A.I.: The next frontierAmparo Alonso Betanzos

CITIC-UDCGrupo LIDIA

The Primeval Soup: The perfect storm

Batch

StreamingAman 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.

Data Industry Landscape

Infrastructure Challenges

Data storage

High performance

in interchange and sharing

Data format and protocols

Advancing hardware

Regulation and Ethics

Safety

Data rich vs Data

poor

Confidentiality and

scientific transparency

Reproducibility Free data

https://www.linkedin.com/pulse/national-artificial-intelligence-research-development-nco-nitrd/

High dimensionality data

Sparse data

Heterogeneous data

Missing data

Noisy data

Adversarial data

Untrustworthy data

Data Science

• Machine Learning is as valuable as how exploitable its results are.

• Lagging behind in some areas:• Visualization of clusters• Data drift• Results Assurance• Biased data2017 Big Data Coruña. Statistical inference for big-but-biased datahttps://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

Feature Engineering

Distributed FS algorithms

Missing Data

Heterogeneous data

Unbalanced data

Norm

aliz

ed D

isco

unte

d C

um

ula

tive G

ain

(N

DC

G)

• 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

FS Original

FS Median Imputation

FS, SVD imputation

Size matters

• 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

• 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)

Learning to Learn

http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/

https://spectrum.ieee.org/static/ai-vs-doctors

Narr

ow

nic

he v

s G

enera

l A

I

“Armed with machine learning, a manager becomes a supermanager, a scientist a superscientist, an engineer a superengineer. The future belongs to those who understand at a very deep level how to combine their unique expertise with what algorithms do best.” Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

https://www.itnonline.com/content/ new-report-highlights-five-reasons-why-radiology-needs-artificial-intelligence

Human-in-the-loop

• Deep Learning is not the AI future, https://www.kdnuggets.com/2017/08/deep-learning-not-ai-future.html

• The National AI R&D Strategic plan (USA)

https://www.linkedin.com/pulse/national-artificial-intelligence-research-development-nco-nitrd/

• General Data Protection Regulation, UE

http://ec.europa.eu/justice/data-protection/reform/files/regulation_oj_en.pdf

Explainability

Transportation

service robots

Public safety, securityAI Applications

Education

Low-resource communities

AI Applications

Entertainment

Social risk of diminishing interpersonal interactions

AI applications: Employment and workplace

The 6 Laws proposed by EUAll 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/estas-son-seis-leyes-robotica-que-propone-ue-56972

6,3% (16% in Software Industry)

A.I.: The next frontierAmparo Alonso Betanzos

CITIC-UDCGrupo LIDIA