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In addition to reanalysing all the old data using a consistent system, the reanalyses also make use of much archived data that was not available to the original analyses. This allows for the correction of many historical hand-drawn maps where the estimation of features was common in areas of data sparsity.
The functions work on many types of data, including numerical, categorical, time series, textual, and image. [7] Mojo can run some Python programs, and supports programmability of AI hardware. It aims to combine the usability of Python with the performance of low-level programming languages like C++ or Rust. [8]
An atmospheric reanalysis (also: meteorological reanalysis and climate reanalysis) is a meteorological and climate data assimilation project which aims to assimilate historical atmospheric observational data spanning an extended period, using a single consistent assimilation (or "analysis") scheme throughout.
Audio under Creative Commons from 100k songs (343 days, 1TiB) with a hierarchy of 161 genres, metadata, user data, free-form text. Raw audio and audio features. 106,574 Text, MP3 Classification, recommendation 2017 [144] M. Defferrard et al. Bach Choral Harmony Dataset Bach chorale chords. Audio features extracted. 5665 Text Classification 2014
OpenAI Codex is an artificial intelligence model developed by OpenAI.It parses natural language and generates code in response. It powers GitHub Copilot, a programming autocompletion tool for select IDEs, like Visual Studio Code and Neovim. [1]
In March 2020, 15.ai, created by an anonymous MIT researcher, was a free web application that could generate convincing character voices using minimal training data. [42] The platform is credited as the first mainstream service to popularize AI voice cloning ( audio deepfakes ) in memes and content creation , influencing subsequent developments ...
A November 2022 class action lawsuit against Microsoft, GitHub and OpenAI alleged that GitHub Copilot, an AI-powered code editing tool trained on public GitHub repositories, violated the copyright of the repositories' authors, noting that the tool was able to generate source code which matched its training data verbatim, without providing ...
import theano from theano import tensor # Declare two symbolic floating-point scalars a = tensor. dscalar b = tensor. dscalar # Create a simple expression c = a + b # Convert the expression into a callable object that takes (a, b) # values as input and computes a value for c f = theano. function ([a, b], c) # Bind 1.5 to 'a', 2.5 to 'b', and evaluate 'c' assert 4.0 == f (1.5, 2.5)