Jonas Mueller @jomulr
Co-Founder & Chief Scientist @CleanlabAI. Previously @awscloud, PhD @MIT_CSAIL people.csail.mit.edu/jonasmueller Joined April 2020-
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Goodbye Hallucinations! Today, Cleanlab launches the Trustworthy Language Model (TLM 1.0), addressing the biggest problem in Generative AI: reliability. technologyreview.com/2024/04/25/109…
@CleanlabAI made the Forbes AI 50 list! It is an honor to be recognized alongside friends and inspirational companies like @OpenAI, @databricks, and @huggingface. We're just getting started... forbes.com/lists/ai50
Open-Source AI aficionados: you've probably heard of the new Open-Source AI Cookbook from @huggingface At the top of this amazing resource, you'll now find a new notebook: Detecting Issues in a Text Dataset with Cleanlab 👇 huggingface.co/learn/cookbook… [...]
News! @CleanlabAI is ranked 18th globally among AI private companies. cbinsights.com/learn/ai-100-2…
Launched today: Use Data-Centric AI to automatically catch erroneous values in any column of a structured/tabular dataset. No problem if your data is messy with missing values and heterogeneous {numeric, categorical, text, date, ...} fields.
Launched today: Use Data-Centric AI to automatically catch erroneous values in any column of a structured/tabular dataset. No problem if your data is messy with missing values and heterogeneous {numeric, categorical, text, date, ...} fields.
BIG news for open-source practitioners of Data-Centric AI: We just released major updates to cleanlab, the most popular software library for Data-Centric AI (with 8000 GitHub stars thanks to an amazing community) Check out the repo and read on ... github.com/cleanlab/clean…
New major release of cleanlab open-source library is out! We aim to make Data-Centric AI more useful and accessible to all. Don’t do all your data checking manually – also use cleanlab's AI-automated algorithms to ensure you don’t miss any data problems github.com/cleanlab/clean…
New major release of cleanlab open-source library is out! We aim to make Data-Centric AI more useful and accessible to all. Don’t do all your data checking manually – also use cleanlab's AI-automated algorithms to ensure you don’t miss any data problems github.com/cleanlab/clean…
Every Instruction Tuning dataset inevitably has bad examples lurking within it that are harming your LLMs. Now you have a way to easily filter and correct them 👇 cleanlab.ai/blog/filter-ll… Code to reproduce results for the Dolly dataset is linked in our article.
Our Data-Centric AI system can catch the bad images in any dataset. See how it works in our new blogpost
Our Data-Centric AI system can catch the bad images in any dataset. See how it works in our new blogpost
Everybody’s excited about @GoogleDeepMind's new LLM: Gemini #Gemini produces accurate outputs (that beat #GPT4) via an “uncertainty-routed chain-of-thought” algorithm. Cleanlab scientists previously invented a similar algorithm to boost the trustworthiness of outputs from any…
To learn about practical advances in #DataCentricAI at #NeurIPS2023, check out our paper: arxiv.org/abs/2207.10062 This collaboration w/ @MLCommons, @GoogleAI, @AIatMeta, @kaggle + other institutions -- introduces a community benchmarking framework for data-centric AI innovation
AutoGluon 1.0 is live!! Shatters SOTA, wins 75% vs prior release, 63% win-rate vs best-in-hindsight combination of other methods. To our knowledge, this is the biggest leap forward in tabular ML in the past 4 years. See how we did it: github.com/autogluon/auto… #AutoML #AutoGluon
🤔 Did you know the famous ImageNet dataset has some very confusing classes in it? 💥 CHALLENGE: Go look at some examples and WITHOUT PEEKING AT LABELS see if you can determine which belongs to the “missile” vs the “projectile” class, or the “keyboard” vs “space bar” class.
Many #computervision folks work with image segmentation datasets, but never tried annotating the data. Labeling pixels right is hard! Most segmentation datasets are thus full of errors -- our new research paper shows how to automatically detect them. The code is open-source!
Many #computervision folks work with image segmentation datasets, but never tried annotating the data. Labeling pixels right is hard! Most segmentation datasets are thus full of errors -- our new research paper shows how to automatically detect them. The code is open-source!
We've launched some great industry-specific solution accelerators on Databricks Marketplace with @CleanlabAI, @Graphistry and @JohnSnowLabs. Check out these free resources with notebooks and sample data for healthcare, cybersecurity & communications. sprou.tt/18zyKvQGzyl
Shawn Charles🎤🔥 @ShawnBasquiat
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779 Followers 74 Following Author & Lead Developer of @AutoGluon : https://t.co/foLauqqWWe Senior Scientist at AWS AI #autogluon #automl #opensourceThomas Olenik @TomOlenik
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3 Followers 67 FollowingCleanlab @CleanlabAI
2K Followers 159 Following Data is the currency of AI. Cleanlab increases the value of your data, automatically. Come pioneer the future of Data-Centric AI: https://t.co/AXtvL5wegdMIT Introduction to D.. @dcai_course
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766 Followers 20 Following Fast and Accurate ML in 3 Lines of Code #AutoML #opensourceUsing this new trustworthiness score to prioritize human review of LLM outputs can have large (up to 80%) cost savings over other popular methods like OpenAI’s log probs or asking the LLM to self-reflect: cleanlab.ai/blog/trustwort…
Launch day @CleanlabAI! We are solving the biggest problem with productionizing GenAI: reliability/hallucinations. Check it out and give us some feedback! producthunt.com/posts/trustwor…
Exciting day for our team at @CleanlabAI! Our Trustworthy Language Model (TLM) is now live (v1.0)! TLM helps solve the most significant problem with productionizing GenAI: reliability/hallucinations. With TLM, you can get more accurate outputs than GPT-4, along with…
Announcing the Trustworthy Language Model, a solution to the biggest problems in productionizing GenAI: hallucinations and reliability. TLM provides a reliable trustworthiness score for every LLM output and can also produce more accurate outputs than GPT-4.
Goodbye Hallucinations! Today, Cleanlab launches the Trustworthy Language Model (TLM 1.0), addressing the biggest problem in Generative AI: reliability. technologyreview.com/2024/04/25/109…
Here are all the relevant links: Research paper: arxiv.org/abs/2210.06812 Details blog post with code: docs.cleanlab.ai/stable/tutoria…
This Python library works like magic! ✨ With a single line of code & the two matrices you see in the image below, I can find: - a consensus label - quality score for individual & consensus labels - overall quality score for each annotator Introducing CrowdLab! 🚀 A weighted…
@CleanlabAI provides no-code, automated data curation for LLMs and the modern AI stack.
@CleanlabAI made the Forbes AI 50 list! It is an honor to be recognized alongside friends and inspirational companies like @OpenAI, @databricks, and @huggingface. We're just getting started... forbes.com/lists/ai50
Open-Source AI aficionados: you've probably heard of the new Open-Source AI Cookbook from @huggingface At the top of this amazing resource, you'll now find a new notebook: Detecting Issues in a Text Dataset with Cleanlab 👇 huggingface.co/learn/cookbook… [...]
We are honored to be featured in @CBinsights 2024 list of the top 100 private AI companies in the world. Alongside @OpenAI, @AnthropicAI, @huggingface, @MistralAI, @databricks, and other friends.
News! @CleanlabAI is ranked 18th globally among AI private companies. cbinsights.com/learn/ai-100-2…
Boom: Meet the 2024 AI 100 cbi.team/4aoH7Pu From new AI architectures to precision manufacturing, this year’s winners are tackling some of the hardest challenges across industries.
Startup founders. The pandemic is over. GO MEET YOUR CUSTOMERS. Shake their hand. Hug them. Learn what's working and what's not. Free webinar TODAY with myself and BRG, an enterprise @CleanlabAI customer that 2x'd their customers using Cleanlab Studio. register.gotowebinar.com/register/27334…
Claude put together a prompt library. I've tested all of them. Here are 5 prompts you can try ↓ #1. Adaptive Editor
Bad data costs the U.S. $3 Trillion per year. Your company's structured data has errors due to data entry or measurement mistakes, sensor noise, pipeline bugs, etc. Announcing 📣 an AI solution to catch erroneous values in *any* tabular dataset: help.cleanlab.ai/tutorials/data…
Absolutely NOBODY is paying for cbt.chat, well actually one sign up Thousands of users that all got 10 messages free Either we can tweak this or we just invalidated the idea of an AI mental help life coach within a week which is fine too
Slowly been sharing the cbt.chat chat bot username in its Telegram channel to soft launch and test it Payment integration from Telegeam to Stripe works 700 people have now used it I will add @SimpleAnalytic server side event tracking soon to log activity better
A common trend I've seen in AI startups: 1. startup makes a useful open-source project and raises Seed 2. invests in open-source, primarily user growth/ not new features 3. based on growth, raises a Series A 4. kills open source to focus 100% on SaaS sales to raise Series B
Flawed data produces flawed AI, and real-world datasets have many flaws. With one line of code, you can run cleanlab on any dataset to automatically catch these flaws, and thus improve almost any ML model fit to this data. Don't just explore/check data manually, use automation!
Today’s v2.6.0 release includes new capabilities like Data Valuation (via Data Shapely), detection of Underperforming Data Slices/Groups, + more. Our blogpost outlines the new cleanlab techniques to systematically increase the value your existing data: cleanlab.ai/blog/cleanlab-…
BIG news for open-source practitioners of Data-Centric AI: We just released major updates to cleanlab, the most popular software library for Data-Centric AI (with 8000 GitHub stars thanks to an amazing community) Check out the repo and read on ... github.com/cleanlab/clean…