Sunday, April 15, 2018

2018 University of Toronto-Tsinghua University AI Squared Forum


The University of Toronto and Tsinghua University are pleased to announce the launch of the University of Toronto-Tsinghua Entrepreneurship and Innovation AI Squared Forum. This event, held in May at the University of Toronto, will bring together leading researchers, entrepreneurs, innovators, and students to discuss opportunities for research collaboration and partnerships in areas of Artificial Intelligence. The event will be held on May 3rd and May 4th between the two universities. Afterwards the forum will be extended to the general public on May 5th (Saturday).

Background

For 2018 the topic is Artificial Intelligence, building on the intense interest created by the launch of the Vector Institute in Toronto.

The forum will showcase the world-class research programs and entrepreneurial activities of the two institutions through keynote talks, research presentations, and a student innovation event. The latter will focus on how students and faculty are translating their research activities from the university into the real world, including the challenges of launching start-ups and securing funding. With presentations from both the University of Toronto and Tsinghua, participants and forum attendees will be able to compare and contrast the innovation and entrepreneurship landscapes of Toronto and Beijing, and by extension, Canada and China.

University of Toronto and Tsinghua University have been collaborating on education and research since 2004. Opportunities such as the Innovation and Entrepreneurship Forum will strengthen and advance the innovative potential between both institutions, developing a global entrepreneurial ecosystem.

This forum is open to general public on Saturday with the following themes:
  • How AI will impact the business world
  • What are the latest AI research topics
  • How AI talents and enterprises can work seamlessly together
Speakers are from Tsinghua, UofT, Borealis AI (RBC), Deep Genomics, OMERS Ventures, Bibu Labs, ROSS Intelligence, Twenty Billion Neurons, Cyclica .....

Date: May 5, 2018 (Saturday) – AI Squared Forum Open To Public

For more forum information, please visit the forum website: http://www.aisquaredforum.ca/

Seat is limited, please reserve today at https://goo.gl/JGvGvk

Monday, January 15, 2018

Recent AI Info Digest about 2017 Review and 2018 Prediction


ACSIP New Year Party for AI
This party attracted the members who were interested in AI. AI (爱) stands for love in Chinese. ACSIP (Association of Chinese Senior IT Professionals) was founded in Toronto in 2006 by a group of pioneering Chinese IT entrepreneurs and professionals. They hoped to encourage individual career and business development by building a community that facilitates information exchange among those senior IT professionals. 


AI and Deep Learning in 2017 – A Year in Review
A really great summary of all the amazing things that happened in 2017 by Denny Britz

30 Amazing Machine Learning Projects for the Past Year (v.2018)
This is an extremely competitive list and it carefully picks the best open source Machine Learning libraries, datasets and apps published between January and December 2017. Mybridge AI evaluates the quality by considering popularity, engagement and recency. 

Top 10 AI technology trends for 2018 from PwC

Top 10 AI trends for business leaders in 2018 from PwC

Friday, November 3, 2017

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.


Thursday, October 26, 2017

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. 



Friday, October 13, 2017

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.

Friday, September 29, 2017

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.