In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs).

Italian neurosurgeon Sergio Canavero has a dream to extend life by swapping someone's head (or at least their brain) onto a new body. The Italian neurosurgeon Sergio Canavero has been preparing for a surgery that might never happen. Canavero caused a stir in 2017 when he announced that a team he advised in China had exchanged heads between two corpses. But he never convinced skeptics that his technique could succeed--or to believe his claim that a procedure on a live person was imminent. The labeled him the "P.T. Barnum of transplantation." Canavero withdrew from the spotlight.

Deep neural networks (DNNs) defy the classical bias-variance trade-off: adding parameters to a DNN that interpolates its training data will typically improve its generalization performance. Explaining the mechanism behind this "benign overfitting" in deep networks remains an outstanding challenge.

Yes, you can pay $50,000 to clone a pet. But others are using the technology to rescue endangered species. This week, we heard that Tom Brady had his dog cloned. The former quarterback revealed that his Junie is actually a clone of Lua, a pit bull mix that died in 2023. Brady's announcement follows those of celebrities like Paris Hilton and Barbra Streisand, who also famously cloned their pet dogs. But some believe there are better ways to make use of cloning technologies.

The remarkable capability of over-parameterised neural networks to generalise effectively has been explained by invoking a "simplicity bias": neural networks prevent overfitting by initially learning simple classifiers before progressing to

Algorithm 2 describes the prompt completion algorithm introduced in Section 2.2. Algorithm 3 is a variant of the rebinding Algorithm 1 that does not use EM. This decoded clone (and all the clones in its clone set) are then rapidly bound to emit the surprise. Add the pseudocount ϵ to the initial emission matrix and normalize its rows. Figure 9: A. Transition graph of the learned CSCG model with overallocation ratio We present below the tables of results associated with Figure 1.