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Novi sad ai event 1-2018

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Novi sad ai event 1-2018

  1. 1. 31.01.2018
  2. 2. 21st century will achieve times the progress of the 20th century.
  3. 3. NOVI SAD APPLIED INTELLIGENCE COMMUNITY City.AI NOVI SAD shaping up around a community whose goal are : To help local actors develop efficiently the Serbian branch on AI internationally To work around applied AI challenges with the local & global ecosystem actors To democratize AI innovation and close the gap between technology and society To train and challenge the local community
  4. 4. LEVERAGING THE POTENTIAL OF AI IN 40+ CITIES AFRICA Accra - Lagos ASIA Bangalore - Bangkok - Beirut - Chiang Mai - Hanoi - Hong Kong - Jakarta - Johor Bahru - Karachi - Lahore - Manila - Pune - Seoul - Singapore - Taipei AUSTRALASIA Wellington EUROPE Amsterdam - Berlin - Bratislava - Bristol - Brussels - Bucharest - Budapest - Cambridge - Cluj - Cologne - Copenhagen - Hamburg - Iasi - Krakow - Kyiv - London - Madrid - Munich - Novi Sad - Oxford - Paris - Sofia - Stockholm - Stuttgart - Tallinn - Tirana - Valencia - Valletta - Vienna - Vilnius NORTH AMERICA Austin - LA - New York - San Diego - San Francisco SOUTH AMERICA Bogota - La Paz - Sao Paulo
  5. 5. Powered by:
  6. 6. Our team NOVI SAD AI TEAM Jovan Stojanovic Ambassador of Novi Sad-AI Marko Jocic Co-Ambassador of Novi Sad-AI Jovana Miletic Operation manager of Novi Sad-AI
  7. 7. Dejan Vukobratovic PhD Proffesor and researcher, FTN on Belief networks Ivan Peric Deep learning engineer on NLP in Fintech LESSONS LEARNED BY
  8. 8. Outline of the Talk • Who are we? – iCONIC Centre – Hotspot for massive communications and information processing • Our Focus? – Large-scale networks for information acquisition and processing (5G) • Topic This Talk? – Probabilistic Graphical Models and Belief Propagation – Applications and Connections (to Deep Learning?)
  9. 9. Outline of the Talk • Who are we? – iCONIC Centre – Hotspot for massive communications and information processing • Our Focus? – Large-scale networks for information acquisition and processing (5G) • Topic This Talk? – Probabilistic Graphical Models and Belief Propagation – Applications and Connections (to Deep Learning?)
  10. 10. iCONIC iCONIC: intelligent COmmunications, Networking and Information proCessing
  11. 11. Outline of the Talk • Who are we? – iCONIC Centre – Hotspot for massive communications and information processing • Our Focus? – Large-scale networks for information acquisition and processing (5G) • Topic This Talk? – Probabilistic Graphical Models and Belief Propagation – Applications and Connections (to Deep Learning?)
  12. 12. MCC CN nodes RAN nodes MEC MEC 5G Core Network 5G Radio Access Network
  13. 13. 5G Focus No. 1: Machine-Type Communications
  14. 14. https://shop.sodaq.com/en/nb-iot-shield-deluxe.html 3GPP Narrowband IoT (NB-IoT) NarrowBand IOT • Standardized within 3GPP Release 13 (Nov 2016) • Already in testing/ deployment at many mobile operators • Low-Power WAN • Alternative to LoRa, SigFox • “Next big thing” for mobile operators Early market solutions
  15. 15. 5G Focus No. 2: Distributed Information Processing in Mobile Edge Computing (MEC)
  16. 16. Example: Massive Data Acquisition and Distributed Information Processing in Smart Grids IEEE Communications Magazine, Vol. 55, No. 10, October 2017. (http://ieeexplore.ieee.org/document/8067687/)
  17. 17. Outline of the Talk • Who are we? – iCONIC Centre – Hotspot for massive communications and information processing • Our Focus? – Large-scale networks for information acquisition and processing (5G) • Topic This Talk? – Probabilistic Graphical Models and Belief Propagation – Applications and Connections (to Deep Learning?)
  18. 18. Probabilistic Graphical Models • Model dependencies between random variables of a large-scale system x1 x2 xN-1 xN x4 x5 xN-3 xN-2 fs1 fs2 fsM . . . x3
  19. 19. Probabilistic Graphical Models • Variables can be discrete (e.g., ON/OFF) or continuous (e.g., TEMP) x1 x2 xN-1 xN x4 x5 xN-3 xN-2 fs1 fs2 fsM . . . p(xN) p(xN) x3
  20. 20. Probabilistic Graphical Models • We observe (measure) subset of random variables of a large-scale system x1 x2 xN-1 xN x4 x5 xN-3 xN-2 fs1 fs2 fsM . . . x3
  21. 21. Belief Propagation Algorithm • Based on a new evidence, we infer values of all variables in the system x1 x2 xN-1 xN x4 x5 xN-3 xN-2 fs1 fs2 fsM . . . x3
  22. 22. Belief Propagation Algorithm • Based on a new evidence, we infer values of all variables in the system x1 x2 xN-1 xN x4 x5 xN-3 xN-2 fs1 fs2 fsM . . . x3
  23. 23. Belief Propagation Algorithm • After BP converges, we obtain new beliefs about all system variables x1 x2 xN-1 xN x4 x5 xN-3 xN-2 fs1 fs2 fsM . . . x3 p(xN) p(xN)
  24. 24. Applications Large-scale Distributed State Estimation in Smart Grids Power system Probabilistic Graphical Model
  25. 25. Applications Large-scale Distributed State Estimation in Smart Grids
  26. 26. Applications • Error correction in wireless systems • Image segmentation • Troubleshooting – Systems – Medical Diagnosis
  27. 27. Applications • Sensor Data Fusion
  28. 28. BP and Deep Learning • https://sinews.siam.org/Details-Page/deep-deep-trouble • Michael Elad (Technion) blog “Deep, deep trouble: Deep Learning’s Impact on Image Processing, Mathematics and Humanity ” – “Unfortunately, all of these great empirical achievements were obtained with hardly any theoretical understanding of the underlying paradigm. Moreover, the optimization employed in the learning process is highly non- convex and intractable from a theoretical viewpoint.” – “Should we be happy about this trend? Well, if we are in the business of solving practical problems, the answer must be positive. Right? Therefore, a company seeking such a solution should be satisfied. But what about us scientists?” – “This is clearly not the school of research we have been taught, and not the kind of science we want to practice. Should we insist on our more rigorous ways, even at the cost of falling behind in terms of output quality?”
  29. 29. x1 x2 xN x4 xN-2 fs1 fs2 fsM . . . xN-3 x5 xN-1 BP and Deep Learning • In many instances, we know BP is too complex. Is there a shortcut? x3 xN-1 x5 xN-3 x1 xN
  30. 30. Thinking Fast and Slow • Two cognitive mechanisms: • FAST: akin to DNN – Instinctive – Instantaneous – Computationally fast shortcut • SLOW: akin to PGM and BP – Thoughtful but slow – Deep – Natural
  31. 31. Dejan Vukobratovic PhD professor and researcher on Belief networks Ivan Peric Deep learning engineer on NLP in Fintech LESSONS LEARNED BY
  32. 32. Application of Deep Learning to NLP task in commercial projects IVAN PERIĆ, NOVI SAD 2018
  33. 33. AI/ML Engineer – Synechron Serbia, Novi Sad Who am I? Teaching Assistant – Chair of Informatics, Faculty of Technical Sciences, University of Novi Sad
  34. 34. What is Deep Learning?  Main goal – “Completely” simulate human brain  Mathematical models that approximate biological concepts, like neurons  Very big networks of artificial neurons  A lot of computations  The result is a hierarchical feature set extractor, that can be used for classification, transformation to other feature sets, etc.  Known architectures  Multilayer Perceptron Neural Networks - MLPs  Convolutional Neural Networks – CNNs  Recurrent Neural Networks - RNNs
  35. 35. Where are we now?  State of the art deep artificial neural networks contain at most thousands of neurons  Biological human brain contains 15-30 billion neurons
  36. 36. Use Case - Automated Question Answering Engine  Terabytes (or even petabytes) of documents  Banking contracts  Financial reports  Financial news articles  Client emails  Answer questions that might have an answer in these documents  No predefined question list
  37. 37. Automated Question Answering Engine - Idea [Question, Answer, Context] Word Embedding Bidirectional LSTM with Attention Mechanism Answer
  38. 38. Word Embedding  Words need to be coded as numbers to be a valid input to ANN  One-Hot representation (high dimensionality, no semantics in word positions)  Word embedding allows word representation in a dense vector space, and adds contextual similarity to words (word2vec, GloVe)
  39. 39. Bidirectional LSTM (Long Short-Term Memory)  Unidirectional LSTMs capture dependencies only in one direction  Bidirectional LSTMs captures future and past dependencies and work very good in tasks that can benefit from this fact
  40. 40. Attention mechanism in LSTM  Attention mechanism lets network decide which part of the input sequence it should focus on  Focused part is probably more important for the task the sequence model is being trained for than the rest of the sequence  In this case, decoder output depends on a weighted combination of all the input states, not just the last state
  41. 41. Bidirectional Attention Flow mechanism  Machine Comprehension of textual data Published as a conference paper at ICLR 2017
  42. 42. Constraints in Deep Learning application to commercial projects  Technical constraints in commercial use of Deep Learning  Lack of any kind of data  Vast amounts of data, but unlabeled  Data is not opened to the public  Bad infrastructure for model training
  43. 43. Constraints in Deep Learning application to commercial projects  Functional constraints in commercial use of Deep Learning  Lack of understanding of capabilities of Deep Learning  Black box model  Hard or impossible to explain any non-working cases to the client  Hard or impossible to explain any working cases to the client  Traditional Data Science approaches based on statistical models, as well as conventional reasoning in AI and ML (searches, optimization algorithms, fuzzy logic, …) still look more acceptable to clients  Usually easier to explain  They can work very well without big amounts of data
  44. 44. Thank you for your attention.
  45. 45. Dejan Vukobratovic PhD Professor and researcher on Belief networks Ivan Peric Deep learning engineer on NLP in Fintech QA WITH:
  46. 46. Folllow us at Novi Sad city AI

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