A painting created using GANs (generative adversarial networks) sold for $432 000 at Christie’s today.

There is a thread on twitter raising complaints about the originality of the piece. The collective that produced the painting (Obvious) used modified versions of open source code, a lot of which were based on Robbie Barrat’s data scrapper and implementation of dcGAN. This work is itself based on a chain of research originating from Ian Goodfellow back in 2014.

The controversy is a nuanced issue and a very interesting one for me. On one hand, the work sold at auction is by no means…

Notes on the first Third Thursday Salon meeting of 2018. Topic of discussion: Brain Computer Interfaces. Co-organized with Ben Reinhardt.

See the Economist BCI summary: https://www.dropbox.com/sh/rndus3rewaq2yri/AADw2Bu9x52JAYUKO1ayeYNMa?dl=0



Assertion: We’re interface limited:

We don’t have enough resolution to do X

If X is manipulation:

You can do XY control with 50 electrodes <Source>

Biomimetic — go all the way to the brain


Utah Array

Brain has 85 Billion Neurons and 85 Billion Glia

Glia in the central nervous system are divided into four kinds:

  • Astrocytes (20–40%) — Gradient Following Badasses, star shaped, envelop synapses, wrap foreign bodies, are the bane of implant designers…

Differentiable Programming Framework for Deep Learning and Machine Intelligence.

Here’s is a recording of the talk I gave at the Age of AI conference in San Francisco along with transcript.

Rough transcript.

``Deep Learning est Morte! Vive Differentiable Programming”

I’ll be talking about Differentiable programming as a useful framework for deep learning and machine intelligence.

First a little about my background:

-I’m an investor with Amplify Partners.

-early venture firm that invests in technical founders solving technical problems.

-our portfolio centers on compute infrastructure and applied AI

some examples are:

  • Primer (texts summarization from diverse modalities)
  • DeterminedAI — which deploys deep learning models on prem.
  • Embodied Intelligence — applies Deep Reinforcement Learning for robotics in…

Amplify Partners leads $7 million initial funding for covariant.ai.

How can we build a robot that learns? Before answering this question, let’s start with a simpler one. How might a robot catch a baseball? Nowadays, the standard approach would model the problem with complex differential equations that account for factors like gravity, air resistance and so on. Successfully catching the ball depends on an incredibly precise plan executed in a deterministic environment, which is why a lot of tasks are too difficult to automate. But this is not how we, humans, catch a baseball. Humans, to our credit, figured out…

Lisha li

Venture capitalist focused on AI startups. PhD UC Berkeley math and deep learning. www.lishali.com

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