NIPS 2017 Digest 2017/12/15

The 2017 NIPS Conference took place in Long Beach on December 4-9.

NIPS 2017 — Day 1 Highlights

NIPS 2017 — Day 2 Highlights

NIPS 2017 — Day 3 Highlights

NIPS 2017 — notes and thoughts by olgalitech

NIPS 2017 Notes by David Abel

Ali Rahimi's talk at NIPS(NIPS 2017 Test-of-time award presentation)

Yann LeCun response to Ali Rahimi's NIPS lecture

Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/11/03

2017 The State of Data Science & Machine Learning
This year, for the first time, Kaggle conducted an industry-wide survey to establish a comprehensive view of the state of data science and machine learning. It received over 16,000 responses and learned a ton about who is working with data, what’s happening at the cutting edge of machine learning across industries, and how new data scientists can best break into the field. The below report shares some of their key findings and includes interactive visualizations so you can easily cut the data to find out exactly what you want to know.

CapsNet-Tensorflow
A Tensorflow implementation of CapsNet based on Geoffrey Hinton's paper Dynamic Routing Between Capsules

Blockchain, Machine Learning, Robotics, Artificial Intelligence And Wireless Technologies Will Reshape Digital Business In 2018
Blockchain, together with artificial intelligence, machine learning, robotics, and virtual and augmented reality, have the potential to deliver disruptive outcomes and reshape digital business in 2018. And companies that have not started the digital investment cycle are at high risk of being disrupted.

Awesome Machine Learning for Cybersecurity
A curated list of amazingly awesome tools and resources related to the use of machine learning for cybersecurity.

Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper.
This is a fairly faithful reimplementation of the system described in the Alpha Go Zero paper "Mastering the Game of Go without Human Knowledge". For all intents and purposes, it is an open source AlphaGo Zero.

Can I get a job as a Data scientist after doing the John Hopkins (10 courses) Data Science specialization from Coursera?
You can read this answer from Scott Breunig, Data Scientist at Snapdocs.

Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/10/26

AlphaGo Zero: Learning from scratch
In the paper, published in the journal Nature, deepmind team members demonstrate a significant step towards this goal.

Reimplementation of the system described in the Alpha Go Zero paper
For all intents and purposes, it is an open source AlphaGo Zero.

All the Linear Algebra You Need for AI
The purpose of this notebook is to serve as an explanation of two crucial linear algebra operations used when coding neural networks: matrix multiplication and broadcasting.

IEEE VIS 2017: Best Papers and Other Awards
This first part covers the opening, which included presentations of the best papers from all three tracks plus a new Test of Time award category.

Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent
Not surprisingly, many think the talent shortage won’t be alleviated for years.

Are too many people training to become data scientists?
Definitely not. In fact, there is a major shortage of analytical talent across the board. 

Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/10/13

Artificial intelligence can say yes to the dress
The technology, developed by Vue.ai’s Anand Chandrasekaran and Costa Colbert, uses a machine learning approach called generative adversarial networks, or GANs. 

The History of Deep Learning — Explored Through 6 Code Snippets
In this article, we’ll explore six snippets of code that made deep learning what it is today. We’ll cover the inventors and the background to their breakthroughs. Each story includes simple code samples on FloydHub and GitHub to play around with.

China’s AI Awakening
The West shouldn’t fear China’s artificial-intelligence revolution. It should copy it.

Interview: Yoshua Bengio, Yann Lecun, Geoffrey Hinton
October 10, 2017 for the first time ever, RE•WORK brought together the ‘Godfathers of AI’ to appear not only at the same event, but on a joint panel discussion. At the Deep Learning Summit in Montreal, we saw Yoshua Bengio, Yann LeCun and Geoffrey Hinton come together to share their most cutting edge research progressions as well as discussing the landscape of AI and the deep learning ecosystem in Canada.

Deep RL Bootcamp - Lectures
August 2017   |   Berkeley CA

The Data Scientist's Guide to Apache Spark
This repo contains notebook exercises for a workshop teaching the best practices of using Spark for practicing data scientists in the context of a data scientist’s standard workflow. By leveraging Spark’s APIs for Python and R to present practical applications, the technology will be much more accessible by decreasing the barrier to entry.

Recent AI, Deep Learning, Machine Learning Study Guide 2017/09/29

15 minute guide to choose effective courses for machine learning and data science
Advice from Tirthajyoti Sarkar for young professionals in non-CS field who wants to learn and contribute to data science/machine learning. Curated from personal experience.

The Complete Guide on Learning Deep Learning
This guide by Susan Li covers almost all the courses for Deep Learning.

NVIDIA Deep Learning Institute (DLI)
The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve the world’s most challenging problems with deep learning.

Meet with Harry Shum and Xuedong Huang in Seattle

During my recent trip to Seattle, I was glad to meet Harry Shum, Executive VP, Microsoft Artificial Intelligence and Research Group Group and listened to his excellent keynote speech in the 2nd North America Tsinghua Alumni Convention.

During the same convention, I also met Xuedong Huang, a Microsoft Technical Fellow in AI and Research.

I was so impressed by Dr.Huang's short presentation (most in Chinese) about Microsoft Translator in the breakout session about AI.

Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/09/08

Scientists Use Artificial Intelligence To Spot Alzheimer's Before Onset of Symptoms
Scientists from the Douglas Mental Health University Institute’s Translational Neuroimaging Laboratory at McGill have developed an algorithm that reliably detects signs of dementia before its onset. The technology could be used to help families prepare for treatment options, and to help researchers select better candidates for clinical trials that test drug effectiveness.

Facebook creates AI that negotiates in unknown new language
Facebook AI Research (FAIR) has been working on artificial intelligence (AI) agents that negotiate for the best deal, using all the complexities of language, reasoning, and deception that humans use.

The Seven Deadly Sins of Predicting the Future of AI
Predicting the future is really hard, especially ahead of time.

What machines can tell from your face
Life in the age of facial recognition. Technology is rapidly catching up with the human ability to read faces. In America facial recognition is used by churches to track worshippers’ attendance; in Britain, by retailers to spot past shoplifters. This year Welsh police used it to arrest a suspect outside a football game. In China it verifies the identities of ride-hailing drivers, permits tourists to enter attractions and lets people pay for things with a smile. Apple’s new iPhone is expected to use it to unlock the homescreen. 

Python overtakes R, becomes the leader in Data Science, Machine Learning platforms
While in 2016 Python was in 2nd place ("Mainly Python" had 34% share vs 42% for "Mainly R"), in 2017 Python had 41% vs 36% for R. 

Object detection: an overview in the age of Deep Learning
There’s no shortage of interesting problems in computer vision, from simple image classification to 3D-pose estimation. One of the problems we’re most interested in and have worked on a bunch is object detection. 

Background removal with deep learning
Background removal is a task that is quite easy to do manually, or semi manually (Photoshop, and even Power Point has such tools) if you use some kind of a “marker” and edge detection.  However, fully automated background removal is quite a challenging task.