A Re-post with courtesy from Quantum Bayesian Networks: IBM and Google Caught off Guard by Rigetti Spaghetti — Quantum Bayesian 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 […] …

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Required share fom The Morning Paper: Snorkel: rapid training data creation with weak supervision — the morning paper

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 […] …

Import AI 106: Tencent breaks ImageNet training record with 1000+ GPUs; augmenting the Oxford RobotCar dataset; and PAI adds more members — Import AI

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 […] …

Latest DeepMind research on computer vision and scene rendering

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 …

Statistical and Machine Learning Models for Time Series Forecasting

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 …

Time Series Analysis in Python and R

Time series analysis is one of the most important toolkits for the Data Scientist. For the more experienced data analyst and /or scientist this is a no brainer obvious fact. But the volume, the complexity and the demands of other parts of this massively important field may obscure at times the best working memory - …

ReBlog from The Morning Paper: DeepTest: automated testing of deep-neural-network-driven autonomous cars — the morning paper

DeepTest: automated testing of deep-neural-network-driven autonomous cars Tian et al., ICSE’18 How do you test a DNN? We’ve seen plenty of examples of adversarial attacks in previous editions of The Morning Paper, but you couldn’t really say that generating adversarial images is enough to give you confidence in the overall behaviour of a model under […] …

Thoughts on Machine Learning and Artificial Intelligence

Thoughts about Machine Learning(ML) and Artificial Intelligence(AI): is it right to say that in order for the frameworks to generalize better about the data that is their input, they must somehow try to go beyond the need for strict dimensionality reduction algorithms they currently employ? My bet is yes, and specifically within the context of …

The Information is back: PyData Ann Arbor, Machine Learning, Crowdsourcing and Cartoons

  It is a new year: 2018. And The Intelligence of Information is back with some new posts. Hopefully with better quality and significance for all the interested audience: that past audience, the future and else. I want to improve the coverage of the topics already familiar with this blog: Data Science, Machine Learning and …

Public Blockchains in Germany (link to Digital Distributed Asset )

PUBLIC BLOCKCHAINS IN GERMANY Source: Public Blockchains in Germany