Living with ChatGPT: Detecting AI text without destroying trust | Evan Crothers, Gov of Canada

Channel:
Subscribers:
19,600
Published on ● Video Link: https://www.youtube.com/watch?v=CmZRGHpRH6A



Duration: 0:00
681 views
0


Advances in natural language generation (NLG) have resulted in machine generated text that is increasingly difficult to distinguish from human–authored text. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. The great potential of state-of-the-art NLG systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. This session includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability. For details, please see the paper:\nhttps://arxiv.org/pdf/2210.07321.pdf by Evan Crothers, Nathalie Japkowicz, and Herna Viktor. Security guru Bruce Schneier referred to the paper as “a solid grounding amongst all of the hype”. View here: https://www.schneier.com/crypto-gram/archives/2023/0115.html#cg2023 \n\nSpeakes:\nEvan Crothers\nGovernment of Canada\n\nModerators:\nWojciech Samek\nTechnical University Berlin\n\n#TrustworthyAI #ChatGPT\n\nJoin the Neural Network! \nhttps://aiforgood.itu.int/neural-network/\nThe AI for Good networking community platform powered by AI. \nDesigned to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.\n\n Watch the latest #AIforGood videos!\n\n\nExplore more #AIforGood content:\n AI for Good Top Hits\n   • Top Hits  \n\n AI for Good Webinars\n   • AI for Good Webinars  \n\n AI for Good Keynotes\n   • AI for Good Keynotes  \n\n Stay updated and join our weekly AI for Good newsletter:\nhttp://eepurl.com/gI2kJ5\n\n Discover what's next on our programme!\nhttps://aiforgood.itu.int/programme/\n\nCheck out the latest AI for Good news:\nhttps://aiforgood.itu.int/newsroom/\n\nExplore the AI for Good blog:\nhttps://aiforgood.itu.int/ai-for-good-blog/\n\n Connect on our social media:\nWebsite: https://aiforgood.itu.int/\nTwitter: https://twitter.com/AIforGood\nLinkedIn Page: https://www.linkedin.com/company/26511907 \nLinkedIn Group: https://www.linkedin.com/groups/8567748 \nInstagram: https://www.instagram.com/aiforgood \nFacebook: https://www.facebook.com/AIforGood\n\nWhat is AI for Good?\nWe have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets.\nMore than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals.\nAI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland.\n\nDisclaimer:\nThe views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.




Other Videos By AI for Good


2023-03-06Use of Big Data and Artificial Intelligence for inclusive fintech
2023-03-05iNaturalist as a tool for conservation impact and the role of machine learning
2023-03-01Enabling intrusion detection in 5G networks via novel datasets
2023-02-28Enabling a responsive and agile performance evaluation of AI-based digital diagnostics
2023-02-28Long Short-Term Memory networks for large-scale rainfall-runoff modeling | Earth & Sustainability
2023-02-27Sustainable robots: What does it take for a robot to be sustainable? | AI for Good Webinar
2023-02-21Using ML to parameterize explicit convection in climate models | Mike Pritchard, NVIDIA
2023-02-20AI for Autonomous and Assisted Driving: Findings of ITU-T Focus Group FG-AI4AD
2023-02-20Drawing reproducible conclusions from observational clinical data | AI for Good Discovery
2023-02-19Detection and attribution of biodiversity change: a role for AI | AI for Biodiversity
2023-02-15Living with ChatGPT: Detecting AI text without destroying trust | Evan Crothers, Gov of Canada
2023-02-13Exploring the future of biologically-inspired soft robots for good
2023-02-06Affordable universal healthcare access through autonomous mobile clinics | AI FOR GOOD WEBINAR
2023-02-05AI for improved health and well-being at all ages | Ministry of Science and ICT
2023-01-31The role of AI in achieving the Sustainable Development Goals
2023-01-31AI for Good Global Summit 2023
2023-01-30The future of robots for good: The quest for embodied AI | AI for Good Webinars
2023-01-22‘Multiverse Barrier Free’: A new era of human-machine interaction for manufacturing
2023-01-17Fighting wildfires with intelligent robots | AI for Good Webinar
2023-01-11AlphaTensor: discovering mathematical algorithms with reinforcement learning | AI for Good Webinar
2022-12-15The #AIforGood Community is making a Global Impact on Sustainable Development