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Lecture 3: Wednesday 8th January 2020, PM
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Artificial Intelligence: Success, Limits, Myths and Threats - Lecture 1
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- 1 DATE: 06 January 2020, 16:00 to
- 2 Lecture 1 Public Lecture: 6 January 2020, PM
- 3 Lecture 2: Tuesday 7th January 2020, PM
- 4 Lecture 3: Wednesday 8th January 2020, PM
- 5 Artificial intelligence: success, limits, myths and threats Lecture 1
- 6 ICTS
- 7 ICTS Campus in Bangalore
- 8 What is the Goal of the ICTS?
- 9 Enabled by 3 interactive missions:
- 10 During the past decade ICTS has achieved some measure of success in all its three missions!
- 11 Programs:
- 12 Sample Programs...
- 13 Programs in Machine Learning
- 14 ICTS-Infosys Foundation Lecture series:
- 15 Research
- 16 ICTS People: Faculty
- 17 Students and Postdocs
- 18 ICTS as a platform for new initiatives:
- 19 Science Outreach
- 20 Public Lectures
- 21 Kaapi with Kuriosity
- 22 Abdus Salam Memorial Lectures
- 23 Einstein Lectures
- 24 Vishveshwara Lectures
- 25 D.D. Kosambi Lectures
- 26 Mathematics of Planet Earth MPE 2013, Bengaluru
- 27 Bangalore Area Science Habba
- 28 Mathematics Circles
- 29 ICTS Organization
- 30 ICTS Resources
- 31 Thank You!
- 32 Artificial Intelligence: Success, Limits, Myths and Threats
- 33 Chapter One - Myths and Reality
- 34 The new era of AI
- 35 ImageNet Database and Challenge
- 36 Convoy of self-driving trucks completes first European cross-border trip
- 37 The new era of AI
- 38 2 - Language understanding
- 39 AlphaGo
- 40 July 2019 : Pluribus
- 41 Chapter Two - Machine learning
- 42 Machine Learning
- 43 Test phase=present new picture, that the machine has not yet seen
- 44 Chapter Three - The Machines: Artificial neural networks
- 45 Everyone recognizes
- 46 Artificial neural networks
- 47 Frank Rosenblatt's perception
- 48 What is new since Rosenblatt's perceptron?
- 49 Neural network reading digits
- 50 Performance on handwritten digits
- 51 Deep neural networks
- 52 Bigger networks, more parameters. Larger database!
- 53 New computing paradigm. Collective representation of information, going to larger scales. Robust.
- 54 Chapter Four - Why deep networks are not yet? a panacea
- 55 Three main problems:
- 56 1- Huge amount of labelled data is necessary for learning in deep networks
- 57 Oh, look at ko bamoule! Do you see ko bamoule?
- 58 Chapter Five - About scientific Intelligence
- 59 Quote from Chris Anderson -The end of Theory: The data deluge makes the scientific method obsolete
- 60 Thought experiment :
- 61 We are still very very far from General Artificial Intelligence
- 62 Conclusion - So, what is going to happen?
- 63 Predicting the future
- 64 Predicting the future ?
- 65 A major concern for the present:
- 66 In 2018:
- 67 Take-home message
- 68 The End
- 69 Q&A
