Large language models based on transformers have achieved great empirical successes. However, as they are deployed more widely, there is a growing need to better understand their internal mechanisms in order to make them more reliable. These models appear to store vast amounts of knowledge from their training data, and to adapt quickly to new information provided in their context or prompt. We study how transformers balance these two types of knowledge by considering a synthetic setup where tokens are generated from either global or context-specific bigram distributions. By a careful empirical analysis of the training process on a simplified two-layer transformer, we illustrate the fast learning of global bigrams and the slower development of an induction head mechanism for the in-context bigrams. We highlight the role of weight matrices as associative memories, provide theoretical insights on how gradients enable their learning during training, and study the role of data-distributional properties.

Somewhere on the horizon is a beam of light from another group, scouring the skies for danger over their own patrol zone. There's no public data on the total number of volunteer units – or how many women are involved. But as Russia sends drones packed with explosives almost every night, they help form an extra shield around big towns and cities. From the Witches' position in a field, Yulia tracks two drones on her tablet. They're over the neighbouring region, so there's no imminent danger for Bucha, but the machine guns will stay in place until the alert ends.

Competing in kaggle competitions is fun and addictive! And over the last couple of years, I developed some standard ways to explore features and build better machine learning models. These simple, but powerful techniques helped me get a top 2% rank in Instacart Market Basket Analysis competition and I use them outside of kaggle as well. So, let's get right into it! One of the most important aspects of building any supervised learning model on numeric data is to understand the features well.

Articulating thought in computerbased media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an intelligent learning environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components that are capable of generating knowledge-based feedback and virtual characters that enhance the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed.

On the upcoming SpaceX CRS-10 mission, a rocket will launch carrying the next batch of cargo to the International Space Station. Inside its Dragon capsule's unpressurized trunk will be a critical Earth-facing instrument--one that maps ozone molecules and other compounds in the atmosphere. Its name is the Stratospheric Aerosol and Gas Experiment III, or SAGE III. When the Dragon arrives at the ISS, a robot arm will reach into the trunk, pull out the experiment's parts, put them together, and install them on the outside of the habitat. Scientists at NASA's Langley Research Center will watch streaming video of their baby being assembled, breaths held till everything is in place.

Awards season is underway, with all roads leading to Oscar night on Feb. 26. Keep up with all the buzz and predictions here, where you'll find the latest on the guild awards, critics' picks, the Golden Globes and more. 'Manchester By the Sea,' 'Fences' and'Moonlight' lead SAG awards nominations NAACP Award nominees include'Moonlight,' 'Birth of a Nation' 'Manchester By the Sea,' 'Fences' and'Moonlight' lead SAG awards nominations NAACP Award nominees include'Moonlight,' 'Birth of a Nation' The reactions continue to roll in as actors across Hollywood process Wednesday morning's nominations for the Screen Actors Guild Awards. Nominees were grateful to SAG, but perhaps even more appreciative of the phenomenal casts they were a part of.