-connections.ibm.com/files/app#/file/1218827c-5a02-4941-b888-4eb3e402f105 Preview on YouTube here https://www.youtube.com/watch?v=JLEpanWl9Fs To link the videos to the images, put the MOV files on the computer you will be using to present (or if you're bringing a memory stick and presenting from someone else's machine, put the vids on the memory stick), right click the screen capture image in the PPT, select "action settings" and hit the "hyperlink to" radio button. In the drop down box, scroll to the "other file" option and then browse to select the right video file. It helps if you have the video files already launched and sized to fit the screen BEFORE you start presenting so that there won't be a lag as the files open or a chance that they are not full screen. After you link the files, you can't change their location on the computer or the link will break.
• #26: Main Point: We have a brand new offering that we’re announcing called the Watson Discovery Advisor. It’s not a general availability product yet but it’s an new area we’re focusing to help researchers accelerate insights and stay current by synthesizing millions of documents into summary hypotheses and supporting evidence. We’re working with innovative organizations like leading publisher Elsevier, research university NC State, and Biotech firm Life Technologies to create this new offering that: Helps accelerate research by synthesizing millions of documents into summary hypotheses and evidence Helps keep researchers current by automatically updating findings Makes research more collaborative by exporting findings with colleagues
• #27: YouTube link – https://www.youtube.com/watch?v=vLE7VuppRzU Download link: http://cattail.boulder.ibm.com/cattail/#view=collections/7B2B14E081303DDD8173FCD2093F23B6 To link the videos to the images, put the MOV files on the computer you will be using to present (or if you're bringing a memory stick and presenting from someone else's machine, put the vids on the memory stick), right click the screen capture image in the PPT, select "action settings" and hit the "hyperlink to" radio button. In the drop down box, scroll to the "other file" option and then browse to select the right video file. It helps if you have the video files already launched and sized to fit the screen BEFORE you start presenting so that there won't be a lag as the files open or a chance that they are not full screen. After you link the files, you can't change their location on the computer or the link will break.
• #30: We speak to the various parties in the ecosystem and how they feed into the ecosystem’s core components
• #33: Helps me discover (fresh insights) Find patterns that I don’t even know to look for Freedom to explore and follow my train of thought Operates in timely fashion (real-time) Real-time analytics as data flows through an organization Enterprise-class Hadoop that runs 4x faster Speed of thought analytics Establishes trust (act with confidence) Governance across complete data lifecycle inc. Hadoop Security and privacy with compliance Transparency and context to decision-making process
• #36: Main point: As impressive as Watson’s capabilities are today, we’re continuing to push the boundaries of what is possible. We are working toward expanding Watson’s cognitive capabilities such as reasoning abilities including dialogue to help users along the journey toward arriving at their goal. It’s providing responses graphically through visualization capabilities. And it’s helping users explore their many data portfolios to help identify bodies of evidence for further analysis and evaluation.
• #38: Main point: We’re not done. We’re continuing to expand Watson’s capabilities. Nearly 1/3 of IBM’s research group will be focused on cognitive systems which ensures that Watson’s capabilities continue to expand and grow into new areas. We’re pursuing initiatives to enable Watson to gather more human-like capabilities like seeing and hearing. For example, we’re working on extracting knowledge from visual data like heart patients’ cardiograms. And we’re increasing Watson’s ability to grow through experience and even show characteristics of creativity with examples like “cognitive cooking” to let Watson develop new recipes based on tastes and dietary restrictions. The possibilities are as endless as the imagination.
• #40: Main point: Join the conversation and take the next step. Further speaking points:. Get involved and learn more about ways that Watson can help your business today. Learn more on the web. Join the conversation on twitter and facebook. See how Watson was created and is having a real impact on youtube. And above all, contact your IBM representative to your priorities and goals and how Watson can help play a part in meeting them.