The hottest ML models Substack posts right now

And their main takeaways

Category

Top Technology Topics

Open LLM company playbook

Democratizing Automation • 126 implied HN points • 01 Nov 23

🕹 Technology AI Open Source ML models Feedback loops

To succeed as an open LLM company, have a specific niche and positioning strategy.
Training high-quality models is essential for adoption and success in the market.
Interacting with the community, releasing model weights, and benchmarking against closed models can lead to improved products, crowdsourced evaluations, and better public relations.

The emptiness at the heart of emotion recognition

Apperceptive (moved to buttondown) • 22 implied HN points • 08 Dec 23

🕹 Technology AI Machine Learning ML models

Emotion recognition technology in machine learning is based on a logical fallacy.
The foundational work of Paul Ekman in emotion recognition lacks scientific validity.
ML models for emotion recognition fail to capture the complexity of human emotional expressions and relationships.

How Hugging Face and Kaggle Bolster the Open Source Machine Learning Community

The Strategy Deck • 39 implied HN points • 26 Jul 23

🕹 Technology Machine Learning Open Source Data science ML models

Open source ML hubs like Hugging Face and Kaggle provide platforms for managing, sharing, and deploying ML models.
Hugging Face focuses on models, datasets, deployment infrastructure, and community engagement.
Kaggle empowers learners, developers, and researchers with educational resources, open source models, and a competitive platform.

Detecting Deepfakes in a cheap and effective way [StoryTime Saturdays]

Technology Made Simple • 59 implied HN points • 04 Sep 22

🕹 Technology Deepfakes Detection Research ML models Artifacts

Deepfakes have serious legal and social consequences, especially with advancements in creating synthetic audio and video.
There are cheap and effective ways to detect Deepfakes by looking for 'fingerprints' left by image tampering.
Research shows promise in using simple models to detect Deepfakes by identifying differences between generated and real images.

Peak Data

East Wind • 2 HN points • 25 Oct 23

🕹 Technology AI Data Quality ML models Training Data

The quality and percentage of human-generated data on the internet may have reached a peak, affecting the efficacy of future AI models.
Models may face challenges with outdated training data and lack of relevant information for solving newer problems.
Potential solutions include leveraging RAG models, proactive data contribution by platform vendors, and maintaining incentives for human contributions on user-generated content platforms.

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ML models don't need that much data to be better than you

ML Powered • 2 HN points • 20 Mar 23

🕹 Technology AI ML models Data Speech Text

ML models can perform better than humans with less data
Speech: Machines require more data but can perform better
Text: Humans read more than ML models, but ML models have a broader knowledge

Is there a castle behind ML moat

Tech, investment and a little bit of everything. • 1 HN point • 07 May 23

🕹 Technology ML models Open Source

Having a 'moat' to protect a 'castle' is essential in business.
Companies should focus on providing services for ML models rather than competing in building them.
Google can invest in providing affordable methods to run ML models instead of competing with specific models.