Brainy Breakdown: 5/26/2017

Brainy Breakdown: 5/26/2017

DeepMind’s AlphaGo achieves superhuman performance, becoming “the god of the Go game” A surprisingly reasonable discussion about the singularity. Note that they don’t use the word. Related: AI can be overhyped. On the positive side, Sebastian Thrun on the power of AI. Privacy concerns who? Google takes on AutoML to...
Paper Review: An Integration of Deep Learning and Neuroscience, Part 3

Paper Review: An Integration of Deep Learning and Neuroscience, Part 3

In our previous post we talked about how the brain might optimize cost functions. Now we’ll explore how cost functions may be generated, represented, and change over time in the brain. Marblestone et al outline several ways that cost functions could be generated.  In particular, they talk about specialized circuitry...
Paper Review: An Integration of Deep Learning and Neuroscience, Part 2

Paper Review: An Integration of Deep Learning and Neuroscience, Part 2

One of the key sticking points in discussions comparing machine learning and the brain is how the notion of “learning” differs between computational and biological systems. In section 2 of their paper, Marblestone et al. grapple with this issue in detail. For our introduction post on this paper, go here....
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Quick Hit: 100 Days of Divided Attention

Quick Hit: 100 Days of Divided Attention

The week before the 2016 election, I proposed my dissertation. I had ambitious goals and I was ready to put in the work to achieve them. A December 2017 graduation date was in my sights. But on November 9th, I entered into an unfamiliar brain state: constant, low-level panic. An itching feeling that I needed...
Quick Hit: Bad Advice from a Bad Mentor

Quick Hit: Bad Advice from a Bad Mentor

As anyone in a PhD program knows, your experience is highly dependent on your relationship with your advisor. We discussed in our earlier post, How to Choose a PhD Advisor, ways to hopefully make the “right” (best?) choice. While there are many advisors in the world, not all can be called mentors. The difference is...
Paper Review: An Integration of Deep Learning and Neuroscience, Part 1

Paper Review: An Integration of Deep Learning and Neuroscience, Part 1

Nicole: More and more I see that people are very concerned with the biological plausibility of neural networks. I think this comes from the fact that we as machine learners are finally achieving human-level performance on some tasks. It has renewed faith in the idea that the best way to “solve” intelligence is to copy...
Neural Nets 101

Neural Nets 101

A neural network is a computational model inspired by neurons, and the neuronal circuits observed in biological systems.  The history behind neural networks is long and storied and could be its own blog post (and of course it is already its own blog post), so we won’t get into that here.  Instead, let’s just cover...
Neurons 101

Neurons 101

Neurons are the building blocks of the brain. They take information they receive from upstream neurons and summarize it as a spike that is sent  to downstream neurons. (image courtesy of wikimedia commons) Let’s talk about the anatomy of neurons:   Nucleus: Like any cell in your body, each neuron has a nucleus, where its...
t-SNE and t-leaves

t-SNE and t-leaves

I stumbled upon a great blog post which discusses t-SNE.  If you’re not familiar with it, t-SNE is an algorithm that take high dimensional data (like fMRI images) and projects them down to a 2D space which is easier to view.  Of course, to make that sort of projection, some information from the high dimensional...