The Google Policy Fellowship is now accepting applications in North America. 2015 will mark the eighth summer of this program, which places undergraduate and graduate students at key tech policy think tanks and NGOs. Applications are open now through March 12, 2015.
Please help recruit top talent to this program! Our blog post has details about the program and application process that you can share with friends, alumni networks, and anyone else who is interested. Note that while Google facilitates the program and sponsors the fellows, we do not participate in the selection process. Host organizations choose their fellows directly.
This program has been great way to grow the next generation of tech policy advocates and build relationships with them at an early stage. In fact, a large number of Google Policy Fellows have become full time policy staff at their host organizations. Program alumni also have gone on to work for regulatory agencies, in academia, and at start ups.
If you have friends who are interested in the program and have questions beyond what is covered on the site, please feel free to connect them directly to [email protected].
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Last summer, students from all over the US and Canada gathered to explore pressing questions at the intersection of technology and policy. Whether working on data security standards at the National Consumers League or innovation economy issues at the R Street Institute, students gained hands-on experience tackling critical technology policy questions.
2015 is just beginning, but these issues show no signs of slowing down. We’re excited to announce the 8th annual Google Policy Fellowship, which connects students interested in emerging technology policy issues with leading nonprofits, think tanks, and advocacy groups.
Applications are open today for North America, and students of all levels and disciplines are welcome to apply before Thursday, March 12th.
This year’s organizations include:
American Association of People with Disabilities
American Enterprise Institute
American Library Association
Center for Democracy and Technology
Center for Data Innovation
Electronic Frontier Foundation
Engine
Future of Music Coalition
Georgetown Center on Privacy & Technology
Global Network Initiative
Internet Education Foundation
Internet Keep Safe Coalition
Mercatus
National Consumers League
National Hispanic Media Coalition
Open Technology Institute, New America Foundation
Public Knowledge
R Street Institute
Samuelson-Glushko Canadian Internet Policy & Public Interest Clinic
TechFreedom
Technology Policy Institute
The Citizen Lab
US Hispanic Chamber of Commerce
More fellowship opportunities in Asia, Africa, and Europe will be coming soon. You can learn about the program,application processandhost organizations on the Google Public Policy Fellowship website.
Google really is just useless now, huh
I search for something on Google. The first 2 pages are sales links. The rest of the results are nonsense because Google decided to replace my keywords with “synonyms” or “similar terms” that are basically the opposite of what I was trying to search for. I contemplate setting my laptop on fire and pulling out the typewriter.
I had this experience just the other day it was ENRAGING
So, there’s a bunch of commands you can use for Google if it starts acting up. The best ones are -thing and “thing” If you notice dumb synonyms in your query get rid of it with Minus, and put quotes around the exact thing that NEEDS to be in the search results. You can also bypass this by using the Advanced search which does the exact same thing, but we’re busy people and we don’t have time for that shit.
I know about search syntax. I also remember a time when I was able to find useful information without spending an extra 5 minutes peppering my query with it literally every time I want to use the service.
Yeah. I’m legitimately very good at research and in the last five years Google has gotten steadily worse with a big drop about two years ago, and if you’re trying to find older information (say you want data on how masking impacted flu rates in Japan in 2009) you are basically screwed because its novelty weighting had gotten so much stronger.
Not to mention that a lot of search syntax does not work anymore. I can type in the same string of words in quotations on Google and on Bing; Bing will get me what I actually want, Google will claim it doesn’t exist. Bing is better than Google at finding the stuff I want.
I’m Not Afraid of AI Overlords— I’m Afraid of Whoever’s Training Them To Think That Way
by Damien P. Williams
I want to let you in on a secret: According to Silicon Valley’s AI’s, I’m not human.
Well, maybe they think I’m human, but they don’t think I’m me. Or, if they think I’m me and that I’m human, they think I don’t deserve expensive medical care. Or that I pose a higher risk of criminal recidivism. Or that my fidgeting behaviours or culturally-perpetuated shame about my living situation or my race mean I’m more likely to be cheating on a test. Or that I want to see morally repugnant posts that my friends have commented on to call morally repugnant. Or that I shouldn’t be given a home loan or a job interview or the benefits I need to stay alive.
Now, to be clear, “AI” is a misnomer, for several reasons, but we don’t have time, here, to really dig into all the thorny discussion of values and beliefs about what it means to think, or to be a mind— especially because we need to take our time talking about why values and beliefs matter to conversations about “AI,” at all. So instead of “AI,” let’s talk specifically about algorithms, and machine learning.
Machine Learning (ML) is the name for a set of techniques for systematically reinforcing patterns, expectations, and desired outcomes in various computer systems. These techniques allow those systems to make sought after predictions based on the datasets they’re trained on. ML systems learn the patterns in these datasets and then extrapolate them to model a range of statistical likelihoods of future outcomes.
Algorithms are sets of instructions which, when run, perform functions such as searching, matching, sorting, and feeding the outputs of any of those processes back in on themselves, so that a system can learn from and refine itself. This feedback loop is what allows algorithmic machine learning systems to provide carefully curated search responses or newsfeed arrangements or facial recognition results to consumers like me and you and your friends and family and the police and the military. And while there are many different types of algorithms which can be used for the above purposes, they all remain sets of encoded instructions to perform a function.
And so, in these systems’ defense, it’s no surprise that they think the way they do: That’s exactly how we’ve told them to think.
[Image of Michael Emerson as Harold Finch, in season 2, episode 1 of the show Person of Interest, “The Contingency.” His face is framed by a box of dashed yellow lines, the words “Admin” to the top right, and “Day 1” in the lower right corner.]
Read the rest of I’m Not Afraid of AI Overlords— I’m Afraid of Whoever’s Training Them To Think That WayatA Future Worth Thinking About
But I was at a wedding, so I had to pretend I’m fine, contorting my face to look something like this:
Another time, I got one of those “being let go” texts from a guy on my birthday. It had been days since our last date and as (lack of) luck would have it, he texted me while i was out with friends for my birthday celebration.
AND, once I got dumped by a bf right before he was supposed to meet my entire family for dinner…he didn’t make the dinner but everyone else did…
So yeah. Timing has not been an ally in my dating experiences.
My solar system necklace being worn by Megan Ansdell in this Google cloud video!
this leaves out the most crucial tip you’ll ever need:
-site:pinterest.*
excludes the entirety of pinterest’s evil domainverse from image search
Reblogging for the Pinterest addition