Generic AI Talk

This is set of slides to talk about the history of AI, the ARPANET, FOSS and where were are today.

Transcript

1. Exploring the Power of Artificial Intelligence and Open Source: A Game Changing Duo Harish Pillay h.pillay@ieee.org @harishpillay@floss.social
2. Sometime in 1948
3. Grey Walter's tortoises - https://www.youtube.com/watch?v=lLULRlmXkKo
6. 1950 • Alan Turing proposes the Turing Test (1950) a. In the 1950 “MIND - A QUARTERLY REVIEW OF PSYCHOLOGY AND PHILOSOPHY”, Alan Turing asks “Can Machines Think?”
7. 1950 • Alan Turing proposes the Turing Test (1950) a. In the 1950 “MIND - A QUARTERLY REVIEW OF PSYCHOLOGY AND PHILOSOPHY”, Alan Turing asks “Can Machines Think?” b. He rephrases that statement instead to “The Imitation Game”. [0]
8. The Imitation Game released 2014
10. 1956 • The Dartmouth Workshop (1956) held at the Dartmouth College
11. 1956 • The Dartmouth Workshop (1956) held at the Dartmouth College a. This is lead by Prof John McCarthy, and he invited Marvin Minsky, Claude Shannon, Nathaniel Rochester
12. 1956 • The Dartmouth Workshop (1956) held at the Dartmouth College a. This is lead by Prof John McCarthy, and he invited Marvin Minsky, Claude Shannon, Nathaniel Rochester b. By the end of the workshop, which lasted 8 weeks, they coined the term "artificial intelligence" [1]
13. 1956 • The Dartmouth Workshop (1956) held at the Dartmouth College a. This is lead by Prof John McCarthy, and he invited Marvin Minsky, Claude Shannon, Nathaniel Rochester b. By the end of the workshop, which lasted 8 weeks, they coined the term "artificial intelligence" [1] • Early AI programs: Logic Theorist and checkers-playing program
14. https://www.scienceabc.com/innovation/ what-is-artificial-intelligence.html
17. 1960s • The “perceptron”, an algorithm invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt, funded by the United States Office of Naval Research was implemented, in software, on an IBM 704.
18. 1960s • The “perceptron”, an algorithm invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt, funded by the United States Office of Naval Research was implemented, in software, on an IBM 704. • That was subsequently implemented in custom-built hardware known as the "Mark 1 Perceptron”.
19. 1960s • The “perceptron”, an algorithm invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt, funded by the United States Office of Naval Research was implemented, in software, on an IBM 704. • That was subsequently implemented in custom-built hardware known as the "Mark 1 Perceptron”. • It was one of the first artificial neural networks to be produced.
20. 1960s • The “perceptron”, an algorithm invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt, funded by the United States Office of Naval Research was implemented, in software, on an IBM 704. • That was subsequently implemented in custom-built hardware known as the "Mark 1 Perceptron”. • It was one of the first artificial neural networks to be produced. • First AI Winter kicks in as the systems have severe limitations and research funding began to dwindle.
21. Mark 1 Peceptron - at the Smithsonian Museum
22. 1970s - 1980s
23. 1970s - 1980s • Research focus moves to Symbolic AI and Knowledge Representation
24. 1970s - 1980s • Research focus moves to Symbolic AI and Knowledge Representation • Expert systems - Solving problems in specific domains
25. 1970s - 1980s • Research focus moves to Symbolic AI and Knowledge Representation • Expert systems - Solving problems in specific domains • Limitations of knowledge engineering and reasoning hampered adoption
26. 1970s - 1980s • Research focus moves to Symbolic AI and Knowledge Representation • Expert systems - Solving problems in specific domains • Limitations of knowledge engineering and reasoning hampered adoption • 2nd AI Winter happens
28. ARPANET - https://en.wikipedia.org/wiki/ARPANET
29. ARPANET - https://en.wikipedia.org/wiki/ARPANET
30. IEEE Plaque recognizing the birthplace of the Internet at UCLA
31. At the University of California, Los Angeles (UCLA) visiting with Leonard Klienrock in 2017
32. 1980s - 1990s
33. 1980s - 1990s • Statistical learning algorithms gain prominence
34. 1980s - 1990s • Statistical learning algorithms gain prominence • Support Vector Machines (SVMs - a form of perceptron), and decision trees gain popularity
35. 1980s - 1990s • Statistical learning algorithms gain prominence • Support Vector Machines (SVMs - a form of perceptron), and decision trees gain popularity • Increased focus on data-driven approaches
36. 1980s - 1990s • Statistical learning algorithms gain prominence • Support Vector Machines (SVMs - a form of perceptron), and decision trees gain popularity • Increased focus on data-driven approaches • Groundwork laid for future advances
37. https://www.infrapedia.com/
39. https://www.fsf.org/
40. https://www.fsf.org/
41. 1990s • Linux - an open source operating system (runs this laptop)
42. 1990s • Linux - an open source operating system (runs this laptop) • Apache - webservers
43. 1990s • Linux - an open source operating system (runs this laptop) • Apache - webservers • Python, Perl, PHP
44. 1990s • Linux - an open source operating system (runs this laptop) • Apache - webservers • Python, Perl, PHP • Dot.com boom
45. 2000s
46. Remember the dot.com boom of the 1990s?
47. It went bust!
48. https://www.infrapedia.com/
49. 2000s • Increased computational power and availability of large datasets
50. 2000s • Increased computational power and availability of large datasets • Revival of neural networks - Deep learning architectures
51. 2000s • Increased computational power and availability of large datasets • Revival of neural networks - Deep learning architectures • Breakthroughs in image recognition, speech recognition, and natural language processing
53. 2010s
54. 2010s • Big Data revolution - explosion of data volume and variety
55. 2010s • Big Data revolution - explosion of data volume and variety • Cloud computing platforms - Scalable and accessible resources
56. 2010s • Big Data revolution - explosion of data volume and variety • Cloud computing platforms - Scalable and accessible resources • Democratization of AI - Increased accessibility for businesses and researchers
57. 2010s - current • Specialization of deep learning architectures for specific tasks • Deep learning applications in various domains: self-driving cars, healthcare, finance • Ethical considerations of AI - Biasness, Fairness, and Transparency and 8 other metrics
59. 2010s - current - Open Source & Collaboration • Rise of open source AI frameworks and tools (TensorFlow, PyTorch) • Collaborative research and development efforts • Fostering innovation and accelerating progress
60. 2020s - current • Foundational Models - Large/Small Language Models capable of generating text, translating languages, and writing different kinds of creative content • Generative models for creating realistic images and other types of data • Multimodal models that can process and understand different types of data (text, images, audio)
61. 2020s - current • Mixture Of Experts (MOE) - A framework for training and using large, efficient multiple foundational models • Improves scalability and reduces training costs • Potential for wider adoption of complex AI models • OpenMOE [2] - an open source implementation to drive innovation
62. 2020s - current • RAG - Retrieval Augmented Generation • Using open source (as per Open Source Initiative’s definitions [3]) foundational models to train against corpus of private data for subsequent enquiry
63. 2020s - current • Testing of AI solutions for fairness, unbiasedness etc, via open source testing tools • AI Verify’s toolkit [4], is a community driven global effort of the AI Verify Foundation to create a commonly agreed to test framework • Guiding principles of AVF is on AIVerifyFoundation.sg
64. The Future from Today • Sovereign AI - nation states taking charge • Personal AI - kwaai.ai [5] • AI Governance Frameworks v2 - from Singapore’s Personal Data Protection Commission [6]
65. [0] https://academic.oup.com/mind/article/LIX/236/433/986238 [1] https://spectrum.ieee.org/dartmouth-ai-workshop [2] https://arxiv.org/abs/2402.01739 [3] https://opensource.org/deepdive [4] https://github.com/IMDA-BTG/aiverify [5] https://www.kwaai.ai [6] https://www.pdpc.gov.sg/-/media/Files/PDPC/PDF-Files/Resource-for -Organisation/AI/SGModelAIGovFramework2.pdf References
66. https://matrix.to/#/#ai-verify-foundation:matrix.org
67. Harish Pillay h.pillay@ieee.org @harishpillay:matrix.org @harishpillay@floss.social Thank you