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…
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 …
Topics covered include: AI international agreements or standards; existential risk; approach to education in AI; impact of AI generated misinformation; Dark AI.
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…
This talk is on threats and challenges in security due to quantum computing, - addressing the challenges with quantum principles and post-quantum cryptography. For more details about our events or…
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 explains efforts to discover real-time visual data on the Internet so that useful insights can be obtained. For access to past video webinars or to join our Dlist to hear about future…
(Q&A at 29:00 + 42:30) -- quantum-resistant algorithms, lattice-based, multivariate, code-based, hash functions, advantages, research ... Chuck Easttom,…
ICADS ’23: Second International Conference on Applied Data Science For access to past video webinars or to join our Dlist to hear about future programs, please 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 talk covers concepts of autonomous driving in digital railways, and discusses feasibility, challenges and opportunities, including explainability, autonomic computing, and digital twins. For…
ICADS ’23: Second International Conference on Applied Data Science Invited talk For access to past video webinars or to join our Dlist to hear about future programs, please visit…
This 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…
answering your questions on Artificial Emotional Intelligence, Deep Learning, Digital Health -- topics: AI of Covid audio, reducing bias, affective computing…
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…
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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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 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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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…
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 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…
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…
Machine Learning is advancing civilization and is one of the key drivers of the economy today. Machine Learning literacy may one day become essential in the way computer literacy is today. This…
The ensemble techniques covered include bagging, boosting, stacking, and related algorithms such as random forest, Adaboost, and gradient boosting. One-shot and few-shot learning are also briefly…