What if we've let the sociopaths win -- by convincing us that everyone is like them?
That everyone is out to get what they need and want, at the expense of others
That relationships are about what do I get from you, what do you get from me, and is this mutually beneficial and worth putting up with each other
What a load of crap.
What if we seek relationships because we have love to give?
We each have love to give. It is beautiful to find the opportunity to give it to someone.
I've been in relationships where giving love to my partner, day after day, was exhausting. That is not healthy.
All my relationships now, I find joy and energy from giving love. Like the way it feels to hold a baby, or pet an eager dog.
Sometimes a friend has sadness, and then I put energy into comforting her. The arc of the relationship is joy from giving love, on both sides.
What if loving and accepting other people is our need? Can we stop pretending we're innately selfish, stop fleeing pain, and feel the love we need to give each other?
Friday, February 09, 2018
Saturday, January 13, 2018
Personhood
"My present self is so different from the child I once was --
are these even the same person?"
I say, your Personhood is in the change.
As a person you exist in time (and space).
Present self, child self, these are Projections. Slices.
In the wiggle through time is our real existence.
My outside is my interactions (and body).
I reflect words, and touch (and light).
And I radiate!
I emit words
and art
and programs into the world.
Absorb and reflect these
as we wiggle onward together
in at least four dimensions.
are these even the same person?"
I say, your Personhood is in the change.
As a person you exist in time (and space).
Present self, child self, these are Projections. Slices.
In the wiggle through time is our real existence.
My outside is my interactions (and body).
I reflect words, and touch (and light).
And I radiate!
I emit words
and art
and programs into the world.
Absorb and reflect these
as we wiggle onward together
in at least four dimensions.
Sunday, November 20, 2016
to know is to love?
Is there a word for acceptance without approval? embracing without preserving? intimacy without attachment?
To change a software system I need to become part of it, I need to understand how the bits fit together. For example: When we spin up a new AWS instance, we use code in one repository, which activates a system controlled by another repository, which includes a script that shells into another machine where it makes an HTTP call to localhost to a service in a third repository, which digs around in its history and links to artifacts created by code in a fourth repository to package up a fifth repository. Do I approve of this flow? It doesn't matter. I need to accept its existence, without accepting its inevitability. I need to love it enough to understand it, and only then can I confidently change it.
In social systems too, I am part of the system already. I need to understand its workings; I am part of them. It is from here I can exert force for changing them. Resentment, denial, judgement: these hurt me, and they change nothing. I will face my fear, and my pain, and let it pass through me. I will be not surprised, I will be not approving, I will not expect that because it is this way now, that it must always be. I am part of the system but I do not help it self-preserve, in ways that I am able to see because I no longer punish myself with resentment and terror for being able to see them.
Participation is not endorsement.
To understand a complex system, whether society of software, means immersing myself in it, while retaining perspective. It means observing each interaction without surprise, it means intimacy without losing my self. To understand deeply is to love -- to love without needing it to stay the same. There's a concept our culture doesn't have: love, intimacy, familiarity while encouraging change. Even with children we are like "I wish they didn't grow up so fast!" No. Let them grow, it is the changing that holds their greatest beauty.
When I can hold in my head both the way the system works now and the way I wish it to work soon, then I can find the stepping stones and the pressure points to move it from here to there.
(duplicated from tistil, with an additional comment here)
Perhaps the closest word is "engagement."
from: https://www.brainpickings.org/2016/03/16/rebecca-solnit-hope-in-the-dark-2/
To change a software system I need to become part of it, I need to understand how the bits fit together. For example: When we spin up a new AWS instance, we use code in one repository, which activates a system controlled by another repository, which includes a script that shells into another machine where it makes an HTTP call to localhost to a service in a third repository, which digs around in its history and links to artifacts created by code in a fourth repository to package up a fifth repository. Do I approve of this flow? It doesn't matter. I need to accept its existence, without accepting its inevitability. I need to love it enough to understand it, and only then can I confidently change it.
In social systems too, I am part of the system already. I need to understand its workings; I am part of them. It is from here I can exert force for changing them. Resentment, denial, judgement: these hurt me, and they change nothing. I will face my fear, and my pain, and let it pass through me. I will be not surprised, I will be not approving, I will not expect that because it is this way now, that it must always be. I am part of the system but I do not help it self-preserve, in ways that I am able to see because I no longer punish myself with resentment and terror for being able to see them.
Participation is not endorsement.
To understand a complex system, whether society of software, means immersing myself in it, while retaining perspective. It means observing each interaction without surprise, it means intimacy without losing my self. To understand deeply is to love -- to love without needing it to stay the same. There's a concept our culture doesn't have: love, intimacy, familiarity while encouraging change. Even with children we are like "I wish they didn't grow up so fast!" No. Let them grow, it is the changing that holds their greatest beauty.
When I can hold in my head both the way the system works now and the way I wish it to work soon, then I can find the stepping stones and the pressure points to move it from here to there.
(duplicated from tistil, with an additional comment here)
Perhaps the closest word is "engagement."
This is an extraordinary time full of vital, transformative movements that could not be foreseen. It’s also a nightmarish time. Full engagement requires the ability to perceive both.
from: https://www.brainpickings.org/2016/03/16/rebecca-solnit-hope-in-the-dark-2/
Monday, December 29, 2014
Dogcalling
A colleague remarked:
"a golden retriever puppy is the ultimate chick magnet
when mine was 4 months old, no female passer-by could actually pass her by
so every time I took her out for a walk, I had at least three random conversations about dogs"
This sounds great if you want to have conversations, annoying if you just want to take the dog for a walk and mind your own business.
Well hey, one might say, if you don't want people to compliment you on your dog, don't leave the house with a 4-month-old golden retriever puppy.
All right - getting a cute puppy was a choice, and these interruptions won't happen for very long. Probably not even long enough to get sick of them.
Now consider an attractive woman walking down the street, and random people keep talking to her, telling her she's pretty, whistling, calling out "Nice ass!" or "Smile!" These are catcalls. They can be incessant. They can be infuriating when a woman just wants to get where she's going. It isn't twice a day; it's every time she goes outside.
She doesn't want to have a conversation about her ass. Yet she can't leave her ass at home.
Add to that, some catcallers are dangerous. My colleague wasn't worried about admirers kidnapping his dog. A woman does have to worry about a random male forcibly taking her ass, taking her very autonomy away from her. She never knows which of the hundreds of street harassers are also rapists.
If someone's chosen to walk around with a cute dog and you are attracted to that dog, your comments might still be unwelcome, but they're not threatening, and the dog owner can get away from them. When a woman walks around in her own body and you find that attractive, keep it to yourself.
If you want to start a conversation, try getting a cute dog.
If you want to start a conversation, try getting a cute dog.
Saturday, April 12, 2014
Leaving People Out
When there's a dialog about women in technology, there's usually an effort to let more women speak in the discussion than men. This is good, because the point is to give women a voice. Yet it's bad, because as we're trying to make some people feel less left out, we make others feel left out.
Men who care want to contribute to the conversation, too. When they're pushed out of the discussion, they feel excluded and rejected. It's like there's a double standard: only women can speak about how important it is that everyone have a voice.
Well that sucks. It isn't fair. And I want to tell you: it's also essential.
There's a meme permeating our culture, an idea that gets passed down and entrenched without ever being stated. It is: "White men dominate discourse." This is just a fact. And it's a fact that reinforces itself. Men expect to dominate the discourse because they do, and this means they are comfortable speaking up, and they do. And so they dominate the discourse.
This isn't anybody's fault, but it is everybody's loss. Inclusion and balance means more ideas, more sharing, more learning for everyone. This is especially true in tech, where ideas can have a positive impact quickly. Learning and growing is everything in our field.
You don't have to think men should dominate discourse to perpetuate it. This meme spreads itself, it's a feedback loop, a circular causality. Because it's suboptimal for our culture, we can choose to consciously counteract this meme.
Panels of mostly women, discussions where women's contributions outnumber men's, these are one way to counteract the meme. Every conversation where women speak at least as much as men is one that teaches us, subconsciously, "You have a contribution to make. Speak up." Where better to achieve this than in discussions about women in technology? If we can't change the ratio in discussions about gender, where can we?
Still, there's a long way from "women speak at least as much as men" to "shut up! you're a white man so you have no contribution to make!" There are many men who care and can contribute. Why would we discourage them to chime in?
The answer is: math. In a field that's 90% men, any random distribution of comments, even without cultural influence, will be 90% men. In one conversation, say the whole 10% of non-men speak up. If not more than 50% of comments are from men, then only the same number of men may speak. 10% of the total, out of 90% that are men, means that 8 out of 9 men are excluded.
Sometimes the best way to contribute is to remain silent. As a man, if you want to help mitigate the meme of "white men dominate discourse," it often means being quiet. Instead of speaking up, amplify the women's voices with +1s and retweets. Make the audience of your contributions the men around you who aren't listening to discussions about women in tech. Or speak individually to the women you're closest to, get their perspective, and maybe let them get the ideas into the conversation without contributing to male domination of discussions. It isn't your fault. It's the way things are. You can help us adjust them, help make tech more fair for everyone in the long run, by accepting some not-fair-to-you-ness in this particular case.
---------------------
[1] 90% men: In my experience as a senior dev and frequent attender of tech conferences, this is a generous estimate. If you disagree, change it and do your own math. The qualitative conclusion is the same.
Men who care want to contribute to the conversation, too. When they're pushed out of the discussion, they feel excluded and rejected. It's like there's a double standard: only women can speak about how important it is that everyone have a voice.
Well that sucks. It isn't fair. And I want to tell you: it's also essential.
There's a meme permeating our culture, an idea that gets passed down and entrenched without ever being stated. It is: "White men dominate discourse." This is just a fact. And it's a fact that reinforces itself. Men expect to dominate the discourse because they do, and this means they are comfortable speaking up, and they do. And so they dominate the discourse.
This isn't anybody's fault, but it is everybody's loss. Inclusion and balance means more ideas, more sharing, more learning for everyone. This is especially true in tech, where ideas can have a positive impact quickly. Learning and growing is everything in our field.
You don't have to think men should dominate discourse to perpetuate it. This meme spreads itself, it's a feedback loop, a circular causality. Because it's suboptimal for our culture, we can choose to consciously counteract this meme.
Panels of mostly women, discussions where women's contributions outnumber men's, these are one way to counteract the meme. Every conversation where women speak at least as much as men is one that teaches us, subconsciously, "You have a contribution to make. Speak up." Where better to achieve this than in discussions about women in technology? If we can't change the ratio in discussions about gender, where can we?
Still, there's a long way from "women speak at least as much as men" to "shut up! you're a white man so you have no contribution to make!" There are many men who care and can contribute. Why would we discourage them to chime in?
The answer is: math. In a field that's 90% men, any random distribution of comments, even without cultural influence, will be 90% men. In one conversation, say the whole 10% of non-men speak up. If not more than 50% of comments are from men, then only the same number of men may speak. 10% of the total, out of 90% that are men, means that 8 out of 9 men are excluded.
Sometimes the best way to contribute is to remain silent. As a man, if you want to help mitigate the meme of "white men dominate discourse," it often means being quiet. Instead of speaking up, amplify the women's voices with +1s and retweets. Make the audience of your contributions the men around you who aren't listening to discussions about women in tech. Or speak individually to the women you're closest to, get their perspective, and maybe let them get the ideas into the conversation without contributing to male domination of discussions. It isn't your fault. It's the way things are. You can help us adjust them, help make tech more fair for everyone in the long run, by accepting some not-fair-to-you-ness in this particular case.
---------------------
[1] 90% men: In my experience as a senior dev and frequent attender of tech conferences, this is a generous estimate. If you disagree, change it and do your own math. The qualitative conclusion is the same.
Saturday, March 15, 2014
Resisting conclusions
Arguments provide reasons to believe their conclusions. For conclusions we already believe, it's easy to say, "that's a great argument." For conclusions we find unlikely, it takes a strong argument to influence our beliefs.[1]
The strength of evidence required in an argument depends on the likelihood of its conclusion. It doesn't take much to convince an environmentalist that more recycling is valuable. It takes a very strong argument to convince a fiscal conservative that a tax increase is worthwhile. The perceived likelihood of any conclusion, which varies by audience.
We do this without realizing it, in our own heads. We form a hypothesis, estimate its probability, weigh the evidence (and opposing evidence, if we notice it), and then react.
For example, pretend you're a regular attender of a user group for functional programming. A new person walks in to the meeting. Hypothesis: "A developer has come to hear the talks." Likelihood? high. Evidence: presence at user group. Opposing evidence: Dockers and a polo shirt with a company name. Action: welcome him and ask where he works.
Now suppose the person who walked in is also woman. Hypothesis: "A developer has come to hear the talks." Likelihood? low. Evidence: presence; dress is jeans and a tee. Action: say hello and ask her who she recruits for.
It's completely reasonable! I'm not offended. It's reasonable because our internal estimation of "How likely is it that this person is a developer?" is based on all the developers we've seen before. In a k-means based on available information -- appearance and presence -- she's pretty far from the "developer" cluster.
Fortunately, "this person is a developer" is not a strong conclusion, and it doesn't require any action. When the woman says she's a developer, people believe her.
Now consider the hypothesis "This person is a very good developer." That's a stronger conclusion, requiring actions of respect and listening. Therefore, this conclusion demands stronger evidence. Because that subconscious likelihood estimation still includes information about appearance and gender, it's gonna take much stronger evidence before we believe the woman is a very good developer, compared to the evidence needed to believe the man is a very good developer.
We can't help it, our brains do this, our language does this. Appearance and race and gender and subtle class indicators -- these are determiners of that subconscious likelihood estimation.
The only way to combat this, the way to be fair and relatively impartial, is to pull the evaluation of this hypothesis out of the subconscious. To carefully and methodically evaluate the evidence with our reasoning brain[2]. To ignore our instincts and impressions. To work at making a decision about whether this person is a good developer, instead of going with what feels likely.
This is why people who think they have a sexist bias are less likely to exhibit one: they take precautionary measures.[3] Anyone who thinks they aren't biased may skip looking closely at the data and go with their gut. Our gut is always biased toward what it has seen before.
------------------
[1] I'm talking about inductive arguments, not deductive. This information comes out of the Coursera course "Think Again: How to Reason and Argue"
[2] System 2 in "Thinking Fast & Slow"
[3] there was a study showing evaluations of applicants, and male-named applicants were evaluated higher by both men and women. It's in here somewhere I think
The strength of evidence required in an argument depends on the likelihood of its conclusion. It doesn't take much to convince an environmentalist that more recycling is valuable. It takes a very strong argument to convince a fiscal conservative that a tax increase is worthwhile. The perceived likelihood of any conclusion, which varies by audience.
We do this without realizing it, in our own heads. We form a hypothesis, estimate its probability, weigh the evidence (and opposing evidence, if we notice it), and then react.
For example, pretend you're a regular attender of a user group for functional programming. A new person walks in to the meeting. Hypothesis: "A developer has come to hear the talks." Likelihood? high. Evidence: presence at user group. Opposing evidence: Dockers and a polo shirt with a company name. Action: welcome him and ask where he works.
Now suppose the person who walked in is also woman. Hypothesis: "A developer has come to hear the talks." Likelihood? low. Evidence: presence; dress is jeans and a tee. Action: say hello and ask her who she recruits for.
It's completely reasonable! I'm not offended. It's reasonable because our internal estimation of "How likely is it that this person is a developer?" is based on all the developers we've seen before. In a k-means based on available information -- appearance and presence -- she's pretty far from the "developer" cluster.
Fortunately, "this person is a developer" is not a strong conclusion, and it doesn't require any action. When the woman says she's a developer, people believe her.
Now consider the hypothesis "This person is a very good developer." That's a stronger conclusion, requiring actions of respect and listening. Therefore, this conclusion demands stronger evidence. Because that subconscious likelihood estimation still includes information about appearance and gender, it's gonna take much stronger evidence before we believe the woman is a very good developer, compared to the evidence needed to believe the man is a very good developer.
We can't help it, our brains do this, our language does this. Appearance and race and gender and subtle class indicators -- these are determiners of that subconscious likelihood estimation.
The only way to combat this, the way to be fair and relatively impartial, is to pull the evaluation of this hypothesis out of the subconscious. To carefully and methodically evaluate the evidence with our reasoning brain[2]. To ignore our instincts and impressions. To work at making a decision about whether this person is a good developer, instead of going with what feels likely.
This is why people who think they have a sexist bias are less likely to exhibit one: they take precautionary measures.[3] Anyone who thinks they aren't biased may skip looking closely at the data and go with their gut. Our gut is always biased toward what it has seen before.
------------------
[1] I'm talking about inductive arguments, not deductive. This information comes out of the Coursera course "Think Again: How to Reason and Argue"
[2] System 2 in "Thinking Fast & Slow"
[3] there was a study showing evaluations of applicants, and male-named applicants were evaluated higher by both men and women. It's in here somewhere I think
Sunday, July 15, 2012
Neitzche and Turing
From the Prologue of Beyond Good and Evil: "the fundamental condition of all life, perspective."
From the Turing machine: all it sees is its current state and immediate environment. This is perspective. The individual actor, whether an ant or a person in an economy or an agent in a simulation - it sees only its environment, and its own (possibly very complex) internal state. Perspective.
It is these tiny pieces each with their own perspective that come together to create order within a system of increasing entropy, to create life. See? Perspective is the fundamental condition of life.
Reading about complexity theory bangs my drum.
From the Turing machine: all it sees is its current state and immediate environment. This is perspective. The individual actor, whether an ant or a person in an economy or an agent in a simulation - it sees only its environment, and its own (possibly very complex) internal state. Perspective.
It is these tiny pieces each with their own perspective that come together to create order within a system of increasing entropy, to create life. See? Perspective is the fundamental condition of life.
Reading about complexity theory bangs my drum.
thoughts on the turing machine
A simple Turing machine, in each step, takes as input its own state and the state of its environment. Output is its next state, how to change the environment, and how to move to a different environment.
Or: the input to a Turing machine is the tape and its starting position. The output of the turing machine is the tape when it's done.
We can look at the Turing machine at these two levels as input->output. The aggregate input-output is easier to see. The each-step input-output explains how it works.
When we're looking at biological computation, do we ever get to see the individual steps? We're lucky to get a view of the aggregate input/output. How do you debug a cell?
Or: the input to a Turing machine is the tape and its starting position. The output of the turing machine is the tape when it's done.
We can look at the Turing machine at these two levels as input->output. The aggregate input-output is easier to see. The each-step input-output explains how it works.
When we're looking at biological computation, do we ever get to see the individual steps? We're lucky to get a view of the aggregate input/output. How do you debug a cell?
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