Why do we reify? When are you able to read others' minds? Is this a pedestrian? Are sensation and perception tied together? How are new mental templates formed? Are priors 'necessary' for categorization? How do we know, in a badly lit place, if something is a wire or a snake? Are mental images ‘sharp’? Should stochastic processes (also) be understood via their higher-order moments? How are interiors of opaque objects (metal spheres to stars) pictured? Do you internally ‘model’ various parts of your own body? Does our mechanical body ‘produce’ words? Do virtual worlds also need a degree of veridicality? Do verbal and visual systems interact during encoding? How were gravitational waves detected? Is reification language dependent? Does brute force of computation help? When does (sub-symbolic) chairness become a (symbolic) chair? Is the notion of an ‘object’ generalizable across domains/languages? Can stars’ magnetic fields be plotted as precisely as the HR diagram? ‘Are sun-like stars like the Sun’? Do we need the body to conceptualize? Will computer systems need bodies? Are eyes like cameras? How do shrooms work? &, will ’everything’ tie up? SynTalk thinks about these & more questions using concepts from astrophysics & machine learning (Dr. Shravan Hanasoge, TIFR,  Mumbai), computational linguistics (Dr. Inderjeet Mani, Hua Hin) & cognitive psychology (Dr. Varsha Singh, IIT Delhi). Listen in...

#TROO (The Reification Of Objects) --- SynTalk

There is no known learning algorithm that is more flexible and powerful than the human brain. That's quite inspirational, if you think about it--to level up machine learning, maybe we should be going back to biology and letting millions of year of evolution guide the structure of our algorithms. 

This is the idea behind neural nets, which mock up the structure of the brain and are some of the most studied and powerful algorithms out there. In this episode, we’ll lay out the building blocks of the neural net (called neurons, naturally) and the networks that are built out of them. 

We’ll also explore the results that neural nets get when used to do object recognition in photographs.

Neural Nets (Part 1)

In the last episode, we zipped through neural nets and got a quick idea of how they work and why they can be so powerful. Here’s the real payoff of that work:

In this episode, we’ll talk about a brand-new pair of results, one from Stanford and one from Google, that use neural nets to perform automated picture captioning. One neural net does the object and relationship recognition of the image, a second neural net handles the natural language processing required to express that in an English sentence, and when you put them together you get an automated captioning tool. Two heads are better than one indeed...

Neural Nets (Part 2)

In Episode 1.4, we conclude our look at robotics, and we challenge commonly held fears regarding machine intelligence. The “angry god” AI of The Terminator is an over-hyped myth. Here’s why.

[Archive: UTOPIA #4]

Interview with Eric Colson

Meet Eric Colson, Chief Algorithms Officer at Stitch Fix
Eric Colson leads the Algorithms and Data Platform teams at Stitch Fix. Eric is responsible for the multitude of algorithms deployed in nearly every function of the company: merchandise, inventory, marketing, forecasting & demand, operations and, of course, the styling platform. Prior to joining Stitch Fix, he was the Vice President of Data Science & Engineering at Netflix. He’s also held analytical roles at Yahoo!, Blue Martini, Proxicom and Information Resources Inc. Eric holds a B.A. in Economics (SFSU), an M.S. in Information Systems (GGU), and an M.S. in Management Science & Engineering (Stanford).

Episode 5 - Eric Colson

first track of 7 i'm releasing one by one

i work on music better in serial-format i think
and even if i don't, it's worth a shot to try it out at this time

samples "Cock Dumb" by Neural Nets
patreon.com/minderaser
neuralnetsandprettypatterns.tumblr.com

next track will be out whenever it's done

xoxo

[lyrics]

heart’s misplaced in the right place lately
am i really this nice or appliantly lonely,
too outpatient, inconsequential,
favorite pains and past-times past their thresholds
nostalgia, stress sweat, big crybaby
in a class full of NASA employees
TNO crash-lands right into the MIR 
fuck i bit your stem, pipe in my legbeard

down six foot faith
that last spring break
where promisers get made
they get made and they rally

[i've been born again, my hole life]

kept a hold and still
i’m moving couches in my mind
lava-lamping eyes doing
what they were assigned to

well, somebody once told me
the world was guy fieri and
i probably made him up in my head
he was looking kinda fun
put a flame shirt on the sun
and it kept us all warm before we ded

my heart’s misplaced in the right place lately
am i really this nice or appliantly lonely
like i’m laughing, bubbly, but i’m still baring teeth
out those child-bearing gums i tease in my dreams

nativity scenes a family stage
un-woke sleeper agent
inside my braid whoops 

tears start coming
won’t stop coming
fed two wolves
kept car running

talk around the space
in hopes that we’ll move closer to
closing door, open window
or something to jump out

Kept

In the second season of Anatomy of Next, explore every aspect of going to Mars, transforming it into a habitable world, and building a new branch of human civilization. How do we bring a cold, dead planet back to life? Can we build an atmosphere on Mars, thaw the frozen plains, and build an ocean? How do we seed a barren land with life, and make a red Mars green? Then, it’s everything from politics and education to money, music, and architecture. What does it mean to be human on an alien world?

working on new stuff

will be out in spurts and shorts @ some point

f(q) = Is soil a definitive sign of life? (#TCAFG)

SynTalk is a freewheeling interdisciplinary talk show with a philosophical approach to understanding the world from a long term perspective.

Popular tracks tagged #neural nets