Developing cognitive applications v1
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Cognitive applications leverage advanced computing capabilities to analyze vast and complex data, enhancing human decision-making and problem-solving through machine learning and natural language processing. They enable systems to learn from unstructured data, providing insights and interactions that mimic human-like cognition. The document emphasizes the transformative potential of cognitive computing across various industries, including healthcare and marketing, along with the implications for future technology and employment.
Developing cognitive applications v1
- 1. Cognitive Applications Harsha Srivatsa Feb 10 2016
- 2. Cognitive Applications @ Work Amazon ECHO Pepper Robot Amazon Delivery Drones Google Driverless Car IBM Watson Oncology Advisor Face Detection
- 3. Cognitive computing systems learn and interact naturally with people to extend what either humans or machine could do on their own. They help human experts make better decisions by penetrating the complexity of big data. Cognitive computing has these main components: machine learning, natural language processing , reasoning and inferences and semantic contextual understanding. Cognitive computing is positioned as not just a new computing system or computing paradigm but a whole new era of computing. What's changed now, though, is the explosion of data in the world and the rate and pace of change. These are not systems that are programmed; they're systems that learn. These are not systems that require data to be neatly structured in tables or relational databases. They can deal with highly unstructured data, from tweets to signals coming off sensors. In recent years, numerous technological advancements have combined to give machines a greater ability to understand information, and to learn, to reason, and act upon it. These advancements have reached such sophistication that in some cases machines may even appear to think. The broad term used to describe this emerging capability is Cognitive Applications. Perspective on Cognitive Computing and Applications
- 4. Cognitive Applications 101 Speed & Scale: Cognitive Computing harnesses the clear advantage machines have over humans in their ability to perform mundane tasks of arbitrary complexity repeatedly, whether it is the scale of the data or the complexity of the task Interact in a natural way: Cognitive Computing provides technologies that support a higher level of human cognition by adapting to human approaches and interfaces.. Over the next decade it will incorporate essentially all the ways humans sense and interact. Learn and Improve: Cognitive Computing systems focus on inexact solutions to unsolvable problems that utilize machine learning and improve over time. Often they combine multiple approaches and must integrate them efficiently They must learn from humans , in more and more seamless ways. Assist and Augment Human Cognition: Cognitive Computing addresses problems that lie squarely in the province of human intelligence., but where we can’t handle the volume of information, penetrate the complexity or otherwise extend our reach (physically). Innovation has reached a tipping point where machines computing capability are applied to problems which are not inherently solvable Traditionally these problems came from AI (Artificial Intelligence) The hardest AI problems are the easiest for Human Intelligence Computer Vision, Speech Recognition, Natural Language Processing – these problem domain solutions traditionally were not actually associated with “Intelligence” Human Intelligence provides solutions to some of the toughest problems but has difficulties in scalability and consistency Fundamental Principles of Cognitive Applications
- 5. Foundations of Cognitive Applications 5 COG · NI · TIVE / käg-nə-tiv (adjective): of, relating to or involving conscious mental activities (such as thinking, understanding, learning and remembering) Text Images, Surface, Structured Data… Speech Music, Cues, Noise… Sensors: Temperature, Tactile, Texture See hear smelltouch Supervised Learning Unsupervised Learning Deep Learning Re-enforcement Learning
- 6. What’s exciting about Cognitive Applications? Can discover insights and connections in vast amounts of information beyond the capability of human beings Interact naturally with people to learn to do what humans or machine could do on their own. Accelerates, enhances and scales human expertise Apply human-like characteristics to conveying and manipulating ideas They can solve problems with higher accuracy, more resilience, and on a more massive scale. Understand natural language and identify inferences between text passages with human-like high accuracy at speeds far faster and scale far larger than any human. Evidence-based explanations that help to train new professionals can be used in any field where a large or complex body of knowledge is codified. Aggregating and analyzing specialized knowledge and packaging and enabling data as a service. Achieve deeper insight into how things really work.. Develop strategies and design systems for achieving the best outcomes—taking into account the effects of the variable and the unknowable Enhances the cognitive process of professionals to help improve decision making in the moment Scales expertise by quickly elevating the quality and consistency of decision making across the organization
- 7. Typical Features and Capabilities of a Cognitive Application Assimilation of all sorts of data and knowledge that is available from a variety of structured, semi-structured, and unstructured sources Learn from experience with data/evidence and improve its own knowledge and performance without reprogramming. Generate and/or evaluate conflicting hypotheses based on the current state of its knowledge. Report on findings in a way that justifies conclusions based on confidence in the evidence. Discover patterns in data, with or without explicit guidance from a user regarding the nature of the pattern. Emulate processes or structures found in natural learning systems (that is, memory management, knowledge organization processes, or modeling the neurosynaptic brain structures and processes). Extract meaning from textual images, video, voice, and sensor data. Acquisition and analysis of the right amount of information in context with the problem being addressed. iterative process enables the system to learn and deepen its scope so that understanding of the data improves over time.
- 8. Cognitive Applications – Digital Transformation Potential 8 • Rethink current operating models • Processes Improvements Increase productivity and achieve better performance • Better connections and outstanding customer experience • Superior Customer Insights Deliver a superior customer experience Ultra-personalization Empower all classes of users • Uncover patterns, opportunities and correlations previously impossible to find Analyze everything within and outside organization boundaries
- 9. Examples of Cognitive Applications Pattern Description Marketing and Sales Advisor Creating the most effective targeted marketing campaigns and effective sales strategy by leveraging multiple sources of data from analyst reports, social data, blogs, reviews, and market research, and leveraging Watson’s user profiling, message resonance, and psycholinguistic capabilities Smarter Merchandising Advisor Leveraging forward-looking structured and unstructured data to enhance intelligent merchandising and management decisions related to product, pricing, and inventory management Smarter Operations Advisor Streamlining business processes and compliance in relation to services, policies, procedures, and benefits through an employee-facing quick natural language Q&A self-service interface Procurement Advisor Empowering employees in the support, visibility, and strategic-sourcing process (In the medical industry, professionals tasked with fulfilling procurement needs struggle to navigate clinical evidence, research, analysis, and price data, which is scarce, highly decentralized, and biased. The supply chain decision-making process is complicated by competing priorities and inefficient collaboration within hospitals. In procuring implantable devices alone, an estimated $5 billion is wasted annually due to these inefficiencies.) Human Resources Advisor Streamlining HR processes by leveraging procedures, benefits, policies, and other intellectual property in a quick natural-language interface Research Advisor Enabling analysts and researchers to gain new insights through correlation of large amounts of unstructured data. Whether in health care/life sciences, M&A, accounting and compliance, wealth management, or market competitive analysis–there is a vast corpus of information to provide a professional opinion based on a set of facts.
- 10. IBM Watson Cognitive Computing Platform 10 Cognitive Exploration Cognitive Engagement Cognitive Curation Cognitive Analytics Augmented Intelligence Learning Systems Healthcare Financial Services Public Sector Other Engagement Discovery Policy Other Domain Specialization Cognitive Services Tooling Models | Annotators | Content Assemble | Ingest | Train | Deploy | Admin Perceiving Gaining a new level of insight into our world Reasoning Drawing inferences to reach new conclusions Relating Adapting/personalizing interactions by individual Learning Continuously improving insight with experience Data Lake Requirements Inventory Prioritization Preparation IBM Watson Solutions IBM Watson Products IBM Watson Platform (built on BlueMix) IBM Watson Foundation Methodology Information Lifecycle Management Why is IBM Watson of Interest? IBM Watson enables cutting- edge innovation by combining the best aspects of Big Data, Advanced Analytics, Natural User Interfaces. Contextual and insightful knowledge extraction and insight enablement from unstructured and structured data sources. Enabling the complete Cognitive Engagement experience for users including Cognitive User Interfaces, Cognitive Applications, Decision Intelligence
- 11. IBM Watson Value Proposition 11 What Makes IBM Watson unique? Advanced open-domain Q& A system with deep NLP capabilities Incorporate Natural Engagement interfaces with speech recognition, visual recognition etc. Incorporate visualization, reasoning, ability to relate to users Deeper and broader understanding of information content
- 12. Cognitive Applications Example (1) Personalized Automated Self-Service
- 13. Cognitive Application Example (2) 13 Cognitive Agent Assisted Insurance Claims Processing
- 14. Future Impact of Cognitive Applications 14 Capabilities Dimensions What opportunities exist to create more engaging and personalized experiences for your constituents? What data aren’t you leveraging that – if converted to knowledge – would allow you to meet key objectives and business requirements? What is the cost to your organization associated with making non- evidence-based decisions or not having the full array of possible options to consider when actions are taken? What benefit would you gain in being able to detect hidden patterns locked away in your data? How would this accelerate research, product development, customer services and the like? What is your organizational expertise skill gap? What would change if you could equip every employee to be as effective as the leading expert in that position or field?
- 15. How to Develop Cognitive Applications using IBM Watson (1) 1 Understand the components and capabilities of the IBM Watson ecosystem Data Lifecycle Process Understand the problem domain, data content and what value added capabilities can be added using Cognitive Services. IBM Watson Home Page: (http://www.ibm.com/smarterplanet/us/en/ibmwatson)
- 16. How to Develop Cognitive Applications using IBM Watson (2) 2 Study sample applications on the IBM Watson App Gallery (http://www.ibm.com/smarterplanet,/us/en/ibmwatson/developercloud/gallery.html) Think through the application design (Web/Mobile platform, UI/UX, User Input, Cognitive API’s, Analytics/Insight etc. API documentation, Sample applications, How-To’s, Support forums
- 17. How to Develop Cognitive Applications using IBM Watson (3) 3 Trial/Developer Account on IBM BlueMix cloud platform (https://console.ng.bluemix.net/) Leverage Watson API catalog, Data and Analytics Services, Data Platforms, Dev Envionments in Java/Python) Develop and Deploy application on BlueMix platform (Storage, DevOps, Runtime….) Join the IBM Watson Developer Ecosystem,deploy app on IBM Watson gallergy, monetize!
- 18. Future Scape: The Cognitive Ecosystem Cognitive Internet –of-Everything Smarter Homes, Smarter Cities, Smarter Planet Driverless vehicles Intelligence Devices
- 19. Artificial intelligence • The central goals of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. • As a minimum, an AI system must be able to reproduce aspects of human intelligence. • This raises the issue of how ethically the machine should behave towards both humans and other AI agents • Humans should not assume machines or robots would treat us favorably, because there is no prior reason to believe that they would be sympathetic to our system of morality. • Hyper-intelligent software may not necessarily decide to support the continued existence of mankind, and would be extremely difficult to stop. • Physicist Stephen Hawking, Microsoft founder Bill Gates and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could "spell the end of the human race". • Specialized AI applications, robotics and other forms of automation will ultimately result in significant unemployment as machines begin to match and exceed the capability of human workers to perform most routine and repetitive jobs. • Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self- improvement. • The new intelligence could thus increase exponentially and dramatically surpass humans. This will give birth to Technological Singularity. • Technological Singularity could be the beginning of the end of mankind.
- 20. AI vs. Machine Learning vs Machine Intelligence Classic AI Machine learning Machine Intelligence Models Watson DeepQA Deep Learning Hierarchical Temporal Memory (HTM) Associated terms Expert systems Artificial Neural Networks (ANN) Machine intelligence Biological Neural Network Data sources Rules from experts Large datasets Data streams Training Programmed by experts Derived from labeled databases Derived from unlabeled data streams Outputs Answers to questions Classification Prediction Anomaly detection Classification Batch vs. continuous learning Batch Batch Continuous Need to know what you are looking for Yes Requires labeled data No Many individual models Hard Hard Easy Biological basis None Simple Realistic Provides roadmap to machine intelligence No No Yes