Why does a psychopath win every time against a narcissist?

A narcissist deals with shame by offloading it on to other people, a psychopath deals with it by repeatedly bashing your head on the pavement.

Psychopaths have deeply internalised shame which is dealt with by violence. It isn’t that psychopaths do not register shame, but that it is only a fleeting emotion, quickly replaced by rage.

Psychopathy-related personality traits and shame management strategies in adolescents – PubMed
The purpose of this study was to examine whether there is a correlation between the amount of psychopathy-related personality traits and the type of shame management in adolescents. Two hypotheses were examined; first, that there is a positive correlation between psychopathy-related personality trai …

Psychopaths are not the cool calm robots of Quora fairy tales, they are creatures of hair trigger rage. Attempting to shame a psychopath is incredibly foolhardy, and rather than harsh words, or feigned indifference, you may be met by very real violence.

Narcissists like to appear to be at the top of the pecking order by affecting the superficialities of power, psychopaths have a system of working out who is there by raw, real world might, physical or otherwise.

Psychopaths are hierachical, like reptiles or apes – you will fight for your position and inciting shame in another is a challenge for their spot, or a refutation of the status they appear to be claiming.

A narcissist in a group of psychopaths who attempted to shame or devalue them in any way would soon receive a physical challenge to their assumed superiority. With psychopaths it’s put up or shut up. Got to pay the cost to be the boss.

As Mac Davidson says, there are no narcissists in prison.

If you ever find yourself in the unfortunate situation of being surrounded by psychopaths, be very civil and polite, do your best not to act haughty or superior as they are very sensitive to shame. Psychopaths do not care about being thought to be a good person, they only care about where they sit in the pecking order. Chances are you’re at the bottom, so act accordingly. Self depreciation is your go to here.

Beat them with brains,

Robert

Google hired Timnit Gebru to be an outspoken critic of unethical AI. Then she was fired for it.

Gebru is one of the most high-profile Black women in her field and a powerful voice in the new field of ethical AI, which seeks to identify issues around bias, fairness, and responsibility.

Two months ago, Google promoted Timnit Gebru, co-lead of a group focused on ethical artificial intelligence, after she earned a high score on her annual employee appraisal. Gebru is one of the most high-profile Black women in her field and a powerful voice in the new field of ethical AI, which seeks to identify issues around bias, fairness, and responsibility.

In his peer review of Gebru, Jeff Dean, the head of Google Artificial Intelligence, left only one comment when asked what she could do to have a greater impact, according to documents viewed by The Washington Post: Ensure that her team helps make a promising new software tool for processing human language “consistent with our AI Principles.”

In an email thanking Dean for his review, Gebru let him know that her team was already working on a paper about the ethical risks around the same language models, which are essential to understanding the complexity of language in search queries. On Oct. 20, Dean wrote that he wanted to see a draft, adding, “definitely not my area of expertise, but would definitely learn from reading it.”

Six weeks later, Google fired Gebru while she was on vacation.

I can’t imagine anybody else who would be safer than me,” Gebru, 37, said. “I was super visible. I’m well known in the research community, but also the regulatory space. I have a lot of grass-roots support — and this is what happened.”

In an internal memo that he later posted online explaining Gebru’s departure, Dean told employees that the paper “didn’t meet our bar for publication” and “ignored too much relevant research” on recent positive improvements to the technology. Gebru’s superiors had insisted that she and the other Google co-authors either retract the paper or remove their names. Employees in Google Research, the department that houses the ethical AI team, say authors who make claims about the benefits of large language models have not received the same scrutiny during the approval process as those who highlight the shortcomings.

Her abrupt firing shows that Google is pushing back on the kind of scrutiny that it claims to welcome, according to interviews with Gebru, current Google employees, and emails and documents viewed by The Post.

It raises doubts about Silicon Valley’s ability to self-police, especially when it comes to advanced technology that is largely unregulated and being deployed in the real world despite demonstrable bias toward marginalized groups. Already, AI systems shape decision-making in law enforcement, employment opportunity and access to health care worldwide.

That made Gebru’s perspective essential in a field that is predominantly White, Asian and male. Women made up only 15 percent of the AI research staff at Facebook and 10 percent at Google, according to a 2018 report in Wired magazine. At Google, Black women make up 1.6 percent of the workforce.

Although Google publicly celebrated Gebru’s work identifying problems with AI, it disenfranchised the work internally by keeping it hierarchically distinct from other AI initiatives, not heeding the group’s advice, and not creating an incentive structure to put in practice the ethical findings, Gebru and other employees said.

Google declined to comment, but noted that in addition to the dozen or so staff members on Gebru’s team, 200 employees are focused on responsible AI.

Google has said that it did not fire Gebru, but accepted her “resignation,” citing her request to explain who at Google demanded that the paper be retracted, according to Dean’s memo. The company also blamed an email Gebru wrote to an employee resource group for women and allies at Google working in AI as inappropriate for a manager. The message warned the group that pushing for diversity was no use until Google leadership took accountability.

Rumman Chowdhury, a former global lead for responsible AI at Accenture and chief executive of Parity, a start-up that helps companies figure out how to audit algorithms, said there is a fundamental lack of respect within the industry for work on AI ethics compared with equivalent roles in other industries, such as model risk managers in quantitative hedge funds or threat analysts in cybersecurity.

“It’s being framed as the AI optimists and the people really building the stuff [versus] the rest of us negative Nellies, raining on their parade,” Chowdhury said. “You can’t help but notice, it’s like the boys will make the toys and then the girls will have to clean up.”

Google, which for decades evangelized an office culture that embraced employee dissent, has fired outspoken workers in recent years and shut down forums for exchange and questioning.

Nearly 3,000 Google employees and more than 4,000 academics, engineers and industry colleagues have signed a petition calling Gebru’s termination an act of retaliation by Google. Last week, nine Democratic lawmakers, including Sens. Elizabeth Warren (Mass.) and Cory Booker (N.J.) and Rep. Yvette D. Clarke (N.Y.), sponsor of the Algorithmic Accountability Act, a bill that would require companies to audit and correct race and gender bias in its algorithms, sent a letter to Google chief executive Sundar Pichai asking the company to affirm its commitment to research freedom and diversity.

Like any good researcher, Gebru is comfortable in the gray areas. And she has been using her ouster as an opportunity to shed light on the black box of algorithmic accountability inside Google — annotating the company’s claims with contradictory data, drawing connections to larger systemic issues, and illuminating the way internal AI ethics efforts can break down without oversight or a change in incentives to corporate practices and power structures.

Big Tech dominates AI research around advancements in machine learning, image recognition, language translation — poaching talent from top universities, sponsoring conferences and publishing influential papers. In response to concerns about the way those technologies could be abused or compound bias, the industry ramped up funding and promotion of AI ethics initiatives, beginning around 2016.

Tech giants have made similar investments in shaping policy debate around antitrust and online privacy, as a way to ward off lawmakers. Pichai invoked Google’s AI principles in an interview in 2018 with The Post, arguing for self-regulation around AI.

Google created its Ethical AI group in 2018 as an outgrowth of an employee-led push to prioritize fairness in the company’s machine learning applications. Margaret Mitchell, Gebru’s co-lead, pitched the idea of a team of researchers investigating the long-term effects of AI and translating those findings into action to mitigate harm and risk.

The same year, Pichai released a broadly worded set of principles governing Google’s AI work after thousands of employees protested the company’s contract with the Pentagon to analyze surveillance imagery from drones. But Google, which requires privacy and security tests before any product launch, has not mandated an equivalent process for vetting AI ethics, employees say.

Gebru, whose family’s ethnic origins are in Eritrea, was born and raised in Ethiopia and came to Massachusetts as 16-year-old after receiving political asylum from the war between the two African countries. She began her career as an electrical engineer at Apple and received her PhD from the Stanford Artificial Intelligence Lab, studying computer vision under renowned computer scientist Fei-Fei Li, a former Google executive and now co-director of Stanford’s Human-Centered AI Institute, which receives funding from Google.

Gebru did her postdoctoral research at Microsoft Research as part of a group focused on accountability and ethics in AI. There, she and Joy Buolamwini, then a masters student at MIT Media Lab, co-wrote a groundbreaking 2018 study that found that commercial facial recognition tools sold by companies such as IBM and Microsoft were 99 percent accurate at identifying White males, but only 35 percent effective with Black women.

In June, IBM, Microsoft and Amazon announced that they would stop selling the software to law enforcement, which Dean credited to Gebru’s work. She also co-founded Black in AI, a nonprofit organization that increased the number Black attendees at the largest annual AI conference.

Gebru said that in 2018, Google recruited her with the promise of total academic freedom. She was unconvinced, but the company was opening its first artificial intelligence lab on the African continent in Accra, the capital of Ghana, and she wanted to be involved. When she joined Google, Gebru said, she was the first Black female researcher in the company. (When she left, there were still only a handful of Black women working in research, out of hundreds.)

Gebru said she was also drawn to working with Mitchell. Both women prioritized foresight and building practical solutions to prevent AI risk, whereas the operating mind-set in tech is biased toward benefits and “rapid hindsight,” in response to harm, Mitchell said.

Gebru’s approach to ethical AI was shaped by her experiences. Hardware, for instance, came with datasheets that documented whether components were safe to use in certain situations. “When you look at this field as a whole, that doesn’t exist,” said Gebru, an electrical engineer. “It’s just super behind in terms of documentation and standards of safety.”

She also leaned on her industry experience when collaborating with other teams. Engineers live on a product cycle, consumed with putting out fires and fixing bugs. A vague requirement to “make things fair” would only cause more work and frustration, she thought. So she tried to build institutional structures and documentation tools for “when people want to do the right thing.”

Despite their expertise, the Ethical AI group fought to be taken seriously and included in Google’s other AI efforts, employees said.

Within the company, Gebru and her former colleagues said, there is little transparency or accountability regarding how decisions around AI ethics or diversity initiatives get made. Work on AI principles, for instance, falls under Kent Walker, the senior vice president of global affairs, whose vast purview includes lobbying, public policy and legal work. Walker also runs an internal ethics board of top executives, including Dean, called the Advanced Technology Review Council, which is responsible for yes or no decisions when issues escalate, Gebru said. The Ethical AI team had to fight to be consulted on Walker’s initiatives, she said.

“Here’s the guy tasked with covering Google’s a–, lobbying and also … working on AI principles,” Gebru said. “Shouldn’t you have a different entity that pushes back a little bit internally — some sort of push and pull?” What’s more, members of Walker’s council are predominantly vice presidents or higher, constricting diversity, Gebru said.

In her conversations with product teams, such as a group working on fairness in Machine Learning Infrastructure, Gebru said she kept getting questions about what tools and features they could build to protect against the ethical risks involved with large language models. Google had credited it with the biggest breakthrough in improving search results in the past five years. The models can process words in relation to the other words that come before and after them, which is useful for understanding the intent behind conversational search queries.

But despite the increasing use of these models, there was limited research investigating groups that might be negatively impacted. Gebru says she wanted to help develop those safeguards, one of the reasons she agreed to collaborate with the research paper proposed by Emily M. Bender, a linguist at the University of Washington.

Mitchell, who developed the idea of model cards, like nutrition labels for machine learning models, described the paper as “due diligence.” Her model card idea is being adopted more widely across the industry, and engineers needed to know how to fill out the section on harm.

Gebru said her biggest contribution to both her team and the paper has been identifying researchers who study directly-affected communities.

That diversity was reflected in the authors of the paper, including Mark Diaz, a Black and Latino Google researcher whose previous work looked at how platforms leave out the elderly, who talk about ageism in blog posts, but don’t share as much on sites such as Twitter. For the paper, he identified the possibility that large data sets from the Internet, particularly if they are from a single moment in time, will not reflect cultural shifts from social movements, such as the #MeToo movement or Black Lives Matter, which seek to shift power through changes in language.

The paper identified four overarching categories of harm, according to a recent draft viewed by The Post. It delved into the environmental effect of the computing power, the inscrutability of massive data sets used to train these models, the opportunity costs of the “hype” around claims that these models can understand language, as opposed to identifying patterns, and the danger that the real-sounding text generated by such models could be used to spread misinformation.

Because Google depends on large language models, Gebru and Mitchell expected that the company might push back against certain sections or attempt to water down their findings. So they looped in PR & Policy representatives in mid-September, with plenty of time before the deadline for changes at the end of January 2021.

Before making a pre-publication draft available online, Gebru first wanted to vet the paper with a variety of experts, including those who have built large language models. She asked for feedback from two top people at OpenAI, an AI research lab co-founded by Elon Musk, in addition to her manager at Google, and about 30 others. They suggested additions or revisions, Gebru said. “I really wanted to send it to people who would disagree with our view and be defensive,” Gebru said.

Given all their upfront effort, Gebru was baffled when she received a notification for a meeting with Google Research Vice President Megan Kacholia at 4.30 p.m. on the Thursday before Thanksgiving.

At the meeting, Kacholia informed Gebru and her co-authors that Google wanted the paper retracted.

On Thanksgiving, a week after the meeting, Gebru composed a six-page email to Kacholia and Dean outlining how disrespectful and oddly secretive the process had been.

Specific individuals should not be empowered to unilaterally shut down work in such a disrespectful manner,” she wrote, adding that researchers from underrepresented groups were mistreated.

Mitchell, who is White, said she shared Gebru’s concerns but did not receive the same treatment from the company. “Google is very hierarchical, and it’s been a battle to have any sort of recognition,” she said. “We tried to explain that Timnit, and to a lesser extent me, are respected voices publicly, but we could not communicate upwards.”

How can you still ask why there aren’t Black women in this industry?” Gebru said.

Gebru said she found out this week that the paper was accepted to the Conference on Fairness, Accountability and Transparency, as part of its anonymous review process. “It’s sad, the scientific community respects us a lot more than anybody inside Google,” she said.

Niall Ferguson, “The Square and the Tower”

20:32
it reveals apart from anything else one
of the fundamental problems with network
structures they are bad at self defense
one reason we inclined towards
hierarchical structure through most of
history is that they are quite good at
defense it’s not the first time the
Russians hacked a network I tell the
story of how the most exclusive
intellectual network of all time the
Cambridge apostles the most lofty
high-minded intellectually extraordinary
network got hacked by the KGB this is a
wonderful example of how networks can
attack other networks three of the
Cambridge spies three of the famous five
were members of that society to which
John Maynard Keynes had belonged in the
1920s and Lytton Strachey but the 1930s
the KGB had penetrated it one of the
most successful intelligence operations
of all time much more successful than
what they did in twenty sixteen which by
the way backfired in their faces
completely and it takes a network in
this kind of a world to defeat a network
I’m quoting Stan McChrystal who learnt
that lesson the hard way in his battle
against al-qaeda in Iraq that it’s a
wonderful story that he tells in his own
autobiography it took that very
hierarchical institution the US Army a
long time to realize that it could not
beat its adversary in Iraq other than by
in some ways imitating its network
structure

25:45
but if you are interested in him there’s
a whole section on why it was that
network’s decided 2016 election and one
of my concluding thoughts is the real
lesson of 2016 is no Facebook no
he-who-must-not-be-named
without the network platforms not just
Twitter but especially Facebook the
outcome of that election which has
changed all our lives would have been
different you’re gonna have amassed the
great German philosopher said that
changes in the structure of the public
sphere were often the most decisive
things in history
and I agree with you
organ harbor Mass and this book is
really about changes in the structure of
the public sphere ladies and gentlemen
we are living through one of the
greatest changes in the public sphere
ever to happen
it is as profound in its
way as the change wrought by the
printing press
the printing press was supposed to
create a priesthood of all believers the
internet a global community if history
has anything to teach us
it is the sobering thought that we may
be just at the beginning of a period of
network disruption polarization crazy
stuff going viral and widening
inequality and if that makes you feel
nervous

30:18
story he said the real problem is that
because of the way that Facebook works
and also Google because of the way that
the algorithm is sending you stuff that
is designed to get you engaged on an
individualized basis according to your
data
we each inhabit our own private sphere
and the disaggregation that you describe
is further advanced than we know what
made the advertising so potent in 2016
not only by the way in the United States
it happened in the UK too in the Briggs
that referendum was the ability that the
brexit campaign had and the Trump
campaign had to target advertising very
very specifically
and then tweak the advertising and on
the basis of its effectiveness this is a
completely changed public sphere
political advertisements are no longer
things we all see and can discuss at the
watercooler

each of us begins to inhabit his or her
own reality with our own customized
newsfeed this is a deeply dangerous
development because it means the public
sphere as such ceases to exist or
retreats into the domain of traditional
media traditional media of course slowly
being destroyed because they lose with
every passing month their share of
advertising revenue to the network
platforms so I sense a more profound
crisis of democracy than we get
appreciate because we are focused on
what I think are relatively small issues
the Russian intervention the Russian
intervention wasn’t decisive the number
of advertisements and the number of
people who saw them were release really
small percentages of all the content
that was being produced indigenously by
Americans on Facebook not least the
people that you alluded to so I think
this is a deeply troubling development
and it’s where the book ends book ends
by saying if we allow this networked
world to advance it will transpire that
the real enemy of democracy is the
Russians the real enemy is actually the
way the network our platform algorithms
sub dividers dice and slice us and give
each of us our own version of reality
so
thanks for the great question yes sir
I’m going to ask quick question since
the you had me the power of networks
it’s one step to presume that there is
possibility of large-scale conspiracies
do you believe that large-scale
conspiracies capable to change the
history can happen or happened before oh
I’m so glad you asked I’m so glad you
asked that question because part of the
reason for
writing this book is precisely that
conspiracy theorists have dominated the
literature on social networks for such a
long time I was really struck when I was
researching this bias statistic that I’m
going to get right in 2011 just over
half of Americans agreed with the
statement that quote much of what
happens in the world today is decided by
a small and secretive group of
individuals and I belong to it I do I
must do because I go not this year
because I’m busy selling books to the
World Economic Forum in Davos it’s worse
than that I go to the Bilderberg meeting
it’s quite likely that having written a
book about the Rothschilds and Henry
Kissinger and knowing George Soros that
I am a member of the Illuminati who are
of course controlled by space aliens
wait stop you lost me at space aliens so
here’s the extraordinary thing most of
the work that you can find out there on
the internet on any of the things I just
talked about from the Rothschilds to the
Illuminati is by crazy people and the
conspiracy theory landscape is kind of
fun to wander through but it is entirely
divorced from scholarship in conspiracy
theory land you just make stuff up
which is I mean I guess it’s
entertaining but it isn’t history part
of the problem there is that real
historians who are more nervous and and
risk-averse temperamentally than this
historian shy away therefore from
writing about any of these things so you
don’t actually get many books about the
role of the Freemasons in the American
Revolution that are non crazy there are
relatively few rigorous studies of the
Illuminati and so forth so one reason I
wanted to write this book was that so
much that there is about social networks
s the conspiracy theory industry when
you actually do serious historical
research which you can do on say the
Illuminati you discover that they were
a small South German secret society set
up in the 1770s with the goal of
secretly infiltrating the Masonic lodges
of Europe and spreading thereby the most
radical doctrines of the Enlightenment
including atheism so the Illuminati did
exist but they’re only ever about 2,000
members they spent a lot of time doing
really strange rituals inspired by
Freemasonry and giving one another
strange code names and they were
completely shut down by the Bavarian
authorities in the 1780s making it
highly unlikely that they caused the
French Revolution as was subsequently
alleged so part of the point of this
book is to show that we can write the
history of those secret societies but we
must not exaggerate their power but that
isn’t really a conspiracy to rule the
world run out of Davos I know I’ve been
I mean and frankly if that’s what they
call ruling the world I mean they should

Saving a Tree in Postgres Using LTREE

In my last post, I showed you how to install and enable a Postgres extension called LTREE. LTREE allows me to save, query on and manipulate trees or hierarchical data structures using a relational database table. As we’ll see, using LTREE I can count leaves, cut off branches, and climb up and down trees easily – all using SQL right inside my application’s existing Postgres database!

But trees are natural, haphazard, branching structures with countless leaves, while database tables are man-made rectangles full of numbers and text. How can I possibly save a beautiful tree structure into an ugly, boring database table?

Path Enumeration

Let’s return to the example tree from the first post in this series:

The LTREE extension uses the path enumeration algorithm, which calls for each node in the tree to record the path from the root you would have to follow to reach that node.

What are the options for storing hierarchical data in a relational database?

Generally speaking, you’re making a decision between fast read times (for example, nested set) or fast write times (adjacency list). Usually, you end up with a combination of the options below that best fit your needs. The following provides some in-depth reading:

Why Trump Supporters Believe He Is Not Corrupt

What the president’s supporters fear most isn’t the corruption of American law, but the corruption of America’s traditional identity.

On Wednesday morning, the lead story on FoxNews.com was not Michael Cohen’s admission that Donald Trump had instructed him to violate campaign-finance laws by paying hush money to two of Trump’s mistresses. It was the alleged murder of a white Iowa woman, Mollie Tibbetts, by an undocumented Latino immigrant, Cristhian Rivera.

On their face, the two stories have little in common. Fox is simply covering the Iowa murder because it distracts attention from a revelation that makes Trump look bad. But dig deeper and the two stories are connected: They represent competing notions of what corruption is.

.. The Iowa murder, by contrast, signifies the inversion—the corruption—of that “traditional order.” Throughout American history, few notions have been as sacrosanct as the belief that white women must be protected from nonwhite men. By allegedly murdering Tibbetts, Rivera did not merely violate the law. He did something more subversive: He violated America’s traditional racial and sexual norms.
Once you grasp that for Trump and many of his supporters, corruption means less the violation of law than the violation of established hierarchies, their behavior makes more sense. Since 2014, Trump has employed the phrase rule of law nine times in tweets. Seven of them refer to illegal immigration.
.. Why were Trump’s supporters so convinced that Clinton was the more corrupt candidate even as reporters uncovered far more damning evidence about Trump’s foundation than they did about Clinton’s? Likely because Clinton’s candidacy threatened traditional gender roles. For many Americans, female ambition—especially in service of a feminist agenda—in and of itself represents a form of corruption. 
“When female politicians were described as power-seeking,” noted the Yale researchers Victoria Brescoll and Tyler Okimoto in a 2010 study, “participants experienced feelings of moral outrage (i.e., contempt, anger, and/or disgust).”
Cohen’s admission makes it harder for Republicans to claim that Trump didn’t violate the law. But it doesn’t really matter. For many Republicans, Trump remains uncorrupt—indeed, anticorrupt—because what they fear most isn’t the corruption of American law; it’s the corruption of America’s traditional identity.