This panel discussion is part of ICADS ’23: Second International Conference on Applied Data Science by IEEE CS Silicon Valley Chap. For access to past events or to join our Dlist to hear about…

Insightful discussions on the potential of Agentic AI in enhancing scientific discoveries, artistic creativity, and business applications through autonomous optimization and evolutionary processes…

Topics covered include: AI international agreements or standards; existential risk; approach to education in AI; impact of AI generated misinformation; Dark AI;... For access to past events or to…

This talk covers topics in data warehousing, OLAP, and dimension modeling. For access to past events or to join our Dlist to hear about future programs, visit…

The Hotstate Machine is an advanced runtime loadable microcoded algorithmic Finite State Machine. Learn about its applications, how it can help LLMs, the Hot Neuron, Amdahl's law, NoISA, and more.…

The panelists delve into a range of questions on leveraging the predictive power of GenAI for personalized medicine, enhanced diagnostics, ethical dilemmas, and more.

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The talk addresses how Natural Language Processing (NLP) can be extended to low-resource languages, which so far have yet to attract enough NLP research. For more details about our events or to…

The talk given as part of NFIC ’24 at Stanford University explores how generative AI can transcend language and literacy barriers by transforming complex agricultural research into…

This IEEE Computer Society, Silicon Valley Chapter talk introduces post-quantum cryptography (PQC). Quantum computing is an emerging technology having large-scale computing capabilities and has…

Retrieval-Augmented Generation (RAG), Large Language Models (LLM), Agent AI, Prompt Engineering For access to past events or to join our Dlist to hear about future programs, visit…

answering your questions on Artificial Emotional Intelligence, Deep Learning, Digital Health -- topics: AI of Covid audio, reducing bias, affective computing,…

The talk introduces quantum machine learning (QML) that merges quantum computation and machine learning. An IEEE Computer Society, Silicon Valley Chapter event. For more details about our events…

The talk investigates the challenges faced by refugee populations globally, emphasizing ethnocentric biases. Focusing on Syrian refugees and contrasting media coverage of the Ukrainian crisis, the…

The talk provides a deep dive into a comprehensive set of Machine Learning model evaluation metrics.
 

For past events visit https://r6.ieee.org…

This talk is part of the ICADS ’23: Second International Conference on Applied Data Science For access to past events or to join our Dlist to hear about future programs, please visit…

The talk presents the speaker’s research on how to teach Large Language Models to be faithful problem solvers and perform reasoning from non-human feedback.

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Lakshmanan Sethu from Google, Chinmay Nerurkar from Microsoft, and Ruchi Agarwal from Netflix, and Vishnu Pendyala from SJSU discuss information fabrication in Large Language Models. For past…

The talk covers concepts of autonomous driving in digital railways, and discusses feasibility, challenges and opportunities, including explainability, autonomic computing, and digital twins. For…

The talk presents a new class of multifunctional hearing and health devices with embedded sensors and artificial intelligence. With an extremely energy-efficient embedded deep neural network…

After a brief intro highlighting issues and solutions for data privacy in machine learning settings, Dr. Gautam Kamath answered several questions related to his research on data privacy, machine…

The talk covers streaming algorithms and data sketching techniques that enable real-time analysis of enormous data streams without storing them entirely. They use clever approximation methods to…

The talk explores Eigen Decomposition, PCA, Kernel PCA, Eigen Faces, t-SNE, UMAP, Manifold Learning, a taxonomy of dimensionality reduction techniques, and discuss how the speaker used them for…

This NFIC’24 talk given at Stanford University discusses the various dimensions of and difficulties in making a large language models (LLM) more trustworthy and introduces a new…

The talk presents insights into understanding music using machine learning to analyze and categorize various aspects of music. The research paper that the speakers co-authored on a similar topic…