"We encourage the United States government to adopt a similar whole-of-government approach"
This paragraph on adversial attacks with #NLP sounds familiar.
It almost sounds like.. exactly what I reported in The Ruby Files re how Twitter weaponized NLP.
Adversial NLP attacks can come from within the organization - the bad actor is *not* always external.
Machine Learning Warfare:
"The model misinterprets the content of the image and misclassifies it. An attacker can tailor the expected behavior of an algorithm to achieve a number of outcomes."
"Adversial #AI targets areas of the attack surface that have never previously had to be secured, the AI models themselves.
From now on, organizations need to include these in their security budgets- or risk them being exploited by attackers."
"Poisoned training data"
"We would support NIST developing a sandboxing scheme for AI"
"....test and pilot AI algorithms and tools..."
Next, let's review some highlights in the academic research paper:
If you read the research paper carefully, you will notice a common pattern emerge.
Policy rec. in the name of AI fairness- and a rec. to change results of the model if the output does not align w/ their definition of "fairness."
NIST was repeatedly flagged as an N-Gram in the government agency category.
Why was Twitter using Machine Learning to monitor for NIST mentions?
Do you understand how dangerous this is?
AI “ethicists” have completely circumvented elected officials for well over 5 years now to implement whatever aligns w/ their worldview.
All in the name of “AI fairness”
I want to be clear in my language on this. When I say they- I am referring to a small group of people in ML who have hijacked the tech for nefarious purposes. I am not referring to the entire industry. There are many other people doing great work and find this deplorable.
I believe in the power of AI/ML. I want America to win the AI war. But we won’t lead with nefarious actors infiltrating the government.
They are using ML as a weapon to deploy personal ethics in the name of “AI fairness.”
DARPA isn’t the main issue and in fact it seems like DARPA was screwed over in the process. Something was botched in the transition.
This transition was botched.
DARPA Intel and the core backbone of the domain naming system appears to have been given away and then set up as a charity. But the “charity” is not actually a not for profit. This essentially means the not for profit commercialized DARPA Intel. That never should have happened.
I handed my most important Ruby files to Mike Benz believing he actually cared about the country.
First, he asked if he could share my files with his Intel friends at NSA. Next, he introduced me to John Solomon.
Benz promised to cover The Ruby Files. Instead - he buried my work and proceeded to come up with excuse after excuse as to why he couldn’t.
Next thing I know - he’s on Tucker Carlson and being called the top AI Censorship expert in the world.
He blocked me when I called him out and never spoke to me again.
I was told that my Ruby Files reporting would be on Hannity.
I was also told I basically needed to work with them and do it their way. Something felt off about all of it. I didn’t proceed.
My work was never on Hannity.
My life continued to get worse and worse.
Why? Because they knew what I had and I refused to play ball.
I have stayed silent about this for over a year. I will continue to stay silent about the worst parts of this because I don’t want to deal with this litigious gang of mobsters. Trust me when I say that these are not good people. I am not crazy. They could make any decent person look crazy. This is what they are best at.
Thousands of pages from Google's internal Content API Warehouse detailing over 14,014 attributes related to Google's ranking systems. The screenshots I posted are from those files.
Why does this matter? What is the context?
Two of the attributes include signals related to Covid authority and election scoring.
So what?
This potentially means Google had an internal config for domain level authority scoring pertaining to elections and vaccines.
Who cares?
People interested in the topic of Internet censorship should ideally care.