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.
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.
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.
The Rise of the Data Engineer Over the past 5 years working in Silicon Valley at Airbnb, Facebook and Yahoo!, and having interacted profusely with data teams of all kinds working for companies like Google, Netflix, Amazon, Uber, Lyft and dozens of companies of all sizes, Maxime Beauchemin is observing a growing consensus on what “data engineering” is evolving into, and felt a need to share some of my findings. The Downfall of the Data Engineer In this post, Maxime Beauchemin want to expose the challenges and risks that cripple data engineers and enumerates the forces that work against this discipline as it goes through its adolescence. Machine Learning for Humans Simple, plain-English explanations accompanied by math, code, and real-world examples by Vishal Maini. How Machines Learn: A Practical Guide Karlijn Willems lists seven steps (and 50+ resources) that can help you get started in this exciting field of Computer Science, and ramp up toward becoming a machine learning hero. How AI can aid, not replace, humans in recruitment One industry where the use of the technology is being actively explored is recruitment, where enterprises are drawing on its capabilities in various ways to help them find new staff. Report shows that AI is more important to IoT than big data insights We think that big data is the only thing we need for all of our insights. But in the world of Internet of Things (IoT), that is not the case.
Four deep learning trends from ACL 2017 (part 1) Four deep learning trends from ACL 2017 (part 2) In this two-part post, Abigail See describes four broad research trends that she observed at the conference (and its co-located events) through papers, presentations and discussions. The content is guided entirely by her own research interests; accordingly it’s mostly focused on deep learning, sequence-to-sequence models, and adjacent topics.
The Present and Future of Quantum Computing for AI Quantum computing is still in it’s infancy, and no universal architecture for quantum computers exists right now. However, their prototypes are already here and showing promising results in cryptography, logistics, modelling and optimization tasks. For AI researchers optimization and sampling is particularly important, because it allows to train Machine Learning models much faster with higher accuracy. Artificial intelligence could be the future of banking By leveraging AI, banks can engage with consumers in a faster and more consistent manner. They can use “bots” at contact centres for basic inquiries to free up employees for more complicated questions. They can use robo-advisers to provide basic investment services at lower cost.
Pandas tips and tricks This post includes some useful tips for how to use Pandas for efficiently preprocessing and feature engineering from large datasets.
Aug. 2017 Hive User Group Meeting @HortonWorks 1. Hive on Spark, production experience @Uber (Xuefu Zhang) 2. Reair and its usage for Uber's multi data center replication (Zheng Shao) 3. ACID, use cases in data management (Carter Shanklin) 4. Optimized Hive replication (Anishek Agarwal) 5. LLAP: Locality is dead (in the cloud) (Gopal Vijayaraghavan) 6. Don't reengineer, reimagine: Hive buzzing with Druid's magic potion (Slim Bouguerra)
Thoughts after taking the Deeplearning.ai courses The fast AI course mainly teaches you the art of driving while Andrew’s course primarily teaches you the engineering behind the car. DeepMind and Blizzard open StarCraft II as an AI research environment DeepMind's scientific mission is to push the boundaries of AI by developing systems that can learn to solve complex problems. To do this, we design agents and test their ability in a wide range of environments from the purpose-built DeepMind Lab to established games, such as Atari and Go. Hype or Not? Some Perspective on OpenAI’s DotA 2 Bot As I started looking more into what exactly the DotA 2 bot was doing, how it was trained, and what game environment it was in, I came to the conclusion that it’s an impressive achievement, but not the AI breakthrough the press would like you to believe it is. That’s what this post is about.
Deep Reinforcement Learning, Decision Making, and Control In this tutorial, we will cover the foundational theory of reinforcement and optimal control as it relates to deep reinforcement learning, discuss a number of recent results on extending deep learning into decision making and control, including model-based algorithms, imitation learning, and inverse reinforcement learning, and explore the frontiers and limitations of current deep reinforcement learning algorithms.
Deep Learning for Health Care Applications: Challenges and Solutions In this tutorial, we will discuss a series of problems in health care that can benefit from deep learning models, the challenges as well as recent advances in addressing those. We will also include data sets and demos of working systems. Sequence-To-Sequence Modeling with Neural Networks Sequence-To-Sequence (Seq2Seq) learning was introduced in 2014, and has since been extensively studied and extended to a large variety of domains. Seq2Seq yields state-of-the-art performance on several applications such as machine translation, image captioning, speech generation, or summarization. In this tutorial, we will survey the basics of this framework, its applications, main algorithmic techniques and future research directions. Interpretable Machine Learning In this talk, we first suggest a definitions of interpretability and describe when interpretability is needed (and when it is not). Then we will review related work, all the way back from classical AI systems to recent efforts for interpretability in deep learning. Finally, we will talk about a taxonomy for rigorous evaluation, and recommendations for researchers. We will end with discussing open questions and concrete problems for new researchers. Deep Learning with Andrew Ng In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton
What's Next For Deep Learning? Answer by Ian Goodfellow, AI Research Scientist at Google Brain, on Quora: There are a lot of things that are next for deep learning. Instead of thinking of moving forward in one direction, think of expanding outward in many directions.
Deep Reinforcement Learning: An Overview (by Yuxi Li, version 3, July 15, 2017) We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, in particular, differentiable neural computer (DNC), unsupervised learning, transfer learning, semi-supervised learning, hierarchical RL, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems (a.k.a. chatbots), machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems. We mention topics not reviewed yet. After listing a collection of RL resources, we present a brief summary, and close with discussions.
ImageNet Object Localization Challenge This year, Kaggle is thrilled to be the official host of all three ImageNet Challenges for the first time including the other two competitions: Object Detection Challenge Object Detection from Video Challenge Deep Learning for NLP Best Practices This post is a collection of best practices for using neural networks in Natural Language Processing. It will be updated periodically as new insights become available and in order to keep track of our evolving understanding of Deep Learning for NLP.
This is Part 1 of 3 in a series of posts that looks at the landscape of the artificial intelligence industry and the companies and institutes developing products that are moving the needle of knowledge of machine intelligence and consciousness forward for humanity.
This list contains companies working on artificial intelligence and machine learning products primarily for business use, non-specific to any industry. Industry specific AI will be the final part of this series.
New Frontiers for Deep Learning in Robotics In this workshop a wide range of renowned experts will discuss deep learning techniques at the frontier of research that are not yet widely adopted, discussed, or well-known in our community. How AI And Deep Learning Are Now Used To Diagnose Cancer Without a doubt one of the most exciting potential uses for AI (Artificial Intelligence) and in particular deep learning is in healthcare.
In the opening keynote of the Big Data Toronto conference, however, Luming Wang of Uber Technologies Inc. said companies should aim their earliest AI efforts at assisting employees so they can do their jobs better.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
There’s been a lot of talks about artificial intelligence (AI). Most notably, Google’s AlphaGodefeated Lee Se-dol last year. The latest one was the announcement of Toronto based Vector Institute for Artificial Intelligence which was backed by Geoffrey Hinton, the Godfather of Deep Learning and funded by government as well as large companies. Executives of most Internet giants believe the next decades will be a golden age for AI.
But how is this relevant to the senior IT professionals? They might think AI is only for those PhDs working for Google Deepmind, Facebook, or professors in the Stanford Lab. If they don’t have an academic background in AI and mathematics, does it mean that the AI trend is not within their reach? Is their job going to be impacted by AI?
To help its 2000+ members better position themselves to participate in rather than being hit by the AI wave, Association of Chinese Senior IT Professionals(ACSIP) partnered with IBM Canada to held the first AI presentation of series of seminars at IBM Amphitheater in Markham on May 14, 2017. The two parties had worked together to promote the Big Data technology as well as Watson Analytics in the past. This event was another successful joint venture and drew more than 150 IT professional in GTA to attend. As honorable guests, Dr. Yuxi Li, the author of recently published paper “Deep Reinforcement Learning: An Overview” and three contributors from Synced, China’s first media entity that focuses on reporting machine intelligence and related technologies, also came to support the event.
IBM has been playing a key role and heavily investing in the AI. Watson, the world's first cognitive system, is the fruit of over 50 years of IBM research in AI. Saeed Aghabozorgi, PhD is a Data Scientist in IBM. He introduced the audience about the Big Data University (BDU), an IBM community initiative, which is also helping promote the AI technologies through free courses such as Deep Learning with TensorFlow. The course content is free, access to tool sets used within the courses is free.
The first speaker Kent Yu is the co-founder of ACSIP. He is.a software architect and certified Scrum Master, working for a world leading software company serving Fortune 500 clients. He is also an entrepreneur who founded and sold 2 startups. Although he possesses a MBA degree and a Computer Science degree, he doesn’t have any PhD degree. He is now a student of Jeremy Howard, former Kaggle president and chief data scientist, and founder of Enlitic. Starting his presentation by Bach Music Test, Kent showed how AI has become very good at imitating human composers. He then uncover the deep learning myth by comparing the human neuron with artificial neuron. Through the demos, he continued to explain how neural training works and how Convolutional Neural Network (CNN) improves computer vision. Regarding AlphaGo, Kent mainly touched the reinforcement learning.
Different from Kent, the second speaker Joseph Santarcangelo use mathematical approach to explain neural networks. Joe has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD. His main point was “a neural network is a function that can use to approximate ‘something’ using a set of parameters”. Starting from the equation of a line, he focused on classification area to explain how neural network works.
Given the purpose of this event, Kent also shared his own experience on AI learning. He proved that coders without prior AI background could also learn how to build state-of-art deep learning models beating the best academic results. To help those audience who might like moving forward in the AI field, Kent gave his advice - Focus on Your Strengths. In other words, if you are good at programming, you had better start from reading codes rather than reading papers.
"I can understand the content of any photograph and I’ll try to describe it as well as any human. I’m still learning so I’ll hold onto your photo but no personal info."
The Promise of AI By Frank Chen, this presentation shares more about the promise of artificial intelligence, beyond the hype. It's a ~45-minute narrated walkthrough of what companies are doing with AI today and what’s bubbling up from the research community that’s just a few years out.
CS 20SI: Tensorflow for Deep Learning Research This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.
AI-powered trading raises new questions Around 1,360 hedge funds rely on computer models to trade stocks and other investments. High-Performance Models This document and accompanying scripts detail how to build highly scalable models that target a variety of system types and network topologies. The techniques in this document utilize some low-level TensorFlow Python primitives. In the future, many of these techniques will be incorporated into high-level APIs. News in AI and machine learning Reporting from 29th March 2017 through May 9th 2017 by Nathan Benaich
