This week, I covered the story of Casey Harrell --a man with ALS who is "the first power user" of a brain implant, according to the researchers who worked with him. Harrell is paralyzed and unable to speak coherently without the device. He has now spent almost three years using a brain-computer interface (BCI) that enables him to "speak," surf the web, and perform his job as a climate activist, largely independently. Since Harrell was implanted with the device, in July 2023, a team at the University of California, Davis, has worked with him to adjust and improve its offerings. They've refined its accuracy, for example.

There Aren't a Lot of Reasons to Get Excited About a New Amazon Smartphone The company is reportedly building a new AI-powered mobile device. If Amazon follows through on the plan, experts warn it would be next to impossible to break into a crowded market. Reuters reports that Amazon's Devices and Services unit is working on a smartphone--dubbed Transformer--with Amazon's Alexa+ AI assistant and shopping as a major focus of the experience. It's unclear what this smartphone would cost, how much Amazon is spending to develop Transformer, and what operating system it will run. There's no word on when it will launch, and there's still also a chance the project could be scrapped altogether.

The conventional approach to imputing longitudinal data is to employ a parametric model with mixed-effects, such as the linear mixed-effects model.

Privacy protection of users' entire contribution of samples is important in distributed

However, the problem of provably editing a DNN to satisfy a property remains challenging.

Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.

Stein V ariational Gradient Descent (SVGD) is a nonparametric particle-based deterministic sampling algorithm.

Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite this connection, most existing approaches in both RLHF and PbRL primarily focus on optimizing a mean reward objective, neglecting scenarios that necessitate risk-awareness, such as AI safety, healthcare, and autonomous driving. These scenarios often operate under a one-episode-reward setting, which makes conventional risk-sensitive objectives inapplicable.