The Truth About Deep Learning – Quantified

Come on people — let’s get our shit together about deep learning. I’ve been studying and writing about DL for close to two years now, and it still amazes the misinformation surrounding this relatively

This post is not about how Deep Learning is or is not over-hyped, as that is a welldocumented debate. Rather, it’s a jumping off point for a (hopefully) fresh, concise understanding of deep learning and its implications. This discussion/rant is somewhat off the cuff, but the whole point was to encourage those of us in the machine learning community to think clearly about deep learning. Let’s be bold and try to make some claims based on actual science about whether or not this technology will or will not produce artificial intelligence. After all, aren’t we supposed to be the leaders in this field and the few that understand its intricacies and implications? With all of the news on artificial intelligence breakthroughs and non-industry commentators making rash conclusions about how deep learning will change the world, don’t we owe it to the world to at least have our shit together? It feels like most of us are just sitting around waiting for others to figure that out for us.

Source: The Truth About Deep Learning – Quantified

 

A reference dataset of 5.4 million human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree | bioRxiv

Improvement of variant calling in next-generation sequence data requires a comprehensive, genome-wide catalogue of high-confidence variants called in a set of genomes for use as a benchmark. We generated deep, whole-genome sequence data of seventeen individuals in a three-generation pedigree and called variants in each genome using a range of currently available algorithms. We used haplotype transmission information to create a “platinum” variant catalogue of 4.7 million single nucleotide variants (SNVs) plus 0.7 million small (1-50bp) insertions and deletions (indels) that are consistent with the pattern of inheritance in the parents and eleven children of this pedigree. Platinum genotypes are highly concordant with the current catalogue of the National Institute of Standards and Technology for both SNVs (>99.99%) and indels (99.92%), and add a validated truth catalogue that has 26% more SNVs and 45% more indels. Analysis of 334,652 SNVs that were consistent between informatics pipelines yet inconsistent with haplotype transmission (“non-platinum”) revealed that the majority of these variants are de novo and cell-line mutations or reside within previously unidentified duplications and deletions. The reference materials from this study are a resource for objective assessment of the accuracy of variant calls throughout genomes.

Source: A reference dataset of 5.4 million human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree | bioRxiv