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CPL/Kleisli
Collection Programming Language ,is a high-level language for forulating queries
Kleishli, the system that implements CPL
Functional programming plays 2 roles here:
CPL is a functional language,
and Kleisli is written in Standard ML
CPL/Kleisli als exploits record subtyping.
Emp represents employees by a set of records. Each record must contain a Name and DNum field, but may contain other fields as well. The type system that permits this flexibility and the technique for implementing it efficiently were both adopted directly from research in the functional community.
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AlphaGeometry
the AI system surpasses the state-of -the-art approach for geometry problems, advancing AI reasoning in mathematics
an AI system that solves complex geometry problems at a level approaching a human Olympiad gold-medalist - a breakthrough in AI performance.
AlphaGeometry is a neuro-symbolic system made up of a neural language model and a symbolic deduction engine, which work together to find proofs for complex geometry theorems. Akin to the idea of “thinking, fast and slow”, one system provides fast, “intuitive” ideas, and the other, more deliberate, rational decision-making.
AlphaGeometry builds on Google DeepMind and Google Research’s work to pioneer mathematical reasoning with AI – from exploring the beauty of pure mathematics to solving mathematical and scientific problems with language models. And most recently, we introduced FunSearch, which made the first discoveries in open problems in mathematical sciences using Large Language Models.
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Dot Notation
点标注符
提供了一种获取对象属性和方法的方式。点标注符用啦指明某个属性或者方法隶属于某个特定的类。使用点标注符的语句是对象实例后面紧跟一个点符号（.）以及属性或是方法的名称。
获取对象属性和方法的语句都一样的额。如果点符号后面的语句末尾是圆括号括起来的零个或多个参数的话，那就说明正在访问对象的一个方法。
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Portable Web Documents（PWDs）
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An Open Source text-to-speech system built by inverting Whisper. Previously known as spear-tts-pytorch.
We want this model to be like Stable Diffusion but for speech – both powerful and easily customizable.
We are working only with properly licensed speech recordings and all the code is Open Source so the model will be always safe to use for commercial applications.
Currently the models are trained on the English LibreLight dataset. In the next release we want to target multiple languages (Whisper and EnCodec are both multilanguage).
Whisper for modeling semantic tokens
We utilize the OpenAI Whisper encoder block to generate embeddings which we then quantize with a small 2-layer model to get semantic tokens.
If the language is already supported by Whisper then this process requires only audio files (without ground truth transcriptions).
Using Whisper for semantic token extraction diagram
EnCodec for modeling acoustic tokens
use EnCodec to model the audio waveform. Out of the box it delivers reasonable quality at 1.5kbps and we can bring this to high-quality by using Vocos – a vocoder pretrained on EnCodec tokens
EnCodec block diagram
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prettier-plugin-tailwindcss
tailwindlabs • Updated Jan 9, 2025
