What we’re about
The Santa Clara Valley Chapter of IEEE Computational Intelligence Society is interested in the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing neural networks, connectionist systems, genetic algorithms, evolutionary programming, fuzzy systems, and hybrid intelligent systems in which these paradigms are contained.
We will hold monthly meetings, inviting experts from academia or industry to present their research. Anybody interested is invited to join.
For more information, past event slides and videos, and future events please visit our website: https://ewh.ieee.org/r6/scv/cis/
Upcoming events (1)
See all- IEEE SCV WIE AI SUMMIT 2025Intel SC9, Santa Clara, CA
Physical (pay) and virtual (free) event, Register at:
https://events.vtools.ieee.org/m/479108
Registration ends: August 28, 2025 11:59 PM PDT# IEEE SCV WIE AI SUMMIT 2025
In an era where AI technologies are rapidly transforming industries and redefining possibilities, it is crucial to explore both the innovations driving this change and the responsibilities that come with it. Today, we will delve into a diverse array of topics that highlight the multifaceted nature of AI and its profound impact on our lives.
Our sessions will cover the latest developments in Large Language Models and Foundation Models, exploring efficient fine-tuning, multilingual adaptation, and the role of LLMs as knowledge bases. We will also examine the evolution of AI agents, focusing on autonomous task completion, multi-agent collaboration, and the integration of external knowledge for robust decision-making.
In the realm of Vision and Multimodality, we will explore the integration of text, image, and video understanding, as well as advanced techniques like zero-shot learning and self-supervised learning. Our discussions on MLOps for LLMs will provide insights into best practices for training, deploying, and evaluating large models.
We will also address the critical areas of Knowledge-Grounded Reasoning, On-Device Learning, and the ethical dimensions of AI, including bias mitigation, privacy preservation, and the detection of misinformation.
Talk tracks are broadly classified but not limited to,- Large Language Models (LLMs) & Foundation Models
Efficient Fine-tuning of LLMs for Low-Resource Languages, LLM Alignment & Instruction-Tuning: Challenges and Advances, Scaling Laws: Understanding Model Size vs. Performance, Multilingual and Cross-Lingual LLM Adaptation, Memory-Augmented LLMs: Enhancing Long-Term Context Understanding, LLMs as Knowledge Bases: Reasoning and Fact-Checking
2. AI Agents
Autonomous AI Agents: Leveraging LLMs for Task Completion, Multi-Agent Communication & Collaboration in NLP, Self-Reflective AI: Reflexion and Self-Improvement in Language Models, Hierarchical & Modular AI Agents: Towards Scalable Systems, LLMs as Orchestrators: AI Workflows with Task-Specific Agents, Grounding LLMs in External Knowledge for Robust Decision-Making3. Vision & Multimodality
Vision-Language Models (VLMs): From CLIP to GPT-4V, Multimodal Agents: Integrating Text, Image, and Video Understanding, Spatial and Temporal Reasoning in Vision-Language Models, Zero-Shot and Few-Shot Learning in Multimodal AI, Self-Supervised Learning for Multimodal Representations, Evaluating Multimodal Models: Metrics & Benchmarks, Neurosymbolic Approaches for Language and Vision Tasks4. MLOps for LLMs
LLMOps: Best Practices for Training & Deploying LLMs, Efficient Inference for Large Models: Pruning, Quantization & Distillation, Retrieval-Augmented Generation (RAG): Enhancing Context Awareness, Memory and Context Window Expansion: Architectures & Trade-offs, Evaluation Metrics for LLMs & Conversational Agents5. Knowledge-Grounded & Reasoning
LLMs for Automated Theorem Proving & Scientific Discovery, Commonsense Reasoning in AI Agents, Symbolic vs. Neural Reasoning, Interpretable Models: Improving Explainability in LLMs, Unifying Knowledge Graphs and LLMs for Structured Reasoning6. On-Device Learning for LLMs and Multi-Modal AI
On-Device LLMs & Edge AI for Language Processing and Multimodal Applications, Security, Privacy & Ethical Considerations for On-Device LLMs
7. Ethics, Bias & Fairness
Bias Mitigation in Large Language Models, Hallucination Detection & Control in LLMs, Privacy-Preserving NLP: Federated Learning & Differential Privacy, AI and Misinformation: Detecting Deepfakes & Generated Content, Ethical Considerations in Deploying NLP for Real-World Applications - Large Language Models (LLMs) & Foundation Models