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Towards an Artificial Brain

The goal of MICrONS is three­fold. One is they asked us to go and mea­sure the activ­i­ty in a liv­ing brain while an ani­mal actu­al­ly learns to do some­thing, and watch how that activ­i­ty changes. Two, to take that brain out and map exhaus­tive­ly the wiring dia­gram” of every neu­ron con­nect­ing to every oth­er neu­ron in that ani­mal’s brain in the par­tic­u­lar region. And then third, to use those two pieces of infor­ma­tion to build bet­ter machine learn­ing. So let it nev­er be said that IARPA is unambitious.

(Data) Trust is the New Oil
Redesigning the data economy to optimize for trust

The pow­er of data has nev­er been big­ger than it is today and I think this can be a great thing, even though it is also cre­at­ing some exis­ten­tial risks.

Humans as Software Extensions

What is this con­di­tion? I would sum­ma­rize it as peo­ple extend­ing com­pu­ta­tion­al sys­tems by offer­ing their bod­ies, their sens­es, and their cog­ni­tion. And specif­i­cal­ly, bod­ies and minds that can be eas­i­ly plugged in and lat­er eas­i­ly be dis­card­ed. So bod­ies and minds algo­rith­mi­cal­ly man­aged and under the per­ma­nent pres­sure of con­stant avail­abil­i­ty, effi­cien­cy, and per­pet­u­al self-optimization. 

How to Survive the 21st Century

Of all the dif­fer­ent issues we face, three prob­lems pose exis­ten­tial chal­lenges to our species. These three exis­ten­tial chal­lenges are nuclear war, eco­log­i­cal col­lapse, and tech­no­log­i­cal dis­rup­tion. We should focus on them.

Surveillance State of the Union

We want­ed to look at how sur­veil­lance, how these algo­rith­mic deci­sion­mak­ing sys­tems and sur­veil­lance sys­tems feed into this kind of tar­get­ing deci­sion­mak­ing. And in par­tic­u­lar what we’re going to talk about today is the role of the AI research com­mu­ni­ty. How that research ends up in the real world being used with real-world consequences.

Kaleidoscope: Positionality-aware Machine Learning

Positionality is the spe­cif­ic posi­tion or per­spec­tive that an indi­vid­ual takes giv­en their past expe­ri­ences, their knowl­edge; their world­view is shaped by posi­tion­al­i­ty. It’s a unique but par­tial view of the world. And when we’re design­ing machines we’re embed­ding posi­tion­al­i­ty into those machines with all of the choic­es we’re mak­ing about what counts and what does­n’t count. 

AI Blindspot

AI Blindspot is a dis­cov­ery process for spot­ting uncon­scious bias­es and struc­tur­al inequal­i­ties in AI systems.

Data & Society Databite #119: Mary L. Gray on Ghost Work

I’m just going to say it, I would like to com­plete­ly blow up employ­ment clas­si­fi­ca­tion as we know it. I do not think that defin­ing full-time work as the place where you get ben­e­fits, and part-time work as the place where you have to fight to get a full-time job, is an appro­pri­ate way of address­ing this labor market.

What Sci-Fi Futures Can (and Can’t) Teach Us About AI Policy, open­ing and clos­ing comments

AI Policy Futures is a research effort to explore the rela­tion­ship between sci­ence fic­tion around AI and the social imag­i­nar­ies of AI. What those social mea­sures can teach us about real tech­nol­o­gy pol­i­cy today. We seem to tell the same few sto­ries about AI, and they’re not very helpful.

Bridging AI Fact and Fiction

This is going to be a con­ver­sa­tion about sci­ence fic­tion not just as a cul­tur­al phe­nom­e­non, or a body of work of dif­fer­ent kinds, but also as a kind of method or a tool.

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