2019 in AI | Year-End Recaps From Synced, Stanford, Reddit & More!
2019 in Report | 2019 AI Index report
The AI Index, generated by Human-Centered Artificial Intelligence (HAI) from Stanford University, is a starting point for informed conversations about the state of artificial intelligence (AI). The report aggregates a diverse set of metrics, and makes the underlying data easily accessible to the general public.
(2019 AI Index Report) / (2019 AI Index Data)
2019 in ML | Best of Machine Learning in 2019: Reddit Edition
To help sift through some of the incredible projects, research, demos, and more in 2019, here’s a look at 17 of the most popular and talked-about projects in machine learning, curated from the r/MachineLearning subreddit.
2019 in GAN | This X Does Not Exist
Using generative adversarial networks (GAN), we can learn how to create realistic-looking fake versions of almost anything, as shown by this collection of sites that have sprung up in the past month.
2019 in RL | Breakthrough Research in Reinforcement Learning from 2019
TopBots has selected and summarized 10 research papers that we think are representative of the latest research trends in reinforcement learning. The papers explore, among others, the interaction of multiple agents, off-policy learning, and more efficient exploration.
2019 in Application | 10 AI Failures
This is the third Synced year-end compilation of “Artificial Intelligence Failures.” Despite AI’s rapid growth and remarkable achievements, a review of AI failures remains necessary and meaningful.
Continuous Meta-Learning without Tasks
Researchers present meta-learning via online changepoint analysis (MOCA), an approach which augments a meta-learning algorithm with a differentiable Bayesian changepoint detection scheme. The framework allows both training and testing directly on time series data without segmenting it into discrete tasks.
A New Open Benchmark for Speech Recognition with Limited or No Supervision
The largest-ever open source data set for speech technology, Libri-light is built entirely from public domain audio and optimized for developing automatic speech recognition (ASR) systems using limited or no supervision.
Latent Space Visualization, Characterization, And Generation of Diverse Vocal Communication Signals
Researchers present a set of computational methods that center around projecting animal vocalizations into low dimensional latent representational spaces that are directly learned from data. They apply these methods to diverse datasets from over 20 species enabling high-powered comparative analyses of unbiased acoustic features in the communicative repertoires across species.
(University of California, San Diego)
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Open AI Dota 2 Bots Get Leaner & Meaner
The Dota 2-playing bot team “AI OpenAI Five” have already demonstrated expert-level performance in the popular video game at Internet scale and have even learned effective human–AI cooperation skills. Now they’re gotten even stronger, as detailed by Open AI researchers in the new paper Dota 2 with Large Scale Deep Reinforcement Learning .
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