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Era of Artificial Intelligence Lecture 5 and Lecture 6 Pietro Leo

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Slides of my Lecture to the students of the International FT MBA of Polytechnic of Milan (Part 5 and Part 6)

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Era of Artificial Intelligence Lecture 5 and Lecture 6 Pietro Leo

  1. 1. @pieroleo The Era of Artificial Intelligence Lecture 5 and Lecture 6 Pietro Leo IBM Italy Executive Architect and thought leader for Artificial Intelligence Chief Scientist for IBM Italy Research & Business IBM Academy of Technology Leadership Member of ISO/SC42 Artificial Intelligence Standardization Committee www.pieroleo.com
  2. 2. @pieroleo How do we position Artificial Intelligence in the IT Business Context?
  3. 3. @pieroleo Artificial Intelligence Machine Learning Deep Learning Data Mining Data Science Big Data Defining AI & Cognitive, Machine Learning e Deep Learning Cognitive IT systems that aims to build algorithms to predict meaning in features of human languages (spoken, written, visual) and emulates related forms of human reasoning and knowledge representation It is a class of techniques that aims to generate knowledge by training data to recognize the correlation between a set of feature patterns and outcomes. Systems that leverage a combination of AI reasoning and knowledge representation strategies and other analytic and classical computing techniques to solve a complex problem It is a rapidly maturing Machine Learning space, based on neural network techniques, that are taught to find their own features Internal Use Only
  4. 4. @pieroleo BUSINESS PROBLEMS Cloud, hybrid cloud & on premise Infrastructure Data Applications & Processes Traditional Algorithms COMPUTING DATA BUSINESS LOGIC Artificial Intelligence is emerging as a new enterprise platform layer to move and speedup business IT Applications and Processes to a new level Artificial Intelligence / Cognitive Internal Use Only AI positioning in the Enterprise IT context
  5. 5. @pieroleo Cloud Public/Dedicate and Private Infrastructure A highly scalable, security enabled infrastructure that run on a cloud environments Data Tools to acquire, manage, analyze, govern and exchange all kinds of data generated within the Enterprise as well as gathered form outside Artificial Intelligence / Cognitive A set of building blocks that complement traditional computer programming models with the goal to close applications behavior to human language (spoken, written, visual) and reasoning Applications & Processes All applications and processes that run the enterprise, both horizontal, common to a number of industries, and Vertical, specific for a given industry On-Premise Infrastructure The computing infrastructure that runs inside a company data center Traditional Algorithms A set of languages, middleware, and products to write enterprise software programs Transitional DataUnstructured Data IoT Data COMPUTING DATA BUSINESS LOGIC BUSINESS PROBLEMS AI positioning in the Enterprise IT context
  6. 6. @pieroleo Artificial Intelligence vs algorithms progression
  7. 7. @pieroleo 7 Perception Deep Learning & Reasoning Classification Explaining InterpretabilitySymbolic Reasoning Observing Common-Sense Planning Patterns & Sub-patterns Observation AI Algorithms ….. ….. ….. Ethics
  8. 8. @pieroleo 8 Perception Classification InterpretabilitySymbolic Reasoning Common-Sense Planning Patterns & Sub-patterns Observation AI and ML Algorithms ….. ….. ….. Ethics Deep Neural Learning Deep Learning & Reasoning ExplainingObserving
  9. 9. @pieroleo 9 Santiago Ramon y Cajal Camillo Golgi
  10. 10. @pieroleo 10 Deep learning basic process Multiply + Add Sigmoid SoftMax reLu Multiply + Add1 Weights computation 32
  11. 11. @pieroleo 11 Deep learning basic utilization models www.pieroleo.com Given 1:Input and 2:Weights find the 3:Output à Prediction/Discriminative Given 1:Input and 3:Output find the 2:Weights à Learning Given 2: Weights and 3:Output find the 1:Inputs à Generative
  12. 12. @pieroleo 12 Perception Deep Learning & Reason Classification Explain InterpretabilitySymbolic Reasoning Observe Common-Sense Planning Patterns & Sub-patterns Observation AI Algorithms ….. ….. ….. Ethics See: https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-innovation-equation.html
  13. 13. @pieroleo 13 Source: IBM Research automatic sport highlights generation https://www.ibm.com/blogs/research/2017/06/scaling-wimbledons-video- production-highlight-reels-ai-technology/
  14. 14. @pieroleo Key topics in research Learning & Reasoning to support business problems Making Learning More Human- Like People learn by trail and error without a lot of labeled data. We learn continuously throughout their lives, remembering what we’ve learned and leveraging it for new tasks. Interpretability Explaining AI decisions is crucial for customers, government and regulators, enterprises. Optimization Beyond back-propagation Neuro AI Novel AI approaches based on brain function including plasticity, attention, memory, reward processing, motivation Deep Document Understanding People can access the accumulated knowledge of humanity directly, by reading, viewing and listening. And they can apply that knowledge directly to new tasks. Conversational Knowledge Acquisition Acquiring, Applying and Accumulating knowledge during collaboration with humans. Multi-step Reasoning Humans can combine inputs and knowledge from multiple sources to solve sub-problems and larger complex tasks Reliable, Approximate Reasoning Human reasoning can be exact and it can be flexible, AI systems need to be able to span this range
  15. 15. @pieroleo 1 5 Video Face Extraction 12 Jun 2016 21:40 – 22:00 Video Time Tagging Cleveland, OH Video Geotagging Face Identity Attributes Woman, 20-30 Face Expression Pensive Face Extrinsics Full hair, blond, no glasses, no hat Video Object Finding Segmented Objects Bicycle:{ Colour:gray, Brand:Raleigh, Pose: inverted} Object Recognition Multimedia Retrieval To: find examples of scenes in videos with sets of objects fitting descriptions in a list L • Retrieve candidates videos • For each video, and object type • Use appropriate extractor to find spans with that object • Segment those objects out • Run attribute extraction on each obj o giving a • Remember span and o if a satisfies any description in L • Remember span if it contains objects satisfying all descriptions in L To: Answer a query x for user u, • Identify the language of x, l, • Use language to logic for l on x to make an equivalent query y, • Reason to answer y, yielding answers z • Use logic to language to turn each zi into language l equivalent ai • Assemble ai into list a • Find a convenient display d for u • Display x and the list a on d English to Logic What’s the population of Auckland? (nInhabitants Auckland ?nu) Language ID 47 60 90 Japanese {jp} Logic to English (nInhabitants Auckland 1e6) Auckland has a million people. The popn of Auckland is 1m. Problem Solving Methods Machine Reasoning Multi-step reasoning for Skill Composition
  16. 16. @pieroleo Thanks! Pietro Leo IBM Italy Executive Architect and thought leader for Artificial Intelligence Chief Scientist for IBM Italy Research & Business IBM Academy of Technology Leadership Member of ISO/SC42 Artificial Intelligence Standardization Committee www.pieroleo.com

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