Major AI and Machine Learning Research Trends Shaping 2021
Artificial intelligence research in 2021 focuses heavily on improving large language models, addressing algorithmic bias, and advancing computer vision. Key areas include making transformers more efficient and expanding AI capabilities across multiple languages.
Natural language processing research continues to be dominated by large pre-trained transformer models in 2021. Top tech companies focus heavily on increasing model size to boost performance, with expectations running high for the arrival of GPT-4 or similar massive systems. However, researchers increasingly explore ways to improve the underlying transformer architecture itself rather than just scaling up existing designs.
A major priority for the AI community involves addressing the significant downsides of these large language models. Developers actively work on detecting and removing toxicity and biases from AI-generated text, as systems like GPT-3 frequently produce harmful or skewed content. Additionally, scientists investigate how to apply these language models effectively in multilingual settings without requiring explicit cross-lingual supervision.
Efficiency remains a critical challenge as the computational costs of pre-training and fine-tuning transformers demand excessive time and resources. To combat this, researchers introduce innovative methods for accelerating the training of large models. The field also sees growing interest in successful data augmentation strategies for text, which proves much more difficult than image augmentation due to the need for semantically invariant perturbations.