⸿ooper Applied Technology.com(TM SM R)

We Write & Validate Pseudocode for Algorithm Instructions & Rules


with our focus is on Generative Artificial Intelligence (GenAI) &nDeepFakes Detection of synthetic media

ņlgorithmsÖare the basis of

Artificial Intelligence (AI)---Computer Machine Learning (ML)-- & subsets,

computer programming, electronic devices, robotics


emailto: richardcooper@frontier.com or 5852561449 us, we are real people

How do humans learn? education, experience & practice.

†† How do computer systems learn? Algorithm Instructions & Rules,

training data, data mining & practice.

We write pseudocode for algorithms that are the basis to plan out the logic &

validation needed for Artificial Intelligence (AI), subset Machine Learning (ML) of AI,

Generative AI (GenAI) subset of AI, electronic devices & robotics to operate

& perform a task or multiple tasks.

Generative Artificial Intelligence (GenAI).

Generative artificial intelligence (GenAI} a subset of AI can create new content

including conversations, stories, images, videos & music. GenAI technologies can

mimic human intelligence in nontraditional computing tasks like image recognition,

natural language processing (NLP) that supports ML ability to interpret, manipulate &

comprehend human language..

Generative Artificial Intelligence (GenAI) can learn human language, programming

languages, art, chemistry, biology or any complex subject matter & reuse training data

to solve new problems.

For example, GenAL can learn English vocabulary & create a poem from the words

it processes & chatbots, media creation, product development & design &

what additional needs your mind can conceive.


Machine Learning a subset of AI where machines use algorithms & statistical models

to perform tasks without explicit instructions & new tasks through experience.

Deep Learning, a subset of ML, employs multiple layers of neural networks ( 'deep' networks)

to learn from vast amounts of data teaching computer systems

to process data in a way that is inspired by the human brain.

Deep learning models can recognize complex patterns in pictures, text, sounds &

other data to produce accurate insights and predictions.

Machine Learning can be supervised learning algorithms, unsupervised learning

algorithms or reinforced learning algorithms.

DEEPFAKES Misinformation & Deceptiom

Example of Misinformation & DeceptiomóD Day 1944

As a crucial part of their preparations for D-Day (6 June 1944), the Allies developed

a deception plan to draw attention away from Normandy,

code named Operation 'Fortitude' & part of a larger overall deception strategy

Operation 'Bodyguard'.

The PLAN: Fake radio traffic and decoy equipment including inflatable tanks and dummy

landing craft mimicked preparations for a large-scale invasion for the Pas de Calais.

Lieutenant M.E. Clifton James was employed, a bit Australian actor who bore

a striking resemblance to Bernard Montgomery to impersonate the British general.

James donned one of the generalís uniforms & black berets & flew to Gibraltar on

May 26, 1944 & then to Algiers where German intelligence was sure to spot him.

German Intelligence surmised that no attack across the English Channel could be

imminent with the Allied general scouting the Mediterranean.

Double agents delivered misinformation to the enemy to reinforce this deceit

before the Normandy landings.

This misinformation & deception scheme saved thousands of allied lives.

Today Deepfakes combines with Generative Artificial Intelligence (GenAI), deep learning

algorithms, Convolutional Neural Networks (CNNs) & Generative Adversarial

Networks (GANs )to develop digitally manipulated blend of synthetic media.

Deepfakes can identify & learn from large amounts of data to generate realistic

looking fake images, videos, text, emails, audio sounds, ChatGPT & phone calls.

Convolutional Neural Network (CNN) has the ability to recognize patterns in large amounts

of image data such as facial features & has been used for years typically in image

processing to blur & sharpen images & to perform other manipulative operations.

Generative Adversarial Networks (GANs) are specifically designed to imitate the structure

& function of a human brain using two neural networks, one for generating new

images or songs from existing images or songs database & the second neural

network for making images & songs appear real & sound real. 

DeepFakes pose a considerable threat to the authenticity of online content can be used to

spread misinformation & deception, damage reputations & create discord. 

 Deepfakes AI-generated misinformation has a person saying something they did not say,

appearing in a manner different from authentic visuals or diverging from reality

somehow, with the purpose of fooling the media viewer or a technology system.

DeepFakes Detection 

Deepfake detection employs various techniques to identify manipulated media content created

using deep learning algorithms so to differentiate fake from authentic media content.

Deepfakes detection learns from inclusive training datasets to detect media that has been

digitally manipulated into a blend of synthetic media.

Deepfakes Detection will analyze large amounts of reference data & determine what images,

videos, text, emails, audio sounds, ChatGPT & phone calls have been manipulated & fake.

We apply DeepFakes detection to identify fake images by analyzing the dull shadows around

the eyes, unrealistic facial hair, overly smooth or wrinkled skin, fictitious moles,

unnatural lip color & face swapping.

Ask yourself, Is it believable or too good to be true?


Cooper Applied Technology (USA, family business) has transformed from an outdated inefficient analog company into a development to deployment technology team to

write pseudocode for algorithms that are the basis to plan out the logic &

validation needed for artificial intelligence (AI), computer machine learning (ML),

generative AI (GenAI), electronic devices and robotics to follow

to perform a task or multiple tasks.

Our focus is GenAI & Deepfakes Detection


I acquired my practical & business technology knowledge & experience

as a Director, CEO, CTO, VP, employee of various companies before joining

 Cooper Applied Technology(SM TM R)

I earned BS degrees in Accounting & Electrical Engineering at RIT,

  MBA degrees in Finance & Economics at Syracuse University.

Richard Cooper CEOCTO

Cooper Applied Technology updates our web page as Technology continues to update.

Real People here not an AI ChatGPG



https://cooperappliedtechnology.com*no cookies here*

Cooper Applied Technology67 willowcrest dr  ste 1449

Rochester, NY 14618  USA†† V: 585-256-1449

All incoming & outgoing emails are scanned for security & not a source of ROBO calls or SPAM.

Email addresses are not published!  NO PERSONAL OR BUSINESS INFORMATION IS SOLD!