Their novel framework achieves state-of-the-art performance without sacrificing efficiency in public surveillance tasks
Implementing algorithms that can simultaneously track multiple objects is essential to unlock many applications, from autonomous driving to advanced public surveillance. However, it is difficult for computers to discriminate between detected objects based on their appearance. Now, researchers at the Gwangju Institute of Science and Technology (GIST) have adapted deep learning techniques in a multi-object tracking framework, overcoming short-term occlusion and achieving remarkable performance without sacrificing computational speed. Read More
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Tag Archives: Deep Learning
The Modern Mathematics of Deep Learning
We describe the new field of mathematical analysis of deep learning. This field emerged around a list
of research questions that were not answered within the classical framework of learning theory. These
questions concern: the outstanding generalization power of overparametrized neural networks, the role of
depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful
optimization performance despite the non-convexity of the problem, understanding what features are
learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects
of an architecture affect the behavior of a learning task in which way. We present an overview of modern
approaches that yield partial answers to these questions. For selected approaches, we describe the main
ideas in more detail. Read More
Using AI to Track How Customers Feel — In Real Time
The most common methods of tracking customer sentiments has a big blind spot: They can’t pick up on important emotional responses. As a result, qualitative surveys, like Net Promoter Score, end up missing critically important feedback. Even if they provide a positive score, customers often reveal their true thoughts and feelings in the open-ended comment boxes typically provided at the end of surveys, and AI can help companies make use of this valuable data to better predict customer behavior. Specifically, there are six benefits for adopting AI to analyze this feedback: It can 1) show you what you’re missing in your qualitative surveys, 2) help train your employees based on what’s actually important to customers, 3) determine root causes of problems, 4) capture customers’ responses in real time, 5) spot and prevent declines in sales, and 6) prioritize actions to improve customer experience. Read More
MIT Introduction to Deep Learning | 6.S191
How to learn deep learning by reading papers
Create a system in order to be up to date with deep learning research
Deep learning is moving so fast, that the only way to keep up is by reading directly from the people who publish these new findings. If you’re a technical person and want to learn about deep learning in 2021, you need to read papers. Read More
Modern Recommender Systems
A Deep Dive into the AI algorithms that companies like Facebook and Google have built their business around.
As recently as May 2019 Facebook open-sourced some of their recommendation approaches and introduced the DLRM (Deep-learning Recommendation Model). This blog post is meant to explain how and why DLRM and other modern recommendation approaches work so well. Read More
The past, present and future of deep learning
TLDR; In this blog, you’ll be learning the theoretical aspects of deep learning (DL) and how it has evolved, right from the study of the human brain to building complex algorithms. Next, you’ll be looking at a few pieces of research that have been carried by renowned deep learning folks who have then sown the sapling in the fields of DL which has now grown into a gigantic tree. Lastly, you’ll be introduced to the applications and the areas where deep learning has set a strong foothold. Read More
4 Intersecting Domains That You Can Easily Confuse with Artificial Intelligence
Once you start consuming machine learning content such as books, articles, video courses, and blog posts, you will often see the terms like artificial intelligence, machine learning, deep learning, big data, and data science being used interchangeably. These terms represent several closely related areas within the field of artificial intelligence. They are usually used interchangeably without adequate attention paid to their scopes. It’s not entirely the authors’ fault since there is a slight ambiguity about these terms’ differences. With this post, we will put an end to this ambiguity and clarify their scopes, covering: Artificial Intelligence, Machine Learning. Deep Learning, Data Science, and Big Data. Read More
Noam Chomsky on the Future of Deep Learning
For the past few weeks, I’ve been engaged in an email exchange with my favourite anarcho-syndicalist Noam Chomsky. I reached out to him initially to ask whether recent developments in ANNs (artificial neural networks) had caused him to reconsider his famous linguistic theory Universal Grammar. Our conversation touched on the possible limitations of Deep Learning, how well ANNs really model biological brains and also meandered into more philosophical territory. I’m not going to quote Professor Chomsky directly in this article as our discussion was informal but I will attempt to summarise the key take-aways. Read More
AI pioneer Geoff Hinton: “Deep learning is going to be able to do everything”
The modern AI revolution began during an obscure research contest. It was 2012, the third year of the annual ImageNet competition, which challenged teams to build computer vision systems that would recognize 1,000 objects, from animals to landscapes to people.
In the first two years, the best teams had failed to reach even 75% accuracy. But in the third, a band of three researchers—a professor and his students—suddenly blew past this ceiling. They won the competition by a staggering 10.8 percentage points. That professor was Geoffrey Hinton, and the technique they used was called deep learning. Read More
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