This last summer I started joyfully to watch and apprehend as much as possible about the lectures on Deep Reinforcement Learning delivered by Dr. Sergey Levine at the University of Berkeley California. The list of lectures is available at YouTube, but the from the fall of last year 2017. The lectures usually feature some … Continue reading Deep Reinforcement Learning class at Berkeley by Sergey Levine – Lecture 16 Bootstrap DQN and Transfer Learning
The Massachusetts Institute of Technology (MIT) has been given a series of lectures titled MIT 6.S099: Artificial General Intelligence. It is part of the syllabus of a course on Artificial General Intelligence and Deep Learning delivered by Lex Fridman. It featured a series of conversations with some prominent researchers in the fields of machine learning, … Continue reading A conversation on AI from MIT Artificial General Intelligence Lectures
The two fields of Machine Learning and Quantum Computing are the most important ones for today's computer science in general. A new field of study is actually emerging with the appropriate name of Quantum Machine Learning. The important sub-field of Reinforcement Learning is also being used by researchers in Quantum Computing and today's paper … Continue reading Papers with Code series: Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
Two Minute Papers is a YouTube and Patreon channel, a website, a good repository of some of the latest developments in artificial intelligence and machine/deep learning. It is hosted by a researcher in the field, and given his background most of the content is about computer vision, computer graphics and the applications of these … Continue reading Brain-to-Brain online communication: a reality soon…?
Hello. I am starting today a new series of posts here in The Intelligence of Information. I know there is this hiatus of several months without posting here in this blog. I may have said the reasons for this, so I will skip ahead. Just to remind: this still is a work in progress blog, … Continue reading Papers with Code Series: Self-Attention Generative Adversarial Networks
Recently, Rigetti, the quantum computer company located in Berkeley, CA, made some bold promises that probably caught IBM and Google off guard, as in the following gif Last month (on Aug 8), Rigetti promised a 128 qubit gate model chip “over the next 12 months”. [comment: Quite ambitious. It may turn out that Rigetti cannot […] … Continue reading A Re-post with courtesy from Quantum Bayesian Networks: IBM and Google Caught off Guard by Rigetti Spaghetti — Quantum Bayesian Networks
Snorkel: rapid training data creation with weak supervision Ratner et al., VLDB’18 Earlier this week we looked at Sparser, which comes from the Stanford Dawn project, “a five-year research project to democratize AI by making it dramatically easier to build AI-powered applications.” Today’s paper choice, Snorkel, is from the same stable. It tackles one of […] … Continue reading Required share fom The Morning Paper: Snorkel: rapid training data creation with weak supervision — the morning paper
What takes 2048 GPUs, takes 4 minutes to train, and can identify a seatbelt with 75% accuracy? Tencent’s new deep learning model: …Ultrafast training thanks to LARS, massive batch sizes, and a field of GPUS… As supervised learning techniques become more economically valuable, researchers are trying to reduce the time it takes to train deep […] … Continue reading Import AI 106: Tencent breaks ImageNet training record with 1000+ GPUs; augmenting the Oxford RobotCar dataset; and PAI adds more members — Import AI
The latest DeepMind research paper on computer vision [1] and neural scene rendering appears to be ground breaking and a milestone for the field of computer vision. For anyone already acquainted with the application of deep neural networks for computer vision will know, the training process of those networks requires the input features of an … Continue reading Latest DeepMind research on computer vision and scene rendering
Following a recent post in this blog about Time Series Analysis, today I return to the same topic. But now from a statical learning and machine learning perspectives. The video is also from PyData, the presenter is the same - Dr. Jeffrey Yau - but this time it was in New York City and it is … Continue reading Statistical and Machine Learning Models for Time Series Forecasting
