Watson - A new era of computing.
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Please download the presentation, instead of viewing online, in order to see the videos and animations. Watson brings a new era of computing to our lives. Cognitive computing changes the way a computer interacts with the world, and how it reacts to it. Besides excelling in answering questions in Jeopardy!, see how IBM is putting Watson to work in finance, medicine, services, and why you may be talking to Watson very soon, and not even notice it!
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Watson - A new era of computing.
- 1. © 2014 International Business Machines Corporation IBM Watson A New Era of Computing César Diniz Maciel Executive IT Specialist IBM Global Techline – US cmaciel@us.ibm.com
- 2. © 2014 International Business Machines Corporation
- 3. © 2014 International Business Machines Corporation Cognitive computing – an ancient human desire AI and Cognitive computing research since the 60s eliza (1964-1969)– First attempt at Natural Language Processing (NLP) First program to claimed to pass the Turing test HAL 9000 – true thinking computer from “2001: A Space Odissey” LCARS - “The Computer” from Star Trek Several areas of expertise related to the field Neural networks Fuzzy logic IBM Neurosynaptic chip Natural Language Processing is a highly sought capability for computer systems
- 4. © 2014 International Business Machines Corporation Changing password • Computer: Password expired, enter new password. • User: tomato • Computer: Password too short, try again. • User: redtomato • Computer: Weak password, try again. • User: 1redtomato • Computer: Password must have an upper case letter. Try again. • User: 1RedTomatoDamnComputer • Computer: Password must not start with numbers. Try again • User: IWillStickThis1TomatoinYourFaceStupidComputer • Computer: Password too long. Try again....
- 5. © 2014 International Business Machines Corporation Watson is ushering in a new era of computing Tabulating Systems Era Programmable Systems Era Cognitive Systems Era 1900 1950 2011
- 6. © 2014 International Business Machines Corporation Watson is cognitive computing 6 Understands natural language Generates and evaluates hypotheses Adapts and learns Watson understands me. Watson engages me. Watson learns and improves over time. Watson helps me discover. Watson establishes trust. Watson has endless capacity for insight. Watson operates in a timely fashion.
- 7. © 2014 International Business Machines Corporation Decision Maker Search Engine Finds Documents containing Keywords Finds Documents containing Keywords Delivers Documents based on Popularity Delivers Documents based on Popularity Has Question Has Question Distills to 2-3 Keywords Distills to 2-3 Keywords Reads Documents, Finds Answers Reads Documents, Finds Answers Finds & Analyzes Evidence Finds & Analyzes Evidence Expert Understands Question Understands Question Produces Possible Answers & Evidence Produces Possible Answers & Evidence Delivers Response, Evidence & Confidence Delivers Response, Evidence & Confidence Analyzes Evidence, Computes Confidence Analyzes Evidence, Computes Confidence Asks NL Question Asks NL Question Considers Answer & Evidence Considers Answer & Evidence Decision Maker The difference between Search Engine and DeepQA
- 8. © 2014 International Business Machines Corporation
- 9. © 2014 International Business Machines Corporation How does it work? • Watson is based on a powerful computing platform running specialized algorithms. • Watson is an application of advanced Natural Language Processing, Information Retrieval, Knowledge Representation and Reasoning, and Machine Learning technologies to the field of open-domain question answering. At its core, Watson is built on IBM's DeepQA technology for hypothesis generation, massive evidence gathering, analysis, and scoring. • Behind the application, Watson runs on POWER processor-based systems. The Watson that played Jeopardy was composed of 90 Power 750 systems in cluster. It runs SuSE Linux • Watson runs multiple threads to perform its evaluation of the question and compose the answer. These threads can be paralelized, which allowed the execution to go from 2 hours on a single core to 3 seconds on the cluster.
- 10. © 2014 International Business Machines Corporation • A workload optimized system • 90 x IBM Power 750 servers • 2880 POWER7 cores • POWER7 3.55 GHz chip • 500 GB per sec on-chip bandwidth • 10 Gb Ethernet network • 15 Terabytes of memory • 20 Terabytes of disk, clustered • Can operate at 80 Teraflops • Runs IBM DeepQA software • Scales out with and searches vast amounts of unstructured information with UIMA & Hadoop open source components • Linux provides a scalable, open platform, optimized to exploit POWER7 performance • 10 racks include servers, networking, shared disk system, cluster controllers
- 11. © 2014 International Business Machines Corporation • This means Watson… • Operates at 80 teraflops. The human brain is estimated to have a processing power of 100 teraflops (100 trillion operations per second). • Has the equivalent in memory (RAM) that the Library of Congress adds in books and media over a 4 month period • Can process 200 million times more instructions per second than the Space Shuttle’s computers. • Parses within 3 seconds the equivalent of the number of books on a 700 yard long book shelf…and pick out the relevant information, and create an answer.
- 12. © 2014 International Business Machines Corporation THE Watson
- 13. © 2014 International Business Machines Corporation Initial Question DeepQA: the technology & architecture behind Watson Hypothesis Generation Hypothesis & Evidence Scoring Final Confidence Merging & Ranking Synthesis Question & Topic Analysis Hypothesis Generation Hypothesis and Evidence Scoring Learned Models help combine and weigh the Evidence Evidence Sources Answer Scoring Deep Evidence Scoring Evidence Retrieval Answer Sources Primary Search Candidate Answer Generation Question Decomposition Hypothesis Generation Hypothesis and Evidence Scoring model model model model model model model model model Answer & Confidence
- 14. © 2014 International Business Machines Corporation Example of DeepQA analysis This fish was thought to be extinct millions of years ago until one was found off South Africa in 1938. Category: ENDS IN "TH" Answer: Coleacanth (Google got this one right) When hit by electrons, a phosphor gives off electromagnetic energy in this form. Category: General Science Answer: Photon (Google found the articles commenting on Watson answering this question :-) Secretary Chase just submitted this to me for the third time--guess what, pal. This time I'm accepting it. Category: Lincoln Blogs Answer: His resignation (again, Google had no clue)
- 15. © 2014 International Business Machines Corporation
- 16. © 2014 International Business Machines Corporation Watson in the business Watson Engagement Advisor - May 21st Watson Engagement Advisor announced Cross-industry and cross- geography play January April July October First Two Commercial Offerings February 8th First 2 healthcare products were announced with MSK and WellPoint 2014 Watson Ecosystem November 14th Watson Ecosystem announced Driving innovation and fostering new possibilities Watson Healthcare Update – Oct. 18 MD Anderson launches Expert Oncology Advisor powered by Watson Watson used to bridge clinical practice and medical research
- 17. © 2014 International Business Machines Corporation
- 18. © 2014 International Business Machines Corporation What is the benefit to customers? Transforming industries and professions. Advancing cognitive computing across the enterprise. Delivering the cognitive experience to the users, enabling better interaction, more efficient answers and providing better value.
- 19. © 2014 International Business Machines Corporation Watson in healthcare A data-rich, societally important field helping Watson change how medicine is: Taught Cleveland Clinic Researched MD Anderson Practiced Memorial Sloan-Kettering Paid WellPoint
- 20. © 2014 International Business Machines Corporation Expert systems in healthcare Automation in medical diagnostics is an area of research with deep impacts to mankind. Studies in this area started in the 50s “Reasoning Foundations of Medical Diagnosis”, Ledley and Lusted, July 1959 Automation of diagnosis based on matching keys (symptoms) and attributes (diseases)
- 21. © 2014 International Business Machines Corporation (Sad) facts about healthcare • Medicine is still full of unexplained cases and situations. Rare diseases, unknown processes of infection, lack of understanding of how the body interacts with chemical components in drugs, etc. • Medical knowledge is increasing a lot, but the ability of a doctor to absorb the relevant information does not increase at the same pace (they are humans, after all). • In some situations, the lack of conclusive research, or the speed of changes in the field makes it hard to physicians to be at optimal performance – Cancer research is an area where advanced research is being done, with great success in some areas and dismal results in others. And sometimes the highly specificity of a particular cancer makes it harder to spread knowledge on diagnosis or treatment. – “Lung cancer causes more deaths than the next three most common cancers combined (colon, breast and pancreatic). An estimated 159,260 Americans are expected to die from lung cancer in 2014, accounting for approximately 27 percent of all cancer deaths.” American Cancer Society, Cancer Facts & Figures 2014 – The average successful diagnosis rate of lung cancer by oncologists is 50%.
- 22. © 2014 International Business Machines Corporation “IBM's Watson is better at diagnosing cancer than human doctors” • “According to Sloan-Kettering, only around 20 percent of the knowledge that human doctors use when diagnosing patients and deciding on treatments relies on trial-based evidence. It would take at least 160 hours of reading a week just to keep up with new medical knowledge as it's published, let alone consider its relevance or apply it practically. Watson's ability to absorb this information faster than any human should, in theory, fix a flaw in the current healthcare model. Wellpoint's Samuel Nessbaum has claimed that, in tests, Watson's successful diagnosis rate for lung cancer is 90 percent, compared to 50 percent for human doctors.” http://www.wired.co.uk/news/archive/2013-02/11/ibm-watson-medical-doctor
- 23. © 2014 International Business Machines Corporation
- 24. © 2014 International Business Machines Corporation Know me Leverage profile data for personalized insight into client wants and needs to contextualize experience Client Watson can transform the way people interact over the lifetime of their relationship Empower Me Interactive, informed natural language dialogue that enables insights at the point of action Engage me Dynamic, evidence-based omni-channel experiences that adapt to client preferences
- 25. © 2014 International Business Machines Corporation Watson Engagement Advisor Helps organizations improve how they answer their clients’ needs 25 Engage under- engaged clients Grow top-line revenue Improve bottom line results
- 26. © 2014 International Business Machines Corporation
- 27. © 2014 International Business Machines Corporation Breakthrough insights are critical across research, clinical and commercial functions • What new ways could we target this disease pathway? • What evidence exists on the way this protein could be modified to inhibit that gene’s activity? Lead & Drug DiscoveryLead & Drug Discovery • How can we quickly identify if this compound has a toxicity issue? • Can we identify this compound’s major adverse events prior to in vivo testing? Safety & Toxicity Assessment Safety & Toxicity Assessment • Are there reasons for the early safety signals that we can quickly identify? • Can we gain insight into the factors that may be contributing to side effects? PharmacovigilancePharmacovigilance • Does this drug have an effect on the pathway of another disease? • Are there similarities in the structure of this new compound to those being used in other therapeutic areas? Drug RepurposingDrug Repurposing • What populations are likely to benefit most from this intervention? • How do these ten treatments compare across thousands of trials? Comparative Effectiveness / Clinical Trial Design Comparative Effectiveness / Clinical Trial Design • What do early studies of competitors reveal about their efficacy and safety? • How are competitors’ trial designs related to the strengths of their efficacy/safety profile? Competitive IntelligenceCompetitive Intelligence Watson Discovery Advisor
- 28. © 2014 International Business Machines Corporation
- 29. © 2014 International Business Machines Corporation 29 The Watson Ecosystem is composed of mutually benefiting, interconnected partners Talent Partners Content Partners App Partners Develop cognitive powered applications powered by IBM Watson Fuel the ecosystem with content to drive cognitive applications Support application partners with developing and designing their solutions Watson Developer Cloud Watson Content Store Watson Talent Hub
- 30. © 2014 International Business Machines Corporation Watson in the cloud
- 31. © 2014 International Business Machines Corporation Beta services available now with more in plan Visualization Rendering Graphical representations of data analysis for easier understanding User Modeling Personality profiling to help engage users on their own terms. Language Identification Identifies the language in which text is written Machine Translation Translate text from one language to another. Concept Expansion Maps euphemisms to more commonly understood phrases Message Resonance Communicate with people with a style and words that suits them Question and Answer Direct responses to users inquiries fueled by primary document sources Relationship Extraction Intelligently finds relationships between sentences components Coming • Concept Analytics • Question Generation • Speech Recognition • Text to Speech • Tradeoff Analytics • Medical Information Extraction • Semantic Expansion • Policy Knowledge • Ontology Creation • Q&A in other languages • Policy Evaluation • Inference detection • Social Resonance • Answer Assembler • Relationship identification • Dialog • Machine Translation (French) • Smart Metadata • Visual Recommendation • Industry accelerators Available today
- 32. © 2014 International Business Machines Corporation …Helps me discover fresh insights • Predictive and content analytics to uncover patterns not yet known • Interactive exploration across all data …Operates in a timely fashion • Real-time analytics as data flows through an organization • Enterprise-class Hadoop that runs 4x faster • In-memory computing for speed of thought analytics …Establishes trust so I can act with confidence • Governance across complete data lifecycle including Hadoop • Security and privacy with compliance • Transparency and context to decision-making process WATSON FOUNDATIONS Decision Management Planning & Forecasting Discovery & Exploration Business Intelligence & Predictive Analytics Content Analytics Information Integration & Governance Data Mgmt & Warehouse Hadoop System Stream Computing Content Management WATSON FOUNDATIONS Decision Management Planning & Forecasting Discovery & Exploration Business Intelligence & Predictive AnalyticsBusiness Intelligence & Predictive Analytics Content Analytics Information Integration & Governance Data Mgmt & Warehouse Hadoop System Stream Computing Content Management Watson Foundations uniquely…
- 33. © 2014 International Business Machines Corporation Chef Watson Cognitive Cooking - http://www.ibm.com/smarterplanet/us/en/cognitivecooking/
- 34. © 2014 International Business Machines Corporation Chef Watson Test-drive Chef Watsion - http://www.bonappetit.com/people/our-readers/article/beta-test-chef-watson
- 35. © 2014 International Business Machines Corporation 35 Explores Reasons Visualizes Watson’s intelligence and capabilities are expanding
- 36. © 2014 International Business Machines Corporation IBM Neurosynaptic chip http://www.research.ibm.com/articles/brain-chip.shtml
- 37. © 2014 International Business Machines Corporation This will be Watson Sees Hears Experiences Understands natural language Generates and evaluates hypotheses Adapts and learns Reasons Explores Visualizes
- 38. © 2014 International Business Machines Corporation
- 39. © 2014 International Business Machines Corporation
Notes de l'éditeur
- Watson Business Unit Launch video can be downloaded here: http://cattail.boulder.ibm.com/cattail/#view=mdholme@us.ibm.com/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.
- 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.
- 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>
- 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.
- 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
- 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
- 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.
- 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
- 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.
- We speak to the various parties in the ecosystem and how they feed into the ecosystem’s core components
- 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
- 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.
- 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.
- 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.