{"id":147,"date":"2023-06-27T15:38:28","date_gmt":"2023-06-27T15:38:28","guid":{"rendered":"https:\/\/socio.org.uk\/ddp2023\/?page_id=147"},"modified":"2023-07-18T15:24:37","modified_gmt":"2023-07-18T15:24:37","slug":"workshop","status":"publish","type":"page","link":"https:\/\/socio.org.uk\/ddp2023\/workshop\/","title":{"rendered":"Workshop"},"content":{"rendered":"\r\n

First Workshop on “Next-Generation Models for Generative AI”<\/strong><\/span><\/h2>\r\n\r\n\r\n\r\n

(In conjunction with the Third International Conference on Digital Data Processing 2023)<\/p>\r\n\r\n\r\n\r\n

University of Bedfordshire, Luton. UK<\/span><\/h3>\r\n

\r\n\r\n<\/h3>\r\n

November 27-29, 2023<\/span><\/h3>\r\n\r\n\r\n\r\n

(IEEE CPS will publish the proceedings, and papers should follow the IEEE template)<\/p>\r\n\r\n\r\n\r\n

AI models use billions of parameters to detect and retrieve text and images. The currently available LL models are experimented with for their efficiency, and at the same time, newer models are being developed. There are many challenges associated with generative AI. The pre-training volume and efficiency are a focus. To leverage the training set with comprehensiveness and accuracy, billions of parameters are injected into the LLM. Current GPTs face criticism, and governments issue a caution to their use. For example, Zhuang Rongwen, China\u2019s cyberspace chief, raises concerns over the power of generative AI and pledges to make it \u2018controllable’.<\/p>\r\n\r\n\r\n\r\n

The agenda for the future Generative AI needs to be clarified. Improved generative tools should be capable of characterising extremist narratives in corpora to reveal different contexts, which may lead to building semantic-rich content for end-users. Testing the current models and their results may contribute to future research. Considering these issues, we framed a workshop to address the theme, Next Generation Models. The workshop themes include but are not limited to the following.<\/p>\r\n\r\n\r\n\r\n

Text, Image, Code, Video, 3D models<\/span><\/strong>
Domain-specific models<\/span><\/strong>
Compositional generative models<\/span><\/strong>
Foundation models<\/span><\/strong>
Energy-based models<\/span><\/strong>
Deep equilibrium models<\/span><\/strong>
Impact of Generative AI on Teaching and Learning<\/span><\/strong>
Knowledge and Semantic Issues in Generative AI<\/span><\/strong>
Future LLM<\/span><\/strong>
AI Ethical Issues<\/span><\/strong>
AI and NLP<\/span><\/strong>
Embedding in AI<\/span><\/strong>
Reinforcement learning<\/span><\/strong>
Data Support and Datasets in AI<\/span><\/strong>
Standards and Benchmarks<\/span><\/strong><\/p>\r\n\r\n\r\n\r\n

Workshop Chairs<\/strong><\/span><\/p>\r\n\r\n\r\n\r\n

Gloria Tengyue Li<\/strong>
North China University of Technology
Beijing, China<\/p>\r\n\r\n\r\n\r\n

Simon Fong<\/strong>
University of Macau
Macau<\/p>\r\n\r\n\r\n\r\n

Hathairat Ketmaneechairat<\/strong>
King Mongkut\u2019s University of Technology
North Bangkok, Thailand<\/p>\r\n\r\n\r\n\r\n

Important Dates<\/strong><\/span><\/p>\r\n\r\n\r\n\r\n

Full Paper Submission: September 10, 2023<\/strong>
Notification of Acceptance\/Rejection: October 10, 2023<\/strong>
Registration Due:\u00a0 November 10, 2023<\/strong>
Camera Ready Due: November 10, 2023<\/strong>
Workshops\/Tutorials\/Demos:\u00a0\u00a0 November\u00a0\u00a0 28, 2023<\/strong>
Main conference: November 27-29, 2023<\/strong>
Post-conference proceedings:\u00a0 December 20, 2023<\/strong><\/p>\r\n\r\n\r\n\r\n

Paper Submission: http:\/\/socio.org.uk\/ddp2023\/paper-submission<\/a><\/strong><\/h3>\r\n\r\n\r\n\r\n

Contact: stm@socio.org.uk<\/a><\/p>\r\n","protected":false},"excerpt":{"rendered":"

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