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Research & Business about Artificial Intelligence: A Point of View

This document provides an overview of artificial intelligence from the perspective of Pietro Leo, an executive architect at IBM Italy. It discusses how AI is perceived in business today and how relevant it is. It outlines different types of AI applications like computer vision, natural language processing, and decision augmentation. It also discusses challenges in developing human-like learning and ensuring the interpretability and ethics of AI systems. The document aims to demonstrate the wide-ranging roles and impacts of AI across industries.

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@pieroleo Research & Business about Artificial Intelligence A Point of ViewPietro Leo Executive Architect - IBM Italy CTO for Artificial Intelligence Chief Scientist for IBM Italy Research & Business IBM Academy of Technology Leadership Twitter: @pieroleo ---- www.pieroleo.com
@pieroleo Image Credits: Pixie Dust by Disney - http://disney.wikia.com/wiki/Pixie_Dust How Artificial Intelligence is perceived from the business world, today?
@pieroleo How relevant is Artificial Intelligence for the business and research, today?
@pieroleo www.mckinsey.com
@pieroleo Source: https://www.mckinsey.com/industries/advanced- electronics/our-insights/artificial-intelligence-the-time-to- act-is-now Enterprise winners will focus on microverticals in promising industries Source: www.mckinsey.com
6 You shared your position with me and can guess your mobility need. I can take you where you need to be Just enjoy your new experience . Stay safe as in your home I know what is needed for you, even before you order it Please, come with me and stay by me. I know your content I can take care of all your digital life Popular examples of Data-driven companies….
7Source: https://www.grushgamer.com/ Democratizing with the cloud the access to AI is contributing to generate new products, services and new kind of companies….
@pieroleo 8 Hungry to Learn: There is a constant growing interest around Artificial Intelligence
@pieroleo 9 Source: https://www.timeshighereducation.com/data-bites/which-countries-and-universities-are-leading-ai-research Volume of publications, China is leading Source: Elsevier/Scopus Country Publications Field-weighted citation impact Switzerland 1,685 2.71 Singapore 2,432 2.24 Hong Kong 2,205 2.00 United States 25,471 1.79 Italy 6,221 1.74 Although, volume sometimes doesn’t correspond to the quality Which countries and universities are leading on AI research? Source: TimesHighereducation
@pieroleo 10 AI: Back to the basic, now!
@pieroleo ALGORITHMS PROBLEMSDATACOMPUTING High Complexity Low Complexity Stack of Dimensions of Information Technology (IT) systems and their growing complexity ANALOG DIGITAL QUANTUM NUMBERS INFORMATION DATA WISDOM 2+2=4 ASK FOR A LOAN DGITAL TRANSFORMATON PROCEDURAL PROGRAM SUPERVISED REASONING
@pieroleo ALGORITHMS PROBLEMSDATACOMPUTING High Complexity Low Complexity From a business perspective AI is THE ingredient that can push the genetic evolution of IT from the status of a tool to a generalized business problem solving environment ANALOG DIGITAL QUANTUM NUMBERS INFORMATION DATA WISDOM 2+2=4 ASK FORA LOAN DGITAL TRANSFORMATON PROGRAM SUPERVISED REASONING AI REEF
@pieroleo Hic sunt leones
@pieroleo ALGORITHMS PROBLEMSDATACOMPUTING High Complexity Low Complexity ANALOG DIGITAL QUANTUM NUMBERS INFORMATION WISDOM PROGRAM SUPERVISE AND TRAIN A MACHINE 2+2=4 ASK FORA LOAN AI REEF AI isn’t magic — it’s a lot of hard work! Here there are lions
@pieroleo ALGORITHMS PROBLEMSDATACOMPUTING Stack of Dimensions of Information Technology (IT) systems and their growing complexity ANALOG DIGITAL QUANTUM NUMBERS INFORMATION WISDOM PROGRAM SUPERVISE AND TRAIN A MACHINE MACHINE LEARNS WITHOUT A TRAINING 2+2=4 ASK FORA LOAN DGITAL TRANSFORMATON High Complexity Low Complexity
@pieroleo Why we need the help of Artificial Intelligence to solve problems?
Investment in digital is a matter of survive for companies, after digitalization what’s next? @pieroleo www.pieroleo.com
Source: McKinsey What’s next? For agriculture Precision Agriculture
Leveraging the Explosion of Data in Medicine An Impossible Task Without Analytics and New advanced Artificial Intelligence Computing Models 1000 Facts per Decision 10 100 1990 2000 2010 2020 Human Cognitive Capacity Electronic Health Records (Clinical Data) Internet of Things (Exogenous Data) The Human Genome (Genomic Data) Capturing the Value of Data: Big Changes Ahead Medical error—the third leading cause of death in the US Source: BMJ 2016; 353 doi: http://dx.doi.org/10.1136/bmj.i2139 (Published 03 May 2016) Cite this as: BMJ 2016;353:i2139
20 Body Mass Index (BMI) Mass (weight - Kg) / height (cm) x height (cm) You are “Normal” if your BMI is between 18.5 and 24.99 Adolphe Quetelet, 1832 @pieroleo www.pieroleo.com
21 Practice Pearls: • BMI - Body mass index is a strong and independent risk factor for being diagnosed with type 2 diabetes mellitus • Type 2 diabetes risk may be incrementally higher in those with a higher body mass index • Understanding the risk factors helps to shorten the time to diagnosis and treatment How precise could be a relative “simple” signal @pieroleo www.pieroleo.com
22 Main point: it is not only a matter of how many data points you consider to take a decision It is a matter of how data we have are approximating the reality The BMI - Body Mass Index is an approximation of our health status, it is inherently a proxy or a condensed information of a huge quantity physiological parameters AI could help Medicine to reduce approximation
@pieroleo Which is the current and future role of Artificial Intelligence in augmenting decision making?
Assistant Tools Collaborator Coach Mediator Emerging types of augmenting decision making AI is augmenting decision making and is opening to new forms of collaboration between humans and machines to solve problems @pieroleo www.pieroleo.com Passive Role Active Role
Assistant Tools Collaborator Coach Mediator AI is augmenting decision making and is opening to new forms of collaboration between humans and machines to solve problems @pieroleo www.pieroleo.com Emerging types of augmenting decision making Passive Role Active Role
Gutman, Codella, Celebi, Helba, Marchetti, Mishra and Halpern, “Skin LesionAnalysis toward Melanoma Detection:A Challenge”, Int. Symposium on Biomedial Imaging (ISBI) 2016 • Deep Learning for skin lesion image analysis • Trained on dermoscopic images of melanoma and other skin cancers • Automates analysis of images of skin lesions • Extracts clinical features • Segments lesions • Predicts disease • Reports disease score • Searches for similar lesions Tool: Skin Lesion Image Analysis for Melanoma Detection
Assistant Tools Collaborator Coach Mediator AI is augmenting decision making and is opening to new forms of collaboration between humans and machines to solve problems @pieroleo www.pieroleo.com Emerging types of augmenting decision making Passive Role Active Role
Watson Oncology A collaboration between IBM and Memorial Sloan Kettering (MSK). Watson for Oncology utilizes MSK curated literature and rationales, as well as over 290 medical journals, over 200 textbooks, and 12 million pages of text to support decisions. • Analyzes the patient's medical record • Identifies potential evidence-backed treatment options • Finds and provides supporting evidence from a wide variety of sources
Assistant Tools Collaborator Coach Mediator AI is augmenting decision making and is opening to new forms of collaboration between humans and machines to solve problems @pieroleo www.pieroleo.com Emerging types of augmenting decision making Passive Role Active Role
Personalized diabetes mobile companion Guardian Connect Cognitive Computing Sugar.IQ • Real-time, smartphone CGM with alerts • Insulin, Meal, Activity, Context • Standalone, for MDI patients • Watson Health Cloud & Analytics • Pattern recognition, insights & Predictions • Engagement & Gamification • Real-time insights, coaching platform • Assists in daily diabetes mgmt • Aggressive capabilities roadmap Insights Glucose Forecasts Hype-hyper Predictions Ask Watson
Collaborating to have an healthy regime
Assistant Tools Collaborator Coach Mediator AI is augmenting decision making and is opening to new forms of collaboration between humans and machines to solve problems Emerging types of augmenting decision making Passive Role Active Role
ViTA Advisor: it is a conversational multi- modal agent to support older as well as a tool to collect meaningful data about the context of an individual ViTA : Virtual Trainer for cognitive impaired patients Sustain Independence and Dignity with affect and purpose, preserve and reinforce individuals and social memories Vita Memory Coach: a system that supports caregivers to collect meaningful facts and memories of an individual and his context Vita Memories Leo, D’Onofrio, Sancarlo, Ricciardi, De Petris, Giuliani, Peschiera, Failla, Renzi and Greco, “ViTA: Virtual Trainer forAging”- FAAL 2017
Coaching our memory but also memory 2/8/18Tales
Assistant Tools Collaborator Coach Mediator AI is augmenting decision making and is opening to new forms of collaboration between humans and machines to solve problems Emerging types of augmenting decision making Passive Role Active Role
Source: IBM & Marchesa Cognitive Dress https://www.ibm.com/blogs/internet-of-things/cognitive-marchesa-dress/ & https://www.ibm.com/watson/stories/dress.html How you perceive see yourself How others see you A Cognitive Dress mediates you
Computers help to Organize & Find Information Make betterdecisions four our business Big Data Decision Lakes Invent New Products & Markets Augment Intelligence Machine Learning(Visual, Multimodal learning,…) NaturalInterfaces… Industry-driven decisions Augment Problem Solving IntelligentMaterials Newform of Computing After mediation Active Intelligence is at the horizon Source: See Pietro Leo, 2017 - https://pieroleo.com/2017/10/05/active-intelligence-is-at-the-horizon/
@pieroleo 38 Example challenges IBM is tackling requiring tech innovation Media & Entertainment Regulatory Compliance Industrial - Maintenance Customer Care Marketing / Business IoT Is my organization compliant with latest regulatory documents Guide me through fixing malfunctioningcomponents Summarize the strategic intent of a company based on recent news articles Bot that can guide a user through buying the right insurance policy Retail Find rust on electric towers, using drones Healthcare Visual Inspection Improve the accuracy of breast cancer screening Predict yieldof fieldbased on images and sensor data Create highlights of sports events
@pieroleo 39 Source: https://www.ibm.com/watson/products-services/ Conversation Integrate diverse conversation technology into your application. Knowledge Get insights through accelerated data optimization capabilities. Vision Identify and tag content then analyze and extract detailed information found in an image. Speech Convert text and speech with the ability to customize models. Language Analyze text and extract meta-data from unstructured content. Empathy Understand tone, personality, and emotional state. Practical attempts to make easy the access to AI: think in terms of AI microservices (or business building blocks) ready to use
@pieroleo ALGORITHMS PROBLEMSDATACOMPUTING Stack of Dimensions of Information Technology (IT) systems and their growing complexity ANALOG DIGITAL QUANTUM NUMBERS INFORMATION WISDOM 2+2=4 ASK FORA LOAN PROCEDURE DGITAL TRANSFORMATON High Complexity Low Complexity PROGRAM SUPERVISED REASONING
@pieroleo 41 Perception Deep Learning & Reason Classification Explain InterpretabilitySymbolic Reasoning Observe Common-Sense Planning Patterns & Sub-patterns Observation AI Algorithms ….. ….. ….. Ethics @pieroleo www.pieroleo.com
@pieroleo 42 Perception Deep Learning & Reason Classification Explain InterpretabilitySymbolic Reasoning Observe Common-Sense Planning Patterns & Sub-patterns Observation AI Algorithms ….. ….. ….. Ethics Deep Neural Learning @pieroleo www.pieroleo.com
@pieroleo 43 Various forms of AI works Kind of problems where Artificial Intelligence is generating a relevant business impact
@pieroleo 44 AI & Computer Vision General Purpose Visual Services Source IBM Research Computer Vision: http://www.research.ibm.com/cognitive-computing/computer-vision/ Medical Image Analysis “a person holding a giraffe in their hand” Video Content Analysis Image Captioning Low-power computer vision - Gesture Recognition Multimodal Analysis
@pieroleo 45 Source: IBM Research automatic sport highlights generation https://www.ibm.com/blogs/research/2017/06/scaling-wimbledons-video- production-highlight-reels-ai-technology/
@pieroleo 46 Source: IBM Research Food Recognition - https://www.ibm.com/blogs/research/2017/05/training-watson-see-whats-plate
@pieroleo 47 r - https://arxiv.org/pdf/1612.00563.pdf “a blue boat is sitting on the side of a building” “a person holding a giraffe in their hand” @pieroleo www.pieroleo.com Rennie, Marcheret, Mroueh, Ross & Goel, “Self-Critical Sequence Trainingfor Image Captioning.” CVPR 2017
@pieroleo Recognizing products on a supermarket shelf for planogram compliance Karlinsky, Shtok, Tzur, and Tzadok, “Fine-grainedrecognitionof thousands of objectcategories with single-example training”, CVPR-2017 Challenge: Fast detection and recognition of thousands of object categories while training on one example per category Approach: Non-parametric probabilistic model for initial detection, CNN-based refinement and temporal integration (where applicable) Results: Achieving state-of-the-art performance on existing retail benchmark and new dataset that we curated
@pieroleo 49Source: https://arxiv.org/pdf/1710.08864.pdf
@pieroleo 50 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
@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 forcustomers, 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
@pieroleo 5 2 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 languageof x, l, • Use languageto logic for l on x to make an equivalent query y, • Reason to answer y, yielding answers z • Use logic to language to turneach 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 定シエムチ曜 玲ロ危氏47貫 っを数満え形 60弘90健ル がぽぞ逮 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
@pieroleo 53 Source: https://www.ibm.com/blogs/think/2017/01/ibm-cognitive-principles/ Source: https://www.research.ibm.com/software/IBMResearch/multimedia/AIEthics_Whitepaper.pdf www.pieroleo.com
@pieroleo ALGORITHMS PROBLEMSDATACOMPUTING Stack of Dimensions of Information Technology (IT) systems and their growing complexity ANALOG DIGITAL QUANTUM NUMBERS INFORMATION WISDOM PROGRAM SUPERVISE AND TRAIN A MACHINE MACHINE LEARNS WITHOUT A TRAINING 2+2=4 ASK FORA LOAN PROCEDURE DGITAL TRANSFORMATON High Complexity Low Complexity
@pieroleo 55 http://science.sciencemag.org/content/345/6197/668 IBM Research Brain Chip - http://www.research.ibm.com/articles/brain-chip.shtml In 2014, IBM presented 1M spiking-neuron chip with a scalable communicationnetwork and interface. The chip has 5.4 billion transistors, 4096 neuro-synaptic cores and 256 million configurable synapses. Neuromorphic Computing – IBM True North Source: Introduction - https://www.youtube.com/watch?time_continue=3&v=jqI0L44yFEo www.pieroleo.com
@pieroleo 56 Source: IBM Research Gesture recognition at Low power devices - https://www.ibm.com/blogs/research/2017/07/brain-inspired-cvpr-2017/ Source Video: https://www.youtube.com/watch?v=g08IW-qRomM Trained a spiking neural network to recognize 10 hand gestures in real-time at 96.5 percent accuracy within a tenth of a second from the start of each gesture, while consuming under 200 mW – much lower power than frame-based systems, which use traditional processors.
@pieroleo 57 • 64 million neurons • 16 billion synapses, • Processor component will consume the energy equivalent of a dim light bulb – a mere 10 watts to power. Source: IBM Research new TrueNorth project update https://www-03.ibm.com/press/us/en/pressrelease/52657.wss U.S. Air Force Research Lab Taps IBM to Build Brain- Inspired AI Supercomputing System www.pieroleo.com
@pieroleo 58 In-memory Computing with 1 Million Devices for Applications in AI Source IBM Research Phase Change Memory: https://www.ibm.com/blogs/research/2017/10/ibm- scientists-demonstrate-memory-computing-1-million-devices-applications-ai/ IBM Research demonstrated that an unsupervised machine-learning algorithm, running on one million phase change memory (PCM) devices, successfully found temporal correlations in unknown data streams. When compared to state-of- the-art classical computers, this prototype technology is expected to yield 200x improvements in both speed and energy efficiency Source: http://www.nature.com/articles/s41467-017-01481-9 PCM Device Collocated Memory and computing
@pieroleo “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly, it’s a wonderfulproblem, because it doesn’t look so easy.” -Richard P. Feynman NATURE ISN’T CLASSICAL, DAMMIT, AND IF YOU WANT TO MAKE A SIMULATION OF NATURE, YOU’D BETTER MAKE IT QUANTUM MECHANICAL, AND BY GOLLY, IT’S A WONDERFUL PROBLEM, BECAUSE IT DOESN’T LOOK SO EASY.” RICHARD P. FEYNMAN “
@pieroleo Intersection of AI & Quantum Computing How can AI accelerate the development of quantum computers? How will quantum computers speed up the training of AI models? IBM Quantum Environment www.research.ibm.com/ibm-qx doi:10.1038/s41534-017-0017-3 Optimization Chemistry Machine Learning
61 CLOSING What are next challenges for Artificial Intelligence for supporting complex business problems?
Source Kate Crawford #NIPS2017 Transparency & Trustworthiness
@pieroleo FUTURE Multi-Task, Multi-Domain Intelligence Automated Application Development Continuously-Adapting Applications Human-Like Task Learning Implicit + Explicit Memory Explainable Continuously Learn & Adapt Induce Rules & Processes; Infer Solutions Modality Independent Automatically-Constructed Architectures Hybrid Infrastructure Acceleration via Novel Devices & Materials Dynamic Data Information Represented by Knowledge TODAY Single-Task, Single-DomainIntelligence Human-Guided Application Development Static Applications Data-Defined End-to-End Tasks Implicit Memory Opaque Train and Deploy Program Control Uni-Modal Static Algorithm-Specific Architectures Traditional Infrastructure Deep Learning Acceleration Static Data Information Represented by Data AI for Business requires Research innovations RESEARCH ALGORITHMS PROBLEMS DATA COMPUTING
@pieroleo @pieroleo Pietro Leo Executive Architect - IBM Italy CTO for Artificial Intelligence Chief Scientist for IBM Italy Research & Business IBM Academy of Technology Leadership Grazie! www.pieroleo.com