Datasets

The latest advances in AI are built on freely available training data. What will happen if it becomes off-limits? Creative workers don’t want AI developers to train models on their works without permission or compensation, or at all. Data is vanishing as they scramble to lock it down. 

How can we build large-scale language and vision models that don’t inherit social biases? Conventional wisdom suggests training on larger datasets, but research challenges this assumption.

The New York Times launched a multi-pronged attack on the use of its work in training datasets. The company updated its terms of service to forbid use of its web content and other data for training AI systems.

Training an agent that controls a robot arm to perform a task — say, opening a door — that involves a sequence of motions (reach, grasp, turn, pull, release) can take from tens of thousands to millions of examples...

Stable Diffusion may amplify biases in its training data in ways that promote deeply ingrained social stereotypes.

Fine-tuning a neural network typically involves retraining every layer on new data. But research shows that networks may perform better when fine-tuning modifies only a subset of layers.

Amid rising questions about the fairness and legality of using publicly available information to train AI models, Japan affirmed that machine learning engineers can use any data they find.

The largest dataset for training text-to-image generators was assembled by volunteers for roughly $10,000. Now it’s implicated in fights over whether copyrighted works can be used for training.

Developers of language models will have to pay for access to troves of text data that they previously got for free. The discussion platform Reddit and question-and-answer site Stack Overflow announced plans to protect their data from being used to train large language models.

Principal component analysis is a key machine learning technique for reducing the number of dimensions in a dataset, but new research shows that its output can be inconsistent and unreliable.

Large datasets often contain overly similar examples that consume training cycles without contributing to learning. A new paper identifies similar training examples, even if they’re not labeled.

Who would disagree that, if all people are mortal and Socrates is a person, Socrates must be mortal? GPT-3, for one. Recent work shows that bigger language models are not necessarily better when it comes to logical reasoning.

The world’s supply of data soon may fail to meet the demands of increasingly hungry machine learning models. Researchers at Epoch AI found that a shortage of text data could cause trouble as early as this year. Vision data may fall short within a decade.

Images in the wild may not come with labels, but they often include metadata. A new training method takes advantage of this information to improve contrastive learning.

I believe that natural language processing in 2022 will re-embrace symbolic reasoning, harmonizing it with the statistical operation of modern neural networks. Let me explain what I mean by this.