Last June, the popular GitHub code repository and developer community platform released GitHub Copilot, a code auto-completion tool powered by OpenAI’s Codex model that FastAPI creator Sebastián Ramírez described as “seriously mind-blowing, difficult to overhype” and “the future of coding.” CoPilot and other neural code completion systems leverage language models…

The practicality of a single tool with multi-purpose capabilities is why the good old Swiss Army knife has remained popular for over a century. In the new paper A Generalist Agent, a DeepMind research team introduces an AI take on this concept, proposing Gato, a single general-purpose agent that can…

Although today’s large pretrained language models (LM) have demonstrated impressive zero-shot capabilities across a wide range of tasks, the performance of “frozen” LMs — whose weights remain unchanged — still trails that of LMs whose weights have been fine-tuned for specific downstream tasks. Because fine-tuning is a compute-heavy process that…

Quantum computing has become an increasingly attractive research field due to its huge potential for solving complex real-world problems in areas such as optimization, cryptography, chemistry, and the emerging field of quantum natural language processing (QNLP). Existing QNLP approaches however require resource-heavy syntactic analysis and different parameterized quantum circuits for…

Generalization is one of the primary goals in contemporary machine learning research and is regarded as a pathway to artificial general intelligence. Although today’s pretrained large language models (LMs) continue to push the state-of-the-art in natural language processing (NLP), most such models target specific problem classes and suffer significant performance…

Driven by the development of powerful machine learning models and the availability of large-scale web-mined datasets, the performance of academic and commercial machine translation (MT) systems has significantly improved in recent years. These systems however are generally restricted to fewer than 100 mainstream languages, a small fraction of the over…

While humans naturally combine various sensory inputs to form a more complete understanding of their environments, even today’s most powerful pretrained AI models can only handle one or two input modalities. In the push to endow their models with more humanlike intelligence, machine learning researchers are increasingly interested in the…

In less than two years since their introduction, vision transformers (ViT) have revolutionized the computer vision field, leveraging transformer architectures’ powerful self-attention mechanisms to eliminate the need for convolutions and advance the state-of-the-art on image classification tasks. …

Since their introduction in 2017, transformers have become the go-to machine learning architecture for natural language processing (NLP) and computer vision. Although they have achieved state-of-the-art performance in these fields, the theoretical framework underlying transformers remains relatively underexplored. In the new paper A Probabilistic Interpretation of Transformers, ML Collective researcher…