AI: The next frontier by Amparo Alonso at Big Data Spain 2017

AI: The next frontier by Amparo Alonso at Big Data Spain 2017

AI: The next frontier

https://www.bigdataspain.org/2017/talk/ai-next-frontier

Big Data Spain 2017
November 16th - 17th Kinépolis Madrid

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Big Data Spain

December 05, 2017
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Transcript

  1. None
  2. A.I.: The next frontier Amparo Alonso Betanzos CITIC-UDC Grupo LIDIA

  3. The Primeval Soup: The perfect storm

  4. Batch Streaming Aman Naimat. “The new Artificial Intelligence Market. The

    Big data Market”. O´Reilly, 2016  During 2017 the tendency of data generation has showed sustained growth.  The appetite of corporates, industry and public sector for data driven initiatives has not decreased.  There is a change of landscape that by 2017 has started to become apparent.
  5. Data Industry Landscape

  6. Infrastructure Challenges Data storage High performance in interchange and sharing

    Data format and protocols
  7. Advancing hardware

  8. Regulation and Ethics

  9. Safety

  10. Data rich vs Data poor Confidentialit y and scientific transparency

    Reproducibilit y Free data https://www.linkedin.com/pulse/national-artificial-intelligence-research-development-nco- nitrd/
  11. High dimensionality data Sparse data Heterogeneous data Missing data Noisy

    data Adversarial data Untrustworthy data Data Science
  12. • Machine Learning is as valuable as how exploitable its

    results are. • Lagging behind in some areas: • Visualization of clusters • Data drift • Results Assurance • Biased data 2017 Big Data Coruña. Statistical inference for big-but-biased data https://www.youtube.com/watch?v=luTJbX3aVKA More work is needed on: • Feature engineering • Regression • Anomaly detection • Practical non convex optimization • Effective parameter selection • Scalable transfer learning • Data integration • Data visualization Reliable Machine Learning
  13. Feature Engineering Distributed FS algorithms Missing Data Heterogeneou s data

    Unbalanced data
  14. Normalized Discounted Cumulative Gain (NDC

  15. • MNIST, 256 relevant features(576pixels) • 20% missing (MAR) •

    Imputation using median and SVD (Singular Value Decomposition) B. Seijo-Pardo, A. Alonso-Betanzos, K. Bennett, V. Bolón-Canedo, I. Guyon, M. Saeed. Analysis of imputation bias for feature selection with missing data. ESANN 2018
  16. FS Original FS Median Imputation FS, SVD imputation

  17. Size matters

  18. • The study of methodologies that increase the scalability of

    ML principles and algorithms. • Scalability should be seen as an abstract concept that not only includes the case of dealing with huge amounts of data points. • Just measuring the challenge in storage units will be a narrow minded view that will be oblivious to the challenge that current times is putting on the shoulders of ML Networks of AI systems Scalability
  19. • Models that can learn under privacy and anonimity constraints

    • Share parameter values, not data • Using aggregated data • Adequate accuracy? • Private data reconstruction? Privacy-preserving ML D. Fernández-Francos, O. Fontenla-Romero, A. Alonso-Betanzos. One-class convex hull-based algorithm for classification in distributed environments. IEEE Transactions on Systems, Man and Cybernetics: Systems (in press)
  20. Learning to Learn http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/

  21. https://spectrum.ieee.org/static/ai-vs-doctors Narrow niche vs General

  22. “Armed with machine learning, a manager becomes a supermanager, a

    scientist a superscie a superengineer. The future belongs to those who understand at a very deep level how to c expertise with what algorithms do best.” Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake https://www.itnonline.com/content/ new-report-highlights-five-reasons-why-radiology-needs-artificial-intelligence Human-in-the-loop
  23. • Deep Learning is not the AI future, https:// www.kdnuggets.com/2017/08/deep-learning-not-ai-futu

    re.html • The National AI R&D Strategic plan (USA) https ://www.linkedin.com/pulse/national-artificial-intelligenc e-research-development-nco-nitrd / • General Data Protection Regulation, UE http://ec.europa.eu/justice/data- protection/reform/files/regulation_oj_en.pdf Explainabilit y
  24. Transportation service robots Public safety, security AI Applications

  25. Education Low-resource communities AI Applications

  26. Entertainment Social risk of diminishing interpersonal interacti

  27. None
  28. pplications: Employment and workp

  29. The 6 Laws proposed by EU All intelligent machine should

    have an emergency switch An intelligent machine could not damage a human being It is forbidden to establish emotional links with a machine or electronic person The biggest machines should have an obligatory insurance Electronic persons will have rights and obligations. Electronic persons and machines should pay taxes http://www.europarl.europa.eu/news/es/news- room/20170109STO57505/delvaux-propone-normas- europeas-para-la-rob%C3%B3tica-y-un-seguro-obligatorio http://computerhoy.com/noticias/life/e stas-son-seis-leyes-robotica-que- propone-ue-56972
  30. None
  31. 6,3% (16% in Software Industry)

  32. A.I.: The next frontier Amparo Alonso Betanzos CITIC-UDC Grupo LIDIA