Griffey "Artificial Intelligence and Machine Learning in Libraries"
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The document discusses the impact of artificial intelligence (AI) and machine learning in libraries, highlighting advancements in deep learning technology and its implications for fields such as medical diagnosis. It emphasizes the transformative potential of AI, while also addressing concerns about ethics, privacy, and the limitations of current technology. The insights in the document suggest a need for careful consideration of AI's integration into research and information processes in libraries.
Griffey "Artificial Intelligence and Machine Learning in Libraries"
- 1. Artificial Intelligence & Machine Learning in Libraries Photo by Kushagra Kevat on Unsplash Director of Strategic Initiatives NISO Jason Griffey @griffey griffey@niso.org Sharjah Library Conference Nov 7, 2019
- 2. Introduction
- 3. Introduction
- 4. Introduction
- 5. Introduction
- 6. Introduction
- 7. Introduction
- 8. Introduction
- 9. Introduction
- 10. Introduction
- 13. StrongWeak
- 16. – Geoffrey Hinton, University of Toronto “Take any old classification problem where you have a lot of data, and it’s going to be solved by deep learning.There’s going to be thousands of applications of deep learning.”
- 18. AI Assistants
- 21. –Geoffrey Hinton, University ofToronto “I think that if you work as a radiologist you are like Wile E. Coyote in the cartoon.You’re already over the edge of the cliff, but you haven’t yet looked down.There’s no ground underneath. It’s just completely obvious that in five years deep learning is going to do better than radiologists. It might be ten years.”
- 22. –Geoffrey Hinton, University ofToronto “I think that if you work as a radiologist you are like Wile E. Coyote in the cartoon.You’re already over the edge of the cliff, but you haven’t yet looked down.There’s no ground underneath. It’s just completely obvious that in five years deep learning is going to do better than radiologists. It might be ten years.” 2017
- 35. Deepfakes
- 38. Research Process
- 39. • Identify Topic • Preliminary Search for Information • Locate Materials • Evaluate Sources • Make Notes • Write Paper • Cite Sources • Proofread and Edit
- 40. Research
- 51. Proofread & Edit
- 55. Inside Libraries
- 58. Conclusions? Photo by benjaflynn - http://flic.kr/p/FAJjc7
- 59. Concerns
- 60. Mistakes
- 61. Black Box
- 62. Ethics Photo by Marc Schäfer on Unsplash
- 63. Filters & Bubbles Photo by Aaron Burden on Unsplash
- 64. Incentives Photo by Aneta Pawlik on Unsplash
- 65. Labor Capital
- 66. Privacy Implications Photo by Zany Jadraque on Unsplash
- 67. –Roy Amara “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” Amara’s Law
- 68. –Roy Amara “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” Amara’s Law
- 69. J A S O N G R I F F E Y G R I F F E Y @ N I S O . O RG H T T P : / / N I S O . O RG D I R E C TO R O F ST R AT E G I C I N I T I AT I V E S N I S O G R I F F E Y @ G M A I L . C O M H T T P : / / J A S O N G R I F F E Y. N E T @ G R I F F E Y