AI Research

Creator
Creator
Seonglae ChoSeonglae Cho
Created
Created
2024 Aug 8 13:11
Editor
Edited
Edited
2026 Jun 25 10:54

The scalability of methods is crucial in evaluating AI research

  • Data generalization is essential, methods that only work in specific environments are useless.
  • Cost-benefit analysis is important, good performance alone doesn't guarantee practicality.
  • Error analysis is mandatory, without understanding why it fails, improvement is impossible.
More inspiration from nature present, more confident on top-down belief that sustains you when experiments contradict you multifaceted beauty (
Ilya Sutskever
)
AI Research Notion
 

Top labs

 
 
https://researchtrend.ai/
 
 

AI research paper dataset from
Arxiv

neuralwork/arxiver · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
neuralwork/arxiver · Datasets at Hugging Face
Don't pivot into AI research
Many computer science students and new grads aspire to move into machine learning. It seems exciting and sexy. You can play a role in bringing in the incoming AGI utopia. Many strive to work in “machine learning or AI research” - a vaguely defined field that includes everything from data engineering, infrastructure and model architecture.
Who is leading in AI? An analysis of industry AI research
Artificial Intelligence (AI) research is increasingly industry-driven, making it crucial to understand company contributions to this field. We compare leading AI companies by research publications, citations, size of training runs, and contributions to algorithmic innovations. Our analysis reveals the substantial role played by Google, OpenAI and Meta. We find that these three companies have been responsible for some of the largest training runs, developed a large fraction of the algorithmic innovations that underpin large language models, and led in various metrics of citation impact. In contrast, leading Chinese companies such as Tencent and Baidu had a lower impact on many of these metrics compared to US counterparts. We observe many industry labs are pursuing large training runs, and that training runs from relative newcomers—such as OpenAI and Anthropic—have matched or surpassed those of long-standing incumbents such as Google. The data reveals a diverse ecosystem of companies steering AI progress, though US labs such as Google, OpenAI and Meta lead across critical metrics.
 
 

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