From Artificial Intelligence to Quantum Computing an IBM Journey in Physics, AI and Science

Presentation for the Symposium on Physics Engineering (Jornadas de Engenharia Física) V, at the IST. Talks about the evolution of AI, quantum computing.

Transcript

1. From Artificial Intelligence to From Artificial Intelligence to Quantum Computing Quantum Computing an IBM Journey in Physics, AI and Science an IBM Journey in Physics, AI and Science Frederico Muñoz | Chief Architect | IBM Technical Expert Council (SPGI) Symposium on Physics Engineering — JEF·V Instituto Superior Técnico | Feb 2018
2. Hello! Hello!
3. < >  frederico.munoz@pt.ibm.com  https://www.linkedin.com/in/fsmunoz/  https://twitter.com/fredericomunoz  https://github.com/fsmunoz
4. About the Technical Expert Council About the Technical Expert Council The Technical Expert Council for Spain, Portugal, Greece and Israel is an affiliate of the IBM Academy of Technology.
5. To visualize the future of IBM you must know something of the past. — Thomas J. Watson, Sr.
6. IBM has a long relation IBM has a long relation with Physics. with Physics.
7. … and Physics Engineering… … and Physics Engineering…
8. … and Artificial Intelligence… … and Artificial Intelligence…
9. … and Science … and Science in general. in general.
10. Five IBM physicists have Five IBM physicists have received the Nobel Prize received the Nobel Prize in Physics in Physics
11. Leo Esaki in 1973 for his work in semiconductors. Gerd Bining and Heinrich Rohrer in 1986 for the scanning tunneling microscope. Georg Bednorz and Alex Mueller in 1987 for research in superconductivity.
12. And not just Nobel And not just Nobel prizes prizes Many essential scientific breakthroughs were born from IBM Research through the decades.
13. Quantum tunneling Quantum tunneling 1958: Leo Esaki’s discovery of the semiconductor junction, called the Esaki diode, finds wide use in electronics applications
14. Fractal Geometry Fractal Geometry 1967 - IBM, researcher Benoît Mandelbrot.
15. Nanotechnology Nanotechnology 1981: Gerd Binnig and Heinrich Rohrer invent the scanning tunneling microscope, revolutionizing our ability to manipulate solid surfaces the size of atoms.
16. Quantum teleportation Quantum teleportation 1993: An international group of six scientists, including IBM Fellow Charles H. Bennett, confirmed the intuitions of the majority of science fiction writers by showing that perfect teleportation is indeed possible in principle, but only if the original is destroyed.
17. Deep Blue Chess Deep Blue Chess 1997: First computer to defeat human World Chess Champion, Garry Kasparov.
18. IBM Blue Gene IBM Blue Gene 2004: Supercomputer to observe protein folding and gene development.
19. … and many others … and many others 1947 Magnetic Core Memory 1957 Landauer Formalism - Conductance must come in Quantized Units 1958 Quantum Tunnelling 1960 Thin Film Heads 1966 Tunable Lasers 1966 Two-Dimensional Electron Gas (2DEG) 1967 Josephson Junctions 1968 DRAM - 1 Transistor RAM 1974 Dennard Scaling (aka Why Moore’s Law also speeds up transistors in Lay Terms) 1978 Scanning Tunneling Microscope (1986 Nobel Prize Winner) 1982 Thermodynamics of Computation 1983 High Temperature Superconductors (1987 Nobel Prize Winner) 1990 Moving Atoms 1991 RFID 1993 Quantum Teleportation 1993 Seminal Contributions to the Theoretical Foundation of Quantum Information Processing 1994 High- Speed Silicon-Germanium Electronics 1997 GMR - Giant Magnetoresistive Heads 1998 Copper Interconnect 2002 SOI: Silicon on Insulator 2002 Theory of Nanoscale Material 2007 High-K Gate Dieletric 2008 Racetrack Memory 2008 Cooling 3D Chips 2011 Non-Planar Devices 2012 Holey Optochip - 1 Terabit per Second Optical Bus 2013 Millimeter Wave
20. more recently, and in the field of Artificial more recently, and in the field of Artificial Intelligence Intelligence
21. we had a well-known we had a well-known breakthrough breakthrough
22. Watson Jeopardy! Watson Jeopardy! Challenge Challenge
23. Watson and the Jeopardy! Challenge Watson and the Jeopardy! Challenge
24. First computer to defeat TV game show Jeopardy! champions. Research teams are working to adapt Watson to other information-intensive fields, such as telecommunications, financial services and government.
25. From that we have built From that we have built something special something special
26. a whole portfolio of Data a whole portfolio of Data Science, AI and Machine Science, AI and Machine Learning solutions. Learning solutions.
27. … integrated and working together and with … integrated and working together and with researchers researchers
28. (that’s the vision) (that’s the vision)
29. Data → Information → Knowledge Data → Information → Knowledge
30. Everything available on Everything available on the cloud. the cloud.
31. (… or almost everything) (… or almost everything)
32. … but not just in … but not just in any any cloud… cloud…
33. IBM Cloud IBM Cloud
34. APIs IoT Infrastructure AI ready Secure to the core
35. Watson Platform Watson Platform Services Services Integrate Watson into existing applications or develop new ones through the use of comprehensive APIs.
36. some examples of available services some examples of available services (plenty more on the IBM Cloud catalog!) https://www.ibm.com/cloud
37. Visual Recognition: Conversation: Speech to text: Natural Language Classifier: https://visual-recognition- demo.ng.bluemix.net/ https://conversation- demo.ng.bluemix.net/ https://speech-to-text- demo.ng.bluemix.net/ https://natural- language-classifier-demo.ng.bluemix.net/
38. Natural Language Understanding: Personality Insigths: Tone Analyser: https://natural-language-understanding- demo.ng.bluemix.net/ https://personality- insights-demo.ng.bluemix.net/ https://tone-analyzer- demo.ng.bluemix.net/
39. and ready-to-use and ready-to-use starter kits starter kits that make it simple that make it simple and and fun fun https://console.bluemix.net/developer/watson/starter kits
40. (we also use them ourselves)
41. We also have specific We also have specific solutions for getting the solutions for getting the most of unstructured most of unstructured data data
42. Watson Explorer Watson Explorer
43. Mine and explore all your unstructured data Mine and explore all your unstructured data Cognitive exploration Powerful text analytics Machine learning https://www.ibm.com/products/watson-explorer
44. Watson Knowledge Watson Knowledge Studio Studio
45. To become an expert in a given industry or domain, Watson must be trained. The Knowledge Studio helps with the training. https://www.ibm.com/watson/services/knowledge- studio/
46. Easy-to-use tools for annotating unstructured domain literature. Uses those annotations to create a custom machine learning model that understands the language of the domain. Create rule-based models that finds entities in documents based on rules that you define.
48. and to easily get insights from data and to easily get insights from data
49. Watson Watson Analytics Analytics
50. Smart data analysis and visualization Quick to use Guided and automatic predictive analytics Natural Language dialogue. https://watson.analytics.ibmcloud.com/
51. but the focal point of all but the focal point of all
52. and a huge part of IBM’s and a huge part of IBM’s vision vision
53. IBM Watson IBM Watson Data Platform Data Platform
54. collaboration collaboration
55. integration integration
56. discovery discovery
57. openness openness
58. full lifecycle full lifecycle
59. (from getting the data to showing it) (from getting the data to showing it)
60. in IBM Cloud in IBM Cloud
61. of course of course
62. Data governance Data preparation Data analysis Model creation Building apps
63. Some key components Some key components
64. IBM Data Catalog IBM Data Catalog
65. Discover and manage data sources https://www.ibm.com/cloud/data-catalog
66. Data Science Experience Data Science Experience
68. Interactive and collaborative platform for the different roles
69. Data Scientists Data Engineers Analysts Machine Learning Engineers https://datascience.ibm.com/
70. now that we know a bit more about what we now that we know a bit more about what we have have
71. let’s talk about how we let’s talk about how we have been using this to have been using this to bring real results and bring real results and tackle real challenges tackle real challenges
72. globally and in Portugal globally and in Portugal
73. Some examples Some examples
74. Fraud detection Fraud detection
75. Project with SIBS. Real-time fraud detection. Rule-based, predictive-based and AI-driven "best fit" model. Applies machine learning to go beyond "black box" models.
76. Healthcare, Pharmaceuticals & Life Science Healthcare, Pharmaceuticals & Life Science
77. Several projects, including an innovative project with a large pharmaceutical focused on public awareness and information through a bot with domain knowledge.
78. Some additional ongoing projects: Some additional ongoing projects: Adding cognitive support for patients with limited mobility. Improve accessability using real-time AI-driven assistance.
79. Chatbots Chatbots
80. In different industry segments we have active projects around chatbots. Automate first-line assistance. Provide an expert assistance with the ability to understand ambiguous queries. Add a natural-language interface to an existing solution.
81. Automation Automation Using Watson APIs we have active projects around Automation using visual recognition. Autonomous identification of equipment degradation.
82. Classification Classification For a major industrial company: Automatic classification of documents using a taxonomic tree and natural language processing. Streamline classification of documents by suggesting classes based on thre analyses of the documents (which are unstructured data).
83. We have many active We have many active projects in these and projects in these and other fields other fields
84. in fact, we have a Cognite Asset Factory in fact, we have a Cognite Asset Factory θ
85. right next to you right next to you
86. We’ve been here for a while We’ve been here for a while
87. and helped to achieve some rather important and helped to achieve some rather important things things
88. some (extremely) big some (extremely) big
89. some (extremely) small some (extremely) small
90. but always shaping the but always shaping the future future
91. Quantum Computing Quantum Computing
92. In the summer of 1981, IBM and MIT organized a landmark event called the First Conference on the Physics of Computation. It took place at Endicott House, a French-style mansion not far from the MIT campus.
93. Bennett and others realized that some kinds of computations that are exponentially time consuming, or even impossible, could be efficiently performed with the help of quantum phenomena. A quantum computer would store information in quantum bits, or qubits.
94. — Richard Feynman Nature is quantum, goddamn it! So if we want to simulate it, we need a quantum computer!
95. The Sounds of IBM: IBM Quantum The Sounds of IBM: IBM Quantum
96. IBM Q IBM Q An industry-first initiative to build commercially available universal quantum computers for business and science.
97. 20 qubit available, 50 qubit developed QISkit: open to anyone for development. IBM Q Network: advancing quantum computing together
98. These are all reasons These are all reasons behind IBM’s motto behind IBM’s motto
99. THINK THINK
100. Thank you! Thank you!
101. Some final links Some final links
102. Community Community Tools, algorithms and approaches are incresingly more open and social. Cognitive Class: Build Data Science and Cognitive Computing skills for free today Data Scientist Workbench: virtual lab with Data Science tools ready to explore and put to use https://cognitiveclass.ai https://datascientistworkbench.com/
103. Data Science Experience: Learn, create and collaborate IBM Code: Code patterns, tech talks, open source projects, developer advocates, dynamic communities, upcoming events. IBM Cloud: integrate all IBM services with your solution, including Watson & Analytics services https://datascience.ibm.com/ https://developer.ibm.com/code/ https://console.bluemix.net/catalog/
104. References References Fractal animation by Alexandre Tavernier ( ) IBM AI Research: Neurons image by Michelle Kuykendal and Gareth G (Georgia Tech’s NeuroLab): Spring cloud time lapse by Harrison Rowntree ( http://coolfractalanimations.blogspot.pt/2014/05/ set-animation-color-changes.html https://researcher.watson.ibm.com/researcher/view id=6813 https://www.youtube.co v=yy994HpFudc https://www.youtube.com/watch?v=Qu7mcKZgqv0