40 Presentations Not to Miss, on LLM/RAG and Gen AI Technology

By top leaders in the field, from Meta, Nvidia, Google, Intel, HuggingFace, LlamaIndex, Amazon, Microsoft, and top universities (Berkeley, Stanford, Cornell, CMU, and more). Covering the latest trends in the field with focus on open-source, for AI professionals, AI/ML scientists, data scientists, engineers and developers. All in one event on Sep 18-19 in San Francisco.

Register here with a 20% discount, using my code AI_1. Lower price for students and people in academia.

1. Meta Llama 3 and the Future of Responsible AI Development - Spencer Whitman & Vincent Gonquet, Meta
2. HieroGlyph2Text: A PyTorch-Powered Pipeline for Automated Egyptian Hieroglyph Translation from Image
3. NeMo-Aligner: A Scalable Toolkit for Model Alignment - Gerald Shen & Jimmy Zhang, NVIDIA
4. ExecuTorch Beta and on-Device Generative AI Support - Mergen Nachin & Mengtao (Martin) Yuan, Meta
5. Mobile Computational Photography with PyTorch: Low-Light Denoising - Alexis Baudron, Sony
6. The Lightning AI OSS Stack for Accelerating the AI Lifecycle - Luca Antiga, CTO, Lightning AI
7. Enabling AI Everywhere with PyTorch and Intel - Kismat Singh,VP of Engineering for AI Frameworks, Intel
8. Responsible AI - Kate Rooney, CNBC; Kush Varshney, IBM T. J. Watson Research Center; Sara Hooker, C4AI; Aleksander Madry, OpenAI; and Rishi Bommasani, Stanford University
9. The Impact and Challenges of Open Source Generative Datasets and Models - Aaron Gokaslan, Cornell University
10. Running State-of-Art Gen AI Models on-Device with NPU Acceleration - Felix Baum, Qualcomm
11. TorchInductor CPU Backend Advancements: New Features and Performance Improvements - Jiong Gong & Leslie Fang, Intel
12. Extending PyTorch with Custom Python/C++/CUDA Operators - Richard Zou, Meta
13. Welcome to the PyTorch Ecosystem for LLM Fine-tuning Mini Summit - Kartikay Khandelwal, Meta
14. The State of the Llama Ecosystem - Joe Spisak, Meta
15. The Challenges of Building an Opinionated Open Source LLM Framework - Wing Lian, Axolotl AI
16. Hacks to Make LLM Training Faster - Daniel Han, Unsloth AI
17. Universally Deploy Large-language Models via ML Compilation - Tianqi Chen, CMU & OctoAI
18. Navigating the Architectural Timeline of LLMs - Sebastian Raschka, Staff Research Engineer, Lightning AI
19. Building an Advanced Knowledge Assistant - Jerry Liu, Co-Founder & CEO, LlamaIndex
20. Ray: A Distributed Framework for Heterogeneous Computing - Ion Stoica, Professor, UC Berkeley
21. The Rise of Transformers in the Growing PyTorch Ecosystem - Arthur Zucker, Hugging Face
22. LLMs on Edge with AI Accelerators - Chen Lai, Kimish Patel & Cemal Bilgin, Meta
23. Distributing a Million Open Models in the Wild: Lessons Learned from the Hugging Face Hub - Omar Sanseviero, Hugging Face
24. Empowering Developers: Tools and Resources for Running Generative AI on Arm CPUs - Pareena Verma, Arm
25. Implementing and Using Iterable Datasets: What Could Go Wrong? - Nicolas Hug, Meta
26. Optimized PyTorch Inference on aarch64 Linux CPUs - Sunita Nadampalli, Amazon (AWS)
27. AOTriton: Ahead of Time Triton Kernel Libraries on ROCm - Jeff Daily, AMD
28. PyTorch-Wildlife: A Collaborative Deep Learning Framework for Conservation - Zhongqi Miao, Microsoft
29. Optimizing AI Inference for Large Language Models - Mudhakar Srivatsa, Distinguished Engineer, IBM
30. Scaling & Benchmarking - Wei-Lin Chiang & Lisa Dunlap, UC Berkeley; James Bradbury, Anthropic; Tri Dao; Aparna Ramani & Soumith Chintala, Meta
31. Building PyTorch Computer Vision Algorithms for 100 Skin Shades - Emmanuel Acheampong, roboMUA
32. vLLM: Easy, Fast, and Cheap LLM Serving for Everyone - Woosuk Kwon, UC Berkeley & Xiaoxuan Liu, UCB
33. Torchtitan: Large-Scale LLM Training Using Native PyTorch 3D Parallelism - Wanchao Liang, Meta & Linsong Chu, IBM Research
34. PyTorch Support by Google Enabling Performance from Cloud to Edge - Mark Sherwood & Shauheen Zahirazami, Google
35. Understanding and Optimizing PyTorch Models with Thunder - Luca Antiga, Lightning AI
36. Understanding the LLM Inference Workload - Mark Moyou, NVIDIA
37. Lightning Talk: d-Matrix LLM Compression Flow Based on Torch.Fx: Simplifying PTQ/QAT - Zifei Xu & Tristan Webb, d-Matrix Corporation
38. Intel GPU in Upstream PyTorch: Expanding GPU Choices and Enhancing Backend Flexibility - Eikan Wang & Min Jean Cho, Intel
39. Unlocking the Enigma: Crafting Unbiased, Transparent, and Explainable Large Language Models - Rashmi Nagpal, Patchstack
40. The Ethical Implications of AI and the Environment: A Focus on Water - Amber Hasan, Ethical Tech AI & Senegal Tuklor Williams, Broken Pencil Pictures llc

The cherry on the cake: PyTorch Flare Party Sponsored by Hugging Face. If you cannot attend, please inquire about livestream sessions.

Announcement

On the same topic but organized by a different organization, here is the newest event in my webinar series: LLMs in Fraud Detection: Model Comparisons. No cost, online. Recording will be available for registrants who cannot attend the live presentation.

Register here.