Abstract: A computerized system includes a generative large language model (LLM). The LLM includes an encoder configured to provide a vector input to a content addressable memory system and a decoder configured to receive a vector output from the content addressable memory system and generate a data output. The content addressable memory system is configured to receive the vector input and generate a vector output based on data contents of the content addressable memory system. The content addressable memory system is configured to respond to receiving at least one memory update command by updating the data contents of the content addressable memory system.

Abstract: A computerized system includes a generative large language model (LLM). The LLM includes an encoder configured to provide a vector input to a content addressable memory system and a decoder configured to receive a vector output from the content addressable memory system and generate a data output. The content addressable memory system is configured to receive the vector input and generate a vector output based on data contents of the content addressable memory system. The content addressable memory system is configured to respond to receiving at least one memory update command by updating the data contents of the content addressable memory system.

Abstract: A method, computer program product, and computer system for triggering actions in a sequence of time steps within a multi-armed bandit process. In a current time step: a context input is received; a hidden Markov model (HMM) parameter transformation is executed to compute a latent state probability vector and HMM parameters using a conditional probability distribution, context input, values of latent state probability vector, and HMM parameters from a previous time step; an action is selected; an electromagnetic signal is sent to a hardware machine directing the hardware machine to perform the action; a dynamic reward resulting from the hardware machine having performed the action is received; a mean reward estimate as a function of the dynamic reward and the latent state probability is updated; and an update of the latent state probability vector in dependence on the dynamic reward, the action, and the mean reward estimate vector is computed.

Abstract: Hair spray compositions, particularly non-aerosol compositions containing partially to fully neutralized copolymers of 70 to 90 percent methylmethacrylate and 10 to 30 percent methacrylic acid dissolved in aqueous alcohol containing up to 35 percent water.