Debbie Foster

The time-symmetric, retrocausal framework advanced by Aharonov and his colleagues is sometimes called the two-state vector formalism

Decades earlier, I'd moved to Japan in part because it was the most inward and subtle culture I'd met: the relation of surface to depth remains beguilingly uncertain there and I can never begin to imagine I can get to the bottom of things. Yet my neighbors around Kyoto hold on to their privacy by saying little and expressing, with their faces, even

our brains have evolved this quick-acting system to examine mood and energy, which provides a general sense, in a split second, of someone’s emotional state. That’s usually enough to figure out how to align, and whether we should feel safe or alarmed.

As a reader, I am always diffusing into the world of fiction. As a writer, I cannot solidify into direct statements. As a person, I cannot solidify into someone who makes anything happen. In my most lost moments, I see myself disintegrating and drifting into everything and everyone else, floating unseen and dispersed through the world the way I

Freedom to act under the direction of reason rather than the coercion of authorities was one of the great themes of Enlightenment thought.

It is, however, a matter of historical record that people have been impacted by their extraordinary experiences, discovering the biblical "peace of God that passeth all understanding," altering their way of life. Thus a more nuanced approach is to accept these reports as authentic and honest descriptions. They teach us that our central nervous

to the extent that we are identified as a ‘self’, we are merely a performance of the possibilities latent in language.

The intellectual histories of computer science and linguistics are thus studded with attempts to wrangle natural language into the straightjacket of formal language, all of which—like a flailing prisoner—it managed to resist.

… to build AI systems with social cognition, so that they can interact meaningfully with the user over the long term, we first need to solve two foundational problems in how memory systems work. These problems, called oneshot learning and continual learningare currently (as of early 2024) missing in publicly available LLMs.