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Computing, Data, Algorithms, and, Problem Solving
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Computing, Data, Algorithms, and, Problem Solving
- 1. @pieroleo Computing, Data, Algorithms, and, ProblemSolving Pietro Leo Executive Architect - IBM Italy CTO for Artificial Intelligence IBM Academy of Technology Leadership Head of IBM Italy Center of Advanced Studies A guest lecture prepared for Information Technology & Digital Strategy @pieroleo www.pieroleo.com
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- 3. @pieroleo ALGORITHMS My dentist toldme how to perfectly clean your teeth….. COMPUTING Me, a toothbrush, toothpaste and water… PROBLEMS I have to brush my teeth…. DATA Teeth can get sick, caries, tartar… @pieroleo www.pieroleo.com
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- 5. @pieroleo ALGORITHMS PROBLEMSDATACOMPUTING • QUANTUMLAWS • DIGITAL • ANALOG High Complexity • WISDOM • KNOWLEDGE • INFORMATION • NUMBERS • TALK WITH A DIGITAL HUMAN • DGITAL TRANSFOR MATON • ASK FOR A LOAN • 2+2=4 • MACHINE LEARNS WITHOUT A TRAINING • SUPERVISE AND TRAIN A MACHINE • PROGRAM Low Complexity Dimensions of IT systems and their complexity @pieroleo www.pieroleo.com
- 6. @pieroleo ALGORITHMS PROBLEMSDATACOMPUTING ALGORITHMSPROBLEMSDATACOMPUTING ALGORITHMS PROBLEMSDATACOMPUTING 1950-1960 1960-1970 1970-1980 ALGORITHMS PROBLEMSDATACOMPUTING 1980-1990 ALGORITHMS PROBLEMSDATACOMPUTING 1990-2000 2000-2010 ALGORITHMS PROBLEMSDATACOMPUTING 2010-…. IT Complexity waves ALGORITHMS PROBLEMSDATACOMPUTING • QUANTUM LAWS • DIGITAL • ANALOG • WISDOM • KNOWLEDGE • INFORMATION • NUMBERS • TALK WITH A DIGITAL HUMAN • DGITAL TRANSFOR MATON • ASK FOR A LOAN • 2+2=4 • MACHINE LEARNS WITHOUT A TRAINING • SUPERVISE AND TRAIN A MACHINE • PROGRAM @pieroleo www.pieroleo.com
- 7. @pieroleo ALGORITHMS PROBLEMSDATACOMPUTING • QUANTUMLAWS • DIGITAL • ANALOG High Complexity • WISDOM • KNOWLEDGE • INFORMATION • NUMBERS • TALK WITH A DIGITAL HUMAN • DGITAL TRANSFOR MATON • ASK FOR A LOAN • 2+2=4 • MACHINE LEARNS WITHOUT A TRAINING • SUPERVISE AND TRAIN A MACHINE • PROGRAM Low Complexity Dimensions of IT systems and their complexity @pieroleo www.pieroleo.com
- 8. 8 Data We live in a cloud ofdata @pieroleo www.pieroleo.com
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- 11. Non è possibilevisualizzare questa immagine. @pieroleo www.pieroleo.com Sentiment Tone Euphemisms Hedonism Extroversion Face Recognition Openness Colloquialism Reasoning Strategies Language Modeling Dialog Intent Latent Semantic Analysis Phonemes Ontology Analysis Linguistics Image Tags Question Analysis Self-transcendent Affective Status
- 12. 12 Image source: http://personalexcellence.co/blog/ideal-beauty/ City Lifestyle ZIPcode Costal vs Inland Marital status Generation Location Family Size Gender Income Level Competitors Age Loyalty & Card Activity Revenue Size Life Stages Eductation Legal status Sector Industry Subscriptions Date onSite Wish List Size of Network Check-ins App usage duration Number of Apps on Device Deposits/Withdrawals Device Usage Purchase History Following Followers Likes Number of Hashtags used History of Hashtags Search Strings entered Sequence of visits Time/Day log in Time spent on site Time spent on page Frequency of Search Videos Viewed Photos liked Sentiment Tone Euphemisms Hedonism Extroversion Face Recognition Openess Colloquialism Reasoning Strategies Language Modeling Dialog Intent Latent Semantic Analysis Phonemes Ontology Analysis Linguistics Image Tags Question Analysis Self-transcendent Affective Status DNA Proteome Microbiome Clinical/Biochemical Data Steps Nutrition Genetics Runs X-rays (CT scans) sound (ultrasound), magnetism (MRI), Radioactive (SPECT,PET) light (endoscopy,OCT) Environment Bio-Images Source: Bipartisan Policy Center, “F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013) @pieroleo www.pieroleo.com
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- 14. @pieroleo Rapid growth ofexogenous data is transforming healthcare 6 Terabytes 60% Exogenous Factors 1100 Terabytes Volume, Variety, Velocity, Veracity: Educational records, Employment Status, Social Security Accounts, Mental Health Records, Caseworker Files, Fitbits, Home Monitoring Systems, and more… 0.4 Terabytes Electronic Medical / Health Records, Physician Management Systems, Claims Systems and more… 30% Genomics Factors 10% Clinical Factors IBM Watson Health // SOURCE: ©2015 J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health Affairs 21, no. 2 (2002):78–93 Data Generated per Life @pieroleo www.pieroleo.com
- 15. @pieroleo Leveraging Exogenous Datafor Chronic Care (Type 2 Diabetes; Primary & Secondary Prevention) 60% Exogenous Factors 30% Genomics Factors 10% Clinical Factors IBM Watson Health // SOURCE: ©2015 J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health Affairs 21, no. 2 (2002):78–93 Glucose Monitoring Calorie Intake Stress Levels Physical Activity Other vital signs Social Interaction Affinity (retail) Sleep Pattern @pieroleo www.pieroleo.com
- 16. @pieroleo 16 >80% Unstructured Data +External Data “Untouched” Data + Stream of Data Enterprise Data Machine Data People Data @pieroleo www.pieroleo.com
- 17. @pieroleo Data is thereand we need to make the best out of it @pieroleo www.pieroleo.com
- 18. @pieroleo We produce andconsume Data for a specific purpose @pieroleo www.pieroleo.com
- 19. @pieroleo Source: http://datacoup..com Value ofData Pietro Leo's Second Income! @pieroleo www.pieroleo.com
- 20. @pieroleo For Science, Big Datais the microscope of the 21st century Wine DNA Tracing @pieroleo www.pieroleo.com
- 21. @pieroleo Source: Cornell University- Maize kernal infected with Aspergillus flavus,which produced aflatoxin.http://www.plantpath.cornell.edu/labs/milgroom/Research_aflatoxin.html And http://www.special-clean.com/special- clean/en/mold/mold-lexicon-1.php For science, Big Data is the microscope of the 21st century @pieroleo www.pieroleo.com
- 22. @pieroleo Source: http://www.pnas.org/content/114/38/10166.full New kindof prediction microscope: With the DNA raw data you could predict some individual traits
- 23. @pieroleo ALGORITHMS PROBLEMSDATACOMPUTING • QUANTUMLAWS • DIGITAL • ANALOG High Complexity • WISDOM • KNOWLEDGE • INFORMATION • NUMBERS • TALK WITH A DIGITAL HUMAN • DGITAL TRANSFOR MATON • ASK FOR A LOAN • 2+2=4 • MACHINE LEARNS WITHOUT A TRAINING • SUPERVISE AND TRAIN A MACHINE • PROGRAM Low Complexity Dimensions of IT systems and their complexity @pieroleo www.pieroleo.com
- 24. Investment in digital is amatter of survive for companies @pieroleo www.pieroleo.com
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- 26. Investment in private Fintech companies increase 10x inpast 5 years >20B
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- 28. Fintech are «UNBUNDLING » tranditional banks @pieroleo www.pieroleo.com
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- 30. « UNBUNDLING »is general phenomena that is impacting every sectors or corporations Source: https://www.cbinsights.com/blog/smart-home-market-map-company-list/ SMART HOMES @pieroleo www.pieroleo.com
- 31. « UNBUNDLING »logistics Source: https://www.cbinsights.com/blog/startups-unbundling-fedex/ @pieroleo www.pieroleo.com
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- 33. The largest accommodation company owns noreal estates @pieroleo www.pieroleo.com
- 34. Marriott CIO acknowledged Airbnb asa new competitor @pieroleo www.pieroleo.com
- 35. 35 You shared your position withme 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 @pieroleo www.pieroleo.com
- 36. Automation will bringbig shifts to the world of work, as AI and robotics change or replace some jobs, while others are created. Source: McKinsey & co: Jobs Lost, Jobs created: workforce transactions in a time of automation, 2017 By 2030, 75 million to 375 million workers will need to switch occupational categories. 60% percent of occupations have at least 30% of constituent work activities that could be automated @pieroleo www.pieroleo.com
- 37. Leveraging the Explosionof 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
- 38. 38 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
- 39. 39 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 “simple” signal @pieroleo www.pieroleo.com
- 40. Assistant Tools Collaborator Coach Mediator Emerging types ofCognitive Systems Augment Decision Making is opening to new forms of collaboration between humans and machines to solve problems @pieroleo www.pieroleo.com
- 41. Assistant Tools Collaborator Coach Mediator Augment Decision Makingis opening to new forms of collaboration between humans and machines Emerging types of Cognitive Systems @pieroleo www.pieroleo.com
- 42.
- 43. Assistant Tools Collaborator Coach Mediator Augment Decision Makingis opening to new forms of collaboration between humans and machines Emerging types of Cognitive Systems @pieroleo www.pieroleo.com
- 44. @pieroleo 2011 2015 2016 - AlphaGO=4Lee Se-Dol=1 1997 - IBM=2.5 Kasparov=2.5 1997 AlphaGO uses self-trained net to evaluate positions and moves on 30M historical games DeepBlue uses a hard-coded objective function written by a human coupled with High Performance Computing 2016 10 10170 1040 Applying or having wisdom in real world is not only an AI game COMPUTING & MATH WISDOM IBM Watson – Jeopardy! SEMANTICS
- 45. Watson Oncology A collaboration betweenIBM 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
- 46. 46 The Medical Sieve § Builda fast anomaly detection engine – Quickly filters irrelevant images – Highlights disease-depicting regions – Flags coincidental diagnosis § Intended as a radiology assistant – Clinicians still do the diagnosis – Machine reduces workload – Machine performs triage/decision support Given history of the patient and images of a study Is there an anomalous image here? If so, where is the anomaly ? Describe the anomaly The Medical Sieve @pieroleo www.pieroleo.com
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- 48. @pieroleo www.pieroleo.com https://www.thenorthface.com/xps I am goingto New York next May Man Walking, go around vest I'll find a jacket that fits those conditions. Are you looking for a men's or women's jacket? Okay, I got it. What will you use this jacket for? Where and When will you be using this jacket? What styles are you looking for?
- 49. @pieroleo www.pieroleo.com I amgoing to NewYorknext May Man Walking,go around vest Whereand When will you beusing thisjacket? I'll find a jacketthatfits thoseconditions. Areyou looking for a men'sor women's jacket? Okay, Igotit. What will you use thisjacket for? Whatstylesareyou looking for? @pieroleo
- 50. @pieroleo www.pieroleo.com Conversation Intents EntitiesDialogues Personality 5Five Personality Traits Needs Values Language Tone Emotion Social propensities Language styles Translate Conversational News Custom TranslationPatents Language Deep Understanding Speech-to-text Custom pronunciations Voice Transformation Expressive Voice Voice synthesis Keyword Spotting Telephony Broadband Vision Face Recognition Image Similarity Image Classification Custom eyes Keyword Extraction, Entity Extraction, SentimentAnalysis, Concept Tagging, Relation Extraction, Taxonomy Classification, Author Extraction….. Custom Analysis WATSON Kind of skills Source: https://www.ibm.com/watson/developercloud/services-catalog.html @pieroleo
- 51. @pieroleo Watson Chef withBon Appétit Live at: https://www.ibmchefwatson.com/tupler @pieroleo www.pieroleo.com
- 52. Assistant Tools Collaborator Coach Mediator Augment Decision Makingis opening to new forms of collaboration between humans and machines Emerging types of Cognitive Systems @pieroleo www.pieroleo.com
- 53. 53 2 solutions thataddress the biggest needs in Diabetes Care Integrated & personalized diabetes care program with coachingservices& risk stratification for healthcare systems to help high risk/at-risk individualswith diabetesimprove their lives & reduce the cost of care Personalizeddiabetes mobile companionwith real time glucose insights for individuals with diabetes to help make daily diabetesmanagement easierand more effective Medtronic Turning PointBridging the gap in between doctor visits enabling Self-Management & Better Care 53© 2016 International Business Machines Corporation @pieroleo www.pieroleo.com
- 54. Sugar.IQ : IntelligentMobile Assistant 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 @pieroleo www.pieroleo.com
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- 56. Assistant Tools Collaborator Coach Mediator Types of CognitiveSystems Augment Decision Making is opening to new forms of collaboration between humans and machines @pieroleo www.pieroleo.com
- 57. ViTA Advisor: itis 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 @pieroleo www.pieroleo.com
- 58. Memories are abridge among generations 12/15/1Tales @pieroleo www.pieroleo.com
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- 60. Assistant Tools Collaborator Coach Mediator Augment Decision Makingis opening to new forms of collaboration between humans and machines Emerging types of Cognitive Systems @pieroleo www.pieroleo.com
- 61.
- 62. Assistant Tools Collaborator Coach Mediator Augment Decision Makingis opening to new forms of collaboration between humans and machines Emerging types of Cognitive Systems @pieroleo www.pieroleo.com
- 63. Source: Soul Machine- https://www.youtube.com/watch?v=fNWjKtVWToc @pieroleo www.pieroleo.com
- 64. 64 @pieroleo www.pieroleo.com Source: Soul Machine- https://www.youtube.com/watch?v=0tUSsqOLZC8 @pieroleo
- 65. Sphie Source Sophie SouleMachine - https://www.youtube.com/watch?v=JiYANsW2F5A @pieroleo www.pieroleo.com
- 66. @pieroleo ALGORITHMS PROBLEMSDATACOMPUTING • QUANTUMLAWS • DIGITAL • ANALOG High Complexity • WISDOM • KNOWLEDGE • INFORMATION • NUMBERS • TALK WITH A DIGITAL HUMAN • DGITAL TRANSFOR MATON • ASK FOR A LOAN • 2+2=4 • MACHINE LEARNS WITHOUT A TRAINING • SUPERVISE AND TRAIN A MACHINE • PROGRAM Low Complexity Dimensions of IT systems and their complexity @pieroleo www.pieroleo.com
- 67. @pieroleo 67 There is aconstant growing interest around Artificial Intelligence @pieroleo www.pieroleo.com
- 68. @pieroleo 68 Various forms ofAI works @pieroleo www.pieroleo.com
- 69. @pieroleo 69 AI system &sighting 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 www.pieroleo.com
- 70. @pieroleo 70 Source: IBM Researchautomatic sport highlights generation https://www.ibm.com/blogs/research/2017/06/scaling-wimbledons-video- production-highlight-reels-ai-technology/ @pieroleo www.pieroleo.com
- 71. @pieroleo 71 Source: IBM ResearchFood Recognition - https://www.ibm.com/blogs/research/2017/05/training-watson-see-whats-plate @pieroleo www.pieroleo.com
- 72. @pieroleo 72Source: IBM ResearchImage Caption generation paper - 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
- 73. @pieroleo 73 Santiago Ramon yCajal Camillo Golgi
- 74. @pieroleo 74 Perception Deep Reason Classification Explain InterpretabilitySymbolic Reasoning Observe Common-Sense Planning Patterns &Sub-patterns Observation AI Algorithms ….. ….. ….. Ethics @pieroleo www.pieroleo.com
- 75. @pieroleo 75 Perception Deep Reason Classification Explain InterpretabilitySymbolic Reasoning Observe Common-Sense Planning Patterns &Sub-patterns Observation AI Algorithms ….. ….. ….. Ethics Deep Neural Learning @pieroleo www.pieroleo.com
- 76. @pieroleo 76 Deep learning basicprocess Forward Propagation Backward Propagation Multiply + Add Sigmoid SoftMax reLu Multiply + Add Errors @pieroleo www.pieroleo.com
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- 78. @pieroleo 78 Perception Deep 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 www.pieroleo.com
- 79. @pieroleo 79 Source: https://www.ibm.com/watson/products-services/ Conversation Integrate diverse conversation technologyinto 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 exercise: explore how an AI set of basic services looks like @pieroleo www.pieroleo.com
- 80. @pieroleo ALGORITHMS PROBLEMSDATACOMPUTING • QUANTUMLAWS • DIGITAL • ANALOG High Complexity • WISDOM • KNOWLEDGE • INFORMATION • NUMBERS • TALK WITH A DIGITAL HUMAN • DGITAL TRANSFOR MATON • ASK FOR A LOAN • 2+2=4 • MACHINE LEARNS WITHOUT A TRAINING • SUPERVISE AND TRAIN A MACHINE • PROGRAM Low Complexity Dimensions of IT systems and their complexity @pieroleo www.pieroleo.com
- 81. @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 “
- 82. Quantum Algorithms A quantum computercan solve certain problems much faster. Deckwith 99 black cards 1 red Ace Problem: find red Ace Average-casescenario: Classical Algorithm: 51 card draws GroverQuantum Algorithm:10 card draws @pieroleo www.pieroleo.com
- 83. Quantum Algorithms A quantum computercan solve certain problems much faster. Deck#1 100 black cards Deck#2 50 black cards 50 red cards Problem: which deck? Worst-casescenario: Classical Algorithm: 51 card draws Deutsch-JozsaQuantum Algorithm:1 card draw @pieroleo www.pieroleo.com
- 84. Exponential speed-up: A task taking 300 years (233 seconds) on a classical computer might take a minute (~ 30 seconds) on a quantum computer The problem of multiplication vs factoring: 937 x 947 = N (easy) 887339 = p x q (harder) Modulus (1024 bits): de b7 26 43 a6 99 85 cd 38 a7 15 09 b9 cf 0f c9 c3 55 8c 88 ee 8c 8d 28 27 24 4b 2a 5e a0 d8 16 fa 61 18 4b cf 6d 60 80 d3 35 40 32 72 c0 8f 12 d8 e5 4e 8f b9 b2 f6 d9 15 5e 5a 86 31 a3 ba 86 aa 6b c8 d9 71 8c cc cd 27 13 1e 9d 42 5d 38 f6 a7 ac ef fa 62 f3 18 81 d4 24 46 7f 01 77 7c c6 2a 89 14 99 bb 98 39 1d a8 19 fb 39 00 44 7d 1b 94 6a 78 2d 69 ad c0 7a 2c fa d0 da 20 12 98 d3 1024bit public key: = p × q →just short of impossible Shor’s algorithm jumpstarted the interest in quantum computing Classical Record: 320 digits (𝟐 𝟏𝟎𝟔𝟏; >300 CPU years) exp(𝐶 𝑏./0) 𝐶 𝑏0 Quantum Algorithms @pieroleo www.pieroleo.com
- 85. @pieroleo Traveling Salesman Problem - Visit all cities just once - Choose the shortest path -Come back to starting point 17 x … x 5 x 4 x 3 x 2 x 1 = 17! = 355’687’428’096’000 possible paths A quantum computer can explore all routes simultaneously while a classical computer has to try them sequentially. 18 selected cities in Switzerland 16 ausgewählte Städte in der Schweiz @pieroleo www.pieroleo.com
- 86. @pieroleo Molecular Dynamics, Drug Design & Materials Simulation Challenges This laptop couldsimulate a 25 electron system,Titan a 43 electron system but no classicalcomputereverbuilt could simulate a 50 electron system exactly. Caffeine is a moderately sized molecule. It is more complex than water, but simpler than DNA or proteins. The complexity involved to simulate its energy, structure and interactions is beyond the capability of any computer with current technology. To simulate Caffeine on Quantum might take 160 Qubits, doing so with classical with current methods require around 10^48 bits (as there are atoms on planet Earth)
- 87. @pieroleo Some of theearliest examples of these include optimization, chemistry, and machine learning. Quantum Computing holds the potential to solve today’s intractable computational challenges
- 88. @pieroleo How quantum mechanicslaws (such as superposition, entaglement, interference, etc..) could help to build new computational methods to solve these problems more efficiently? @pieroleo www.pieroleo.com
- 89. @pieroleo 0 1 Quantum QUBIT ‘1’ ‘0’ ‘0’ + ‘1’ Classic BITS Superposition: each qubitin 2 states simultaneously How Quantum laws can speedup Information representation? @pieroleo www.pieroleo.com
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- 94. @pieroleo Whereas a 56-qubit quantumcomputer can theoretically perform 2^56 operations simultaneously, IBM's accomplishment involved dividing this task into 2^19 slices that each essentially consisted of 2^36 operations. This strategy meant the researchers only needed about 3 terabytes of memory for their simulated quantum computer. In contrast, earlier in 2017, a 45-qubit simulation at the Swiss Federal Institute of Technology in Zurich required 500 terabytes of memory. @pieroleo www.pieroleo.com
- 95. @pieroleo State-of-the-art quantum computing innovation continues at IBM Research Published inthe journal Nature in September, 2017 @pieroleo www.pieroleo.com
- 96.
- 97. Source Kate Crawford#NIPS2017
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- 99. @pieroleo Source: http://www.ted.com/talks/sherry_turkle_alone_together Sherry Turkle: Connected, butalone? These days phones in our pockets are changing our minds and hearts offer us three gratifying fantasies and NEW challenges and risks for us: 1) We can put our attention where we want to be 2) We always be heard 3) We never left to be alone
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- 102. @pieroleo @pieroleo Pietro Leo Executive Architect IBMItaly CTO for Artificial Intelligence Head of IBM Italy Center of Advanced Studies IBM Academy of Technology Leadership Grazie! www.pieroleo.com