X-ray vision has long seemed like a far-fetched sci-fi fantasy, but over the last decade a team led by Professor Dina Katabi from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has continually gotten us closer to seeing through walls.
Their latest project, “RF-Pose,” uses artificial intelligence (AI) to teach wireless devices to sense people’s postures and movement, even from the other side of a wall. Read More
Daily Archives: March 17, 2019
Can Data Lakes Solve Machine Learning Workload Challenges?
Year after year, the field of ML is progressing at break-neck speed, and new algorithms and techniques are entering the space at a high frequency. Also, machine learning workloads are becoming increasingly more prevalent. However, there are significant challenges in democratizing machine learning and reliably scaling and deploying ML workloads. Read More
AI uses Wi-Fi data to estimate how many people are in a room
You can tell a lot about people from their Wi-Fi connections — including, as it turns out, how many of them are standing near an access point. In a newly published research paper (“DeepCount: Crowd Counting with WiFi via Deep Learning“) on the preprint server Arxiv.org, scientists describe an AI activity recognition model — DeepCount — that infers the population size of rooms from wireless data.
Their work comes not long after researchers at Ryerson University in Torontodemonstrated a neural network that can determine whether smartphone owners are walking, biking, or driving around a few city blocks by using Wi-Fi data, and after Purdue University researchers developed a system that uses Wi-Fi access logs to suss out relationships among users, locations, and activities. Read More
Deep learning using CNN : Learn to remember it visually
There is tremendous growth in people searching or showing interests about deep learning & AI in last few years. Every day hundreds of new articles get published on it in social media & press media. Above chart broadly explains as why search trend is ever growing for deep learning & AI. Read More
Trained neural nets perform much like humans on classic psychological tests
Neural networks were inspired by the human brain. Now AI researchers have shown that they perceive the world in similar ways. Read More
Understanding Generative Adversarial Networks (GANs)
Yann LeCun described it as “the most interesting idea in the last 10 years in Machine Learning”. Of course, such a compliment coming from such a prominent researcher in the deep learning area is always a great advertisement for the subject we are talking about! And, indeed, Generative Adversarial Networks (GANs for short) have had a huge success since they were introduced in 2014 by Ian J. Goodfellow and co-authors in the article Generative Adversarial Nets. Read More
A Beginner’s Guide to Generative Adversarial Networks (GANs)
Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”). GANs were introduced in a paper by Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio, in 2014. Referring to GANs, Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years in ML.” Read More
Generative Adversarial Nets
A new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. Read More
Style-based GANs – Generating and Tuning Realistic Artificial Faces
Generative Adversarial Networks (GAN) are a relatively new concept in Machine Learning, introduced for the first time in 2014. Their goal is to synthesize artificial samples, such as images, that are indistinguishable from authentic images. A common example of a GAN application is to generate artificial face images by learning from a dataset of celebrity faces. While GAN images became more realistic over time, one of their main challenges is controlling their output, i.e. changing specific features such pose, face shape and hair style in an image of a face. Read More
ThisPersonDoesNotExist.com uses AI to generate endless fake faces
The ability of AI to generate fake visuals is not yet mainstream knowledge, but a new website — ThisPersonDoesNotExist.com — offers a quick and persuasive education. Read More
Rick's Cafe AI 