Large language models (LLMs) pretrained on massive data are being used in countless real-world applications. However — as computer scientists have known for decades — not all data is equal, and this ...

Text-to-image generation has been one of the most active and exciting AI fields of 2021. In January, OpenAI introduced DALL-E, a 12-billion parameter version of the company’s GPT-3 transformer ...

Source: Solving the Equation: The Variables for Women’s Success in Engineering and Computing report (2015), American Association of University Women The gender imbalance among machine learning ...

Although deep learning models are playing increasingly important roles across a wide range of decision-making scenarios, a critical drawback is their inability to provide human-understandable ...

Generative AI, including Language Models (LMs), holds the promise to reshape key sectors like education, healthcare, and law, which rely heavily on skilled professionals to navigate complex ...

In the paper Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds, the researchers identify a key observation that inspired the design of the novel domain ...

This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by ...

Knowledge distillation is a classic approach for transferring knowledge from a powerful teacher model to a smaller student model. While it might be assumed that a stronger teacher model would ...

The contemporaneous development in recent years of deep neural networks, hardware accelerators with large memory capacity and massive training datasets has advanced the state-of-the-art on tasks in ...

Speaking at the London Mathematical Society in 1947, Alan Turing seemed to anticipate the current state of machine learning research: “What we want is a machine that can learn from experience . . .

Large-scale language models (LLMs) have achieved impressive performance in automated code generation by bootstrapping human knowledge and learning from extremely large datasets. Might it be possible ...