正在加载视频...

视频加载失败

Tether Data, AI model training platform preview. This PaaS will be available to any company interested in (pre-)training own models. Bonus, at the core of this platform we're leveraging Holepunch's tech for all data-structures to make training and models highly-resilient and unstoppable. Soon available via Northern Data Group ,...

28,092 次观看 • 1 年前 •via X (Twitter)

8 条评论

Revive ꧁IP꧂ 的头像
Revive ꧁IP꧂1 年前

Did u just launched a pumpfun token?

ARK Electronics 的头像
ARK Electronics1 年前

Excited about the latest tech for your drone product? Our NDAA-compliant, US-made flight controllers are designed to accelerate your path to market and provide a solid platform for developing your autonomous software. Check them out! #Drones #UAV #UAS #Robotics #MadeInUSA

Lumin 的头像
Lumin1 年前

Tether’s AI Play – The Next Great Decentralization Move? 🧵 1/ Tether is stepping into AI model training. Not just any AI—unstoppable, decentralized, and highly resilient. This isn’t just about AI. This is about who owns intelligence in the next era.

Eleftherios 的头像
Eleftherios1 年前

we should talk about some of the things we have in the works with @radicle for local first code collaboration and agent development. there is a good synergy.

AldoBalto | AB marketing 的头像
AldoBalto | AB marketing1 年前

Unstoppable for real! 👏🏻👏🏻 Should we start expecting a new product every month? 😁

rufus. 的头像
rufus.1 年前

hey @paoloardoino follow to dm 🫡

Adam TechGrowth 的头像
Adam TechGrowth1 年前

Will Tether Data's use of Holepunch's tech lead to significant improvements in model resilience?

Dontspam Dontjunk 的头像
Dontspam Dontjunk1 年前

Thank you! PS. I really enjoyed the Bitcoin Lugano a few months back! Great show! Great speech!

相关视频

Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step workflows and examine how attention mechanisms, loss objectives, and training data must evolve to support long-form reasoning and planning. Aakanksha shares insights on the difference between context retrieval and actual reasoning, the importance of "trajectory" training data, and why scaling remains essential for discovering emergent agentic capabilities like error recovery and dynamic tool learning. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 02:26 - Reflection 04:54 - Limitations of post-training for building agents 07:31 - Rethinking pre-training in agents 10:51 - Scaling 11:27 - Evolving attention mechanisms for agentic capabilities 12:39 - Memory as a tool 14:13 - Loss objectives and training data 15:50 - Fine-tuning loss in agent performance 19:37 - Training data 21:29 - Augmenting dominant training data source 24:11 - Overcoming challenges in training on synthetic data 25:47 - Benchmarks 30:44 - Scaling laws in large models versus small models 33:20 - Long-form versus short-form reasoning 37:57 - Agent’s ability to recover from failure 40:15 - Hallucinations and failure recovery 43:53 - Tool use in agents 46:38 - Coding agents 48:37 - How researchers can contribute to agentic AI

The TWIML AI Podcast

42,346 次观看 • 5 个月前