from a developer's pov: is machine learning reshaping the world? - simone scardapane -...

From a Developer's POV: is Machine Learning Reshaping the World?

Simone Scardapane

ROME 24-25 MARCH 2017


The age of analytics: Competing in a data-

driven world (McKinsey Report)

http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world

ROME 24-25 MARCH 2017When do you need machine learning?

Two big challenges in machine learning (Léon Bottou)

http://icml.cc/2015/invited/LeonBottouICML2015.pdf

ROME 24-25 MARCH 2017Software trend 1: simpler ML libraries

ROME 24-25 MARCH 2017Can we predict the skill level of a player?


Thompson, J.J., Blair, M.R., Chen, L. and Henrey, A.J., 2013. Video game telemetry as a critical tool in the study of complex skill learning. PloS one, 8(9), p.e75129.

ROME 24-25 MARCH 2017We still need to choose a classifier! :-(

ROME 24-25 MARCH 2017Auto Machine Learning

An overview of the AutoML system taken from: Feurer, M., Klein, A., Eggensperger, K.,

Springenberg, J., Blum, M. and Hutter, F., 2015.Efficient and robust automated

machine learning. In Advances in Neural Information Processing Systems (pp. 2962-

2970).

ROME 24-25 MARCH 2017Software trend 2: feasible deep learning

ROME 24-25 MARCH 2017Can we distinguish between them?

Labeled faces in the wild

http://vis-www.cs.umass.edu/lfw/

ROME 24-25 MARCH 2017Software trend 3: MODULAR deep learning


PathNet: Evolution Channels Gradient Descent in Super Neural

Networks [arXiv preprint]

https://arxiv.org/abs/1701.08734

ROME 24-25 MARCH 2017Software trend 4: machine learning as a service

ROME 24-25 MARCH 2017Software trend 5: reinforcement learning repositories


Universe (OpenAI)

DeepMind Lab (Google DeepMind)

https://universe.openai.com/

https://deepmind.com/blog/open-sourcing-deepmind-lab/

ROME 24-25 MARCH 2017Some words of caution


Breaking linear classifiers on ConvNet

https://karpathy.github.io/2015/03/30/breaking-convnets/


McDaniel, P., Papernot, N. and Celik, Z.B., 2016. Machine learning in

adversarial settings. IEEE Security & Privacy, 14(3), pp. 68-72.

ROME 24-25 MARCH 2017The technical debt of machine learning


Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. ... it is dangerous to think of these quick wins as coming for free. ... it is common to incur massive ongoing maintenance costs in real-world ML systems.

Hidden Technical Debt in Machine Learning Systems (NIPS 2015)

ROME 24-25 MARCH 2017Will machine learning replace programmers?


DeepCoder: Learning to Write Programs [arXiv preprint]

https://arxiv.org/abs/1611.01989

from a developer's pov: is machine learning reshaping the world? - simone scardapane -...

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