AI: The next frontier https://www.bigdataspain.org/2017/talk/ai-next-frontier Big Data Spain 2017 November 16th - 17th Kinépolis Madrid

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=luTJbX3aVKAMore 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. NormalizedDiscountedCumulativeGain(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
NarrownichevsGeneral

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

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 taxeshttp://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

31. 6,3% (16% in Software Industry)

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