Machine Learning snapshot, June 2018

Kian Katanforoosh and Andrew Ng have been teaching CS230:Deep Learning, at Stanford. The project reports and posters list has just come out, summarizing work done by the students with help from the teaching team. More than 160 projects. It will be interesting to see which of these mature into applications.

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Image from Painting Outside the Box: Image Outpainting with GANs (Mark Sabini, Gili Rusak) which was awarded first place in Outstanding Posters

CS230 https://web.stanford.edu/class/cs230/

Reports and posters http://cs230.stanford.edu/proj-spring-2018.html

The 4th Research and Applied AI Summit  https://raais.co/ just finished in London. The 125 slideset on the State of AI is a decent current snapshot of much more evolved work than the Stanford posters. https://www.stateof.ai/

Artificial Intelligence for Institutional Investors

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Venue – the National Press Club, 13th floor ballroom

The Markets Group runs events for institutional investors. I took part in a panel about Artificial Intelligence and Machine Learning, and in a round table discussion.

Diagrams and useful background sources listed below.

Definition of Artificial Intelligence, to illustrate that machine learning is a subset of AI. Sourced from the thorough review of AI in the NHS  http://www.reform.uk/wp-content/uploads/2018/01/AI-in-Healthcare-report_.pdf

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Examples of AI and ML in use

At least 75% of the audience uses Netflix – which applies machine learning to improve its users’ experience of streaming as well as for content selection. Its results are driven by an extreme emphasis on keeping existing and attracting new customers. The data its customers generate by using it are used to make recommendations to them. Artwork personalization https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76  Image discovery https://medium.com/netflix-techblog/ava-the-art-and-science-of-image-discovery-at-netflix-a442f163af6

View story at Medium.com

Alibaba and Tencent Unlike the US companies, they have built their support for retailing primarily based on the customer’s mobile device; so they use facial recognition for identification, image scanning and matching for item selection, precise location specification in shopping venues and integration with payment apps to enhance the customer’s buying experience, both online and in person at a store.  http://technode.com/2018/02/14/alibaba-new-retail/ http://www.bain.com/publications/articles/embracing-chinas-new-retail.aspx (joint report – Bain and Alibaba)

Recent reviews – longer reads  Recent developments are a direct result of the enormous improvement in computing capability at drastically reduced costs, a direct result of Moore’s Law – which continues.

Longer term effects of automation – 10 – 20 year horizon – Bain March 2018 http://www.bain.com/publications/articles/labor-2030-the-collision-of-demographics-automation-and-inequality.aspx

13 artificial intelligence trends reshaping industries and economies – CBInsights February 2018 https://www.cbinsights.com/reports/CB-Insights_State-of-Artificial-Intelligence-2018.pdf

Capabilities at the end of 2017 – summary in slide format from Jeff Dean (Stanford, Google Brain team) http://learningsys.org/nips17/assets/slides/dean-nips17.pdf

History

1950 Alan Turing asked “Can Machines Think”  Computing Machinery and Intelligence paper https://www.csee.umbc.edu/courses/471/papers/turing.pdf
1956 Initial definition of Artificial Intelligence at a workshop  at Dartmouth College https://en.wikipedia.org/wiki/Dartmouth_workshop   Remember the AI Winters in the 1970s, 1990s

 

Scaled Machine Learning

DallyNvidiaScaledML

Bill Dally, Nvidia

Matroid and the Stanford center for image system engineering ran the 3rd year of the ScaledML conference yesterday, 24 March 2018. It was a concentrated survey of work in progress in machine learning, with admirably little overt advertising. The overall impression is of enormous actual potential, orthogonal to enormous hype and inflated expectations, significant uncertainty about what will actually get done, and of a lot of work in progress on necessary infrastructure in hardware, architecture, languages, systems, and education.

Agenda and speaker list

08:45 – 09:00 Introduction Reza Zadeh Matroid
09:00 – 10:00 Ion Stoica Databricks
10:00 – 11:00 Reza Zadeh Matroid
11:00 – 11:30 Andrej Karpathy Tesla
11:30 – 12:00 Jennifer Chayes Microsoft Research
13:00 – 14:00 Jeff Dean Google
14:00 – 14:30 Anima Anandkumar Amazon
14:30 – 15:00 Ilya Sutskever Open AI
15:00 – 15:30 Francois Chollet Google
16:00 – 17:00 Bill Dally Nvidia
17:00 – 17:30 Simon Knowles Graphcore
17:30 – 18:00 Yangqing Jia Facebook

From my notes :
The successor to the AMP Lab at Berkeley is RISE lab, building Real-time Intelligent Secure Explainable applications to make low-latency decisiongs on live data with strong security. (Ion Stoica). Note the remark about Explainable; this came up as a common theme.
Being able to examine detector errors and mistakes came up again in Reza Zadeh’s Matroid demonstration – this was the only live product shown. A user can build a detector with multiple attributes to pick out images from streaming video.
Bill Dally (Chief Scientist, Nvidia) reckons that Moore’s Law is dead; Simon Knowles (Graphcore) gave a more reasoned explanation about possible performance gains from hardware improvements over the next 10 years.

References
Agenda http://scaledml.org/
RISE Lab https://rise.cs.berkeley.edu/ 

Graphcore hardware, use of BSP – Simon Knowles   https://supercomputersfordl2017.github.io/Presentations/SimonKnowlesGraphCore.pdf

Jeff Dean’s slides https://www.matroid.com/scaledml/2018/jeff.pdf

Bill Dally’s slides https://www.matroid.com/scaledml/2018/bill.pdf

Anima Anandkumar https://www.matroid.com/scaledml/2018/anima.pdf

Ion Stoica https://www.matroid.com/scaledml/2018/ion.pdf

Francois Cholet on Keras https://www.matroid.com/scaledml/2018/francois.pdf

Ilya Sutskever https://www.matroid.com/scaledml/2018/ilya.pdf

Jennifer Chayes https://www.matroid.com/scaledml/2018/jennifer.pdf

Yangqing Jia https://www.matroid.com/scaledml/2018/yangqing.pdf