SCONUL Summer Conference 2018 - Nicole coleman
•
1 like•267 views
Keynote: Act deliberately and preserve things. Academic Libraries in an age of artificial intelligence Nicole Coleman, Digital Research Architect, Stanford University Libraries and Research Director, Humanities + Design
Recommended
More Related Content
What's hot
What's hot (20)
Similar to SCONUL Summer Conference 2018 - Nicole coleman
Similar to SCONUL Summer Conference 2018 - Nicole coleman (20)
More from sconul
More from sconul (20)
Recently uploaded
Recently uploaded (20)
SCONUL Summer Conference 2018 - Nicole coleman
- 1. Nicole Coleman Stanford Libraries Academic libraries in an age of artificial intelligence Act deliberately and preserve things.
- 2. Preamble
- 5. Nicole Coleman Stanford Libraries Academic libraries in an age of artificial intelligence Act deliberately and preserve things.
- 6. Ray Kurzweil Director of Engineering at Google “Within several decades, information based technologies will encompass all human knowledge and proficiency, including, ultimately, the pattern recognition powers, problem solving skills and emotional and moral intelligence of the human brain itself. This singularity will allow us to transcend the biological limitations of our bodies and our brains. We will gain power over our fates. Our mortality will be in our own hands. We will be able to live as long as we want and we will be able to fully understand human thinking and will be able to vastly expand and extend its reach.”
- 7. “Artificial intelligence, the dream of computer scientists for over half a century, is no longer science fiction. And in the next few years, it will transform every industry… Where engines made us stronger and powered the first industrial revolution, AI will make us smarter and power the next. What will make this intelligent industrial revolution possible? A new computing model — GPU deep learning — that enables computers to learn from data and write software that is too complex for people to code.” Nvidia maker of the GPU
- 9. Robert Harrison Professor in Italian Literature Stanford University “These guys should all take two years out of their lives to undergo an education in literature, maybe scripture and the classics and learn something about how to improve this really tacky imagination they have about what the good life is supposed to consist in.”
- 10. Douglas Hofstadter “If you look at situations in the world, they don’t come framed, like a chess game or a Go game or something like that. A situation in the world is something that has no boundaries at all, you don’t know what’s in the situation, what’s out of the situation.”
- 11. Artificial Intelligence is neither artificial nor intelligent.
- 12. Despite its name, there is nothing “artificial” about this technology — it is made by humans, intended to behave like humans and affects humans. Fei-Fei Li Associate Professor of Computer Science Stanford It is pattern matching within a very wide space of possibility. Edward Feigenbaum the "father of expert systems"
- 13. AI touches our lives every day.
- 14. 1. Machine Learning (It’s training, not learning.)
- 15. 1997 IBM’s Deep Blue beats Garry Kasparov 1986 "Learning representations by back-propagating errors” Rumelhart, Hinton, and Williams 1998 Larry Page and Sergey Brin found Google based on the “PageRank” algorithm 1989 Yann LeCun applies deep learning to reading handwritten zip codes on envelopes 1999 Nvidia GPU 2009 ImageNet Andrew Ing begins using Nvidia GPUs for deep learning 20082005 Amazon launches the Mechanical Turk 1986-2009
- 16. Google’s AlphaGo defeats Lee Sedol 20162010 Deep Learning for Speech recognition George Dahl 2012 Deep Learning for computer vision Geoffrey Hinton Using ImageNet 2011 Google Brain project begins Andrew Ing, Jeff Dean 2013 Google hires Geoffrey Hinton This leads to Siri, Alexa, etc. 2015 Google RankBrain applies deep learning to search 2014 Andrew Ing’s team applies unsupervised learning example on cat videos Facebook launched the Newswire app and acquired WhatsApp, the smartphone instant messaging application 2010-2016 George Dahl joins Google Brain
- 17. Google’s AlphaGo defeats Lee Sedol 20162010 Deep Learning for Speech recognition George Dahl 2012 Deep Learning for computer vision Geoffrey Hinton Using ImageNet 2011 Google Brain project begins Andrew Ing, Jeff Dean 2013 Google hires Geoffrey Hinton 2015 Google RankBrain applies deep learning to search 2014 Andrew Ing’s team applies unsupervised learning example on cat videos Facebook launched the Newswire app and acquired WhatsApp, the smartphone instant messaging application 2010-2016 George Dahl joins Google Brain This leads to Siri, Alexa, etc.
- 18. 2. Processing power I don’t have 1,000 computers lying around, can I even research this?
- 19. 1997 IBM’s Deep Blue beats Garry Kasparov 1986 "Learning representations by back-propagating errors” Rumelhart, Hinton, and Williams 1998 Larry Page and Sergey Brin found Google based on the “PageRank” algorithm 1989 Yann LeCun applies deep learning to reading handwritten zip codes on envelopes 1999 Nvidia GPU 2009 ImageNet Andrew Ing begins using Nvidia GPUs for deep learning 20082005 Amazon launches the Mechanical Turk 1986-2009
- 20. Google’s AlphaGo defeats Lee Sedol 20162010 Deep Learning for Speech recognition George Dahl 2012 Deep Learning for computer vision Geoffrey Hinton Using ImageNet 2011 Google Brain project begins Andrew Ing, Jeff Dean 2013 Google hires Geoffrey Hinton This leads to Siri, Alexa, etc. 2015 Google RankBrain applies deep learning to search 2014 Andrew Ing’s team applies unsupervised learning example on cat videos Facebook launched the Newswire app and acquired WhatsApp, the smartphone instant messaging application 2010-2016 George Dahl joins Google Brain
- 21. Ben Vigoda (MIT) Today, one million microchips = the compute power of the human brain at the cost of $1 billion. (100 Billion Neurons in the human brain. 10 Billion transistors in a modern microchip. 100,000 transistors to do the work of one neuron.) In 2026: It will take only 10,000 chips at a cost of $10 million In 2036: It will take only 100 chips at a cost of $100,000 In 2046: It will be 1 chip at a cost of $1000 One personal computer will have the same compute capacity as one person. We may be able to shrink that timeline down to 20 years.
- 22. 3. Lots of Data It’s not who has the better algorithm, it’s who has more data.
- 23. 1997 IBM’s Deep Blue beats Garry Kasparov 1986 "Learning representations by back-propagating errors” Rumelhart, Hinton, and Williams 1998 Larry Page and Sergey Brin found Google based on the “PageRank” algorithm 1989 Yann LeCun applies deep learning to reading handwritten zip codes on envelopes 1999 Nvidia GPU 2009 ImageNet Andrew Ing begins using Nvidia GPUs for deep learning 20082005 Amazon launches the Mechanical Turk 1986-2009
- 24. Google’s AlphaGo defeats Lee Sedol 20162010 Deep Learning for Speech recognition George Dahl 2012 Deep Learning for computer vision Geoffrey Hinton Using ImageNet 2011 Google Brain project begins Andrew Ing, Jeff Dean 2013 Google hires Geoffrey Hinton This leads to Siri, Alexa, etc. 2015 Google RankBrain applies deep learning to search 2014 Andrew Ing’s team applies unsupervised learning example on cat videos Facebook launched the Newswire app and acquired WhatsApp, the smartphone instant messaging application 2010-2016 George Dahl joins Google Brain
- 25. ImageNet Does ImageNet own the images? Can I download the images? No, ImageNet does not own the copyright of the images. ImageNet only provides thumbnails and URLs of images, in a way similar to what image search engines do. In other words, ImageNet compiles an accurate list of web images for each synset of WordNet. For researchers and educators who wish to use the images for non- commercial research and/or educational purposes, we can provide access through our site under certain conditions and terms.
- 26. How does it work?
- 31. “Neural Networks” from the Animated Math series by Grant Sanderson at 3blue1brown.com
- 34. Results Pattern matching/search has proven to be extremely flexible
- 38. Supasorn Suwajanakorn, Steven M. Seitz, and Ira Kemelmacher- Shlizerman. 2017. Synthesizing Obama: Learning Lip Sync from Audio. ACM Trans. Graph. 36, 4, Article 95 (July 2017), 13 pages. Thies, Justus, et al. "Face2face: Real-time face capture and reenactment of rgb videos." Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on. IEEE, 2016.
- 39. And it need not be based on actual recorded audio. March 7, 2018
- 40. Limitations “You are what you eat. The data these models are fed is junk food.”
- 41. “The danger I see with machine learning is that it gives rise to the qualified back-formation ‘human learning,’ for which machine learning does not bode particularly well.” John Willinsky Professor Graduate School of Education Stanford University
- 42. Herbert Dreyfus, What Computers Still Can't Do, 1992 “Expertise consists in being able to respond to relevant facts.”
- 47. basketball
- 48. “We thought the problem was that the class ‘basketball’ only contained pictures of black people. But no. The class contains as many pictures of white people as black people. The bias appears because black people are under-represented in the data set as a whole.” Moustapha Cissé Research Scientist Facebook AI Research Lab (or FAIR) Paris
- 50. Timnit Gebru Fairness Accountability Transparency and Ethics (FATE) Microsoft Research
- 51. AI researchers employ not only the scientific method, but also methodology from mathematics and engineering. However, the use of the scientific method - specifically hypothesis testing - in AI is typically conducted in service of engineering objectives. Growing interest in topics such as fairness and algorithmic bias show that engineering-focused questions only comprise a subset of the important questions about AI systems. This results in the AI Knowledge Gap: the number of unique AI systems grows faster than the number of studies that characterize these systems' behavior. To close this gap, we argue that the study of AI could benefit from the greater inclusion of researchers who are well positioned to formulate and test hypotheses about the behavior of AI systems. Closing the AI Knowledge gap Z. Epstein, B. H. Payne, J. H. Shen, A. Dubey, B. Felbo, M. Groh, N. Obradovich, M. Cebrian, I. Rahwan (2018). Closing the AI Knowledge Gap. arXiv:1803.07233 [cs.CY]
- 52. The role of the library “We have the capability, now we need to know what questions to ask.”
- 53. Peter Norvig Director of Research at Google “Engineers need to become more like natural scientists. The task is no longer bug fixing but observing and guiding.” “Applications of AI are not engineering problems, they are subject matter expert problems.” Andrew Ng Professor of Computer Science, Stanford
- 54. This signals a paradigm shift in technology from engineering to design. In other words, it’s up to us.
- 55. 1. Discovery
- 56. Philip A. Schreur Associate University Librarian for Technical and Access Services from “The Academy Unbound: Linked Data as Revolution” 2012 “Linked data has the potential to revolutionize the academic world of information creation and exchange. Basic tenets of what libraries collect, how they collect, how they organize, and how they provide information will be questioned and rethought. Limited pools of bibliographic records for information resources will be enhanced by data captured at creation. By harvesting the entire output of the academy, an immensely rich web of data will be created that will liberate research and teaching from the limited, disconnected silos of information that they are dependent on today.”
- 57. 2. Preservation
- 58. Ten years is not enough.
- 61. Deeper and Higher Resolution Layers Improved Layer Tracing Schroeder et al. (in prep) Published Film Record High Resolution Scan From “Observing Antarctic Ice-sheet Conditions Using Ice-Penetrating Radar” presented at the American Physical Society April Meeting 2018 Dustin Schroeder, Stanford University
- 62. 3. Curation
- 64. The keys to human intelligence are not only to be found in fields like neuroscience, cognitive science, and psychology. They are evidenced in how we organize knowledge and how scholars connect their own ideas to the past and to their contemporaries. The library holds this information and we can ‘operationalize’ it in the design of better systems of discovery that provide meaningful context for information.