Several people told me to add "open source performance is still lagging compared to commercial models". Is there any fundamental reason that stops open source models from catching up? The biggest reason I can think of is what @soumithchintala told me: open source models don't
@chipro
-
Open Source vs Commercial AI Models: Privacy and Security Considerations
By
–
I’m making a list of things to consider when using open source models and commercial models. What else should I add? Commercial models
1. Data privacy: employees might accidentally include confidential information in the prompt, e.g. when Samsung employees leaked the company’s -
Enterprise AI applications easiest to evaluate gain adoption first
By
–
I believe that the most common enterprise AI applications today aren’t the ones that solve the most important problems or make the most money. The most common applications are the ones that are easiest to evaluate. 1. Recommender system: evaluated by increase in engagement or
-
New Book on AI Engineering with Foundation Models
By
–
I’m excited to share that I’m working on a new book about building applications with foundation models! AI Engineering builds upon Machine Learning Systems Design, but with a focus on large scale, ready made models. The book covers: – The new AI stack (e.g. how it differs from
-
Theseus GPU-native query engine benchmarks massive data processing
By
–
Excited to show what our team has been working on over the last 2.5 years: Theseus, our GPU-native query engine! This benchmark compares data queries of different scales — 10TB, 30TB, and 100TB — on Spark (run on CPUs) and Theseus (run on GPUs). Moving the same queries from
-
Decentralized Web of Trust: Personal Reputation Systems Explained
By
–
I like the idea of the web of trust. How would trust be established/signed? Would that be like page rank but for trust? Would trust be personal: e.g. I might not necessarily trust the same website my mom does?
-
Data synthesis challenges for AI startups
By
–
Problems I'd do if I'm to do a startup again (though I probably won't any time soon because startups are hard). If you’re solving any of them, I’d love to chat. 1. Data synthesis: AI has become really good both at generating and annotating data. The challenge now is to make sure
-
845 Generative AI Repos on GitHub: Growth Analysis
By
–
I went through the most popular AI repos on GitHub, categorized them, and studied their growth trajectories. Here are some of the learnings: 1. There are 845 generative AI repos with at least 500 stars on GitHub. They are built with contributions from over 20,000 developers,
-
Human Preference Predictor Architecture and Results
By
–
This is the architecture of my preference predictor. You can read more about how it works and the results here. https://
huyenchip.com/2024/02/28/pre
dictive-human-preference.html
… As always, feedback is much appreciated! -
Bradley-Terry Model Ranking for AI Model Preference Predictions
By
–
Using the preference predictions for different model pairs for a given prompt, I fit a Bradley-Terry model (the same ranking algorithm that Chatbot Arena uses) to compute a model ranking specific to that prompt.