Me, the bitch from the 1300s

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ç„æ„ / Permanent Vacation
Claire Keane
Today's Document

if i look back, i am lost

romaâ
YOU ARE THE REASON
NASA
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Acquired Stardust
tumblr dot com
we're not kids anymore.

titsay
hello vonnie
Game of Thrones Daily

Kaledo Art

pixel skylines
will byers stan first human second
styofa doing anything
seen from Malaysia

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@pointertovoid
Me, the bitch from the 1300s
This beach in Canada is filled with crystal blue tide pools and itâs so magical
Anyways, Kazuki Takahashi is speaking about against facism and Shinzo Abe which is really awesome considering he created one of my favorite works of all time
Above is an illustration posted on his instagram; Dark Magician Girl is saying â Japanâs become a country thatâs difficult to live inâ and Dark Magician is saying âa fascist regime = the dark dimensionâ [Translation source]
Just an FYI on FaceApp...
Not to be that person but just thought Iâd give people a heads up, just in case youâd hate for your face to be a profile pic for some Russian bot account on social media....
AI photo editor FaceApp goes viral again on iOS, raises questions about photo library access â TechCrunch
People should be careful and aware of what permissions theyâre giving an app and itâs important to be cynical about these sort of things (like, I bet Faceapp is at least harvesting the data you send it for some non-Faceapp related use) but it isnât terribly different from any other app you might install on your phone and upload photos to. Some of this thread is true and some of it is... not... i.e., it canât actually collect photos you havenât given it permission to in iOS (but it does upload the photos you put in the app to the cloud*)
hereâs an article about FaceApp for more
*one thing to note here is that applying AI facial filters is a pretty computationally intensive process and that could be an excuse to process them in the cloud rather than locally, so that fact alone is not necessarily malignant tho again it may very well be in this case
Threads in time, Natalie Ciccoricco (because)
Diagnoses are a matter of classification and categorization (Foucault 1982; Madigan 1992). BPD is currently understood through an arrangement of human behavior that classifies like individuals into typologies of deficit. A personality disorder diagnosis declares the deficit to be a fundamental feature of a person rather than a transient state. When a clinician, armed with this model and definition, makes a diagnosis of BPD, for instance, the power to classify derived from this knowledge can influence how individuals view themselves in relation to societal standards. In Foucaultâs (1982) sense, the client may therefore internalize the problem discourse and come to understand themselves as deficient and that deficiency as a fundamental quality.
The etiology of BPD is a highly studied field by researchers, and even critics of BPD have adopted a causal model that names childhood abuse as a risk factor for BPD (Shaw and Proctor 2005). The public comes to think that BPD is the understandable and inescapable result of a stressor, when in fact it is a diagnosis dependent on the mere judgment of a clinician. This is to say, âthere is no disorder ⊠unless somebody with authority applies a psychiatric conceptualizationâ (Burstow 2005, 1299).
Of importance here is that a BPD diagnosis is situated within the dominant Western discourse on identity, a conception of selfhood that values autonomy and goal-directed behavior. These characteristics are closely tied to cultural norms of self-provision through work. In order for members of society to be self-sufficient and goal-directed, personality and identity must be conceptualized as relatively stable, inherent aspects of oneself that emerge through behaviors, traits, and other external manifestations (White 1999; Bradley and Drew 2006). In traditional treatment, clinicians decode and interpret these manifestations in relation to their deviation from societyâs norms for behavior (Madigan 1992).
For example, self-injury and suicidal behaviorsâtwo diagnostic criteria of BPDâare seen as pathological actions that undermine the valued sense of selfhood. Disrupting the dominant narrative of goal-directed behavior, the self-directed injury is seen as an inability to be an agentic, goal-directed individual. Some types of self-harmâsuch as overworking at oneâs place of employment to the point of causing physical ailments, neglect of interpersonal relationships, and loss of sleepâare not seen as pathological because these acts resonate with cultural values, such as self-sacrifice for a greater goal. But because the self-directed nature of self-injury cannot be reconciled with other cultural norms, self-injury is seen as a manifestation of severe pathology; the person must be viewed as disordered for such an action to make sense (Madigan 1992).
Studies of BPD offer us reasons to rethink these dominant conceptions of pathological behavior and the supposed stability of identity. We know now that BPD symptoms diminish over time such that âafter about 10 years, as many as half of the individuals no longer have a pattern of behavior that meets full criteriaâ for BPD (American Psychiatric Association 2013). Another study showed that among an adult cohort, 73 percent were in remission from symptoms after six years (Zanarini et al. 2003), which undermines the narrative that personality is largely unvarying. Furthermore, many symptoms of BPD are normative during adolescence, such as chaotic relationships, recklessness, and extreme emotional shifts, but deemed unacceptable in adulthood.
Feminist critics of BPD offer an alternative perspective, generally viewing the diagnosis of BPD as pathologizing the ways that women respond to gendered abuse and oppression. Shaw and Proctor (2005) theorize the diagnosis as a form of social control: â[BPD] can be applied to women who fail to live up to their gender role because they express anger and aggression. Conversely, the diagnosis is also given to women who conform âtoo strongly,â by internalizing anger, and expressing this through self-focused behavior such as self-injuryâ (485). They show how the diagnosis of BPD presents a double-bind: women with BPD who engage in behaviors that are not stereotypically feminineâself-injury, multiple sexual partners, external expressions of angerâare cast in the archetype of the overemotional hysterical woman.
Here, it is evident that the feminist framework, like other radical frameworks, ties the individual problem to a broader political context. Rather than a pathology that is endogenous to the individual, a feminist perspective theorizes these behaviors as a response to, or relationship with, gendered power relations.
heres a TOTALLY BLANK image!! there is DEFINITELY NO REASON for u to click it!!!!!!!!! NOT AT ALL!!!!!!!!!!!
Did tumblr introduce new optional bg colours just to undermine this post
Charlotte Moorman, Performance wearing artist Nam June Paikâs âTV Celloâ and âTV Glasses,â New York, 1971
Follow @womenartandtechnology on instagram to learn more.
One of the (many) things that bothers me about our current approaches to deep learning is that they sort of arbitrarily assume a godâs eye view of things in various places that actual neurons in the brain wouldnât have access to. And under one interpretation this might mean that our approaches are better for not being chained by the laws of physics, but on another interpretation it could mean that however the brain does it leads to better results for not being chained by the assumptions of formal probability theory (after all, the brain does objectively have better results [it does stuff like come up with formal probability theory, for example], and also probability theory is clearly and openly confused about what itâs doing). Softmax is a good example of this sort of thing. Like, letâs say youâre training an image classifier. Usually a classification networkâs output is just whatever the neurons happen to output, and each neuron represents some particular class. So if you input a picture and the network returns a very high value output on the neuron thatâs supposed to light up when you input a picture of a bird you presume the network has detected a bird. And thatâs fine but for various reason we generally like to have a normalized output on the final layer (and at various points in hidden layers), so that the network doesnât end up saying something like âIâm 94% sure itâs a bird, and 40% sure itâs a giraffeâ because it doesnât make sense for the network to say its 134% sure itâs either of the two. And yet thatâs what itâs saying.Â
So we apply softmax to the output to make sure everything adds up to 100% (the difference between softmax and vanilla normalization is that we exponentiate the values first to magnify the differences). And that sounds entirely reasonable if your idea of reasonable is âwhatever conforms to probability theory,â but beyond that itâs a jarringly external operation. Itâs not like the neurons themselves are organized to give a softmaxed output, because the neurons in one layer donât know about the values of the neurons in the same layer. Sure, you could probably add more layers responsible for normalizing the output of exaggerated differences to try to stick to a minimal set of rules for the heck of it, but why? if you take a step back you have to notice that human brains simply donât do this. Human brains for the most part stick to incoherent outputs that donât sum to 100% (unless the brain is made to think really hard and double check the options to unnaturally force a consistent output) .
And, I donât know, I feel like thereâs something important there but itâs difficult to think about without getting into a masturbatory and implausible treatise on Godel / completeness vs consistency / non-monotic logic. Â
My broader point is (to plagiarize Hinton) that the biggest obstacle deep learning currently faces is that it works. Itâs developed tools that are effective but clearly wrong, and this leads to weird situations where researchers are sort of resistant to fresh, completely unproven approaches. This is sort of understandable, we donât know that what we have is definitely broke, so why fix it? But like, if your definition of not broke is restricted to the parts that work then âŠI donât know that sounds a lot like itâs broke? Regardless, almost everyone (except for OpenAI, apparently) agrees we canât keep relying on algos restricted by the designs of GPUs and TPUs. LIke yeah, the algorithms scale fine, but with such a large scalar value, the power requirements are absurd.Â
I really give huge props to Intel here for dumping a bunch of money into their experimental Loihi architecture. Loihiâs a neuromorphic chip dedicated to spiking neural networks, which are really different from the backpropagation approaches weâre currently stuck on. But the thing about spiking neural networks is that there are like 5 papers in total about them and itâs not clear that theyâre viable, and in part thatâs because the hardware we currently have isnât well suited to them. Chicken and egg problem. So Intel is literally just making a bunch of these things at absurd cost and next to zero evidence of their viability beyond âuhhh ⊠human brains do it so ⊠â and giving them to researchers to play with, and hoping something comes of it. Itâs weird to see a giant established company take a huge risk like that simply because they realized the chicken canât come before the egg. Anyway, the first batch went out to researchers like a months ago (and I really wish someone would blog about their experiences so far). Youâd think everyone would be super happy and encouraging about all of this effort but actually Intel has gotten considerable flak for bothering, largely because a lot of researchers would really rather Intel make hardware thatâs actively better suited to the approaches weâre already using. All of this is to point out the irony that the biggest names in Machine Learning are getting stuck in a local minimum.Â
It sort of looks like progress, I think.