il deep learning ed una nuova generazione di ai - simone scardapane

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  • Il deep learninged una nuova generazione di AI

    Presenter: Simone Scardapane

    Data Driven Innovation Conference, 24 Febbraio 2017

  • Deep learning is the new trend

  • Una prima domanda

    Da dove nasce tutto questo interesse?

  • Il paradosso di Moravec

    "In the 60s, Marvin Minsky assigned a couple of undergrads [to program] a computer to use a camera to identify objects in a scene.

    He figured they'd have the problem solved by the end of the summer.

    Half a century later, we're still working on it."

    https://xkcd.com/1425/ (2014)

    https://xkcd.com/1425/

  • Riconoscere oggetti - oggi

  • Non solo riconoscere: descrivere

    Download TensorFlow Code

    https://github.com/tensorflow/models/tree/master/im2txt

  • Non solo descrivere: inventare!

    An introduction to Generative Adversarial Networks

    http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/

  • Inventare semplice

    Una rete "generativa" crea un'immagine verosimile a partireda rumore.Una seconda rete cerca di discriminare fra immagini reali edimmagini sintetiche.

    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. andBengio, Y., 2014. Generative adversarial nets. In Advances in Neural Information ProcessingSystems (pp. 2672-2680).

    http://papers.nips.cc/paper/5423-generative-adversarial

  • Immagini, musica

    Lintelligenza artificiale che scrive musica come i Beatles e Duke Ellington[La Stampa, 24/09/2016]

    Scientists at SONY CSL Research Lab have created the first-ever entire songs composed by Artificial Intelligence: "Daddy's Car" and "Mister Shadow".

    The two songs are excerpts of albums composed by Artificial Intelligence to be released in 2017.

    http://www.lastampa.it/2016/09/24/tecnologia/idee/lintelligenza-artificiale-che-scrive-musica-come-i-beatles-e-duke-ellington-BYseOFAOzZLZMxzELWs6HJ/pagina.html

  • giochi!

    How AlphaGo Mastered the Game of Go with Deep Neural Networks

    http://airesearch.com/ai-groups/how-alphago-mastered-the-game-of-go-with-deep-neural-networks/http://airesearch.com/ai-groups/how-alphago-mastered-the-game-of-go-with-deep-neural-networks/

  • Una seconda domanda

    Ok, ci hai convinto. Ma come siamo arrivati a

    questo punto in cos poco tempo?

  • Un passo indietro: reti neurali artificiali

    Ogni connessione un parametro: adattandole in base ai dati, possiamo "apprendere" dagli errori.

  • Cosa ci dice la biologia?

    Un elemento essenziale:strati multipli di elaborazione

    Urbanski, M., Coubard, O. A., & Bourlon, C. (2014). Visualizing the blind brain: brain imaging of visual field defects from early recovery to rehabilitation techniques. Frontiers in integrative neuroscience, 8.

    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4179723/

  • 2012-2017: crescita esponenziale

  • Inception

    Gooing Deeper Into Convolutions [Google Research Blog]

    https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.htmlhttps://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

  • Ragione 1: dati

  • Ragione 2: potenza di calcolo

    https://developer.nvidia.com/cudnn

    https://developer.nvidia.com/cudnn

  • Ragione 3: software!

    Creazione di un modello in Keras:

    model = Sequential() model.add(Dense(20, input_dim=16, init='uniform', activation='relu'))model.add(Dense(1, init='uniform', activation='sigmoid'))

    model.compile(loss='binary_crossentropy', optimizer='adam')model.fit(X, Y, nb_epoch=50, batch_size=100)

    Allenamento:

  • ML as a service

  • ML as a service (2)

  • Una terza domanda

    I benefici sono sotto gli occhi di tutti, ma i possibili rischi?

  • La rivoluzione del ML

    People worry that computers will get too smart and take over the world, but the real

    problem is that they're too stupid and they've already taken over the world.

    [Pedro Domingos, The Master Algorithm]

  • Machine Bias

    Machine Bias[ProPublica]

    https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

  • Equal Opportunity

    Equality of Opportunity in Machine Learning[Google Research Blog]

    https://research.googleblog.com/2016/10/equality-of-opportunity-in-machine.html

  • Machine Trust

    A known urban legend: neural networks "learns" to recognize tanks because all training data from one class was taken on sunny days.

    https://www.jefftk.com/p/detecting-tanks

  • "Spiegare" un classificatore

    Ribeiro et al. (2016): "Why should I trust you?"Lipton (2016): "The Mythos of Model Interpretability"

    https://arxiv.org/abs/1602.04938https://arxiv.org/abs/1606.03490

  • Adversarial ML

    Attacking machine learning with adversarial examples[OpenAI blog]

    https://openai.com/blog/adversarial-example-research/

  • Mancanza di senso comune

    Lake, B.M., Ullman, T.D., Tenenbaum, J.B. and Gershman, S.J., 2016. Building machines that learn and think like people. arXiv preprint arXiv:1604.00289.

    https://arxiv.org/abs/1604.00289

  • Mancanza di senso comune (2)

    Movie written by algorithm turns out to be hilarious and intense[ArsTechnica, 06/09/2016]

    http://arstechnica.com/the-multiverse/2016/06/an-ai-wrote-this-movie-and-its-strangely-moving/

  • Imparare il senso comune

    Lerer, A., Gross, S. and Fergus, R., 2016. Learning Physical Intuition of Block Towers by Example. arXiv preprint arXiv:1603.01312.

    https://arxiv.org/abs/1603.01312

  • Privacy

    Narayanan, A. and Shmatikov, V., 2008. Robust de-anonymization of large sparse datasets. In 2008 IEEE Symposium on Security and Privacy, 2008 (pp. 111-125). IEEE.

    Privacy Concerns Put the Kibosh on the Netflix Prize [Mashable]

    http://mashable.com/2010/03/12/netflix-prize-canceled/

  • Approfondimenti

    Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J. and Man, D., 2016. Concrete problems in AI safety. arXiv preprintarXiv:1606.06565.

    Crawford, K. and Calo, R., 2016. There is a blind spot in AI research. Nature, 538(7625), p. 311.

    https://arxiv.org/abs/1606.06565https://www.ncbi.nlm.nih.gov/pubmed/27762391

  • Get involved

    Cerchiamo proposte per:

    Eventi Sponsor Laboratori Talk

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