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Responsible & Safe AI at GSFC Univ Vadodra
The document outlines a presentation on responsible and safe AI delivered at an inaugural ceremony in Vadodara, Gujarat, discussing concepts like alignment problems and the risks associated with advanced AI systems. It highlights the importance of interpretability, bias in large language models, and the need for context in user interactions. Additionally, it calls for collaboration and research opportunities in the field of responsible AI.
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Responsible & Safe AI at GSFC Univ Vadodra
- 1. Responsible & SafeAI 1 Aug 2024 Inaugural Ceremony GSFCU ACM Student chapter Vadodra, Gujarat Ponnurangam Kumaraguru (“PK”) #ProfGiri CS IIIT Hyderabad Vice President, ACM India TEDx Speaker https://precog.iiit.ac.in/ /in/ponguru @ponguru
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- 4. Know the Audience Students/ Faculty / Others Any of you have attended my talk(s) before? 4
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- 6. What is Responsible& Safe? 6
- 7. What is Responsible& Safe AI? 7
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- 24. Activity Please do anyprompting in any of these or other platforms, get them to give you biased response, do not do gender bias HINT: There are very nice prompts that students have come up with in the past 24
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- 31. What is analignment problem? 31
- 32. What is analignment problem? 32 https://youtu.be/yWDUzNiWPJA?si=wSDO4i_EMrHzHYDP
- 33. High- level instanti ation: ‘RLHF’ pipelin e First step: instructiontuning! Second + third steps: maximize reward 33 https://arxiv.org/pdf/2203.02155
- 34. Rouge AIs We risklosing control over AIs as they become more capable. Proxy gaming: YouTube / Insta – User engagement – Mental health 34
- 35. What is goingon? https://www.youtube.com/watch?v=lnyuIHSaso8&t=75s 35
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- 37. Forms of unlearning Exactunlearning Approximate unlearning Unlearning via differential privacy Empirical unlearning, where data to be unlearned are precisely known (training examples) Empirical unlearning, where data to be unlearned are underspecified (think “knowledge”) 37
- 38. Graph Unlearning What isit? 38
- 39. Graph Unlearning 39 Node featureunlearning Node unlearning Edge unlearning
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- 41. New techniques andparadigms for turning model weights and activations into concepts that humans can understand https://en.wikipedia.org/wiki/Neural_network https://en.wikipedia.org/wiki/Artificial_neural_network 41 Interpretability
- 42. Interpretability: Mechanistic Reverse-engineer neuralnetworks Explaining neurons and connected circuits 42
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- 48. Bias in LLMs Currentsystems like ChatGPT employ guardrails, and do not respond to biased content Users on the Web leave out key contexts, which make LLMs think the content is biased This negatively affects user engagement LLMs must be able to explore and ask more questions Our work aims to make LLMs bias-aware – context resolves confusion! 48
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- 51. 51 Search for: PonnurangamKumaraguru https://www.linkedin.com/in/ponguru/ https://twitter.com/ponguru
- 52. Interested in workingwith us? 52 Full time Research Associates PhD Students Interns
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Editor's Notes
- #43 Neurons communicate through electrical currents called action potentials, which are either excitatory or inhibitory. Excitatory currents are those that prompt one neuron to share information with the next through an action potential, while inhibitory currents reduce the probability that such a transfer will take place.