IBM Watson Analytics
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A modified version of IBM Watson Analytics presentation. It covers its development, history, how it works and screen shot of IBM Watson Analytics Application. The presentation included a sample of a Food Production Index of the Philippines from 1999 to 2013 Analytics presentation conducted at Asia Pacific College, Manila, Philippines. The report was used in Advance Emerging Technology class at the University of the East, Manila, Philippines.
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IBM Watson Analytics
- 1. IBM Watson Analytics For-Ian V. Sandoval | Student University of the East - Manila
- 2. 2 Super Computer developed by IBM Research Named for IBM’s founder: Thomas J. Watson Initially created for Jeopardy! Game Show Dr. David Ferrucci leads the Watson project Introduction
- 3. 3 Development Search engines deliver thousands of results that match keywords University’s have worked on a consistent question answering software for years Programmed by 25 IBM scientists Not connected to the internet
- 4. 4 Brief History of IBM Watson R&D Demonstration Commercialization Cross-industry Applications IBM Research Project (2006 – ) Watson for Healthcare (Aug 2011 –) Watson Industry Solutions (2012 – ) Watson for Financial Services (Mar 2012 – ) Expansion Jeopardy! Grand Challenge (Feb 2011) New IBM Division
- 5. 5 Jeopardy! Challenge Set out to answer complex Jeopardy! Questions Language is hard for computers because of “intended meaning” During trials, it won 70% of practice games February 2011: First computer to compete against humans in Jeopardy! Defeated shows greatest two champions Ken Jennings and Brad Rutter
- 8. 8 Data is rapidly becoming the foundation for a Smarter Planet Watson
- 9. 9 Businesses are “dying of thirst in an ocean of data” 1 in 2 business leaders don’t have access to data they need 83% of CIOs cited BI and analytics as part of their visionary plan 2.2X more likely that top performers use business analytics 80% of the world’s data today is unstructured 90% of the world’s data was created in the last two years 20% amount of data traditional systems leverage today
- 10. 10 Understands natural language and human communication Adapts and learns from user selections and responses Generates and evaluates evidence-based hypothesis …built on a massively parallel architecture optimized for IBM POWER7 IBM Watson combines transformational technologies 1 2 3
- 11. 11 In 2012, Watson became smarter, faster, and more scalable 90% Nurses follow Watson’s Recommendations 75% Reduction in time to market with new cancer therapies 6 Instances of Watson deployed in the last 12 months Scalable 605,000 pc. evidence 2M pages of text 25,000 training cases 14,700 clinician hours 240% faster 75% smaller Runs on single server Smarter Faster Scales on demand Millions of Trx. per month In Cloud or on premise PC, tablet or smartphone 1 Based on preliminary pilot results, may not be representative of all situations
- 12. 12 Improve Decisions and Outcomes Oncologists Therapy Designer Watson Healthcare Products – 1H 2013 Assists in identifying individualized treatment options for patients diagnosed with cancer Nurses Assists with efficient trials and reduces time to market with new cancer therapies Streamlines manual review processes between a physician and health plans Improve Diagnosis and Treatments Accelerate Research and Insights Watson Clinical Insights Advisor Watson Diagnosis & Treatment Advisor Watson Care Review and Authorization Advisor
- 13. 13 WATSON PRODUCTS AND INFRASTRUCTURE Watson for Healthcare Watson for Client Engagement Watson for Industry Advisor Solutions Advisor Solutions Utilization Oncology Cardiac Diabetes CallCenter HelpDesk Knowledge Technical Model Train LearnSource Workload Optimized Systems Analytics MobileNLP & Machine Learning Big Data Cloud ASK Services DECISION ServicesDISCOVER Services Watson For Financial Svcs. Advisor Solutions Banking Institutional Retirement Institution
- 14. 14 Watson Business Model: Cloud Delivery and Outcome Based Pricing Dynamic Capacity Automate and control service provisioning Flexible Consumption Support alternative delivery and value pricing models Time to Value Enable incremental automation and business agility Hybrid Delivery Extend & integrate on-premise solution with cloud offering
- 15. 15 © 2012 International Business Machines Corporation 1 Watson is ushering in a new era of computing . . . . . . enabling new possibilities and outcomes 1900 1950 2011
- 16. 16 REGISTRATION
- 18. 18 Upload Data
- 19. 19 How to Edit Data ● Exploration ● Prediction ● View
- 20. 20 Exploration
- 22. 22 Prediction
- 24. 24 Sample Data Visualization and Analytics
- 25. 25 Food Production Index Definition Food production index measures the changes in the production of food commodity in a given year relative to base year.
- 27. 27 T The graph shows the trend of food production index by commodity. The trend for all commodities are significantly increasing specifically the tilapia production.
- 28. 28 The graph shows values of food production index by commodity?
- 29. 29 Food production index drivers.
- 30. 30 Impact of Commodity and Year Binned to Food Production Index
- 31. 31 4.40 % 15.17% 22.85 % 12.15 % The graph shows the increase in percentage of food production index (combined commodities) with five years interval.
- 32. 32 Production index compare by year and commodity.
- 33. 33 Production index compare by year and commodity.
- 34. 34 Production index compare by year and commodity.
- 35. 35 2013 has the highest Food Production Index average
- 37. 37 Decision Tree
- 38. 38 Why choose Watson Analytics? ● Understand natural language ● Genarate and evaluates hypotheses ● Adapts and learn ● Automatic Modeling ✔ Auto selection of best models and detection of strongest relationships: Decision Tree (CHAID) and Key Driver ✔ Reports (based on linear and logistic regression)
- 39. 39 MARAMING SALAMAT PO! You can find me at: https://web.facebook.co m/forian.sandovalforian. sandoval@lspu.edu.ph
Editor's Notes
- Main point: Bringing about a transformation in what was as a society expect of technology does not happen overnight. Watson has been an iterative growth process that continues this day and into the future. Further speaking points: Watson was a research project in IBM starting in 2006. The effort was led by a team of 15 IBM researchers working in collaboration with a pool of top universities as a “Deep QA” project. Jeopardy! was selected as the ultimate test of the machine’s capabilities because it relied on many human cognitive abilities traditionally seen as out of scope for machines such as ability to discern double meanings of words, puns, rhymes, and inferred hints. It also demanded extremely rapid responses and the ability to process vast amounts of information to make connections typically requiring a lifetime of immersion in pop culture and participation in the general human experience. With Jeopardy! in the past, IBM and Wellpoint, one of the US’s largest health insurers, announced a partnership to pilot Watson for use among member hospitals and the insurance organization itself with a goal of improving patient outcomes and health treatments. As a byproduct, it is also expected to improve the productivity of healthcare professionals. From Healthcare, Watson is expected to branch into other industries that rely on analytic solutions to managing unstructured data. Additional information: Financial services organizations and call center operations are seen as high potential areas.
- Main point: Data is growing at an astounding rate. It is growing so fast that we often lack the ability to use it to its full potential. The highly unstructured nature of this data makes the challenge that much more difficult. This is a real problem for business. It makes informed decisions more difficult to make. Business leaders need a way to find hidden patterns and isolate the valuable nuggets that they need to make business decisions. Further speaking points: Yet, the rewards for finding a way to harness the data into useful information are great; 54% of companies in this year’s study with MIT/Sloan are using analytics for competitive advantage… and that number has surged 57% in just the past 12 months. “Dying of thirst in an ocean of data”… It’s an apt analogy. Data is everywhere. 90% of it didn't exist just two years ago. The vast majority of it is totally useless for any given goal and therefore amounts to noise and a hindrance to finding the key useful information needed in a specific time and place. Additional information: See information and stats
- Main Point: 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 Solutions that learn with each iteration Capable of navigating human communication Dynamically evaluating hypothesis to questions asked Responses optimized based on relevant data Ingesting and analyzing Big Data Discovering new patterns and insights in seconds Further speaking points:. Looking at these one by one, understanding natural language and the way we speak breaks down the communication barrier that has stood in the way between people and their machines for so long. Hypothesis generation bypasses the historic deterministic way that computers function and recognizes that there are various probabilities of various outcomes rather than a single definitive ‘right’ response. And adaptation and learning helps Watson continuously improve in the same way that humans learn….it keeps track of which of its selections were selected by users and which responses got positive feedback thus improving future response generation Additional information: The result is a machine that functions along side of us as an assistant rather than something we wrestle with to get an adequate outcome
- 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.