Welcome to the weekly newsletter of Deep Learning AI and Blockchain Convergence. We hope that this newsletter will appeal to all those interested in Deep Learning developments and its relationship to decentralized consensus architectures.
Artificial Life is one of those long running research endeavors with no obvious purpose but with extremely intriguing results. The latest is Bert Chan’s exploration with continuous cellular automata…
I will make the case that the roadmap towards AGI will have to be done through the development of stepping stone capabilities. These capabilities should be inspired by human infant cognitive…
What is the biggest problem with deep learning networks? I propose that it is: “learning how to forget”. Deep learning networks are very good at remembering things, it is so good at this capability…
A new generation of smart machines, fuelled by rapid advances in artificial intelligence and robotics, could potentially replace a large proportion of existing human jobs, according to a new report.
Chris Dixon, arguably the most prominent venture capitalist focusing on blockchain, is friggin’ tall. Prior to sitting down with him inside the spa-like Silicon Valley offices of Andreessen Horowitz…
Automatic language translation is important to Facebook as a way to allow the billions of people who use our services to connect and communicate in their preferred language. To do this well, current machine translation (MT) systems require access to a considerable volume of translated text (e.g., pairs of the same text in both English and Spanish). As a result, MT currently works well only for the small subset of languages for which a volume of translations is readily available.
The seven tools of causal inference with reflections on machine learning Pearl, CACM 2018 With thanks to @osmandros for sending me a link to this paper on twitter. In this technical report Judea Pearl reflects on some of the limitations of machine learning systems that are based solely on statistical interpretation of data. To understand…
(Terence teaches in University of San Francisco’s MS in Data Science program and Prince is an alumnus. You might know Terence as the creator of the ANTLR parser generator.)
Note from Jeremy: I’ll be teaching Deep Learning for Coders at the University of San Francisco starting in October; if you’ve got at least a year of coding experience, you can apply here.
“Whatever you are studying right now if you are not getting up to speed on deep learning, neural networks, etc., you lose,” says Mark Cuban.
“We are going through the process where software will automate software, automation will automate automation.”
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