#Financial Fraud

[dougielombax]

No, kitten.

Tax evasion and hating poor people is NOT a substitute for a personality.

Nor is it a personality trait.

Now run along, Daddy is currently trying to find patterns in things that aren’t there.

[destielmemenews]

"Trump said, “We have a clause in the contract, it’s like a buyer beware clause. It says, ‘When you take a look at the financial statement, don’t believe anything you read’ — this is up front. ‘Don’t believe anything you read.’ Some people call it a ‘worthless clause,’ because it makes the statement, and anything you read in the statement, worthless. It says, ‘Go out and do your own research, go out and do your own due diligence, you have to study the statement carefully. Do not believe anything.’”

He said moments later that the clause “immediately takes you out of any fraud situation and any litigation.”

Facts First: Trump inaccurately described this clause, as Judge Arthur Engoron noted in a ruling last week in which he found Trump liable for fraud. The clause does not say “don’t believe anything you read,” its actual language is significantly softer than Trump claimed. And there is no apparent basis for Trump’s suggestion that the clause inoculates him against any litigation at all."

source

[clothless-sock]

Nature as it should be

[prettiestboytoy2]

Imagine being one to give your money to Euron, Elizabeth Holmes, Sam Bankman-Fried, Adam Neumann and then single handedly causing 2001 and 2008 financial crisis AND then having an audacity to call yourself "Smart-money"

Its like giving an Nobel prize in Medicine to a guy who invented Lobotomy.

[religion-is-a-mental-illness]

By: Adam B. Coleman

Published: Sep 18, 2023

The real measure of an individual’s character isn’t what he portrays to the public but how he treats people in private.

Truly righteous people treat others with respect and dignity when there is no one else around and no social credit to be earned for doing the right thing.

This distinction matters — especially for people who’ve made a career lecturing others on the appropriate way to treat people, especially those perceived as having less power in society.

But when no one was looking and nothing was to be gained, it seems Ibram X. Kendi used his power and privilege as the director of a think tank to exploit and mistreat the people who worked under him as if they were people who are beneath him.

Amid confirmation of layoffs being made at Boston University’s Center for Antiracist Research, former and current faculty have spoken out about Kendi’s mismanagement, “exploitation” and enrichment.

“There are a number of ways it got to this point, it started very early on when the university decided to create a center that rested in the hands of one human being, an individual given millions of dollars and so much authority,” stated Spencer Piston, a BU political science professor. 

A Former assistant director of narrative at the center and a BU associate professor of sociology and African American and black diaspora studies, Saida Grundy, also described a lack of structure, leading to her working additional hours that were unreasonable, especially for the pay she was receiving.

“It became very clear after I started that this was exploitative and other faculty experienced the same and worse,” Grundy lamented.

With tens of millions of dollars flowing in from major donors shortly after the center’s founding in 2020 from Twitter founder Jack Dorsey, the Rockefeller Foundation and biotech company Vertex, Grundy also saw the missed opportunity to directly help black students at Boston University. 

“Those donations could have been going to benefit black students.”

Grundy is correct that much of the donation money could have been utilized in objectively more helpful ways to serve the people Kendi claimed to be advocating for. But the line between rhetoric and action was a line that Kendi never had any intentions of crossing.

Kendi used the dogma of antiracism to project a new moral standard at a time when many Americans momentarily questioned their behavior and culpability.

As he demanded that everyone should check their privilege and feel socially accountable for the exploitation of people, he was simultaneously exploiting the emotions of a nation to solidify his nobility status among the upper class in academia.

Kendi’s boutique moral philosophy on historical events and human interaction has only made him notable among the upper class.

Those elites declare racial enlightenment over the naïve majority who prefer to treat people like they’d want to be treated.

The antiracism think tank operated more like an antiracism piggybank with only one man listed as its financial beneficiary.

Kendi’s interests have become clearer as time has gone on: His “research center” was for the benefit of one black person, not black people.

Remember the $90 million windfall Patrisse Cullors and the Black Lives Matter organization scored and their frivolous spending habits with donation money, buying mansions and funneling cash to board and family members?

Activist Shaun King has also repeatedly been accused of raising money for recipients and causes that never saw it.

This is a similarly disappointing realization after tens of millions of dollars have been placed in the hands of an advocate who has shown little regard to produce a return for his bold aspirations.

Kendi had systemic control over his own research center yet used his position to take advantage of the people whom he was leading and continued to reap the academic clout that legitimizes his profiting in over $32,000 a speech.

Kendi suggests that people should become more race-conscious to be better anti-racists, but I believe it’s more important to be elitist-conscious.

We need to be aware of the behavioral patterns and condescending rhetoric of the people who think they know better than us about everything.

If we were all good anti-elitists, we’d ignore the utopian rhetoric of social progressives and anti-racists and focus on their behavior.

This readjustment would help us quickly realize that race is a tool to distract us from noticing they are getting rich from dividing us into categories of human characteristics.

The only remedy to moral elitism is moral anti-elitism: This is how we have an anti-elitist society.

Adam B. Coleman is the author of “Black Victim to Black Victor” and founder of Wrong Speak Publishing. Follow him on Substack: adambcoleman.substack.com.

==

It was never about doing anything useful. It was always akin to buying indulgences from the Catholic Church.

[financial-advisor]

Mastering Middle-Class Budgeting: A Path to Financial Freedom

[corporationsarepeople]

“The house is worth a billion, a billion and a half, 750 million; it’s worth a fortune,” adding that the clubhouse he lives in is “the most expensive house probably in the world.”

Numbers. What do they even mean? How do they work? Trump doesn’t know. Does anyone know?

[oneminutemoneymagazine]

The Pensions Dashboard: A Constructive Alternative

The Pensions Dashboard is a UK government plan to set up one large database from which every person can access their UK occupational pension data from all of their past and present employers.  The previous post on this blog criticised it on the grounds of complexity and greatly increased risk of fraud.  It could also have mentioned the lack of consent.  The government has not asked me whether I want my personal data put on their new database!

Having said this, there is a risk that, when a pension plan member reaches retirement age, the employer and administrators may have lost contact with them.  There are ad hoc arrangements whereby administrators can ask the DWP to help trace a missing member.  This should be formalised and expanded.  Only small amounts of data such as names, dates of birth and national insurance numbers would be needed.  Either individuals or plan administrators could contact the new government agency and the resources needed would be a mere fraction of those for the proposed Pensions Dashboard.  It should also be possible to implement it well before the current dashboard target date of October 2026.

In the meantime, employees should carefully keep their own records and advise plan administrators of changes of address etc.  There is also a government website called “Find pension contact details”.  It isn’t perfect, but it can often be helpful.  The link is

https://www.gov.uk/find-pension-contact-details

(24/07/2023)

[squaredawayblog]

Here are tips for negotiating with a debt collector. But consumers who don’t get results can file a complaint with the federal government. 

[dougielombax]

HA!!!

Get FUCKED!!!!!!

Throw the book at him!

[dbunicorn]

The mutual incestuous relationship between corporations, politicians, philanthropy and money printing seems to be a problem. How the fuck could I be wrong. The assholes in the Democratic party grew a fucking spine? 🤣🤣🤣🤣

It's a hot button issue

PS I watched the presidential debate. Disgrace eh? Is the irony lost on anyone?

It reminds me of the CRAZY, CRAZY wrong white lotus episode when armand gets caught eating ass.

Uninhibited. 🤣🤣🤣🤣

In plain fucking sight. Forward!!!!!

[lighterr]

亿万富翁赢了官司，死了全家（几乎）

转自 Original 共度时艰 贩财局 2024年08月28日…

[jcmarchi]

Method prevents an AI model from being overconfident about wrong answers

New Post has been published on https://thedigitalinsider.com/method-prevents-an-ai-model-from-being-overconfident-about-wrong-answers/

Method prevents an AI model from being overconfident about wrong answers

People use large language models for a huge array of tasks, from translating an article to identifying financial fraud. However, despite the incredible capabilities and versatility of these models, they sometimes generate inaccurate responses.

On top of that problem, the models can be overconfident about wrong answers or underconfident about correct ones, making it tough for a user to know when a model can be trusted.

Researchers typically calibrate a machine-learning model to ensure its level of confidence lines up with its accuracy. A well-calibrated model should have less confidence about an incorrect prediction, and vice-versa. But because large language models (LLMs) can be applied to a seemingly endless collection of diverse tasks, traditional calibration methods are ineffective.

Now, researchers from MIT and the MIT-IBM Watson AI Lab have introduced a calibration method tailored to large language models. Their method, called Thermometer, involves building a smaller, auxiliary model that runs on top of a large language model to calibrate it.

Thermometer is more efficient than other approaches — requiring less power-hungry computation — while preserving the accuracy of the model and enabling it to produce better-calibrated responses on tasks it has not seen before.

By enabling efficient calibration of an LLM for a variety of tasks, Thermometer could help users pinpoint situations where a model is overconfident about false predictions, ultimately preventing them from deploying that model in a situation where it may fail.

“With Thermometer, we want to provide the user with a clear signal to tell them whether a model’s response is accurate or inaccurate, in a way that reflects the model’s uncertainty, so they know if that model is reliable,” says Maohao Shen, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on Thermometer.

Shen is joined on the paper by Gregory Wornell, the Sumitomo Professor of Engineering who leads the Signals, Information, and Algorithms Laboratory in the Research Laboratory for Electronics, and is a member of the MIT-IBM Watson AI Lab; senior author Soumya Ghosh, a research staff member in the MIT-IBM Watson AI Lab; as well as others at MIT and the MIT-IBM Watson AI Lab. The research was recently presented at the International Conference on Machine Learning.

Universal calibration

Since traditional machine-learning models are typically designed to perform a single task, calibrating them usually involves one task-specific method. On the other hand, since LLMs have the flexibility to perform many tasks, using a traditional method to calibrate that model for one task might hurt its performance on another task.

Calibrating an LLM often involves sampling from the model multiple times to obtain different predictions and then aggregating these predictions to obtain better-calibrated confidence. However, because these models have billions of parameters, the computational costs of such approaches rapidly add up.

“In a sense, large language models are universal because they can handle various tasks. So, we need a universal calibration method that can also handle many different tasks,” says Shen.

With Thermometer, the researchers developed a versatile technique that leverages a classical calibration method called temperature scaling to efficiently calibrate an LLM for a new task.

In this context, a “temperature” is a scaling parameter used to adjust a model’s confidence to be aligned with its prediction accuracy. Traditionally, one determines the right temperature using a labeled validation dataset of task-specific examples.

Since LLMs are often applied to new tasks, labeled datasets can be nearly impossible to acquire. For instance, a user who wants to deploy an LLM to answer customer questions about a new product likely does not have a dataset containing such questions and answers.

Instead of using a labeled dataset, the researchers train an auxiliary model that runs on top of an LLM to automatically predict the temperature needed to calibrate it for this new task.

They use labeled datasets of a few representative tasks to train the Thermometer model, but then once it has been trained, it can generalize to new tasks in a similar category without the need for additional labeled data.

A Thermometer model trained on a collection of multiple-choice question datasets, perhaps including one with algebra questions and one with medical questions, could be used to calibrate an LLM that will answer questions about geometry or biology, for instance.

“The aspirational goal is for it to work on any task, but we are not quite there yet,” Ghosh says.   

The Thermometer model only needs to access a small part of the LLM’s inner workings to predict the right temperature that will calibrate its prediction for data points of a specific task. 

An efficient approach

Importantly, the technique does not require multiple training runs and only slightly slows the LLM. Plus, since temperature scaling does not alter a model’s predictions, Thermometer preserves its accuracy.

When they compared Thermometer to several baselines on multiple tasks, it consistently produced better-calibrated uncertainty measures while requiring much less computation.

“As long as we train a Thermometer model on a sufficiently large number of tasks, it should be able to generalize well across any new task, just like a large language model, it is also a universal model,” Shen adds.

The researchers also found that if they train a Thermometer model for a smaller LLM, it can be directly applied to calibrate a larger LLM within the same family.

In the future, they want to adapt Thermometer for more complex text-generation tasks and apply the technique to even larger LLMs. The researchers also hope to quantify the diversity and number of labeled datasets one would need to train a Thermometer model so it can generalize to a new task.

This research was funded, in part, by the MIT-IBM Watson AI Lab.

[scamsupdateindia]

(via Himansh Verma Navrattan Group Rs 1800 Crore Fraud Scandal)

[signode-blog]

Guard Against Financial Frauds as Data Leakage Becomes Rampant: Insights from RBI Officials

In an era where digital transactions and online banking have become the norm, safeguarding financial information has never been more critical. The Reserve Bank of India (RBI) officials recently highlighted the increasing threats of financial fraud due to rampant data leakage. This blog post delves into the nuances of this pressing issue and offers strategies to protect your financial data. The…

[forensicfield]

The government is sending out messages to alert people about a new scam. In this scam, fraudsters may impersonate police officers, threaten individuals, extort money, and request KYC/account details. It's important to take note of such details and immediately report any suspected fraudulent communications through the Chakshu facility on www.sancharsaathi.gov.in.

In case you have already lost money (got scammed), then report on 1930 or www.cybercrime.gov.in issued in Public Interest by Govt of India.

Stay informed about your rights, and be safe....