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.
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/
• 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
• 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
• 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)
“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
• 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
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
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