Ben Sparks explains (and codes) the so-called SIR Model being used to predict the spread of cornavirus (COVID-19) in this Numberphile video.
Up and Atom talks about predicting and modeling the spread of epidemics like the coronavirus (COVID-19) outbreak we are experiencing now.
Eddie Woo explains survivor bias in this short video.
In honor or Pi Day, here is Calculus Rhapsody.
For Mathematics, trees are more useful than strings.
Professor Thorsten Altenkirch takes us through a functional approach to coding them in Python.
On Friday, someone asked me about linear regression with neural networks.
I didn’t have a good answer – I knew that you *could* do linear regression but neural networks, but never had actually done it in practice.
Promising to learn more, I came across this video by giant_neural_network on YouTube.
Why is it that we can see these multiple histories play out on the quantum scale, and why do lose sight of them on our macroscopic scale?
Many physicists believe that the answer lies in a process known as quantum decoherence.
Does conscious observation of a quantum system cause the wavefunction to collapse? The upshot is that more and more physicists think that consciousness – and even measurement – doesn’t directly cause wavefunction collapse.
In fact probably there IS no clear Heisenberg cut. The collapse itself may be an illusion, and the alternate histories that the wavefunction represents may continue forever. The question then becomes: why is it that we can see these multiple histories play out on the quantum scale, and why do lose sight of them on our macroscopic scale? Many physicists believe that the answer lies in a process known as quantum decoherence.
Quantum computing, a subject as confusing as it is intriguing.
In this fascinating and entertaining talk, Scott Aaronson elucidates the potential and the limits of quantum computing.
In a sober fashion, he gives an overview of the state of research, telling us not only what we could expect from quantum computers in the future, but also what we probably shouldn’t.
Scott Aaronson is the David J. Bruton Centennial Professor of Computer Science at The University of Texas at Austin, USA, and director of its Quantum Information Center. He is well-known for his “complexity zoo,” which helps to classify problems that can be solved by computers, both quantum and classical, according to how hard it is to solve them.
Scott is an accomplished academic researcher who published dozens of influential papers and won various notable awards, like the Alan T. Waterman Award in 2012. Before his current position at UT Austin, he taught at the Massachusetts Institute of Technology for nine years. In 2004, he received his Ph.D. from the University of California at Berkeley and held positions at the University of Waterloo and the Institute for Advanced Study in Princeton.
It’s not surprising that the profound weirdness of the quantum world has inspired some outlandish explanations – nor that these have strayed into the realm of what we might call mysticism.
One particularly pervasive notion is the idea that consciousness can directly influence quantum systems – and so influence reality.
PBS Space Time examines where this idea comes from, and whether quantum theory really supports it.
Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it’s important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I’ll explain the work of these researchers, we’ll write some code, do some math, do some visualizations, and by the end I’ll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!
Likelihood Ratios for Out-of-Distribution Detection paper: https://arxiv.org/pdf/1906.02845.pdf
The researcher’s code: https://github.com/google-research/google-research/tree/master/genomics_ood