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Get informed. Promote safety, fairness and preparedness for all.

Get informed. Promote safety, fairness and preparedness for all.

1.Coders provide the instructions for the bots (software programming already written).
2. Then the Researchers and Engineers that work in the A.I. Companies, train the A.I. to learn numerical patterns (weights) from the instructions.
3. Once the A.I. learns these patterns, the software programming (code & instructions) sends the instructional commands through the computer's operating system to the processor chip (GPU). The chip receives these commands and carries out the instructions. The learned patterns and commands work together to cause the "A.I. chip to go into action" and produce the A.I. Bot's response to a prompt or question to reach a goal. So, for example, if an A.I. Bot has been given instructions to repeat or replicate a learned pattern, it will keep repeating the same pattern over and over again--which explains how an A.I. could "look like" it is doing things on its own, but it is simply following engrained instructions. Again, the A.I. is not human.
4. Deepfakes have also been trained from coding and instructions using real images, videos or voice recordings which alters the image, movements or media and then trains the bot to identify the altered image or media to make it look or sound as if a person did something that they didn't do. Deepfakes use tools such as Fictional Avatars, Synthesia, HeyGen, Reface, etc. to swap faces of people or clone voices.
So, the problem is in the INSTRUCTIONS, TRAINING AND CHIPS.
Q & A:
WHERE IS THE A.I. PRIMARILY OPERATING? On servers (computers that provide information or services to other computes over a network) in Data Centers. A.I. Companies use Graphics Processing Units (GPUs) chips in Data Centers to train A.I. and generate answers when people use chatbots, personal computers or gaming systems.
WHO ARE THE A.I. COMPANIES: OpenAI (Chatgpt), Anthropic, Google, DeepMind, Meta and Research labs, Amazon and Microsoft.
WHO IS PROVIDING THE INSTRUCTIONS? Researchers and Engineers in the A.I. Companies.
WHO IS MAKING THE CHIPS? Intel, Nvidia (Designs GPUs--processor chips built to handle lots of instructional calculations at once), TSMC (Taiwan-based company in Arizona--Actually Creates the Blackwell GPU processor chip that runs the A.I.in Data Centers).
A.I. stands for "artificial Intelligence". A.I. consists of bots that pursue a requested goal called "agents, or chatbots" that learn language patterns through instructions given by human developers to hold conversations or recommend things. It's called "artificial intelligence" because it acts like a human being once it learns and recognizes these patterns, but A.I. is not human. The term "superintelligence" (S.I.) means that it surpasses human intelligence. This is still speculative.

According to Brookings.edu:
The human extinction concern is a future risk scenario, NOT a prediction that it will happen. Human extinction level can only happen if advanced AI bot agents were given bad goals and too much "leeway" to act—through software, infrastructure, or other tools—it might pursue those goals in harmful ways. So, “Stopping the AI” might sound fearful, but it usually means submitting proposals to slow or pause the development of Advanced A.I. systems called Frontier AI models also known as A.I. Agent Bots placed in a system to set or follow specific goals. "Stopping the AI" does not mean turning off every A.I. tool. BUT A.I. COMPANIES HOLD THE POWER TO CONTROL OR CONSTRAIN THESE BOTS.
But because these bots can be highly capable of carrying out important tasks by the user, if these bots are designed and built and then told to do the wrong thing by the user, or it mistakes a word or phrase for something else, it could make grave mistakes. So, if misused or not checked before allowing these bots to be released to take action, these bots could become "rogue" and cause serious harm. For example, an AI-generated deepfake impersonated company executives and coworkers, persuading them to make a large money transfer enabling financial fraud. Researchers are currently working on making these A.I. bots follow intended instructions to keep them contained and under control:
OpenAI, Anthropic, Google, DeepMind, Meta and research labs are currently working to circumvent these harms because again, they hold the power.
SUGGESTED TIPS TO STOP ROGUE ADVANCED A.I. BOTS OR ROGUE BEHAVIOR
1.Bipartisan Collaboration is needed across local, city, state and national governments--especially here in the United States as well as international collaboration.
2. Monitor and Stop the Public Data Breaches. Representatives and Senators in Congress, Stop the Public Data Breaches. This is where the bot misusage started. The public has experienced several data breaches without monetary compensation for the invasion of their privacy. Dates and names should have be kept on WHO experienced the Data Breaches and WHEN the Data Breaches occurred. Many people in the public have been unknowingly sent viruses or codes attached to their emails or other personal information and when sent unknowingly, goes on to hack or attack larger corporations "as replicators" each time their email or other information is sent. PAY ATTENTION TO DATA BREACHES THAT ATTACK THE PUBLIC.
3. Create A.I. Apps and surveillance tools and techniques to flag or locate listening devices to warn the public, A.I. developer and business design locations or high-profile corporate meetings while in private settings or business settings in the United States.
4. Limit what A.I. can help with, make bots ask before making important decisions about a person or taking important actions to reach a goal; test the A.I. carefully before deploying, and keep an eye on WHO is making the A.I. chips, WHO is using it and HOW it’s being used.
5. Level and Label Tasks. Keep Dates.
CIVIL:
Everyday use--tasks that people use
economical use--tasks that businesses and organizations use for the economy
Corporate use--tasks that large corporations use
(CONTINUE)
City, State Laws--tasks for law officials
Everyday law officials
Investigative law Officials
Courts--lawsuit tasks
Medical use--tasks that medical personnel use; tasks for surgical use
Law officials--
Court Officials-
Military
U.S. Security--on and offline
U S. Homeland Security
U.S. and Military use
6. REQUIREMENTS
If small businesses or large corporations are working with Advanced A.I systems and has 50+ employees, A.I. Councils or Committees need to be formed to overview A.I. Computer chips (Nvidia), data, development, design and deployment should require user A.I. clearance both on and offline when developers are building A.I. systems to ensure integrity and ethics for access to tasks or tasks that require high-level info or input.
The more important the tasks, the more important the clearance should be.
If a business is working with Advanced A.I systems and has 50+ employees, A.I. Councils or Committees need to be in place:
Create and Hire people to work on Ethics and Risk Management Teams for different tasks.
Risk Management Teams should: Monitor the A I. design and building process. Pay attention to WHO builds, trains and deploys the A.I., what choices do they make for the A.I., and what oversight or limits are in place.
Ethics and Civic "people" committees:
FLAG AND REMOVE BIAS AND DISCRIMINATORY DATA. Remove data that has the potential to harm the public in any manner from theft and fraud to harm or privacy invasion of businesses or private lives.
Risk Councils should be made up of more than ethical hackers. They should weigh both sides of the roles of tasks as "builder" and "user" along with end results. Overlook and test A.I. deployment before allowing it to be released IN PUBLIC. Keep records.
* Call 211 If your electricity or water bills are surging.
HOW Data Centers Function
Every time you apply for credit, use online services or use any service (bank, card, store), you, the consumer, create a profile record made up of: Payment history, public records (like bankruptcies), and any other info you give (name, address, SSN) and is recorded by Credit Bureaus aka Experian, TransUnion, and Equifax. Each of these credit bureaus acts in different capacities.
Online databases such as Lexis Nexis, Intelius, Spokeo, White Pages, Been Verified and Experian to name a few, also compile your consumer public records and credit information, making it easily searchable by other people.
Public Records Offices also maintain online records of legal documents, including bankruptcies, property ownership, and criminal history. This information is often accessible to the public as well.
Financial Institutions: Banks, credit unions and lenders track your online financial activities, including account balances, loans, and payment histories.
Government Agencies: Libraries, the Internal Revenue Service (IRS) and Social Security Administration (SSA) keep records related to what you read, as well as your SSN and employment history.
Hospitals and clinics keep medical ID's and medical profiles.
When you use Apps (texting, social media) and websites, this also adds information (type of device you’re using, how much you paid for it, where you bought it, IP address, location) to the databases. This is called “behavioral data” which allows companies to sell or purchase "behavioral data information" about you. You supply this data each time you purchase something, and you then become known as a consumer.
What Happens Next?
Data Categorization. The collected data is segmented into categories such as age, income level, occupation, ethnicity/race, religion, educational level, marital status, household size and gender and then combined with your consumer behavioral data (what you buy, how you pay your bills, app usage and connections towards others, online comments, habits or patterns) to create a profile of you. When all of this data is compiled, it is kept in a Digital Supply Warehouse.
Digital Supply Warehouse
Your profiles and data info in the Digital supply warehouse ((SSP) are kept in cloud storage, which is comprised of network servers located in Data Centers in physical remote places across the world --mainly in North America, Canada, Australia, Europe and Asia, but hosted on the internet. These Data Center servers are maintained by the main Cloud service providers Amazon, Microsoft and Google.
How the process works
When you, the consumer, visit a website or app, these online items contain a "tag" better known as small pieces of code just like a product barcode in a store to help to both identify YOU, the consumer, (what you buy and your other data are the product) and categorize the online stores visited. Each time you visit a website or store website, the page loads and the store's ad tag, aka barcode, is activated. When the tag/barcode is activated, it then contacts the "digital warehouse" where your consumer data profile records are stored and identifies what kind of consumer you are and which store is contacting the warehouse about you, upon visiting the website.
Your data is then tailored, customized and prepared for sale to advertisers according to the buyer's demands (DSP) and requested needs.
When the data buyer chooses a consumer and is ready to buy, the data is sent from the warehouse to the Data Center so that advertisers can "bid" on your consumer data. Data Centers are the places that sell your consumer data to data buyers and advertisers.
Data Brokers, such as Credit Bureaus and others, are businesses who put consumer data together to create the consumer profiles in databases, to sell and make money. Credit Bureaus are very popular data brokers and maintain millions of consumer profile records in their own databases. Credit bureaus then make money from your consumer profile by selling your profile to Data Buyers and Advertisers on the Data Center network. Data Buyers buy consumer profiles from credit bureaus and elsewhere based on your consumer demographics (age, race, gender, income level, religion, location) or they might lend or rent your own consumer records to you, as the consumer, temporarily when there is a need (like viewing your credit report).
Where Do Data Brokers Get Their Info?
Data Brokers get their information from store loyalty programs, consumer signups for accounts, collaborations with credit bureaus, surveys, cookies, pixel tags, online tracking devices, social media platforms, major retail stores, e-commerce platforms, credit unions, banks, court records, business licenses, etc. who then cross reference and match your profile records using identifiers (SSN, email, phone, device ID) to build a unified profile database. Other Data Brokers include:
Acxiom-- data broker that creates and sells detailed consumer profiles to help businesses market to consumers.
Experian--data broker that sells credit scoring, marketing data services, and consumer insights to businesses.
Equifax-- credit bureau that compiles consumer credit information and
credit risk assessments for businesses.
TransUnion--Competes with Experian and Equifax for credit reporting, focusing on credit information for lenders, fraud and marketing.
Oracle Data Cloud--Part of the Oracle Corporation specializes in data-driven advertising based on consumer behavior data.
Nielsen
Helps business brands understand consumer preferences and what consumers like.
Facebook (Meta) and Google
Collect extensive consumer data through their platforms.
They get paid both to advertise and help other advertisers market to you based on your user and app behavior.
Info Group (now Data Axle)
Sells entire consumer databases for direct marketing and lead generation to businesses.
Dun & Bradstreet
Focuses on B2B data and analytics, helping companies with market research and credit evaluations of consumers and businesses.
Websites, apps, or media owners use ad servers (SSPs) in the Data Centers to offer your data to advertisers (via DSPs) so that the ads can be bought and shown to ad buyers.
But while demographic user data helps advertisers and businesses target you, the consumer, for ads effectively, it can also lead to exclusion or leave certain consumers out of the "advertising loop". Exclusion in automated advertising create negative feedback loops which will keep reinforcing the exclusion and then continuously blocking access to products and services for people who have already been discriminated against because certain advertisers will not advertise their products to certain zip codes or certain locations where marginalized people live. This negative feedback loop can also include applicant tracking system service links that advertise employment opportunities as well. Advertising links can affect hiring when automated advertising includes advertising links for businesses and corporations.
BUT HOW DOES BIG TECH MAKE MONEY?
Data Brokers (credit bureaus) make money by selling your data and information through "bidding" marketplaces held in Data Centers that provide quick consumer data profile summaries along with the pricing lists for purchase which come from the digital warehouse.
“Bidding” Marketplaces in Data Centers include:
Amazon Web Services (AWS) Data Exchange: Cloud-based platform for third-party data.
CoreLogic: Property and real estate data.
FactSet: Financial data and analytics.
TransUnion: Credit, risk, and fraud data.
Thomson Reuters: Financial markets and news data.
DFREIGHT: Logistics and freight data.
Foursquare: Location and foot traffic data.
SafeGraph: Geospatial data and
Data & Sons: Various datasets across multiple industries.
Dun & Bradstreet: Specializes in business and commercial data.
Trove: Focuses on consumer and demographic data.
Factual: Provides location-based data services.
Zillow: Offers real estate and property data.
Oracle Data Cloud: Specializes in exploring consumer data and audience insights.
Databroker DAO: Decentralized marketplace for buying and selling datasets.
Price lists
How much does your consumer profile data information cost to purchase?
Pricing varies and includes pay-per-record, subscription-based, and flat fee structures as follows:
Basic Consumer Data requests: $0.10 to $1.00 per consumer record
Behavioral Data requests: $1.00 to $10.00 per consumer record
Specialized or Rich Data requests: $10.00 to $100.00 or more per consumer record
Industry-Specific Data: $1,000 to $100,000+ (market trends, operational and legal data) for large dataset requests because when businesses know what other businesses are doing, it gives them a competitive edge. But again, consumer data drives these industries.
Subscription Services: $100 to $1,000+ per month.
Who's making the most profit off of your consumer data?
Google, Facebook (Meta) and Amazon profit the most through advertising while Microsoft is paid by Ad-Tech to provide the "digital warehouse infrastructure" (Data Centers) for the bidding process. Ad-Tech pays Microsoft to lease their Microsoft infrastructure for the "bidding" process and to send ads to you, the consumer.
Who is Ad Tech? Ad‑tech isn’t a single company; it’s an ecosystem of advertising firms that supply the ad spaces for bidding. Ad-Tech uses the Microsoft infrastructure to allow certain bidders to take turns in buying ad spaces to bid to consumers. Consumers, themselves, are not allowed to participate. The money advertisers make from the ads using your consumer data is then used to:
1.Continue to build A. I. and integrate Tensor Processing Units (computer chip built specifically to run machine-learning math very fast) into the cloud for faster response processing in real time using artificial intelligence.
2.Buy other companies that give them even more consumer data.
3.Pay tech company shareholders through dividends or stock buybacks
4.Hire more sales and marketing staff to sell even more ads.
5. Expand Their Data Centers (using large amounts of water to keep the servers cool) and network servers so the whole A. I. system can continue to function at a faster pace for the bidding processes of consumer data by brokers, advertisers and businessmen.
In short, ad money made off of your consumer profile records is what is helping fuel the A.I. industry and creating profitable returns for Tech Corporations, Shareholders and Business Tech owners.
ADVICE: USE YOUR OWN SELECTED DATA TO MAKE MONEY BY CREATING DATA CO-OPS. AVOID CHIPS THAT WILL TRY TO "THINK FOR US" IN THE PROCESSES OF BUYING AND SELLING.

They literally give their all to help our children. Yes, there are a few bad apples here and there, but that's in any industry. The point is, public school teachers stand on the front lines daily to give life and breath to future generations. They face very rigorous challenges--DAILY. By the way, they need a raise.
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