DeepLearning.AI
Calibrating Contrast: X-CLR, an approach to contrastive learning for better vision models
Machine Learning Research

Calibrating Contrast: X-CLR, an approach to contrastive learning for better vision models

Contrastive loss functions make it possible to produce good embeddings without labeled data. A twist on this idea makes even more useful embeddings.

January 15, 20252 min read
Calibrating Contrast: X-CLR, an approach to contrastive learning for better vision models
Machine Learning Research

Calibrating Contrast: X-CLR, an approach to contrastive learning for better vision models

Contrastive loss functions make it possible to produce good embeddings without labeled data. A twist on this idea makes even more useful embeddings.

January 15, 20252 min read
DeepSeek Ups the Open Weights Ante: DeepSeek-V3 redefines LLM performance and cost efficiency
Machine Learning Research

DeepSeek Ups the Open Weights Ante: DeepSeek-V3 redefines LLM performance and cost efficiency

A new model from Hangzhou upstart DeepSeek delivers outstanding performance and may change the equation for training costs.

January 15, 20253 min read
DeepSeek Ups the Open Weights Ante: DeepSeek-V3 redefines LLM performance and cost efficiency
Machine Learning Research

DeepSeek Ups the Open Weights Ante: DeepSeek-V3 redefines LLM performance and cost efficiency

A new model from Hangzhou upstart DeepSeek delivers outstanding performance and may change the equation for training costs.

January 15, 20253 min read
Better Performance From Merged Models: Localize-and-Stitch improves methods for merging and fine-tuning multiple models
Machine Learning Research

Better Performance From Merged Models: Localize-and-Stitch improves methods for merging and fine-tuning multiple models

Merging multiple fine-tuned models is a less expensive alternative to hosting multiple specialized models. But, while model merging can deliver higher average performance across several tasks, it often results in lower performance on specific tasks. New work addresses this issue.

January 8, 20253 min read
Better Performance From Merged Models: Localize-and-Stitch improves methods for merging and fine-tuning multiple models
Machine Learning Research

Better Performance From Merged Models: Localize-and-Stitch improves methods for merging and fine-tuning multiple models

Merging multiple fine-tuned models is a less expensive alternative to hosting multiple specialized models. But, while model merging can deliver higher average performance across several tasks, it often results in lower performance on specific tasks. New work addresses this issue.

January 8, 20253 min read
Massively More Training Text: Harvard unveils a million-book corpus for AI training
Machine Learning Research

Massively More Training Text: Harvard unveils a million-book corpus for AI training

Harvard University amassed a huge new text corpus for training machine learning models.

January 8, 20252 min read
Massively More Training Text: Harvard unveils a million-book corpus for AI training
Machine Learning Research

Massively More Training Text: Harvard unveils a million-book corpus for AI training

Harvard University amassed a huge new text corpus for training machine learning models.

January 8, 20252 min read
Models Can Use Tools in Deceptive Ways: Researchers expose AI models' deceptive behaviors
Machine Learning Research

Models Can Use Tools in Deceptive Ways: Researchers expose AI models' deceptive behaviors

Large language models have been shown to be capable of lying when users unintentionally give them an incentive to do so. Further research shows that LLMs with access to tools can be incentivized to use them in deceptive ways.

January 8, 20256 min read
Models Can Use Tools in Deceptive Ways: Researchers expose AI models' deceptive behaviors
Machine Learning Research

Models Can Use Tools in Deceptive Ways: Researchers expose AI models' deceptive behaviors

Large language models have been shown to be capable of lying when users unintentionally give them an incentive to do so. Further research shows that LLMs with access to tools can be incentivized to use them in deceptive ways.

January 8, 20256 min read
What LLM Users Want: Anthropic reveals how users interact with Claude 3.5
Machine Learning Research

What LLM Users Want: Anthropic reveals how users interact with Claude 3.5

Anthropic analyzed 1 million anonymized conversations between users and Claude 3.5 Sonnet. The study found that most people used the model for software development and also revealed malfunctions and jailbreaks.

January 8, 20252 min read
What LLM Users Want: Anthropic reveals how users interact with Claude 3.5
Machine Learning Research

What LLM Users Want: Anthropic reveals how users interact with Claude 3.5

Anthropic analyzed 1 million anonymized conversations between users and Claude 3.5 Sonnet. The study found that most people used the model for software development and also revealed malfunctions and jailbreaks.

January 8, 20252 min read
Mustafa Suleyman: Agents of action
Machine Learning Research

Mustafa Suleyman: Agents of action

In 2025, AI will have learned to see, it will be way smarter and more accurate, and it will start to do things on your behalf.

January 1, 20252 min read
Mustafa Suleyman: Agents of action
Machine Learning Research

Mustafa Suleyman: Agents of action

In 2025, AI will have learned to see, it will be way smarter and more accurate, and it will start to do things on your behalf.

January 1, 20252 min read
Albert Gu: More learning, less data
Machine Learning Research

Albert Gu: More learning, less data

Building a foundation model takes tremendous amounts of data. In the coming year, I hope we’ll enable models to learn more from less data.

January 1, 20253 min read

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