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🧠 What is TheSequence? The guide to our content universe
With ML the question is: how to keep up? TheSequence helps with that. We put this guide together to navigate you through our newsletters.
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The Sequence Chat: Raza Habib, Humanloop on Building LLM-Driven Applications
Humanloop is one of the emerging platforms that allow developers to build large scale applications on top of LLMs.
Edge 297: Tool-Augmented Language Models
Can LLMs master knowledge tools?
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The LLama Effect: How an Accidental Leak Sparked a Series of Impressive Open Source Alternatives to ChatGPT
Edge 264: Inside Muse: Google’s New Text-to-Image Super Model
Edge 256: The Architecture and Methods Powering ChatGPT
📢 Event: ML practitioners from Affirm, Block, Remitly, Tide & more share their learnings from building risk & fraud detection systems
The Most Exciting Alliance in AI
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📺 See how programmatic labeling is the key to using LLMs [Live Demo]
Even with the rapid advancements to AI made possible by LLMs and Foundation Models, data remains the key to unlocking real value for enterprise AI. Join…
The Next RLHF Effect: Three Breakhroughts that can Unlock the Next Wave of Innovation in Foundation Models
Sundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial…
This week on TuringPost
Hi there, Last week, we introduced Turing Post – the newsletter that complements TheSequence and covers other topics that help you make informed…
📝 Guest Post: Stop Hallucinations From Hurting your LLM Powered Apps*
LLM hallucinations pose a big threat to the successful adoption of the new wave of LLM apps. In this post, the Galileo team dives into how one can…
Edge 296: Inside OpenAI's Method to Use GPT-4 to Explain Neuron's Behaviors in GPT-2
The technique is one of the first attempts to utilize LLMs as a explainability foundation.
The Sequence Chat: Rohan Taori on Stanford's Alpaca, Alpaca Farm and the Future of LLMs
Alpaca was one of the first open LLMs to incorporate instruction following capabilities. Now one of the project's main researchers shares his insights…
Edge 295: Self-Instruct Models
What if LLMs could auto improve their own instruction following capabilities?
TheSequence
TheSequence
The best source to stay up-to-date with the developments in the machine learning, artificial intelligence, and data science world. Trusted by 144,485 professionals from the main AI labs, universities, and enterprises
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Recap
✅ ML Training at Scale
🤼 GANs
🔄🔄 MLOps
🤓 Self-Supervised Learning
🦾Transformer Architectures Recap
🏆📚 Reinforcement Learning
⏳⌛️Time-Series Forecasting Wrap-Up
💪🏻 AutoML
🔐🔏 Security and Privacy
🤓😎 Emerging ML Methods
📕📖📗 Natural Language Understanding
Interviews
François CholletKeras, TensorFlow and New Ways to Measure Machine Intelligence
Oren Etzioni/CEO of Allen Institute for AI (AI2)on advancing AI research for the common good
Piero Molino on creating Ludwig and the Importance of Low-Code ML
Justin Harris/Microsoft"My vision is for people to be compensated for the data they provide while keeping models free to use"
Joe Doliner/CEO of Pachydermon developing a canonical ML stack and main challenges for mainstream developer adoption
Mike Del Balso/Tecton"There is too much depth in this space for feature stores to be just a “feature”
Iskandar Sitdikov/ProvectusHealthcare has it all: NLP, computer vision, recommendations, and a whole lot more
H.O. Maycotte/CEO of Molecula on shifting from “data as fuel” to “features as fuel”
Manu Sharma/Labelbox The future of data labeling automation
Jim Dowling/Logical Clocks ABThe future of feature stores
William FalconWe did our job right if the term MLOps disappears
Krishna Gade/FiddlerChallenges with model explainability
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