There is no denying that machine learning is rapidly reshaping the technological horizon, fueled by increasing availability of data, computing power, and software (e.g., TensorFlow). Classical ML techniques are becoming a common tool for the everyday programmer, at the same time that sophisticated deep learning models are fueling driverless cars, advanced AI players, and more. This talk will survey the ways in which ML is impacting the programming world, as we try to answer the following questions: are we truly witnessing a new AI resurgence? If yes, what should any developer be aware of?

1. From a Developer's POV: is Machine
Learning Reshaping the World?
Simone Scardapane
ROME 24-25 MARCH 2017

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The age of analytics: Competing in a data-
driven world (McKinsey Report)

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5. ROME 24-25 MARCH 2017When do you need machine learning?
Two big challenges in machine learning (Léon Bottou)

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

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

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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.

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

13. 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).

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

15. ROME 24-25 MARCH 2017Can we distinguish between them?
Labeled faces in the wild

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

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PathNet: Evolution Channels Gradient Descent in Super Neural
Networks [arXiv preprint]

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

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

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Universe (OpenAI)
DeepMind Lab (Google DeepMind)

30. ROME 24-25 MARCH 2017Some words of caution

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Breaking linear classifiers on ConvNet

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McDaniel, P., Papernot, N. and Celik, Z.B., 2016. Machine learning in
adversarial settings. IEEE Security & Privacy, 14(3), pp. 68-72.

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

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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)

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

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DeepCoder: Learning to Write Programs [arXiv preprint]

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