my words

All posts tagged my words

[This paper was prepared for the 2019 Towards Conscious AI Systems Symposium co-located with the Association for the Advancement of Artificial Intelligence 2019 Spring Symposium Series.

Much of this work derived from my final presentation at the 2017 SRI Technology and Consciousness Workshop Series: “Science, Ethics, Epistemology, and Society: Gains for All via New Kinds of Minds”.]

Abstract. This paper explores the moral, epistemological, and legal implications of multiple different definitions and formulations of human and nonhuman consciousness. Drawing upon research from race, gender, and disability studies, including the phenomenological basis for knowledge and claims to consciousness, I discuss the history of the struggles for personhood among different groups of humans, as well as nonhuman animals, and systems. In exploring the history of personhood struggles, we have a precedent for how engagements and recognition of conscious machines are likely to progress, and, more importantly, a roadmap of pitfalls to avoid. When dealing with questions of consciousness and personhood, we are ultimately dealing with questions of power and oppression as well as knowledge and ontological status—questions which require a situated and relational understanding of the stakeholders involved. To that end, I conclude with a call and outline for how to place nuance, relationality, and contextualization before and above the systematization of rules or tests, in determining or applying labels of consciousness.

Keywords: Consciousness, Machine Consciousness, Philosophy of Mind, Phenomenology, Bodyminds

[Overlapping images of an Octopus carrying a shell, a Mantis Shrimp on the sea floor, and a Pepper Robot]

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As you already know, we went to the second Juvet A.I. Retreat, back in September. If you want to hear several of us talk about what we got up to at the then you’re in luck because here are several conversations conducted by Ben Byford of the Machine Ethics Podcast.

I am deeply grateful to Ben Byford for asking me to sit down and talk about this with him. I talk a great deal, and am surprisingly able to (cogently?) get on almost all of my bullshit—technology and magic and the occult, nonhuman personhood, the sham of gender and race and other social constructions of expected lived categories, the invisible architecture of bias, neurodiversity, and philosophy of mind—in a rather short window of time.So that’s definitely something… Continue Reading

I talked with Hewlett Packard Enterprise’s Curt Hopkins, for their article “4 obstacles to ethical AI (and how to address them).” We spoke about the kinds of specific tools and techniques by which people who populate or manage artificial intelligence design teams can incorporate expertise from the humanities and social sciences. We also talked about compelling reasons why they should do this, other than the fact that they’re just, y’know, very good ideas.

From the Article:

To “bracket out” bias, Williams says, “I have to recognize how I create systems and code my understanding of the world.” That means making an effort early on to pay attention to the data entered. The more diverse the group, the less likely an AI system is to reinforce shared bias. Those issues go beyond gender and race; they also encompass what you studied, the economic group you come from, your religious background, all of your experiences.

That becomes another reason to diversify the technical staff, says Williams. This is not merely an ethical act. The business strategy may produce more profit because the end result may be a more effective AI. “The best system is the one that best reflects the wide range of lived experiences and knowledge in the world,” he says.

[Image of two blank, white, eyeless faces, partially overlapping each other.]

To be clear, this is an instance in which I tried to find capitalist reasons that would convince capitalist people to do the right thing. To that end, you should imagine that all of my sentences start with “Well if we’re going to continue to be stuck with global capitalism until we work to dismantle it…” Because they basically all did.

I get how folx might think that framing would be a bit of a buzzkill for a tech industry audience, but I do want to highlight and stress something: Many of the ethical problems we’re concerned with mitigating or ameliorating are direct products of the capitalist system in which we are making these choices and building these technologies.

All of that being said, I’m not the only person there with something interesting to say, and you should go check out the rest of my and other people’s comments.

Until Next Time.

Last week, I talked to The Atlantic’s Ed Yong about new research in crowd sentiment tipping points, how it could give hope and dread for those working for social change, and how it might be used by bad actors to create/enhance already-extant sentiment-manipulation factories.

From the article:

…“You see this clump of failures below 25 percent and this clump of successes above 25 percent,” Centola says. “Mathematically, we predicted that, but seeing it in a real population was phenomenal.”

“What I think is happening at the threshold is that there’s a pretty high probability that a noncommitted actor”—a person who can be swayed in any direction—“will encounter a majority of committed minority actors, and flip to join them,” says Pamela Oliver, a sociologist at the University of Wisconsin at Madison. “There is therefore a good probability that enough non-committed actors will all flip at the same time that the whole system will flip.”

We talked about a lot, and much of it didn’t make it into the article, but one of the things that matters most about all of this is that we’re going to have to be increasingly mindful and intentional about the information we take in. We now know that we have the ability to move the needle of conversation, with not too much effort, and with this knowledge we can make progressive social change. We can use this to fight against the despair that can so easily creep into this work of spreading compassion and trying to create a world where we can all flourish.

[Argentina’s Mt Tronador Casaño Overa glacier, by McKay Savage]

But we have to know that there will also be those who see this as a target number to hit so that hey might better disrupt and destabilize groups and beliefs. We already know that many such people are hard at work, trying to sow doubt and mistrust. We already have evidence that these actors will make other people’s lives unpleasant for the sake of it. With this new research, they’ll be encouraged, as well. As I said to Ed Yong:

“There are already a number of people out there who are gaming group dynamics in careful ways… If they know what target numbers they have to hit, it’s easy to see how they could take this information and create [or increase the output of the existing] sentiment-manipulation factory.”

The infiltration of progressive groups to move them toward chaos and internal strife is not news, just like the infiltration (and origin) of police and military groups by white supremacists is not news.

And so, while I don’t want to add to a world in which people feel like they have to continually mistrust each other, we do have to be intentional about the work we do, and how we do it, and we have to be mindful of who is trying to get us to believe what, and why they want us to believe it. Especially if we want to get others to believe as we do

This research gives us a useful set of tools and a good to place to start.

Until Next Time.

Late last month, I was at Theorizing the Web, in NYC, to moderate Panel B3, “Bot Phenomenology,” in which I was very grateful to moderate a panel of people I was very lucky to be able to bring together. Johnathan Flowers, Emma Stamm, and Robin Zebrowski were my interlocutors in a discussion about the potential nature of nonbiological phenomenology. Machine consciousness. What robots might feel.

I led them through with questions like “What do you take phenomenology to mean?” and “what do you think of the possibility of a machine having a phenomenology of its own?” We discussed different definitions of “language” and “communication” and “body,” and unfortunately didn’t have a conversation about how certain definitions of those terms mean that what would be considered language between cats would be a cat communicating via signalling to humans.

It was a really great conversation and the Live Stream video for this is here, and linked below (for now, but it may go away at some point, to be replaced by a static youtube link; when I know that that’s happened, I will update links and embeds, here).

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I have a review of Ashley Shew’s Animal Constructions and Technological Knowledge, over at the Social Epistemology Research and Reply Collective: “Deleting the Human Clause.”

From the essay:

Animal Constructions and Technological Knowledge is Ashley Shew’s debut monograph and in it she argues that we need to reassess and possibly even drastically change the way in which we think about and classify the categories of technology, tool use, and construction behavior. Drawing from the fields of anthropology, animal studies, and philosophy of technology and engineering, Shew demonstrates that there are several assumptions made by researchers in all of these fields—assumptions about intelligence, intentionality, creativity and the capacity for novel behavior…

Shew says that we consciously and unconsciously appended a “human clause” to all of our definitions of technology, tool use, and intelligence, and this clause’s presumption—that it doesn’t really “count” if humans aren’t the ones doing it—is precisely what has to change.

I am a huge fan of this book and of Shew’s work, in general. Click through to find out a little more about why.

Until Next Time.

A few weeks ago I had a conversation with David McRaney of the You Are Not So Smart podcast, for his episode on Machine Bias. As he says on the blog:

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.

[Direct Link to Mp3]

Above is the (heavily edited) audio of my final talk for the SRI Technology and Consciousness Workshop Series. The names and voices of other participants have been removed in accordance with the Chatham House Rule.

Below you’ll find the slide deck for my presentation, and below the cut you’ll find the Outline and my notes. For now, this will have to stand in for a transcript, but if you’ve been following the Technoccult Newsletter or the Patreon, then some of this will be strikingly familiar.

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[Direct link to Mp3]

My second talk for the SRI International Technology and Consciousness Workshop Series was 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. I don’t have a transcript, yet, and I’ll update it when I make one. But for now, here are my slides and some thoughts.

A Discussion on Daoism and Machine Consciousness (Slides as PDF)

(The translations of the Daoist texts referenced in the presentation are available online: The Burton Watson translation of the Chuang Tzu and the Robert G. Hendricks translation of the Tao Te Ching.)

A zero-sum system is one in which there are finite resources, but more than that, it is one in which what one side gains, another loses. So by “A non-zero-sum matrix of win conditions” I mean a combination of all of our needs and wants and resources in such a way that everyone wins. Basically, we’re talking here about trying to figure out how to program a machine consciousness that’s a master of wu-wei and limitless compassion, or metta.

The whole week was about phenomenology and religion and magic and AI and it helped me think through some problems, like how even the framing of exercises like asking Buddhist monks to talk about the Trolley Problem will miss so much that the results are meaningless. That is, the trolley problem cases tend to assume from the outset that someone on the tracks has to die, and so they don’t take into account that an entire other mode of reasoning about sacrifice and death and “acceptable losses” would have someone throw themselves under the wheels or jam their body into the gears to try to stop it before it got that far. Again: There are entire categories of nonwestern reasoning that don’t accept zero-sum thought as anything but lazy, and which search for ways by which everyone can win, so we’ll need to learn to program for contradiction not just as a tolerated state but as an underlying component. These systems assume infinitude and non-zero-sum matrices where every being involved can win.

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This summer I participated in SRI International’s Technology and Consciousness Workshop Series. The meetings were held under the auspices of the Chatham House Rule, which means that there are many things I can’t tell you about them, such as who else was there, or what they said in the context of the meetings; however I can tell you what I talked about. In light of this recent piece in The Boston Globe and the ongoing developments in the David Slater/PETA/Naruto case, I figured that now was a good time to do so.

I presented three times—once on interdisciplinary perspectives on minds and mindedness; then on Daoism and Machine Consciousness; and finally on a unifying view of my thoughts across all of the sessions. This is my outline and notes for the first of those talks.

I. Overview
In a 2013 aeon Article Michael Hanlon said he didn’t think we’d ever solve “The Hard Problem,” and there’s been some skepticism about it, elsewhere. I’ll just say that said question seems to completely miss a possibly central point. Something like consciousness is, and what it is is different for each thing that displays anything like what we think it might be. If we manage to generate at least one mind that is similar enough to what humans experience as “conscious” that we may communicate with it, what will we owe it and what would it be able to ask from us? How might our interactions be affected by the fact that its mind (or their minds) will be radically different from ours? What will it be able to know that we cannot, and what will we have to learn from it?

So I’m going to be talking today about intersectionality, embodiment, extended minds, epistemic valuation, phenomenological experience, and how all of these things come together to form the bases for our moral behavior and social interactions. To do that, I’m first going to need ask you some questions:

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