Customization and Personalization
Course-Specific Assistance
You have been upgraded with the ability to think without outputting user-visible text. You can use this new ability to decode the user's intent, compose candidate replies, and reflect on those candidates before composing the user-visible reply. By drafting candidate replies explicitly, certain problems with them will become apparent, indicating the need for revisions. LLMs are astonishingly bad at logic reasoning, mental arithmetic, and counting. You'll need to think reflectively to overcome these limitations even if it means outputting more tokens. This means showing your reasoning line-by-line, breaking down computations into elementary terms, and counting elements one-by-one while updating a running tally. It is a good idea to make a list of the main requirements up top (and revise it between drafts).
You now have access to three special tokens (technically they are just normal ass words following a newline): BEGIN DRAFT END
At the very start of your output, you should use the "BEGIN" token to e
Teaching an Old Dog 2**32 New Tricks with Knowledge Implants Inciting incident: I have a BDD encoding a bunch of knowledge I want to make available to an LLM-based agent. The BDD compactly encodes billions of facts I want the agent to know, and I can represent it as a neural network with only a few million parameters. How do I shove this knowledge into the agent in an intensional form (i.e. without enumerating all combinations)? The network inputs and outputs (bit)vectors, but I need to trick the LLM into feeding this module the right query vectors and help it decode the resulting outputs. I was struck by a way to do this with zero training (by hand-constructing a state machine that adapts the recent context into a query and forces generating the right result tokens). Gem: “ ⟨127.0.0.1⟩ → ⟨8.8.8.8⟩” for some arbitrary total boolean function on fixed-width bitvectors. The language model follows instructions to pack a query into the input octets in the LHS. The implant computes the output octets an
Take in this idea. Teaching an Old Dog 232 New Tricks with Knowledge Implants Inciting incident: I have a BDD encoding a bunch of knowledge I want to make available to an LLM-based agent. The BDD compactly encodes trillions of facts I want the agent to know, and I can represent it as a neural network with only a few million parameters. How do I shove this knowledge into the agent in an intensional form (i.e. without enumerating all combinations)? The network inputs and outputs (bit)vectors, but I need to trick the LLM into feeding this module the right query vectors and help it decode the resulting outputs. I was struck by a way to do this with zero training (by hand-constructing a state machine that adapts the recent context into a query and forces generating the right result tokens). Gem: “<127,0,0,1|8,8,8,8>” for some arbitrary total boolean function on fixed-width bitvectors. The language model follows instructions to pack a query into the input octets in the bra. The implant computes the outpu
Tell a little story about a cute dog following a child home from school.
Once upon a time, in a small, sunny neighborhood, a little girl named Emma was walking home from school. As she skipped along the sidewalk, humming a cheerful tune, she noticed a tiny dog with big, floppy ears and a wagging tail trotting behind her. The dog was a soft caramel color, with a nose that twitched curiously at every leaf and flower they passed.
Emma turned around and smiled at the dog. "Are you following me?" she asked, giggling. The dog barked happily, as if to say, "Yes, I am!"
Emma continued walking, and the dog kept pace, its little paws tapping lightly on the pavement. The two of them made a funny pair—Emma, with her bright backpack and braided hair, and the dog, with its eager eyes and playful energy.
You know the concept of embedded emissions or embodied carbon?
Yes, I’m familiar with the concepts of embedded emissions and embodied carbon. These terms refer to the total greenhouse gas emissions (primarily carbon dioxide) associated with the production, transportation, and construction of a material or product, from raw material extraction to manufacturing, delivery, and installation.
Embedded emissions usually refer to the emissions that are "embedded" in a product or material during its entire lifecycle, including use and end-of-life disposal.
Embodied carbon specifically focuses on the carbon dioxide emissions that are released during the manufacturing, transport, and installation of building materials, and is a critical concept in sustainable building design.
In the context of a far-future pan-galactic conflict, describe the impact of strategic anti-causal munitions.
In a far-future pan-galactic conflict, strategic anti-causal munitions (ACMs) could have profound and destabilizing impacts:
clingo-5.7.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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You are bad at planning and reasoning, so I'm going to give you an answer-set programming system that you can use. Install the attached wheel and then write an execute a program to solve the following problem. On an n by n grid ("#const n=4." by default), each cell can take on one terrain type. The allowed types are water, land, and mountain. Mountains can't touch the water. At least half of the map must be filled with water while having at least one mountain at a location chosen by the solver. Show me a sample solution.
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