Tag: open source

AI Machine Learning & Data Science Popular Research

Game On! MIT, Allen AI & Microsoft Open-Source a Suite of AI Programming Puzzles

A research team from MIT, Allen Institute for AI and Microsoft Research open-sources Python Programming Puzzles (P3), a novel programming challenge suite that captures the essence of puzzles and can be used to teach and evaluate an AI’s programming proficiency.

AI Research United States

OpenAI Guards Its ML Model Code & Data to Thwart Malicious Usage

The San Francisco-based AI non-profit however has raised eyebrows in the research community with its unusual decision to not release the language model’s code and training dataset. In a statement sent to Synced, OpenAI explained the choice was made to prevent malicious use: “it’s clear that the ability to generate synthetic text that is conditioned on specific subjects has the potential for significant abuse.”

AI Conference

On Compilers: First TVM and Deep Learning Conference

Last December some 9,000 attendees packed a single venue in Montreal for a week-long academic conference. NeurIPS was completely sold out, the latest indication of just how hot AI is nowadays. As AI and machine learning continue to ignite discussion across a wide variety of disciplines, novel approaches to the tech are also garnering interest.

AI China Research

Alibaba Open-Sources Mars to Complement NumPy

Alibaba Cloud recently announced that it has open sourced Mars — its tensor-based framework for large-scale data computation — on Github. Mars can be regarded as “a parallel and distributed NumPy.” Mars can tile a large tensor into small chunks and describe the inner computation with a directed graph, enabling the running of parallel computation on a wide range of distributed environments, from a single machine to a cluster comprising thousands of machines.

AI

2018 In Review: 10 Open-Sourced AI Datasets

In the conclusion to our year-end series, Synced spotlights ten datasets that were open sourced in 2018 and takes a peek into the papers behind them. We hope this list can provide the AI community with insight into what 2019 might hold in store for big data.