IEEE North Jersey Section

[] Celebrate Alexander Graham Bell's Patent - the Birth Certificate of the Telephone in person at AT&T Labs in Middletown New Jersey and view the Story of Alexander Graham Bell at times scheduled 10AM, 12PM, 2PM. Please register ahead so the hosts can prepare for guests at the location. The Movie depicts how Alexander Graham Bell invented the Telephone and changed history. The Movie will be shown in the Auditorium. Visitors will be able to see the AT&T Labs Science & Technology Center and Museum. In addition, up to 99 people who wish to Celebrate the Birth of an Industry and transformation of our world can view the Alexander Graham Bell story on Zoom at 8PM. This Zoom viewing is virtual only. Bldg: 2A, 200 S Laurel Ave, Middletown Township, New Jersey, United States, Virtual: https://events.vtools.ieee.org/m/410058

[] Celebrate Alexander Graham Bell's Patent - the Birth Certificate of the Telephone in person at AT&T Labs in Middletown New Jersey and view the Story of Alexander Graham Bell at times scheduled 10AM, 12PM, 2PM. Please register ahead so the hosts can prepare for guests at the location. The Movie depicts how Alexander Graham Bell invented the Telephone and changed history. The Movie will be shown in the Auditorium. Visitors will be able to see the AT&T Labs Science & Technology Center and Museum. In addition, up to 99 people who wish to Celebrate the Birth of an Industry and transformation of our world can view the Alexander Graham Bell story on Zoom at 8PM. This Zoom viewing is virtual only. Bldg: 2A, 200 S Laurel Ave, Middletown Township, New Jersey, United States, Virtual: https://events.vtools.ieee.org/m/410058

IEEE Maine Joint Communications/Computer Societies Chapter and UMaine AI invite you to a live virtual event on Thursday, March 7, 12-1 PM EDT: Math Information Retrieval: What We Have & What We Need presented by Behrooz Mansouri, Assistant Professor of Computer Science, University of Southern Maine. Abstract: This talk introduces math information retrieval, discussing the challenges, existing systems, and open problems in this domain. Math information retrieval (MIR) presents unique challenges due to the structured nature of mathematical language and symbols. Despite recent advancements like digital libraries and specialized search engines, the field of MIR still deals with ambiguities and accurately addressing users’ information needs. This presentation emphasizes ongoing efforts to develop test collections, search systems, and conduct user studies for MIR. Coordinated with Computer Society Region 1 and Region 2 Chapters Coordinator. The presentation will be followed by Q & A, moderated by Julia Upton, IEEE Maine Joint Communications/Computer Societies Chapter Chair. Please register (there is no registration fee) here: (https://ai.umaine.edu/webinars/) Virtual: https://events.vtools.ieee.org/m/407995

Special Presentation on “LMMs as Universal Foundation Models for AI-Native Wireless Systems” by Dr. Christo K. Thomas (Virginia Tech, USA) Hosted by Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, March 7th, 2024 @ 6 PM EST (3 PM PST) Topic: Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems Abstract: Foundation models such as large language models (LLMs) have recently been touted as game-changers for 6G systems. However, previous efforts on LLMs for wireless networks are limited to directly applying existing language models designed for natural language processing (NLP) applications. Contrary to this, in this talk, we present a comprehensive vision of how to design universal foundation models that are tailored towards the unique needs of next-generation wireless systems, thereby paving the way towards the deployment of artificial intelligence (AI)-native networks. These LMMs are driven by three distinct characteristics: 1) integration of multi-modal sensing data, 2) grounding sensory input via causal reasoning and retrieval-augmented generation (RAG), and 3) instructibility to environmental feedback through logical and mathematical reasoning enabled by neuro-symbolic AI. These attributes are crucial for developing "universal foundation models" capable of addressing interconnected cross-layer networking challenges in AI-native wireless systems while ensuring alignment of objectives across diverse domains. We also discuss preliminary results from experimental evaluation that demonstrate the efficacy of grounding using RAG in LMMs, and showcase the alignment of LMMs with wireless system designs. Furthermore, compared to vanilla LLMs, the enhanced rationale exhibited in the responses to mathematical questions by LMMs demonstrates the logical and mathematical reasoning capabilities inherent in LMMs. Building on those results, we present a sequel of open questions and challenges for LMMs, including intent-based networks, resilient wireless systems, semantic communications, and many more. Co-sponsored by: IEEE Future Networks Speaker(s): Dr. Christo K. Thomas Virtual: https://events.vtools.ieee.org/m/407262

Special Presentation on “LMMs as Universal Foundation Models for AI-Native Wireless Systems” by Dr. Christo K. Thomas (Virginia Tech, USA) Hosted by Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, March 7th, 2024 @ 6 PM EST (3 PM PST) Topic: Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems Abstract: Foundation models such as large language models (LLMs) have recently been touted as game-changers for 6G systems. However, previous efforts on LLMs for wireless networks are limited to directly applying existing language models designed for natural language processing (NLP) applications. Contrary to this, in this talk, we present a comprehensive vision of how to design universal foundation models that are tailored towards the unique needs of next-generation wireless systems, thereby paving the way towards the deployment of artificial intelligence (AI)-native networks. These LMMs are driven by three distinct characteristics: 1) integration of multi-modal sensing data, 2) grounding sensory input via causal reasoning and retrieval-augmented generation (RAG), and 3) instructibility to environmental feedback through logical and mathematical reasoning enabled by neuro-symbolic AI. These attributes are crucial for developing "universal foundation models" capable of addressing interconnected cross-layer networking challenges in AI-native wireless systems while ensuring alignment of objectives across diverse domains. We also discuss preliminary results from experimental evaluation that demonstrate the efficacy of grounding using RAG in LMMs, and showcase the alignment of LMMs with wireless system designs. Furthermore, compared to vanilla LLMs, the enhanced rationale exhibited in the responses to mathematical questions by LMMs demonstrates the logical and mathematical reasoning capabilities inherent in LMMs. Building on those results, we present a sequel of open questions and challenges for LMMs, including intent-based networks, resilient wireless systems, semantic communications, and many more. Co-sponsored by: IEEE Future Networks Speaker(s): Dr. Christo K. Thomas Virtual: https://events.vtools.ieee.org/m/407262