American Statistical Association
New York City
Metropolitan Area Chapter

Chapter Seminar
March 30, 2017

The New York Metro Area Chapter of the American Statistical Association

Is Pleased to Invite You to a Seminar On



Melanie Swan
Philosopher and Economic Theorist
New School for Social Research


The aim of this talk is to discuss deep learning as a statistical method in data science and its philosophical implications. Statistics is a mode of viewing the world. We think that reality is composed of patterns, which can become clear through statistical and data science methods. In statistical data science methods, hypotheses are formulated in terms of probability distributions over possible datasets of empirical facts. In the application of statistical methods, learning is increasingly becoming a component. Statistical learning is applying automated learning algorithms to identify the features of systems and recognize patterns in very-large datasets.

Deep learning is an advanced statistical method that has come into use in recent years. In deep learning, algorithms learn from and make predictions about data, particularly by modeling high-level abstractions in datasets and assigning probability-weighting to nodes as they move through the system modeling. An important challenge in deep learning is that these methods seem to work, but we do not have a good explanation for why, which prevents a wider application to problems beyond the domains of their current success in image recognition (with convolutional nets) and text/speech recognition (with recurrent nets). This talk looks at what happens in deep learning, where there are multiple layers of increasingly abstract representation and non-linear operation used by algorithms to determine an answer. Deep learning is essentially a complexity optimization problem, where some concepts that may explain how deep learning systems operate include backpropagation, supervised-unsupervised learning, attractors, non-convexity, saddle-point local minima-maxima, and energy-based models (probabilities sum to > 1).

The philosophy of statistical deep learning involves the meaning, justification, use, and interpretation of these methods to arrive at empirical scientific knowledge. How are we to understand and trust these methods as an explanatory mechanism for patterns in datasets? Since statistical data science methods are crucial to any field of scientific inquiry as an interpretation mechanism for empirical data, a better understanding of the theory underlying current topics such as deep learning may help to improve our overall understanding.

Presentation Slides

Speaker Biography

Melanie Swan is a Philosopher and Economic Theorist at the New School for Social Research in New York, NY. She is the founder of several startups including the Institute for Blockchain Studies, DIYgenomics, GroupPurchase, and the MS Futures Group. Ms. Swan's educational background includes an MBA in Finance and Accounting from the Wharton School of the University of Pennsylvania, an MA in Contemporary Continental Philosophy from Kingston University London and Université Paris 8, and a BA in French and Economics from Georgetown University. She is a faculty member at Singularity University and the University of the Commons, an Affiliate Scholar at the Institute for Ethics and Emerging Technologies, and a contributor to the Edge's Annual Essay Question.

Deep Learning Review Papers and Other Background Resources

Arel, Itamar, Rose, Derek C., and Karnowski, Thomas P. (2010). Deep Machine Learning: A New Frontier in Artificial Intelligence Research. IEEE Computational Intelligence Magazine. 5(4). Pp. 13-18.

Cassirer, Ernst. (2017, 1923). Substance and Function, and Einstein's Theory of Relativity. London UK: Forgotten Books. (probability, explanation)

Goodfellow, Ian, Bengio, Yoshua, and Courville, Aaron. (2016). Deep Learning (Adaptive Computation and Machine Learning series). Cambridge MA: MIT Press.

Karpathy, Andrej. (2015). The Unreasonable Effectiveness of Recurrent Neural Networks. LeCun, Yann, Bengio, Yoshua, and Hinton, Geoffrey. (2015). Deep Learning. Nature. 521. Pp. 436-444).

Metz, Cade. (2015). Wolfram's Image Recognition Reflects a Big Shift in AI. WIRED.

Schmidhuber, Juergen. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks. 61. Pp. 85-117.

Swan, Melanie. (2015). Philosophy of Big Data: Expanding the Human-Data Relation with Big Data Science Services. IEEE BigDataService, Redwood City CA, Mar 31-Apr 2, 2015.

Thursday, March 30, 2017

5:00 P.M.
Doors open at 4:30 and talk begins at 5:00 P.M.

Pfizer, Inc.
235 East 42nd Street
(between 2nd and 3rd Avenues)
New York, New York 10017

Go to 235 East 42nd Street (between 2nd and 3rd Avenues).
Be sure to have a photo ID.
You will be directed to the Security Reception area where your name will be listed for the meeting.
You will be provided with a badge that is required to go through the security gates.
When you enter through the security gates, please follow the signage,
"ASA NYC Metro Chapter" to 219-Mezzanine Level: Mezz-31.

Registration and Fees
Seating is limited.
Advanced registration is required and there is no fee.

Additional Information
For questions, send an e-mail to

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