new update :

1) With the goal of getting as close to real time in detection as possible, I've found out about MinION dna sequencer device, which is said to be able to produce genomes in seconds from dna samples.
2) I'm going to try to purchase one myself, then later try to convince my country to get more once I develop a pipeline.
3) The process for quick inference/real time nCov detection could look like the following:
(a) Dna from person to be screened → (b) Genome data from MinION device → (c) Trained algorithm that has been built to distinguish between nCov -positive genome data, and healthy or rather nCov-negative genome data → (d) prediction-classes: nCov[~1] or no-nCov [~0]
Based on the available data/nCov genome information, this is the quickest solution I can think about implementing now.
The github page has been updated with the MinION details in the PLANNED steps section. MinION processing is expensive and complex with all the library preparation etc. What is needed is a gene sequencing and analysis device which operates with all the simplicity of a blood glucose meter used by diabetics, maybe with a smartphone application. That would be very nice. The same company who makes MinION, seems to be planning to make something for smart phones as well, called smidgion,
I did notice that the MinION costs $1000 usd. I can manage that for now. t looks like China may have been using MinIONs to get that genome sequence out so quickly:
https://nanoporetech.com/.../novel-coronavirus-ncov-2019

Too big to deploy: How GPT-2 is breaking production
https://towardsdatascience.com/too-big-to-deploy-how-gpt-2-is-breaking-production-63ab29f0897c

Bayesian Deep Learning



https://www.youtube.com/watch?v=MJBXjXmQtKk

An interpretable neural network model through piecewise linear approximation
Guo et al.: https://arxiv.org/abs/2001.07119
#MachineLearning #ArtificialIntelligence #NeuralNetworks

Fast Video Object Segmentation using the Global Context Module. https://arxiv.org/abs/2001.11243

Machine Learning in Neuroscience
https://www.frontiersin.org/research-topics/9012/machine-learning-in-neuroscience

Machine learning + neuroscience

https://www.youtube.com/watch?v=iPdKMs9cEAs

A review of machine learning for neuroscience:
https://www.mdpi.com/2076-3425/9/3/67/htm

Soul Machines
Soul Machines is an AGI research company re-imagining how humans connect and collaborate with machines. With the world's first autonomous animation engine, Soul Machines is bringing Digital Heroes to life to deliver a new era of customer experience.


https://www.youtube.com/channel/UCKjRfR3yKcKdP1VQ9rfnYsQ

If you're interested in mind uploading, then I have an excellent article to recommend. This wide-ranging article is focused on neuromorphic computing and has sections on memristors. Here is a key excerpt:

"...Perhaps the most exciting emerging AI hardware architectures are the analog crossbar approaches since they achieve parallelism, in-memory computing, and analog computing, as described previously. Among most of the AI hardware chips produced in roughly the last 15 years, an analog memristor crossbar-based chip is yet to hit the market, which we believe will be the next wave of technology to follow. Of course, incorporating all the primitives of neuromorphic computing will likely require hardware solutions even beyond analog memristor crossbars..."

Here's a web link to the research paper:

https://aip.scitation.org/doi/abs/10.1063/1.5129306%40are.2020.BIE2019.issue-1

2019 nCoV realtime track system based Scrapy + influxdb + grafana + NLTK + Stanford CoreNLP
https://github.com/hysios/coronavirus

Thinc – deep learning library with type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet.

https://thinc.ai/docs
https://github.com/explosion/thinc