"WOW"-- SEE SOME GREAT Ai and ML Videos linked BELOW)

https://www.youtube.com/watch?v=VwtC_4t2g5M&vl=ea.n shows data regarding possible life on other planets. This video also shows how Ai is used to create robots that look and function like humans. The Allen Telescope Array (ATA) is explained in this video.

Used to map the sky.for potential Aliens.

https://www.seti.org/ata

https://www.youtube.com/

https://www.youtube.com/watch?v=V5aZjsWM2wo

- -SEE MORE GREAT VIDEOS SOON --

rhe O ould train an AI program to classify whether an image depicts a banana or a donut . First, provide the A computer with a large number of sample images of donuts and bananas

THIS IS OUR

CURRENT FEATURED ARTICLE:

-----------------------------

"INTENDED AND UNINTENDED

CONSEQUENCES

of

ARTIFICIAL INTELLIGENCE (AI)

and

MACHINE LANGUAGE (ML)"

by

The Good United States Artificial Intelligence Group

--------------------------------------------------

GoodUSAi.com

The below three named individuals are concurrently Co-Founders and Officers

of

The "Good United States Intelligence Group."

Qin ("Tim") Sheng, Russ Peterman, John E. ("Johnny") Miller

1. Dr. Qin ("Tim") Sheng

* Baylor University, Math Professor and Math Researcher

* Baylor University Center for Astrophysics, Space and Engineering

* Research Editor in Chief at Taylor and Francis`

* Ph.D. in Mathematics at University of Cambridge

Dr. Sheng joined the Baylor faculty in August 2005. Prior to coming to Baylor he held a research position in University of London (1989-1990), a visiting professor position in Baylor University (2003) and faculty positions in National University of Singapore (1990-1995), University of Louisiana (1996-2001) and University of Dayton (2001-2005). He was a recipient of the J. T. Knight Prize in Mathematics (1987) and Lundgren Research Award (1989).

Dr. Sheng was an invited research participant of the Isaac Newton Institute for Mathematical Sciences, Cambridge, England (2007). He was a U.S. Air Force SFFP Research Fellow (2005-2007). Dr. Sheng directed two doctoral dissertations, 8 Master of Science theses and a number of undergraduate research theses.

Dr. Sheng's on-going research is in computational and applied mathematics. In particular, he is interested in splitting and adaptive methods for solving singular partial differential equations. He has been involved in cross-disciplinary projects in scientific and engineering computations. Dr. Sheng has been active in his research fields and community. He is on editorial boards of several scholarly journals and research issues. His special projects have been support by the United States Air Force Research Laboratory and Department of Defense.

Dr. Sheng’s math focus is computational and applied mathematics. His studies have been continuously supported by the University, USAF, DoD, and others. He is married to Helen. They have sons Andy and Dan. He enjoys reading, painting, traveling, photography and spending time with the family.

https://www.baylor.edu/math/index.php?id=54497h

https://www.baylor.edu/math/index.php?id=54019

https://sites.baylor.edu/qin_sheng/

https://www.baylor.edu/research/news.php?action=story&story=220906

EMAIL: Qin_Sheng@Baylor.edu

------------------------------------------

2. Russ Peterman

President of Peterman Consulting Associates,

Round Rock, Tx

2005 - Present

Russ is active in the Austin Metro entrepreneurial space, focused on life sciences, nanotechnology, aviation tech, as well as general and specific challenges with underlying physics components. Russ has served on numerous state and local boards and commissions focused on technology and economic development. He holds five technical USPTO patents, has started numerous technical and non-profit companies, mentored over twenty successful technical and administrative interns, and is currently available for mentoring, consulting and new company formation and funding, among other consulting areas.

----------------------------------------------

* BS -The University of Texas at AustIn

1974

* MS - The University of Texas at Austin -

Russ completed his undergraduate work in physics at UT-Austin. After serving as the Station Science Leader for Applied Research Laboratories/NSF at McMurdo Station, Antarctica he completed his Masters in Physics at UT Austin and then transitioned to the Ph.D program in Electrical and Integrated Circuit Engineering at UT Austin.

* 1978

- University of Colorado (Boulder)

Business Management

* 1995

--------------------------------------

RUSS PETERMAN - EMPLOYMENT

-Research Scientist-

Applied Research Labs

The University of Texas at Austin

1970 - 1978

- Radian International LLC-

(Principle Scientist)

1978-2001

-Vaisala -

(Interim Vice President)

2001-2002

- Peak Weather Resources-

NCAR

(President) 2002-2004-The University of Texas

-McDonald Observatory-

(Supervisor of Observatory)

2004-2006

-Texas Life Sciences Collaboration Center-

(Executive Director)

2007- 2012

- Frame Switch Energy , Inc.-

(President and Founder)

2012-2013

-Texas Bio Corridor Allowance-

(President and Founder)

2012 - 2014

Tango Flight

-(Director of Business Development)-

2016--2018

Tango Flight is a non-profit organization created to support GISD's Project Lead the Way in building a Van's RV-12 Light Sport Aircraft . The aircraft has been built entirely by high school students and experienced mentors, as part of a class with award-winning engineering teacher Dan Weyant at Eastview High School in Georgetown, Texas. In what is envisioned as a ten-year project to build one aircraft per school year for the next ten years.

---------------------------------------------------

3. John ("Johnny") E. Miller

Texas Emeritus Attorne

State Bar of Texas

The Law Office of John E. Miller

Bryan, Texas

EDUCATION

Mr. Miller received a BA in Political Science (with a minor in Oral Communication) from Baylor

University in 1967, a JD in Law from University of Memphis in 1973, a LLM in Urban (Legal) Affairs from University of Missouri KC Law School in 1980, and a CPCM from NCMA in 2015.

LEGAL FOCUS, PUBLICATIONS, AND CIVILIAN AWARDS

Mr. Miller's legal focus is in contracts, procurement, business law, computor software, IP, licensing, export compliance, technology transfer, and Ai & ML legal and contractual issues. In the last 20 years he has published more than 20 professional articles in the National Contract Management Association (NCMA) "Contract Management (CM) Magazine", American Bar Assocjation, and other national magazines. Links to each of these profession publications can be

found at www.con-tracts.com.'

In 1990 he was one of ten of Radian Corporation's 2,000 employees to win a "Corporate Achievement Award." This award was granted to him primarily for his work in safeguarding Radian's intellectual property.

In 2007 he was the co-winner of the Annual National Contracts Management Association - Charles J. Delaney Memorial Award for the most outstanding national article published in the National Contract Management Association Magazine.

Early 2013 he was actively involved in the following Colorado CLE presentation: *Advising Small Companies - Responding to Your Clients' Needs with Practical Advice – February 27, 2013 – Colorado Bar Association CLE - Basics of Contract Drafting and Negotiations: - Analysis of common contract provisions; Strategy in Contract Drafting; and Negotiating Tips - Presented by Colleen Belak, Esq. - http://cle.cobar.org/Seminars/EventInfo/sessionaltcd/BL022713L.aspx - with Johnny Miller’s Sixty Practical Contract Drafting Tips – NCMA CM Magazine in April 2007.

JOHNNY'S MILITARY HONORS and EXPERIENCE

During the first semester of the second year of Law School Johnny was "fortunate enough" to be drafted into the US ARMY on October 31, 1968 (Halloween). In those days military draft educational deferments were not granted to law/legal students.

After completion of US Army Basic Combat Training on January 11, 1969 Colonel Irvine G. Hughes, Infantry (Commanding) selected Johnny as the "TOP MAN" in his US Army Basic Combat Training Company, Ft. Leonard Wood, Missouri.

Mr. Miller received some additional main frame computer communications & electronics repair training at Fort Gordon, Georgia and Fort Ord - Oakland, California. Then he was assigned to Southeast Asia.

When he arrived in Southeast Asia, he was

immediately re-assigned to S-2 Army Security and Intelligence. Then he served in the US Army for two deployments in Southeast Asia from 1969 to 1971 working in S-2 Army Security and Intelligence (Battalion Level) for the US Army Signal Corp. He was awarded the US Army Commendation Medal for his work in Southeast Asia by his Commanding Officer - Lieutenant Colonel Ed Jones. Lieutenant Colonel Jones was an outstanding leader , a savy analyst, and an excellent mentor for his troops.

Mr. Miller was assigned to provide certain US Army S-2 Security and Intelligence support duties in Southeasr Asia during two deployments.

Johnny was permanently stationed in Korat, Thailand. He and his wife Nancy (a US Citizen) lived off post on the local Korat economy. Nancy got a job as a full time kinder-garden teacher (independent contractor) on the Korat, Thailand US Army base - Camp Friendship. Thailand is a very beautiful country and Thai people are outstanding. Thailand is best described as "The Land of Smiles" due to the country's friendly people and fascinating culture.

As a result of his military service in Southeast Asia, Johnny received the Vietnam Service Medal, the Vietnam Campaign Medal, the National Defense Service Medal, and the US Army Commendation Medal. The DoD gave him a "Secret" and "Top Secret" security clearance for military and later civilian use.

After completion of his military service obligations in 1971 he completed the remainder of law school and then practiced law as a small town attorney for six years in a general civil practice in Missouri. He was also an Assistant Professor of Business Law at Central Missouri State University during part of this time period.

At that point onward, starting in 1980, he moved-on as a Texas attorney and started working in Texas for several high-technology companies.

Mr. Miller has continuously worked (with ... "his boots on the ground"...) for more than 40+ years in many big business/advanced technology domains in various cities inluding but not limited to: Austin, Houston, St. Louis, Chicago, Detroit, Memphis, Atlanta, Cleveland, North Carolina, New York, Boston, Denver, Dallas, LA, San Francisco, San Diego, Sacramento, San Antonio, El Paso, Philly, Bangkok, Sydney, Melborne, Mexico City, and other locations with increasing levels of responsibility.

A list of his numerous specific high tech clients and employers is available upon request.

___________________________

COLLEGE BASEBALL

In the 1960s, Johnny played first year baseball for Baylor University during 1963-1964 as a pitcher. He was the starting pitcher in games against University of Texas, Texas A&M, SMU, and others. His coach was Dutch Schroeder.

Then - he played Semi-Pro Baseball for the Waco Braves in 1964-1967 under coach Henry. There Johnny set a team record on one very hot day of hitting a home run over the left field fence and also being the winning pitcher in the same game.

Johnny was invited to and participated in a professional Single "A" try-out as a pitcher for the Burlington Bees Pro Baseball Team in Iowa in the Summer of 1967. Although well regarded by the Burlington Bees, he was not selected to play for the Bees.

-------------------------------------

BIOS

More comprehensive Bios are available for each of the above three Co-Founders upon request.

----------------------------------------------

THREE CO-FOUNDERS

The above three individuals are the Co-Founders and Officers of

"The Good US Artificial Intelligence Group."

This Group is not a legal, business, enterprise, not-profit, foundation, governmental or academic entity of any type but rather merely an independent, informal, unstructured, unbiased group of experienced and well-regarded professionals (in their respective fields) who share a strong common interest of doing and promoting what is "Good" (not bad) for the USA in the areas of Artificial Intelligence (AI), including but not limited to Machine Learning (ML).

The word "Good" generally means morally right, virtuous, beneficial, advantageous, positive,

high quality, excellent, cool, outstanding, super, empathy, ethics, integrity, honesty, social benefit

and so-forth.

The word "Bad" generally means detrimental, harmful, poor quality, inferior, evil, detrimental, immoral, malicious, and so forth.

THE MAIN THRUST OF THIS WEB SITE

IS NOT THE DARK / EVIL SIDE OF AI BUT RATHER RESEARCHING, EDUCATING, TEACHING, TRAINING, PROMOTING, AND APPLYING: (1) SAFETY; (2) INTEGRITY; (3) HONESTY; (4) ETHICS; (5) ZERO BIAS; (6) EMPATHY; (7) RESPONSIBILITY; AND (8) SOCIAL BENEFIT WITH REGARD TO ALL ASPECTS OF GOOD UNITED STATES ARTIFICIAL INTELLIGENCE INCLUDING (BUT NOT LIMITED TO) MACHINE LEARNING, DEEP LEARNING, ROBOTICS, ROBOTIC VISION, NATURAL LANGUAGE PROCESSING, VOICE RECOGNITION, SPEECH RECOGNITION, IoT, FACIAL RECOGNITION, DRONES, AND OTHER AI.

-----------------------------------------------------

DEFINITION OF AI

The definition of "Artificial Intelligence' (AI) can be a little confusing. However let's give it a shot and try to make it easier understood.

JUST REMEMBER ... AI IS THE SIMULATION OF ANY INTELLIGENT TASK THAT: (1) is AI machine and/or software operations that; (2) mimics human brain processes; (3) by learning and; (4) making decisions based on data.

I think it is safe to say that all AI programs tend to operate in one of two ways:

(1) Unsupervised Learning; and

(2) Supervised Learning.

First Method - Using Unsupervised Learning, AI computers train themselves through trial and error. Their computer algorithms learn to perform tasks independently by: (1) collecting Big Data; (2) analyzing the Big Data; (3) creating new algorithms based on test results; and (4). making decisons .

Second Method– Using human Supervised Learning, AI computers learn: (1) by collecting data; (2) by Humans providing data; and (3) by using AI programs, which then scan the data to detect patterns, finding anommolies, and creating classification rules.

For example, (using Supervised Learning) one could train an AI program to classify if an image depicts a banana or a donut. First, provide the AI computer with a large number of sample images of

donuts and bananas Training Ser). One would define each image as a banana or a donut. The AI program would create a Model from this information containing a series of algorithms about whether an image is a donut or banana. For instance, it may note the size of the object, color, and whether it has a hole in the center. Once the A training is done, one would feed it new images of bananas and donuts to determine how accurately the AI system Model classifies the objects. If the AI user accuracy is low one could add data to the Training Set and adjust the algorithm (the set of rules it follows) to improve it.

AI is a subset of Data Science. Data Science is a subset of Computer Science. AI (as a subset of Data Science) is the ability of a machine and/or of computer software/firmware programs to find, assemble, process, calculate, translate, think, reason, learn, problem-solve, identify risks, create speech recognition, develop human-like speech generation, incorporate feedback, remember, reduce errors, exercise continuous improvement, and potentially act (to some extent). It could be performing in a manner that some people would consider it as ... "intelligent."

AI is a field of science covering how computers can make decisions as well as (and sometimes better than) humans. AI needs to be able to understand humans. Machine Learning (ML), a subset of AI, refers to the popular modern-day techniques for creating software that learns from data. It involves learning how to carry out a task from data without being programed to carry out the task. For example, Facial Recognition (FR) is Machine Learning. Applications of Machine Learning normally use a Neural Network (NN). A Neural Network is a computer system mimicking the human brain. An example of Deep Learning is Speech Recognition (SR). Included in the AI definition is the difference between weak/narrow AI and strong/broad AI. Strong AI genuinely simulates human reasoning while weak AI is just focusing on getting a system to work without simulating human cognitive behavior (thinking the way a human would think). Today most of the AI work falls somewhere between weak AI and strong AI. Let's call that "Mid-Range AI." There - that AI definition wasn't so bad - was it?

-------------------------------------

PROFESSOR McCARTHY

The term "Artificial intelligence" dates back to about the mid-1950's. About 1956. AI was first named and identified in 1956 by the Sanford professor / researcher John McCarthy as a sub-field of the larger academic study of computer science.

---------------------------------------------

ROCK & ROLL

(The mid 1950's were famous for the development of creative "Rock & Roll" music. Remember Chuck Berry, Little Richard, Jerry Lee Lewis, Bo Diddley, Elvis, Buddy Holly, Johnny Cash, Connie Francis, etc, etc? (FYI - Little Richard was the King of Rock & Roll). They also were bursting in on the music scene in the mid-1950's. So, it was only fitting for that time period to also be the time of the start of development of creative AI.

----------------------------------------------

IMPORTANCE OF ARTIFICIAL INTELLIGENCE (AI)

Artificial Intelligence (AI) systems will play a major role in every aspect of life on earth. It will change the world in which we live. AI will improve the speed, accuracy, efficiency, and safety of human decision making. However, it will carry the risks of some unintended or unplanned results.

In fact, AI may turn out to be a greater world problem than any of the future concerns, such as: nuclear security, radiation, climate change, civil unrest, chemical - biological warfare, terrorism, cyber-security, asteroid impact, loss of coral and plankton, alien visitors, locus invasion. destroyed food supply, bacteria and virus threats, power grid destruction, ozone depletion, loss of internet, poverty, hunger, lowering water supply, air pollution, sanitation, water shortage, water pollution, nuclear waste, de-forestation, lack of education, loss of rain forests, "Disclosure" (in the ET meaning) , failure of monetary system, world-wide bankruptcy, and so forth.

Is it possible that AI will be an "existential" event once AI reaches the human /general / conscious / super level and beyond? Let us know what you think

----------------------------------------------------------

PROGRESS ALWAYS COMES AT A COST

Paper fundamentally changed the way information was stored and distributed, but its production contributes to de-forestation. Industrialization increased our standard of living, but has led to much pollution and social ills.

The benefits brought by the internet are too many to mention, however, misinformation, false-truth, erosion of privacy, scams, election interference, hackers, pornography, dark web, terrorists, human traffic facilitation, and others are all unintended consequences. Not even medicine is free from side effects.

Rarely at the time of creation / implementation is the designer / inventor / creator / implementer / integrator / reseller the best judge of how the system will be used, or truly knows what good or harm will come of it. Understanding this, we ought to analyze the issues deeply before taking on an unintended bad consequence type project.

---------------------------------------------

WHY NOW? BECAUSE THE TIME IS RIGHT

Unlike the past "AI Winter", AI / ML is possible nowadays due to: (1) giant increases in computational capabilities; (2) huge growths in data (big data); (3) focusing on specific / unique problem issues to be addressed; (4) being able to timely convert those unique issues / problems into targeted knowledge engineering rules (algorithms) so that AI systems can learn; (5) efficiently plugging the resulting rules into appropriate AI systems; and (6) allowing AI systems to learn the rules automatically.

-----------------------------------------------

MAJOR AI COMPONENTS

The following items are some of the major components that make up AI and help to better define AI:

1. Machine Learning (ML)- Machine Learning (ML) is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn fast. It allows systems to learn from data, spot repeating patterns and make decisions without explicit instructions or human intervention. It enables entities/humans to save time and resources.

ML IS THE MOST IMPORTANT ASPECT OF AI .

ML IS THE MOST ESSENTIAL PART OF AI.

2. Big Data - In the article: "Data, Data, Everywhere", Baylor Arts and Sciences, Special Issue, Research in Arts & Sciences, Fall 2018 (by Julie Engebretson) it states: "It is estimated that about 16.3 Zettabyes of data - the equivalent of 16.3 trillion Gigabytes - is produced in the world each year." Wow ! Now that's big data !

Dark Data (DD) is sometimes data that is obtained through numerous computer network operations but because it is so disorganized it is generally not used to obtain insights or for decision making.

According to Oracle: "Put simply, Big Data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them." It includes structured and unstructured data. There is a growing belief that data is more valuable than oil.

Before data can be used in an AI use case it must be cleaned, prepared, and labeled.

Data is the heart of AI. Data is the new electricity. Cleaning data involves eliminating duplicates, deleting extraneous data, and working with data by humans to get it ready for use with the appropriate AI algorithm for a use case.

3. Artificial Neural Networks - Artificial Neural Networks are one of the main tools used in Machine Learning. As the “neural” part of their name suggests, they are brain-inspired systems which are intended to replicate the way that we humans learn. Neural Networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize.

4. Robotics (including Robotic Vision) - From Wikipedia, the free encyclopedia, it states: “Robotics is an interdisciplinary branch of engineering and science that includes mechanical engineering, electronic engineering, information engineering, computer science, and others. Robotics deals with the design, construction, operation, and use of robots, as well as computer systems for their control, sensory feedback, and information processing. These technologies are used to develop machines that can substitute for humans and replicate human actions. Robots can be used in many situations and for lots of purposes, but today many are used in dangerous environments (including bomb detection and deactivation), manufacturing processes, or where humans cannot survive (e.g. in space). Robots can take on any form but some are made to resemble humans in appearance. This is said to help in the acceptance of a robot in certain reputation type behaviors usually performed by people. Such robots attempt to replicate walking, lifting, speech, cognition, and basically anything a human can do. Many of today's robots are inspired by nature, contributing to the field of bio-inspired robotics.” From Wikipedia, the free encyclopedia it states: "Robot vision or a Vision Guided Robot (VGR) System is basically a robot fitted with one or more cameras used as sensors to provide a secondary feedback signal to the robot controller to more accurately move to a variable target position. VGR is rapidly transforming production processes by enabling robots to be highly adaptable and more easily implemented, while dramatically reducing the cost and complexity of fixed tooling previously associated with the design and set up of robotic cells, whether for material handling, automated assembly, agricultural applications, life sciences, and more.

5. Facial Recognition- Now here is a controversial topic. A Facial Recognition (FR) system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which Facial Recognition systems work, but in general, they work by comparing selected facial features from given image with faces within a database. It is also described as a Biometric AI (BAI). It is an application that can uniquely identify a person by analyzing patterns based on the person's facial textures and face shape.

6. Speech Recognition - Speech Recognition (SR) is the ability of a machine or program to identify words and phrases in a spoken language and convert them to a machine-readable format. Rudimentary speech recognition software has a limited vocabulary of words and phrases, and it may only identify these if they are spoken very clearly.

7. Voice Recognition - The ability of hardware and software to recognize the voice of a person (as unique as a fingerprint). It differs from Speech Recognition 's ability to understand words - not the identity of a person.

8. Deep Learning - Networks capable of learning from unstructured data when the network is unsupevised.

9. Internet of Things (IoT)

https://en.wikipedia.org/wiki/Internet_of_things

10. Drones - An Unmanned Aerial Vehicle (UAV), commonly known as a drone, is an aircraft without a human pilot aboard. UAVs are a component of an Unmanned Aircraft System (UAS); which include a UAV, a ground-based controller, and a system of communications between the two. The flight of UAVs may operate with various degrees of autonomy: either under remote control by a human operator or autonomously by onboard computers. Compared to manned aircraft, UAVs were originally used for missions too "dull, dirty or dangerous” for humans. While they originated mostly in military applications, their use is rapidly expanding to commercial, scientific, recreational, agricultural, and other applications, such as policing, peacekeeping, surveillance, product deliveries, aerial photography, agriculture, smuggling, and drone racing. Civilian UAVs now vastly outnumber military UAVs, with estimates of over a million sold by 2017, so they can be seen as an early commercial application of autonomous things, to be followed by the autonomous car and home robot.

11. AI Algorithms - are mathematical instructions that provide step by step procedures for calculations. It is kind-of like a set of step-by-step instructions.

12. Augmented AI - At this point (all things considered), it seems as though one of the most promising methods of beneficially, expeditiously, and effectively making the best use of Artificial Intelligence (AI) is to use it to augment the efforts of the AI user.

AI should, on most cases only be used to assist the human worker. It should not be used to replicate and replace him/her.

Augmented Artificial Intelligence (AAI) is the best way to use AI. Augmented Artificial Intelligence refers to the result of skillfully blending human and machine intelligence. With the the proper use of Augmented Artificial Intelligence, Humans will be safer during disasters, better cared for as they get older, and more independent for they get older.

-------------------------------------------------------------

"SOME OF THE POTENTIAL BENEFITS OF AI

1. Humans will be safer during disasters;

2. Better care for the aging;

3. More independence for the elderly;

4. Reduction of traffic accidents;

5. Could become a tightly coupled cognitive unit with humans;

6. AI can augment our brain;

7. AI can augment our bodies;

8. It can make humans better at everything;

9. Great improvements to orthotics;

10. Wide applications to exoskeletons;

11. Improvement to human senses (vision, hearing, smell, touch, etc);

12. AI can help to solve climate change;

13. AI can quickly review millions of pages of documents;

14. AI can help to cure diseases;

15. Anticipated to greatly assist with space travel;

16. Improved internet access;

17. Use of robots powered by AI in homes;

18. Wide-spread embedding of AI;

19. Prevent environmental catastrophes;

20. Creation of more free time for humans to be more creative;

21. Improves ability to help customer;

22. Improves e-commerce;

23. Chat boxes.;

24. Better decisions and fewer mistakes;

25. Assisting humans with Augmented Artificial Intelligence;

26. Help compose music.

27. Improved gaming and media;

28. Help in the hospitality and restaurant business;

29. Improve the back office;

30. Bank loan determination;

31. Fraud determination;

32. Assisting in ancestry research;

33. AI serving as an enabler;

34. Potential wide-spread access and use of open-source AI world-wide;

35. Airlines can save money annually on fuel savings in-flight and during take-off and landing (such as Quantas Airlines saving $40M in 2018) due to AI);

36. Assisting air traffic control to improve safety, efficiency, and decision making;

37. Use of AI and ML in the oil and gas business to address safety, exploration, discovery, drilling, production, analysis, and distribution.

38. Use of AI and ML can enhance sales by (1) achieving real time data analysis, (2) handling routine repetitive processes, (3) improving customer support, (4) cutting costs, and (5) improving quality.

SOME OF THE POTENTIAL

RISKS AND THREATS TO AI

Here are numerous risks and threats that arise out of the use of AI including Machine Learning (ML),, Deep Learning (DL), Robotics, Robot Vision (RV), Internet of Things (IoT), Signal Processing (SP), Neural Networks (NN), Natural Language (NL) and Unstructured Data (UD) or Dark Data (DD) that need to be anticipated and analyzed:

1. If a motor vehicle accident involves AI, trying to find the liable party is difficult and confusing (i.e. a autonomous car hits another vehicle);

2. AI often has to identify vehicles, people, roads, sidewalks, traffic signals, buildings or other items. To do this, AI relies on robotic vision (cameras, radar, infrared, sonar, etc.), sensors, and recordings. Things may not look the same to AI as it does to humans. AI also can reflect the biases of the software/firmware designer/developer;

3. AI is getting closer to actual human-like consciousness. This is also often called: Super A or General AI;

4. Robots utilizing AI may sometime be seeking certain civil rights;

5. If AI commits or is involved in a crime who is guilty?

6. Privacy rights of people are potentially being eroded with AI;

7. AI has been developing a very rapid rate. AI has outpaced applicable legislation;

8. How will AI generated information be used in court? (No right of cross-examination, etc.);

9. Mass unemployment;

10. Wars;

11. Autonomous weapons;

12. Mass surveillance;

13 Discrimination;

14. Improper actions of robo doctors;

15. Wrongful financial services actions;

16. Constructing algorithms improperly;

17. Improper healthcare issues;

18. Improper insurance issues;

19. Ineffectively regulatory issues;

20. "Reasonable man" and "proximately caused" issues in tort law;

21. Non competitive antitrust AI issues;

22 In addition to the potential benefits of the quantum issues, we also need to keep an eye on the soon to be emerging potentially harmful uses of quantum physics (QP), quantum computers (QC), quantum gravity (QG), quantum mechanics (QM) and other quantum issues;

23. Eventuality of AI hacking and subversive activities involving rules, algorithms, data, and other and/or other components;

24. Potential autonomous killing machines (except in very rare US military cases for cause);

25. In the event of consciousness in AI, seek the embedding of ethics, clean data, bias, responsibility, error prevention, legal compliance, integrity, moral values, ethics, continuous human feedback, and proper social manners;

26. Danger of manipulation of data a motor vehicle accident involves AI, trying to find the liable party is difficult and confusing. (i.e. a autonomous car hits another vehicle);

27. AI often has to identify vehicles, people, roads, sidewalks, traffic signals, buildings or other items. To do this, AI relies on robotic vision (cameras, radar, infrared, sonar, etc.), sensors, and recordings. Things may not look the same to AI as it does to humans. AI also can reflect the biases of the software/firmware designer/developer on be used in court? Autonomous weapons;

28. Mass surveillance;

29. Discrimination;

30. Improper actions of robo doctors creating or using algorithms improperly;

31. Improper healthcare issues;

32. Improper insurance issues;

33. Ineffectively regulatory issues;

34. "Reasonable man" and "proximately caused" issues in tort law;

35. Non competitive antitrust AI issues;

36. In addition to the potential benefits of the quantum issues, we also need to keep an eye on the soon to be emerging potentially harmful uses of quantum physics (QP), quantum computers (QC), quantum mechanics (QM) and other quantum issues;

37. Eventuality of AI hacking and subversive activities involving rules, algorithms, data, and other and/or other components.;

38. Potential autonomous killing machines (except in very rare US military cases);

39. In the event of consciousness in AI, seek the embedding of ethics, clean data, bias, responsibility, error prevention, legal compliance, integrity, moral values, ethics, continuous human feedback, and proper social manners.;

40. Danger of manipulation of data. Data is a huge AI issue.

See:

In "Data, Data, Everywhere", Baylor Arts and Sciences, Special Issue, Research in Arts & Sciences, Fall 2018 (by Julie Engebretson) It states: "It is estimated that about 16.3 Zettabyes of data - the equivalent of 16.3 trillion Gigabytes - is produced in the world each year." Wow !

Now that's Big Data !

41. "Humans Need Not Apply" Issue.

42. If a motor vehicle accident involves AI, trying to find the liable party is difficult and confusing. (i.e. a autonomous car hits another vehicle).

43. Below is a great link to a very thought-provoking article on the AI dangers to democracy and privacy in the US: "Artificial Intelligence: Risks to Privacy and Democracy - Karl Manheim* and Lyric Kaplan" 21 Yale J.L. & Tech. 106 (201

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=327301

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HOW TO POTENTIALLY MITIGATE AI/ML RISKS, DANGER, AND THREATS

1. Understand the vast complexities of AI / ML;

2. Track AI / ML reasoning;

3. Participate in rule making, algorithms development, and data gathering;

4. Use good clean structured data;

5. Allocate AI / ML risk amount to the extent applicable to the parties due to benefits received by the applicable parties;

6. Start soon with Augmented AI applications;

7. Protect AI with IP such as patents and trade secrets;

8. Establish an AI team;

9. Recruit and retain AI talent;

10. Humans will need to get smarter in order to deal with AI;

11. There needs to be a legal revolution with a focus on experiential and lifetime learning;

12. Participate in government regulation of AI;

13. Make AI / ML secure;

14. Develop strong AI / ML testing prior to deployment;

and

15. Think like a robot.

Certain portions of the next-above section (on potential mitigation of AI/ML risks, threats, and dangers) have been summariized and/or derived from: "UnderstanSding the Legal Implications of Artificial Intelligence", Perkins Coie, News – Insights 5/12/2018

-[and]

Two-Minute Guide To Artificial Intelligence, Parmy Olson, Forbes – 10/3/2018

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"SEVEN PATTERNS OF AI. "

SEE: https://www.cognilytica.com/2019/04/04/the-seven-patterns-of-ai

CHECK OUT THE COGNILYTICA WEB SITE LINK JUST BELOW DESCRIBING THE ... "SEVEN PATTERNS OF Ai:"

https://www.cognilytica.com/2019/04/04/the-seven-patterns-of-ai

"THE COGNILYTICA WEB SITE DECRIBES HOW AI APPARENTLY SEEMS TO REPEAT THEMSELVES IN DIFFERENT COMBINATIONS IN ALL OF THE AI USE CASES THAT VARIOUS ENTITIES HAVE CREATED." https://www.cognilytica.com/2019/04/04/the-seven-patterns-of-ai

It is highly recommended that the outstanding ..."COGNILYTICA WEB SITE"... be reviewed to see the interesting AI/ML Use Cases decribed there.

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WATCH THIS GREAT COLD FUSION AI / ML VIDEO !

Don't Miss ThIs Great Video About "General AI / ML. " This may be the first time a computer achieved the General (Human Level) AI/ML."

Deep Mind - World's First General AI ?

https://www.youtube.com/watch?v=T//nUYcTuZJ

(Back-Up Link)

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THE GOOD UNITED STATES ARTIFICIAL INTELLIGENCE GROUP

(GOODUSAI.COM)

INTERESTING AI AND ML WEB SITES SITES

1. IMPROVED MEDICAL DIAGNOSIS CASES WITH AI / ML https://www.youtube.com/watch?v=HrKzXLgGohA

2. TOP 25 MACHINE LEARNING START-UPS

https://www.forbes.com/sites/louiscolumbus/2020/04/26/top-25-machine-learning-startups-to-watch-in-2020/#a046b61f5214

3. AI AND ML TRENDS TO LOOK FOR IN 2020

https://medium.com/@quosphere/ai-and-ml-trends-to-look-for-in-2020-d76cdcceb302

4. 2020 AI & ML TRENDS

https://www.forbes.com/sites/gilpress/2019/12/09/120-ai-predictions-for-2020/#676708c50cf9

5. TEN BEST 2020 FREE ON LINE AL AND ML CLASSES5

https://www.forbes.com/sites/bernardmarr/2020/03/16/the-10-best-free-artificial-intelligence-and-machine-learning-courses-for-2020/#350f41cf6f66

6.. THE FUTURE OF ARTIFICIAL INTELLIGENCE

https://builtin.com/artificial-intelligence/artificial-intelligence-future

7. THE TOP 20 WORK FROM HOME OR REMOTE JOBS

https://www.forbes.com/sites/laurabegleybloom/2020/02/11/work-from-home-top-20-companies-remote-jobs/#4add

8. WHAT IS THE DIFFERENCE BETWEEN AI, ML AND DEEP LEARNING?

https://www.guru99.com/machine-learning-vs-deep-ng.html

9. WHAT ARE THE AI AND ML TRENDS IN 2020?

https://www.forbes.com/sites/mariyayao/2020/01/22/what-are--important-ai--machine-learning-trends-for-2020/#1b7e436e2323

10.. 2020 AI and ML TRENDS

https://www.forbes.com/sites/gilpress/2019/12/09/120-ai-predictions-for-2020/#676708c50cf9

11. HERE ARE THE TOP 10 AI AND ML USE CASES THAT EVERYONE NEEDS TO KNOW ABOUT

https://www.forbes.com/sites/bernardmarr/2016/09/30/what-are-the-top-10-use-cases-for-machine-learning-and-ai/#5d1c36b

12. LOOK AT THESE 10 BEST AI AND ML USE CASES FOR GOOD

https://www.forbes.com/sites/bernardmarr/2020/06/22/10-wonderful-examples-of-using-artificial-intelligence-ai-for-good/#2a82c97a2f95

13. NEED FOR GDPR COMPLIANCE

https://www.forbes.com/sites/ajdellinger/2019/05/31/a-year-later-many-sites-are-still-failing-to-meet-basic-gdpr-requirements/#4e0913501eb9

14. A COMPLETE BEGINNER'S GUIDE TO AI https://www.forbes.com/sites/bernardmarr/2017/04/25/the-complete-begin-guide-to-artificial-intelligence/#2f5c99f4a835

15. AI EMERGES AS A MAJOR PLAYER IN THE RACE TO FIND COVID 19 THERAPIES AND VACCINIES CURE

https://www.forbes.com/sites/brianuzzi/2020/06/12/ai-emerges-as-a-major-player-in-the-race-to-find-covid-19-therapies-and-vaccines/#5b9c40571b57

16. THE INTERNET OFTHINGS (IOT) https://www.google.com/search?q=Internet+of+things&sa=X&rlz=1C5CHFA_enUS923US923&stick=H4sIAAAAAAAAAOOQUeLSz9U3MCrLSzE0MJLMTUzOyMxLVchJTSzKy8xLV8jMSyktLimqjBJPSi0uUUjMVEgsKslMzkkFyigYGRgZnGJkBxmQm111ihHJqFOMHCCOaVpOGky8wKDENA0qblhRVXGKkRPMtDRMs4CyjdKT8yxhSnKNy6Bas0qM4wuNoWrMqypNKn8xSvricmoDC-MiViHPvJLUorzUEoX8NIUSoML04ltskgxKJf965918ale060eY1_G6xKKgbd-OHm-4DwD12lbzCAEAAA&biw=1366&bih=600&tbm=isch&source=iu&ictx=1&fir=H_xg2sddxY189M%252CLq9jBH91k-6gMM%252C_&vet=1&usg=AI4_-kRw-3PTxIhBQjqg-_gQCoDmG25lzQ&ved=2ahUKEwj3yr_Ur9DsAhXxJDQIHbGqA5AQ_h16BAgUEAU#imgrc=H_xg2sddxY189M

17. THIRTY TOP AI AND ML COMPANIES

https://www.smartdatacollective.com/30-top-artificial-intelligence-and-machine-learning-companies/

18. GREAT WIKI IoT ARTICLE

SEE: https://en.wikipedia.org/wiki/Internet_of_things

19. EVERYTHING YOU NEED TO KNOW ABOUT IoT

SEE: https://www.zdnet.com/article/what-is-the-internet-of-things-everything-you-need-to-know-about-the-iot-right-now/#:~:text=The%20Internet%20of%20Things%2C%20or,all%20collecting%20and%20sharing%20data.

20. What is your favored AI / ML URL ?

Tell us why ?

Let us know. Email us.

AI and ML Humor

Q ... Can AI and ML Comprehend All the Unique Aspects of Humor?

Despite the huge capabilities, power and intelligence of Ai and ML, there are some things beyond the capabilities of

Ai and ML such as wisdom, love, empathy, humor, emotions, common sense, leadership, and other soft skills.

Some Random Thoughts About Ai and NL Humor

*Artificial Intelligence is no match for natural stupidity.

*Maybe if we tell everyone that their brain is an App, then they will start using it.

*Q ... What does a woman have who dyes her hair from Brown hair to Blond hair?

A ... Artificial Intelligence."

*Sign on the front door of a company's Human Resources Department:

HUMANS NEED NOT APPLY

-----End of Our First Featured Article------

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