DeepLearning.AI

Letters

Welcoming Diverse Approaches Keeps Machine Learning Strong: What technology counts as an “agent”? Instead of arguing, let's consider a spectrum along which various technologies are “agentic.”

One reason for machine learning’s success is that our field welcomes a wide range of work.

Letters

Welcoming Diverse Approaches Keeps Machine Learning Strong: What technology counts as an “agent”? Instead of arguing, let's consider a spectrum along which various technologies are “agentic.”

One reason for machine learning’s success is that our field welcomes a wide range of work.

Letters

Blenders Versus Bombs, or Why California’s Proposed AI Law is Bad for Everyone: California’s proposed AI law SB-1047 stifles innovation and open source in the name of safety.

The effort to protect innovation and open source continues. I believe we’re all better off if anyone can carry out basic AI research and share their innovations.

Letters

Blenders Versus Bombs, or Why California’s Proposed AI Law is Bad for Everyone: California’s proposed AI law SB-1047 stifles innovation and open source in the name of safety.

The effort to protect innovation and open source continues. I believe we’re all better off if anyone can carry out basic AI research and share their innovations.

Letters

We Need Better Evals for LLM Applications: It’s hard to evaluate AI applications built on large language models. Better evals would accelerate progress.

A barrier to faster progress in generative AI is evaluations (evals), particularly of custom AI applications that generate free-form text.

Letters

We Need Better Evals for LLM Applications: It’s hard to evaluate AI applications built on large language models. Better evals would accelerate progress.

A barrier to faster progress in generative AI is evaluations (evals), particularly of custom AI applications that generate free-form text.

Letters

Project Idea — A Car for Dinosaurs: AI projects don’t need to have a meaningful deliverable. Lower the bar and do something creative.

A good way to get started in AI is to start with coursework, which gives a systematic way to gain knowledge, and then to work on projects.

Letters

Project Idea — A Car for Dinosaurs: AI projects don’t need to have a meaningful deliverable. Lower the bar and do something creative.

A good way to get started in AI is to start with coursework, which gives a systematic way to gain knowledge, and then to work on projects.

Letters

From Prompts to Mega-Prompts: Best practices for developers of LLM-based applications in the era of long context and faster, cheaper token generation

In the last couple of days, Google announced a doubling of Gemini Pro 1.5's input context window from 1 million to 2 million tokens, and OpenAI released GPT-4o, which generates tokens 2x faster and 50% cheaper than GPT-4 Turbo and natively accepts and generates multimodal tokens.

Letters

From Prompts to Mega-Prompts: Best practices for developers of LLM-based applications in the era of long context and faster, cheaper token generation

In the last couple of days, Google announced a doubling of Gemini Pro 1.5's input context window from 1 million to 2 million tokens, and OpenAI released GPT-4o, which generates tokens 2x faster and 50% cheaper than GPT-4 Turbo and natively accepts and generates multimodal tokens.

Letters

Beware Bad Arguments Against Open Source: Big companies are lobbying governments to limit open source AI. Their shifting arguments betray their self-serving motivations.

Inexpensive token generation and agentic workflows for large language models (LLMs) open up intriguing new possibilities for training LLMs on synthetic data...

Letters

Beware Bad Arguments Against Open Source: Big companies are lobbying governments to limit open source AI. Their shifting arguments betray their self-serving motivations.

Inexpensive token generation and agentic workflows for large language models (LLMs) open up intriguing new possibilities for training LLMs on synthetic data...

Letters

Building Models That Learn From Themselves: AI developers are hungry for more high-quality training data. The combination of agentic workflows and inexpensive token generation could supply it.

Inexpensive token generation and agentic workflows for large language models (LLMs) open up intriguing new possibilities for training LLMs on synthetic data. Pretraining an LLM

Letters

Building Models That Learn From Themselves: AI developers are hungry for more high-quality training data. The combination of agentic workflows and inexpensive token generation could supply it.

Inexpensive token generation and agentic workflows for large language models (LLMs) open up intriguing new possibilities for training LLMs on synthetic data. Pretraining an LLM

Letters

Why We Need More Compute for Inference: Today, large language models produce output primarily for humans. But agentic workflows produce lots of output for the models themselves — and that will require much more compute for AI inference.

Much has been said about many companies’ desire for more compute (as well as data) to train larger foundation models.

Welcoming Diverse Approaches Keeps Machine Learning Strong: What technology counts as an “agent”? Instead of arguing, let's consider a spectrum along which various technologies are “agentic.”

Welcoming Diverse Approaches Keeps Machine Learning Strong: What technology counts as an “agent”? Instead of arguing, let's consider a spectrum along which various technologies are “agentic.”

Blenders Versus Bombs, or Why California’s Proposed AI Law is Bad for Everyone: California’s proposed AI law SB-1047 stifles innovation and open source in the name of safety.

Blenders Versus Bombs, or Why California’s Proposed AI Law is Bad for Everyone: California’s proposed AI law SB-1047 stifles innovation and open source in the name of safety.

We Need Better Evals for LLM Applications: It’s hard to evaluate AI applications built on large language models. Better evals would accelerate progress.

We Need Better Evals for LLM Applications: It’s hard to evaluate AI applications built on large language models. Better evals would accelerate progress.

Project Idea — A Car for Dinosaurs: AI projects don’t need to have a meaningful deliverable. Lower the bar and do something creative.

Project Idea — A Car for Dinosaurs: AI projects don’t need to have a meaningful deliverable. Lower the bar and do something creative.

From Prompts to Mega-Prompts: Best practices for developers of LLM-based applications in the era of long context and faster, cheaper token generation

From Prompts to Mega-Prompts: Best practices for developers of LLM-based applications in the era of long context and faster, cheaper token generation

Beware Bad Arguments Against Open Source: Big companies are lobbying governments to limit open source AI. Their shifting arguments betray their self-serving motivations.

Beware Bad Arguments Against Open Source: Big companies are lobbying governments to limit open source AI. Their shifting arguments betray their self-serving motivations.

Building Models That Learn From Themselves: AI developers are hungry for more high-quality training data. The combination of agentic workflows and inexpensive token generation could supply it.

Building Models That Learn From Themselves: AI developers are hungry for more high-quality training data. The combination of agentic workflows and inexpensive token generation could supply it.

Why We Need More Compute for Inference: Today, large language models produce output primarily for humans. But agentic workflows produce lots of output for the models themselves — and that will require much more compute for AI inference.

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