人工智能资料库：第25辑（20170206）
- 【博客】SimGANs: A Game Changer in Unsupervised Learning, Self Driving Cars, and More
简介：
Apple’s Learning from Simulated and Unsupervised Images through Adversarial Training (S+U Learning) lays down the blueprint for training state-of-the-art neural nets from only synthetic and unlabelled data. In this post we will see why this has huge potential, and apply it to an interesting problem: autonomous driving. We will refer to an implementation of SimGAN: https://github.com/wayaai/SimGAN, and to my 2nd favorite company behind waya.ai: comma.ai, throughout the post.
原文链接：https://medium.com/intuitionmachine/simgans-applied-to-autonomous-driving-5a8c6676e36b#.duoewdd7a
2.【视频】Game AI Development With OpenAI Universe | Two Minute Papers
简介：
Game AI Development With OpenAI Universe | Two Minute Papers
原文连接：https://www.youtube.com/watch?v=vaFhLAbPi8w
3.【视频】Artificial Intelligence is the New Electricity - Andrew Ng
简介：
Artificial Intelligence is transforming industry after industry - just like electricity did 100 years ago. Health care, transportation, communications, manufacturing, and quite possibly your own industry - are in the early stages of this transformation. In this presentation, you’ll learn about the major trends in AI, understand what is (and isn’t) possible with machine learning today, and how to distinguish hype from reality.
Speaker:
Andrew Ng
VP & Chief Scientist of Baidu, Co-Founder of Coursera, Adjunct Professor at Stanford, and Artificial Intelligence pioneer
原文链接：http://www.internet-future.org/media-library/mediaitem/2503-artificial-intelligence-is-the-new-electricity-andrew-ng.html
4.【论文】Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey
简介：
Natural language and symbols are intimately correlated. Recent advances in machine learning (ML) and in natural language processing (NLP) seem to contradict the above intuition: symbols are fading away, erased by vectors or tensors called distributed and distributional representations. However, there is a strict link between distributed/distributional representations and symbols, being the first an approximation of the second. A clearer understanding of the strict link between distributed/distributional representations and ymbols will certainly lead to radically new deep learning networks. In this paper we make a survey that aims to draw the link between symbolic representations and distributed/distributional representations. This is the right time to revitalize the area of interpreting how symbols are represented inside neural networks.
原文链接：https://arxiv.org/pdf/1702.00764v1.pdf
5.【博客】Where to get your Data Science Training or Apprenticeship
简介：
I am frequently asked for suggestions regarding academic institutions, professional organizations, or MOOCs that provide Data Science training. The following list will be updated occasionally (LAST UPDATED: 2016 August 16) .
Also, be sure to check out The Definitive Q&A for Aspiring Data Scientists and the story of my journey from Astrophysics to Data Science. If the latter story interests you, then here are a couple of related interviews: “Data Mining at NASA to Teaching Data Science at GMU“, and “Interview with Leading Data Science Expert“.
原文链接：http://rocketdatascience.org/?p=216