AI Research

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.

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.

https://huggingface.co/datasets/neuralwork/arxiver

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.

https://maged.com/pivot-to-ai