everything you needed to know about growth hacking
Every business is different, but the top brands use one of three organizational models to build a data-driven marketing strategy.
how you can get CNN and Wikipedia back links for as little as three dollars
everything you needed to know about growth hacking
There is an increased importance on CLV metrics since 80% of all app users churn within three months. Learn more about implementing CLV into your app marketing strategy.
the complete guide to growth hacking!
Looking back at all the video marketing case studies we published in 2017 provides an interesting glimpse into how brands are evolving their video strategies.
everything you needed to know about growth hacking
Adjusting marketing efforts to account for consumer attention span is easier said than done. These 3 myths hold marketers back from making a shift.
how you can get CNN and Wikipedia back links for as little as three dollars
the complete guide to growth hacking!
We’re watching 2017 last minute Christmas shopping trends closely to help inform your holiday marketing strategy. Here’s what we have found so far.
everything you needed to know about growth hacking
With any data-driven marketing analysis, you don’t just need the right technology, you also need to the right leadership.
how you can get CNN and Wikipedia back links for as little as three dollars
A few weeks ago I had a conversation with
Now that algorithms are everywhere, helping us to both run and make sense of the world, a strange question has emerged among artificial intelligence researchers: When is it ok to predict the future based on the past? When is it ok to be biased?
“I want a machine-learning algorithm to learn what tumors looked like in the past, and I want it to become biased toward selecting those kind of tumors in the future,” explains philosopher Shannon Vallor at Santa Clara University. “But I don’t want a machine-learning algorithm to learn what successful engineers and doctors looked like in the past and then become biased toward selecting those kinds of people when sorting and ranking resumes.”
We talk about this, sentencing algorithms, the notion of how to raise and teach our digital offspring, and more. You can listen to all it here. [Direct Link to the Mp3 Here]
If and when it gets a transcript, I will update this post with a link to that.
Until Next Time.
by Damien
Over at AFutureWorthThinkingAbout, there is the audio and text for a talk for the about how nonwestern philosophies like Buddhism, Hinduism, and Daoism can help mitigate various kinds of bias in machine minds and increase compassion by allowing programmers and designers to think from within a non-zero-sum matrix of win conditions for all living beings, meaning engaging multiple tokens and types of minds, outside of the assumed human “default” of straight, white, cis, ablebodied, neurotypical male:
My starting positions, here, are that, 1) in order to do the work correctly, we literally must refrain from resting in abstraction, where, by definition, the kinds of models that don’t seek to actually engage with the people in question from within their own contexts, before deciding to do something “for someone’s own good,” represent egregious failure states. That is, we have to try to understand each other well enough to perform mutually modeled interfaces of what you’d have done unto you and what they’d have you do unto them.” I know it doesn’t have the same snap as “do unto others,” but it’s the only way we’ll make it through.
[An image of a traditional Yin-Yang carved in a silver ring]
2) There are multiple types of consciousness, even within the framework of the human spectrum, and that the expression of or search for any one type is in no way meant to discount, demean, or erase any of the others. In fact, it is the case that we will need to seek to recognize and learn to communicate with as many types of consciousness as may exist, in order to survive and thrive in any meaningful way. Again, not doing so represents an egregious failure condition. With that in mind, I use “machine consciousness” to mean a machine with the capability of modelling a sense of interiority and selfness similar enough to what we know of biological consciousnesses to communicate it with us, not just a generalized computational functionalist representation, as in “AGI.”
For the sake of this, as I’ve related elsewhere, I (perhaps somewhat paradoxically) think the term “artificial intelligence” is problematic. Anything that does the things we want machine minds to do is genuinely intelligent, not “artificially” so, where we use “artificial” to mean “fake,” or “contrived.” To be clear, I’m specifically problematizing the “natural/technological” divide that gives us “art vs artifice,” for reasons previously outlined here.
The overarching project of training a machine learning program and eventual AI will require engagement with religious texts (a very preliminary take on this has been taken up by Rose Eveleth at the Flash Forward Podcast), but also a boarder engagement with discernment and decision-making. Even beginning to program or code for this will require us to think very differently about the project than has thus far been in evidence.
Read or listen to the rest of A Discussion on Daoism and Machine Consciousness at A Future Worth Thinking About
