COLUMN # 1

TIM'S PHOTO

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

DR. QIN (TIM) SHENG

CO-FOUNDER

 - MEMBER & ADVISOR- 

THE GOOD US AI GROUP

GoodUSAi.com

 Dr. QIN (TIM) SHENG

BS, MS, Ph.D

Nanjing University, China

BS 1982  

Nanjing University, China

MS 1984

University of Cambridge, UK

Post Doc. UCL, London

Ph.D in Mathematics  1990

BAYLOR UNIVERSITY MATHEMATICS PROFESSOR

Computational and Applied Mathematics

BAYLOR DEPARTMENT OF MATHEMATICS

and

BAYLOR UNIVERSITY CENTER FOR ASTROPHYSICS, SPACE & ENGINEERING RESEARCH

BAYLOR UNIVERSITY

WACO, TEXAS

EDITOR IN CHIEF 

AT

 TAYLOR AND FRANCIS

LINK TO 

COMPREHENSIVE BIO FOR DR. TIM SHENG:

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

and ...

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

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AUGMENTED 

ARTIFICIAL

INTELLIGENCE

ADVOCATE

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THREE PERSON LEADERSHIP TEAM  FOR GOODUSAI.COM

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1. DR. QIN (TIM) SHENG

CO-FOUNDER

 - MEMBER & ADVISOR- 

THE GOOD US AI GROUP

GoodUSAi.com

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2. RUSS (RUSSELL)

K. PETERMAN

CO-FOUNDER 

- MEMBER & ADVISOR- 

THE GOOD US AI GROUP

GooUSAi.com

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3. JOHN (JOHNNY) E. MILLER

-- CO-FOUNDER --

- MEMBER & ADVISOR -

THE GOOD US AI GROUP

GoodUSAi.com

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NOTE:

 BE SURE TO REVIEW THE AI CONTENT IN COLUMNS 1, 2, AND 3. 

NEW AI CONTENT IS ADDED  TO THIS WEB SITE WEEKLY.

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COLUMN # 1

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ARTIFICIAL INTELLIGENCE

INTRODUCTION

Artificial Intelligence (AI) systems will play a major role in every aspect of life on earth.  It will change the world we live in. 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, climate change, civil unrest, chemical- biological warfare, terrorism, cyber-security, asteroid impact, loss of coral and plankton,  alien visitors, power grid  destruction, ozone depletion,  loss of internet, poverty, hunger, lowering  water supply, air pollution, sanitation, water pollution, nuclear waste,  or de-forestation, ,  lack of education, loss of rain forests, etc, etc.. 

Is it possible that AI will be an existential event once it reaches the human level and beyond?

MAIN THRUST OF THIS WEB SITE 

THE MAIN THRUST OF THIS WEB SITE IS NOT THE DARK / EVIL SIDE OF AI BUT RATHER  RESEARCHING, EDUCATING, TEACHING, 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,  FACIAL RECOGNITION, DRONES, ARTIFICIAL NEURAL NETWORKS, INTERNET OF THINGS, AND OTHER ASPECTS OF AI, 

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PROGRESS ALWAYS COMES 

AT A COST

Progress always comes at a cost. Paper fundamentally changed the way information was stored and distributed, but its production contributes to deforestation. 

Industrialization increased our standard of living, but has led to much pollution and arguably, even some social ills. The benefits brought by the internet are too many to mention, yet viral misinformation, vast erosion of privacy, and the diminishing patience of society as a whole were all unintended consequences. Not even medicine is free from side effects. This should come as no surprise because hindsight is always twenty-twenty. Rarely at the time of invention is a creator the best judge of how their system will be used, or truly knows what good or harm will come of it. Understanding this, as technologists, we ought to give pause and reflect deeply before taking on a project.

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SOME BASIC DEFINITIONS:

A.  Artificial Intelligence:

1.  Artificial Intelligence (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 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 is machine learning. Applications of machine learning normally use a neural network.  A neural network is a computer system mimicking the human  brain.

An example of deep learning is speech recognition.

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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.

 Major Artificial Intelligence Components:

The following items are some of the  major components that make up AI:

1. Machine Learning (ML) - Machine learning 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 for themselves. 

It allows systems to learn from data, identify repeating patterns and making decisions without explicit instructions or human intervention. It enables entities/humans to save time and resources. 

2. Big Data - 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 replicative 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 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  In based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. 

    6.  Speech Recognition - Speech recognition 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 unsupervised.

9. Internet of Things  (IoT) -  This is Computer components embedded in everyday objects with capabilities of sending and receiving data and performing other activities.  

The oil & gas industry is especially ripe for the application of IoT with potential benefits in safety, the environment, reduced spills, reduced carbon emissions,  improved monitoring, improved drilling strategy, cost reduction, time savings, and increased revenue.               

Here below is a good high level description of how IoT works, as so stated in LEVEREGE's ebook "An Introduction to Internet of Things:"    

"'An IoT system consists of sensors/devices which “talk” to the cloud through some kind of connectivity. Once the data gets to the cloud, software processes it and then might decide to perform an action, such as sending an alert or automatically adjusting the sensors/devices without the need for the human user . 

But if user input is needed or if the user simply wants to check in on the system, a user interface allows them to do so . Any adjustments or actions that the user makes are then sent in the opposite direction through the system: from the user interface, to the cloud, and back to the sensoIrs/ devices to make some kind of change."

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. There are, of course, other aspects of AI that are in various stapes of creation and development.

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FIRST THINGS FIRST:

OK - first things first.  We start first with data science (DS),  according to Russ Rankin and Dr. Stephen Gardener  - Baylor 

Magazine - Fall Issue 2018 -  "Data Drive" - "Data science  is a broadly  interdisciplinary field that draws heavily on statistics  and computer science  and has applications in business, engineering, medicine, law, education, sociology, political science, and other disciplines.   Data science is the foundational field for development of AI, robotics, and other technologies that can mimic or transcend  many aspects of human intelligence." 

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TED MED Video - Artificial Intelligence 

https://m.youtube.com/watch?v=rCkXW079cDM 

This is a very useful video on medical and other AI applications.

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ROCK & ROLL:

 The term "Artificial intelligence"  dates back to about the mid-1950's. About  1956. (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?  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.  AI was first named and identified

 in 1956 by Sanford professor / researcher John McCarthy as a sub-field of the larger academic study of computer science.  

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DEEP LEARNING VIDEO (UNDER AI)  EXAMPLE: 

https://www.newsweek.com/artificial-intelligence-cucumber-farm-raspberry-pi-495289

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INTENDED CIRCUMSTANCE.

AS THE INTENDED AI CIRCUMSTANCE, AI SHOULD BE BENEFICIAL,  ETHICAL, SAFE, GUIDED BY COMMON SENSE,  RESPONSIBLE,  UNBIASED, AND OPERATE WITH COMPLETE INTEGRITY.

 AI duplicates the human thought process and behavior.  It should act in a beneficial human - like way that is safe,  intelligent, brilliant, rational, unbiased, guided by common sense, responsible,  reasonable, timely, and  (of course) ethical with integrity.  

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THE TIME IS RIGHT:

AI 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.

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POTENTIAL FUTURE 

OF AI:

Here is some Accenture November 2018 LinkedIn information on the potential of AI: 

"AI has the potential to boost rates of profitability by an average of 38 percentage points and could lead to an economic boost of US$14 trillion in additional gross value added (GVA) by 2035."

Accenture further stated in the same post: "Artificial Intelligence could double annual economic growth rates by 2035 by changing the nature of work and spawning a new relationship between both human and machine. 

The impact of AI technologies on business is projected to boost labor  productivity by up to 40 percent by fundamentally changing the way work is done and reinforcing the role of people to drive growth in business."

 Accenture additionally stated in the same post: "AI at its core combines intelligent technology with human ingenuity. Probably the most dramatic change that AI is driving for people is the nature of work.  And collaborative intelligence is very important because this is about how we take the best capabilities of a person and the best capabilities of software or machine or AI and put that together to create a new type of job. The 

ideal is that there’s something missing 

from the discussion around AI today 

and it’s what’s happening in the middle between humans and machine. We tend to think about what humans are good at, which is communication, emotion, imagination, generalization, and so forth. And then you think about what machines are good at -- memorization, prediction, transaction, and more. There’s a lot of opportunity in that area in the middle and the discussion that has been missing is about how do you put humans and technology together to do new things and create new capability together. 

Human and AI technology are connected and will change the way we work and solve business problems. AI will also re-imagine society, in how we use it to solve social issues, and in how we apply it responsibly. Responsible AI is necessary for AI to come together across business, people and society 

and be successful.

There was no better place to talk 

about the last 25 years of technology, 

and the next 25 years when we will see 

epic change driven by AI. The Information Age has brought us to where we are today. The next period will define our future and change the world."

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"BENEFITS OF AI  CAN BE ENORMOUS: 

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 brains;

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, 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 customers.

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.

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POTENTIAL AI  RISKS AND THREATS

The numerous legal  and other risks 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 needs to be anticipated and analyzed. 

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  .

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HERE ARE A FEW OF THE AI RISKS AND THREATS

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 getting closer to actual human-like consciousness. This is also often called: Super 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  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).

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.

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 AI CHALLENGES AND DANGERS TO DEMOCRACY AND PRIVACY IN THE US

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 (2019) 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3273016

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HOW TO MITIGATE AI  RISKS AND THREATS

1. Understand the vast complexities of AI;

2.  Track AI reasoning;

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

4. Use good clean data;

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

6. Start soon with Augmented    Intelligence applications, and then move on early  with narrow AI applications before arriving at strong 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 secure;

14. Develop strong AI testing prior to deployment; and 

15.  Think like a robot.

Certain portions of this section on AI legal risks and AI mitigation actions have been summarized, extracted from, and/or derived from:

 A. Understanding the Legal Implications of Artificial Intelligence,

Perkins Coie, News – Insites 5/12/2018

-and-

B.  A Two-Minute Guide To Artificial Intelligence

Parmy Olson, Forbes – 10/3/2018

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FOUR INTERESTING VIEW POINTS ABOUT AI:

1.  “The automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining.”

— Stephen Hawking

2.  “What to do about mass unemployment? This is going to be a massive social challenge. There will be fewer and fewer jobs that a robot cannot do better [than a human]. These are not things that I wish will happen. These are simply things that I think probably will happen.”

— Elon Musk

3.  “You cross the threshold of job-replacement of certain activities all sort of at once.”

— Bill Gates

4.  Here is an interesting comment from the "ProjectAI" web site:

"The coming AI developments may change life and society as we know it faster and more deeply than the industrial revolution, the internet or anything mankind has ever experienced before. It is crucially important to consider the coming AI developments not as a theoretical exercise or academic subject of investigation, but as a very real development which will take a concerted effort by people from all walks of life working together to take action to ensure continued quality of life for mankind."

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"Getting Ready for a Robot-Only Future"   - Ben Dickerson - November 2018 -

"How To prepare for Employment in the Age of Artificial Intelligence"

"Contrary to popular belief, most AI systems currently act as a complement to humans instead of replacing them. According to expert estimates, we are still years away from general artificial intelligence and full automation. But eventually, there will come a day where robots will perform most tasks and the role of humans in the production cycle will be marginal.

It’s very hard to envision the dynamics of a robot-driven economy. But how will humans sustain their lives when robots take all their jobs?

Governments should impose an income tax on robots that replace humans, Bill Gates said in an interview with Quartz. The Microsoft founder proposed that the robot tax could finance jobs to which humans are particularly well suited. This can include taking care of elderly people or working with kids in schools, for which needs are unmet.

Other experts are endorsing the notion of a Universal Basic Income (UBI), or handing out unconditional money to all citizens. The concept has been around for centuries, but it is gaining traction as full automation starts to loom on the horizon.

There are many political, economic and ethical hurdles to the full implementation of the UBI, but pilot programs are underway. Governments as well as private firms are testing the concept in small scale.

We have yet to see how the accelerating evolution of AI will unfold, but what’s for sure is that fundamental changes lie ahead. While we can’t predict the future, we can prepare for its potential outcome as best as we can."

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Don't Miss This Video  About  a Computer Using Human-Like Intuition

Deep Mind - World's First General AI

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

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CHECK THIS GREAT LINK OUT !

HERE IS A GREAT LINK TO THE COGNILYTICA WEB SITE WHERE THEY LAY OUT A GREAT ONE-PAGE DESCRIPTION OF THE SEVEN PATTERNS OF AI.  

TAKE A LOOK AT THIS INFORMATION

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

OK.  Here are all of the previously-existing seven AI types that were recently identified by Cognilytica on their web site that apparently seem to repeat themselves in different combinations in all of the AI use cases that various entities have created.  Some use cases make up just a single AI type for their use case application while others combine a few different AI types together to create their use case.

Here are seven AI types:

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

A.  Goal-Driven System is using machine learning to give the AI system the ability to learn through trial and error in finding the optimal solution to a problem. The AI Chess win is an example of this.

B.   Autonomous Systems (AS) are systems that are able to  

perform a task, goal, or interaction with minimal to no human  

involvement or labor. The primary objective of the  autonomous systems type is to minimize human labor.  Autonomous vehicles and drones are examples of this AI type.

C.  Hyper-Personalization (HP) is defined as using machine learning (ML) to develop a very unique profile of each individual human, and having that special profile learn and adapt for a wide variety of purposes, including content, products, personalized recommendations,  personalized healthcare, finance, and other specific one-to-one activities.  Advertising, finance, medical/health care, and fitness are some of the AI uses of HP.

D.  Predictive Analytics & Decision Support  is using ML to   

understand  how learned patterns of data can assist in          predicting the future.  It also assists humans in making  decisions about the future using what it learned from data and 

other information. It is all about helping humans make better      decisions. This application of AI is primarily intended to assist 

humans to figure out an answer to a problem. The human is still making the decision but it is ML helping the humans make 

the better decision.

E.  Conversational / Human Interaction is the use of machines

in communicating with humans through natural conversation and including voice, text, images, and other forms. The goal is to create communication between machines and humans. This is enabling machines to interact with humans the way that humans interact with each other. 

F.  Pattern & Anomaly Detection is the AI type that has the ability to find which one of the things is like the other and which is not. The goal is to find anomalies in data and indicate what looks out of the ordinary such as fraud detection and risk analysis.  

G.  Recognition is the AI type using machine learning to identify objects or other things within some form of unstructured content.  Use cases include image recognition, facial recognition, sound recognition, audio recognition, item detection, handwriting detection, text recognition, and gesture detection. The recognition AI type is one of the most widely used and adopted of all AI use types.

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ROBOTS OPERATING IN DANEROUS CONDITIONS:

Understanding the Accident of Fukushima Daiic  

Japan Special: Inside Fukushima Daiichi Nuclear Power Plant where robots are helping the clean-up after 2011's meltdown. Plus across the country, robots learn to sumo wrestle, play volleyball and act like humans

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

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AI CAN AUGMENT PUBLISHED MATH RESEARCHERS

Proofs in theoretical math can be overwhelmingly complicated. Even professional mathematicians often do not fully comprehend them. Consequently, researchers just keep faith that the underpinnings of a new proof are correct.

Because of this, when mathematicians reference a published result in their work, readers just take their word for it.  Kevin Buzzard, an Imperial College London mathematician, is worried about this. There can be a huge possibility that many existing mathematical proofs are incorrect.

Much of published math results may be wrong because researchers often cannot check background details used. Buzzard considers that a researcher should use artificial intelligence (AI) to do at least the background information checking. Stay tuned.

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CONTINUE TO TOP OF COLUMN #2

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ABOUT THE GROUP:

 The "Group" (The Good United States Artificial Intelligence Group) is not a company, LLC, partnership, corporation, joint venture, proprietorship or any other legal or business entity. It is just an informal number of individuals loosely assembled that has some unifying relationship. In our case, that underlying relationship is the education, safety, ethics, integrity, responsibility, honesty, non-biasedness, empathy, and social good of Ai (in all of Ai's forms) as Ai transitions into more and more of our lives.  We look forward to good beneficial Ai --- not bad Ai.  

To quote our GoodUSAi.com Co-Founder & Member - Dr. Qin Sheng - "We need to let people know more about our interests, expertise and endeavors. Let’s do the best to support this wonderful country!"

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WORK IN PROGRESS

"Do it -- Fix It -- Try It"

Chaotic action is preferable to orderly inaction 

This web site will always be a ..."Work In Progress."  Changes, updates, additions, and deletions will be made monthly. The last update to this web site was made on: December 9,    2019

======================

(C) COPYRIGHT JOHN E. MILLER, RUSS PETERMAN,  AND DR QIN SHENG

2018 - 2019

ALL RIGHTS RESERVED (EXCEPT AS NOTED HEREIN WHERE ANOTHER PARTY OWNS  A COPYRIGHT  INTEREST )   

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COLUMN # 2  

RUSS'S PHOTO                                                                                                                                                                                     

https://www.linkedin.com/in/russ-peterman-3a97431b  

RUSSELL

(RUSS)

K. PETERMAN

 CO-FOUNDER 

- MEMBER & ADVISOR- 

THE GOOD US AI GROUP

GoodUSAi.com 

RUSS PETERMAN

BS, MS,Business Management

University of Texas - Austin, Texas

BS

University of Texas - Austin, Texas

MS

University of Texas - Austin, Texas 

 {Physics)

University of Colorado

(Business Management)

THE PRESIDENT OF

PETERMAN CONSULTING ASSOCIATES

AUSTIN, ROUND ROCK, GEORGETOWN, TEXAS

LINK TO: 

COMPREHENSIVE BIO FOR RUSS PETERMAN

https://www.linkedin.com/in/russ-peterman-3a97431b?trk=people-guest_profile-result-card_result-card_full-click

_______________

Commercial Glider/Self-launch Sailplane Pilot/Certified Flight Instructor

FAA Issued Jun 1974

AUGMENTED 

ARTIFICIAL

INTELLIGENCE

ADVOCATE

_______________

COLUMN # 2

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\V/

HERE IS A LINK TO 10 CURRENT EVERYDAY AI APPLICATIONS:

https://beebom.com/examples-of-artificial-intelligence/ 

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FOLLOW THE MONEY:

(To some limited extent, some of JohNny's commentary in this "Three Kinds of  Money" section just below  as well as the "AI Solutions Might Not Work Out" section also just below,  may be summarized, paraphrased, derived from or extracted from an "AI Today" oral Podcast: Artificial Intelligence Insights - #57 - titled  -"Is VC Funding for AI Over Heated ? "

October 3, 2018: 

See: this link:

https://itunes.apple.com/us/podcast/ai-today-podcast-artificial-intelligence-insights-experts/id1279927057_

THREE KINDS OF MONEY

"Follow the money.  AI and its subset, machine learning, are getting                                                  a huge amount of: (1) start-up ("non-Unicorn and Unicorn") VC funding,; (2) big company funding  (Amazon, MS, FaceBook, Google, Apple, Oracle, Accenture, Twitter, IBM, etc),  and  (3) government funding (US, China, UK, France, Germany, Japan, Russia, and South Korea ( in that order) today.  

For example (according to ABI Research) , in 2017  in the USA with  its 155  AI VC companies there was  US$ 4.4 billion in just VC Unicorn-type funding (not including big company and government funding).  Chinese AI VC funding from its 19 companies for 2017 was US$  4.9 billion.  

AI  government funding and AI big company investments in   AI funding for 2017  is hard to calculate ... but consider this:  SenseTime is a Chinese big AI company. Its valued at 

US$ 4.5 billion."

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'

AI SOLUTIONS MIGHT NOT WORK OUT:

"Even with all of this  AI  VC funding, it is possible that  the resulting AI solutions might not work out.   

Will AI prove itself out?  Is it the big wave promised and will the AI bubble remain inflated? AI has  tremendous promise.  Many think that the US ROI will be met.  But there is a lot of competition.  

Having the right people in your labor force is important. For example, data engineers and data scientists are both urgently needed. Data scientists do the AI deep analysis. Data engineers do the management  and cleaning of big data for use by data scientists. Data scientists do the deep analysis of clean big data. You need more data engineers than data scientists. Obviously, a data scientist needs to have very high STEM skills.

The governments of the US and China. overwhelmingly dominate the AI  race. China has both its huge tech giants  and government collaborating and working closely.  The privacy issues do not hamper AI in China.  The Chinese are adding AI to the high school curriculum as a mandatory subject.    The US has a promising AI labor force.  There are AI centers in the US  in  places like New York,  Boston (MIT, Harvard), Pittsburgh,  Seattle ,(MS, Amazon)  Washington DC, Austin, northern and southern California. 

The global AI  race is on.  Japan is soaring in AI robotics but has an aging labor force. South Korea is pushing electronics, medical, and health AI and will soon have 5000 new data engineers. UK and France are leading the way in responsible and  ethical AI. Russia believes its future rests on AI and has sizable AI funding  but lacks  the creation of VC funding by companies."

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A SAMPLING OF EIGHT CURRENT AI APPLICATIONS

1. ARGOBOT - AI APPLICATION FOR  FARMING: Tracks the status and health of each individual strawberry to determine the best berry-specific management of each strawberry.

2. ROBORACE - AI APPLICATION FOR AUTO RACES: Race cars drive themselves in a race  tracking and managing  each vehicle in race with no human driver.

3. WILDBOOD -AI APPLICATION FOR MANAGING ZEBRAS AND ELEPHANTS:  AI enabled system identifies each animal and tracks the individual health, location, and status of each animal in an African country.

4. ICEBURG - AI APPLICATION FOR HOCKEY:  Manages the multitude of game statistics and analyzes   pros/cons of each player.

5. SMART DRONE - AI APPLICATION FOR CONTROLLING PHOTOGRAPHIC DRONES; Manages the control  and positioning  of the drone including multiple cameras in order to take the best desired photo..

6. MOTION PICTURE TRAILER DEVELOPMENT:  Motion picture companies are using AI to develop motion picture trailers based on the particular viewers of a trailer.

7. UNDERWATER ROBOTS: Underwater scientists are using AI in underwater robots to control the increasing number of harmful star fish.

8.  USE OF AI TO ASSIST IN 

SPOTTING SHARKS NEAR BEACHES

AI is used on a real time basis to locate and identify sharks that may harm humans near beaches.

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    GOOGLE AND DARPA

In June 2018, it was reported  that Google was offering its resources to the US Department of Defense for Project Maven, a research initiative to develop computer vision algorithms that can analyze drone footage. In response, more than 3,100 Google employees have signed a letter urging Google CEO Sundar  Pichai  to reevaluate the company’s involvement, as “Google should not be in the business of war,” as reported by The New York Times.

Google then banned  its development of AI software that can be used in war, business of war, and weapons.

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ACCENTURE

Accenture has the right idea.  As AI rolls in ... managers will have more time for important , strategic, and creative tasks while the  time-consuming repetitive work is done by AI.  More available time is a huge benefit  of AI. They become super-managers."

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OPEN AI vs. NATIONAL CLOSED AI 

Who will win the AGI race?

The US  WWII Manhattan Project is a good example of a successful private closed national program approach. However, today many prefer an open source type approach to the sharing of Ai information so that there is a democracy of Ai algorithms world-wide and other Ai information.  An Open Ai Charter has even been established among certain world stakeholders. Will everyone be able to play in the same play box together?

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AGI in 2042 ?

It has been said in certain Ai circles that the year 2042 will be when someone reaches AGI.   However in the technology world things happen faster than one anticipates. For example, the Wright brothers originally stated that it would take 50 years before someone could fly in a heaver than air device. After making that statement It only took the Wright brother 3 years to make their historical flight.  

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HYPER-PERSONALIZATION

“Hyper-personalization” is quite valuable in the hands and minds of today’s advertiser employing artificial intelligence (AI). Accenture  feels that about 40% of the United States  consumers have backed off their preferred products and services brands due to a lack by the advertisers' use of the  latest trends of personalization and trust. Plain  old “Personalization” is the inclusion with an advertisement that an advertiser sends to a consumer of allowable personal and transactional information in advertising, coupons, and other communications, such as: Name, Address, Title, Organization, Purchase History, Age, Address, Gender, Occupation, other similar items regarding your product or service. 

On the other hand, Hyper-personalization goes several levels higher. Amazon is a great example of hyper-personalization. It has access to certain data points such as Full Name, Age, Address, Gender, Job, Search Query, Dwell Time on web sites,, Average Time Spent On Search, Past, Purchase History, Brand Loyalty, Average Spend Amount, among other factors. Using this, the advertiser  can create a very accurate profile of you and use it to craft a highly relevant email, mailed ad, phone call, voice mail, text message, etc to the consumer. Some Hyper-personalization experts feel that Hyper-personalization effectively used in an ad contributes more to the consumers' buying decision than the actual content of the ad itself.

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  AI AND SIMULATIONS

We make use of simulations for all kinds of things. One common example today is teaching pilots how to fly. Another simulation is for doctors to practice various operations. So, how about using artificial intelligence (AI) and simulations to teach autonomous vehicles how to drive and to continue to improve its driving skills eventually from NHTSA Level 2 (vehicle does driving and steering while a human monitors) to NHTSA Level 5 (vehicle does it all without human assistance)? 

Sure. However, we will need 5G Networks, advanced GPS, LIDAR, Centimeter-Accurate HD Maps, as well as some other technology advances. It will take millions of simulated vehicle trips but that can be done quickly and concurrently. The hardware and software for Level 5 autonomous vehicles is ready now but the vehicles themselves may not be ready for about 5 years due to NHTSA Level 5 extensive testing  and validation.

In the USA there are about 5.5 million vehicle crashes per year that kills about 30,000 people annually. That is just in the USA. Amazing but true.

Some experts believe that by 2040 motor vehicle accidents will go down by 80% due to the use of AI in autonomous vehicles. The motor vehicle becomes safer and safer as it moves towards Level 5 AI autonomous vehicles. This will affect other things like the reduction in the price of auto insurance,  reduction of the number of auto body repair shops, reduction in the number of vehicle accident personal injury attorneys, reduction of EMS / hospital services resulting from motor vehicle related injuries, double use of the trucks' 11 hour daily driving limits, driverless RV motor homes, and cost savings in certain aspects of the autonomous vehicles since doors, windshields, seats, AC, heater, and many other items would not be required.

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AI  WILL CHANGE PRO BASEBALL

Yes - it's true. AI will substantially improve scouting, recruitment, training, performance analysis, strategy, health, fitness, and safety of pro baseball. For example,  take the new AI pitching strategy  of the New York Yankees. According to long standing baseball traditions and baseball  wisdom, the fastball (4 seam fastball or 2 seam fastball) is the very best professional  baseball  pitch to get an opposing  batter out. Since those late 1870's starting during the formative   years  of baseball  in the USA (when overhand pitching replaced underhand pitching),  the fastball was considered the best pitch to get a batter out. It is the pitch that most pitchers rely on or go to. Generally, no pro team pitches less than 50% fastballs during a 9 inning game  (except for the Yankees and  now 4 other teams are doing it). Yankees ... 43.1% fastballs and Houston ... 47.3% fastballs. The Yankees do this despite that their pitching staff throws an average of 94.3 MPH  fastballs - the fastest in baseball. Through AI analysis of pro baseball's big data the Yankees discovered that opposing batters were hitting the fastball often and missing the pitches with more deceptive movement, ball placement, and varying ball speeds (like the curve ball, slider, change of pace, fork ball, knuckle ball. etc). This is a good example how AI will challenge and change conventional thinking when conventional thinking may not be accurate.

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HERE, IN THE GREAT LINK BELOW, (ACCORDING TO FORBES AND CONTRIBUTOR --- BERNARD MARR), ARE THE TOP 10 AI AND ML NON-MILITARY USE CASES THAT ARE SO IMPORTANT THAT EVERYONE SHOULD KNOW ABOUT THEM:

https://www.forbes.com/sites/bernardmarr/2016/09/30/what-are-the-top-10-use-cases-for-machine-learning-and-ai/#495a8bea94c9

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HOW AI / ML WILL AFFECT PROJECT MANAGEMENT ?

PROJECT MANAGEMENT 

FLOW CHART:  

Project Manager (PM) -> Engage -> Approval -> Scope -> Initiate -> Plan -> Schedule -> Sequence -> Design -> Execute -> Monitor -> Report -> Control -> Quality -> Change Orders -> Product Sellers - Service Providers -Validate -> Manage -> Evaluate-> -  Close Out -> Review Lessons Learned ->Perform Scorecards  

A project manager (PM) is the person who is assigned the responsibility for the creation, operation, completion, delivery,  and success of a project by:

 (1) obtaining the project initial engagement,  (2) obtaining directions/approval of management, (3) defining scope, (4) kicking off the initiation, (5) planning, (6) scheduling, (7) sequencing, (8) designing, (9) executing, (10) monitoring, (11) reporting, (12) controlling, (13) establishing quality, (14) handling of change orders,  (15) managing product sellers/vendors, (16) managing service providers, (17) validating, managing, (18) evaluating, and (19) closing the project (including (20) reviewing the lessons-learned, as well as (21) performing a final project scorecard on the project and each seller/vendor/service provider. 

Such a PM can be found in all types of projects such as (but not limited to) construction, petrochemical, architecture, environmental, mining, manufacturing, aerospace, information technology, software development, computer hardware. insurance, legal, retail, telecommunications, health care, financial, research, and many other different industries that produce/perform/sell/license/lease products and / or services.

The PM should make sure they control “RISKS”.  All topics, issues and risks must be identified, assigned, managed, and resolved. Typically, the PM will manage project tasks under the project by using Microsoft Project Software. 

The role of PM is similar to that of a musical band leader. The band leader doesn't need to play all the instruments in order to conduct the band, so the role of PM needs to manage a team of people who have different roles and skills in a project. 

However, the PM needs the following PM skills: social, point-of-contact with customer, good communicator to all parties (internal, external, customer, stakeholders, etc), coach, mentor, listener, strong domain, technical, project at-hand knowledge & experience, team-player, persistence leadership, strategy,  and business management, proactive, quasi-psychologist, common sense, real-world mentality, problem-solving skills, and facilitator. 

AI / ML will probably affect both PMs and projects, as follows: (1) the human PM will have about 50% more time (according to Accenture LLC)  to address the serious,  and important project issues since the AI / ML will allow the AI / ML project applications to handle and resolve the repetitive minor time-consuming administrative project issues; (2) use of “smart assistants” by the human PM; (3) creation of automatic periodic risks analysis; (4) AI / ML becomes a project valuable team member; (5) use of robots; (6) creative thinking by humans to solve complex problems; (7) valuable tools for reporting and monitoring; (8) use of predictive analysis; (9) evaluating KPI’s; (10)  reducing costs and mistakes; (11) avoiding surprises; (12) early warning of surprises; (13) predictive maintenance; (14) risk removal; (15) tracking progress / performance; (16) use of chat boxes; (17) forecasting; (18) use of experimentation; and (19)  other similar activities.

PM’s will not be replaced by AL / ML but rather PM’s will be assisted by AI / ML.

A better description is that the PM will be assisted by Augmented Intelligence .

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WHAT IS ... “BAT” ?

(It has nothing to do with baseball or flying mammals).

As so reported / blogged on April 2, 2019 by Karen Hao ...

-- (at MY MPCA - mympcapital.

blogspot.com -

- "China’s AI Industry") -- 

a little more than 50% of China’s 190 leading AI/ML companies received private funding from the three largest Chinese AI/ML giant tech companies ( Baidu, Alibaba, and Tencent -   often referred to jointly as “BAT”).

Each of these BAT companies, although widely focused on many aspects of AI/ML, also has a  known expertise. For example, Alibaba is e-commerce; Tencent in social networking; and Baidu in search and information indexing. 

China has set a challenging goal to be the World Leader in AI/ML by 2030. However, China appears to be  "top heavy".  "Top Heavy," in this context, means that China is strong on the AI/ML applications but not as strong as they should be  on the fundamentals that support the AI/ML.  

China  is still behind the US in expanding AI/ML capabilities through fundamental research, algorithms, advanced silicon chips, machine vision, natural-language processing, and other AI/ML fundamental capabilities.

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"2019 STATE OF AI REPORT"

(JUNE 29, 2019)

SEE THE LINK BELOW TO THIS GREAT 136 SLIDE REPORT PREPARED BY NATHAN BENAUCH AND IAN HOGARTH: 

https://www.slideshare.net/StateofAIReport/state-of-ai-report-2019-151804430

THE STATE OF AI REPORT REPORT  IS  PREPARED ANNUALLY BY NATHAN BENAUCH AND IAN HOGARTH

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HOW AI / ML IS WORKING IN THE LEGAL SERVICES ARENA

In a recent  LexisNexis survey (according to John G. Browning and Christene Krupa Downs in their article - "The Future Is Now" in the July 2019 issue of The Texas Bar Journal) only 20 to 25% of the US  in-house legal departments currently use some  AI / ML applications.  In those low percentage of AI / ML  companies, generally only one legal domain area is being addressed.  As compared to a company’s  in-house  Finance and HR departments, those departments of a company are twice as likely to use AI / ML applications than in-house legal.

Take the Coca-Cola’s legal department for example. By using AI / ML applications they have reduced a standard legal agreement for review from 10 person hours to about 15 minutes per document.

In another case, JP Morgan Chase & Co. has saved over 360,000 person hours of legal services time in the last year due to its AI / ML applications and the use of Chase’s own AI / ML platform called “Contract Intelligence”.

Likewise, many outside private counsel firms are using AI / ML to reduce time charged to their clients, be more competitive,  perform due diligence, do document review, perform predictive analysis, do AI / ML assisted legal research, use of AI / ML applications for damages models, do verdict prediction, perform predispositions of judges, create cost-saving, and do many other types of legal AI / ML that make lawyers more efficient and effective. This is especially true due to the huge amounts of legal “Big Data” that is now available. 

Interestingly, outside legal counsel are the most  intrigued with the AI / ML applications related to cost savings and predictive analysis.

The general consensus in the legal community is that, due to AI / ML, legal professionals at the lower service levels may experience declining numbers. However, AI / ML will benefit the legal profession by enhancing what lawyers do and freeing them up for more important, meaningful, creative, and fulfilling work.

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18 WHEEL 40 TON TRUCKS
USING DRIVER-LESS AI AUTONOMOUS TECHNOLOGIES

According to Chris Spear -- President and CEO of the American Trucking Associations (ATA) -- as stated on the ATA web site -- (https://trucking.org). -- the current estimates are that there are 3.5 million truck drivers in the USA. In fact, the USA needs roughly 90,000 more truck drivers yearly just to keep up with ever
increasing demand for drivers.
(See; https://trucking.org).

Unlike trucks driven by humans, self-driving trucks using autonomous vehicle (AV) technologies, can drive more than the current 11 hour daily driving limit. Also, driver-less trucks do not need breaks for food, restroom use, and other personal delays/stops. 

One company ("Too Simple" of San Diego, CA) is already driving one of their Level 4, driver-less loaded trucks on “dock to dock" regular revenue runs in Arizona three to five times weekly (also carrying a safety engineer and safety back-up driver). They expect fully autonomous, driver-less, Level 5, and commercially- ready 18 wheel 40 ton trucks by the end of 2020.

Alternatively, it may be a future in which self- driving trucks using AV technology drive the long highway miles between what they call transfer hubs, where human drivers will take over for the last miles through complex urban and industrial areas. So, self-driving trucks may be complementing humans, not completely replacing them. 

AV technology holds enormous collaborative potential for the trucking industry, its drivers and the motoring public. That could mean improving the truck availability percentage during assigned runs from 50% to 80%.

It is anticipated that the "Perception" distance for trucks driven by humans  could increase from about 1/4 mile achieved by current human drivers to 1/2 mile for very high perception AI driver-less systems. In other words, all things considered, the Perception System to be employed on the driver-less truck is expected to much better vision than a human driver.

With 94% of highway accidents attributed to human error, the successful deployment of AV driver-less technology can drastically reduce fatalities on the road.

Moreover, the AI driver-less technology can deliver other significant returns by reducing traffic congestion, improving driver productivity and decreasing emissions through lower fuel burn.

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CAN YOU PLAY

"G O" ?

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AI ... or ... AAI ?

We currently seem to be a good distance from establishing advanced intelligence systems that can independently process, reason, and create in the same capacity and capability (or better) of the human brain (i.e. artificial intelligence - AI).

However, is matching (or improving upon) the capacity and capability of human intelligence the most useful AI application (all things considered)?  Should AI be the conscious singularity  wide ultimate intelligence goal for businesses and other entities?

How about considering the growing worthy alternate concept of: “Augmented Artificial Intelligence” (AAI)?  AAI focuses on supplementing human intelligence so as to augment humans.  AAI maintains humans  at the center of the appropriate management and decision making.

The innovative technologies implementing AI and AAI are the quite similar, however, the applications of AI and AAI are quite different. AI aims to create systems that run without humans. On the other hand, AAI aims to create systems that make humans better. Isn't our goal to make humans better? What do you think is the best approach? AI or AAI?

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WILL THE UNITED STATES WIN, PLACE OR SHOW IN THE AI / ML 

HORSE RACE ?

As of this point in time in late 2019, despite the persistent hype, there appears to be several countries currently struggling to achieve a global advantage and become the leader in the artificial intelligence (AI) and machine learning (ML) “horse race..

Those AI / ML -focused countries clearly understand that AI / ML is composed of several foundational technologies that can sky-rocket competitiveness, increase productivity, improve health care, protect national security, improve transportation, care for the environment, favorably address climate issues, provide adequate energy, control space, and help solve many other societal challenges (just to name a few of the many current world challenges).

It is quite interesting to objectively compare China, the European Union (EU) countries (currently including the UK, Germany, Norway, and Sweden ), and the United States (US) in terms of their ranked standing in the AI /ML economy by examining the following eight categories: (1) talent, (2) research, (3) scholarly journal articles, (4) development, (5) adoption, (6) big data, (7) hardware, and (8) committed AI / ML strategy. 

The bottom line summary is that despite China’s bold AI / ML initiative (claiming it will become the most dominate AI / ML force in the world BY 2030), the US still leads the world (at least for now). 

China comes in second, and the EU countries lag further behind in third place. Ranked behind the EU countries (in no order of priority) are Russia, Japan, South Korea, Singapore, India, Israel, Canada, Indonesia, Brazil, Mexico, and Taiwan.

This AI / ML ranked order could change in the next few years as:  (1) China appears to be making more rapid progress than either the US or the EU countries (28 member states); and, (2) the China AI / ML labor force becomes even larger.  That’s 1.43 billion general population in China  compared to 323 million general population in the US. China’s 1.43 billion general population makes a sizable dent in the total world general population of 7 billion and creates massive amounts of big data (the driving force behind AI / ML).

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HOW TO SUBSTANTIALLY REDUCE HARMFUL AI BIASES -— AKA (“GARBAGE IN … GARBAGE OUT")

Harmful human biases can: (1) be innocently and inadvertently found inserted in an artificial intelligence (AI) system; or (2) be formed intentionally with potential intended harm and found inserted in an artificial intelligence (AI) system. Ideally, such AI biases, once discovered and identified are then corrected by humans, AI systems, and/or by humans and AI systems in a joint collaborative manner. 

Although bias is considered by many to be AI's “Achilles Heel” or AI’s “weak link in the chain”, once the AI bias is located it can in most cases be mitigated. 

Unfortunately , often the AI bias passes into active use in an AI use case application without notice, identification, analysis, and correction … “like a large ship quietly passing in the night.” 

Additionally, an AI system itself can be creatively and efficiently used as a virtual software tool to help humans to identify the remainder of AI bias system problems (not earlier identified and previously cured by humans). This preventive procedure using AI typically may reduce the harmful impact of human biases. However, it should be noted, unless AI biases are properly and promptly addressed in a timely manner by appropriate humans and AI, then such a less-than-needed amateur type effort can potentially make the resulting AI bias problems even worse. 

For the purposes of this article the example of correcting AI baises in the hiring/employment activity will be used. 

Miscellaneous Take Away Guidelines: 

1. AI biases can potentially creep into AI algorithms and data in several ways and should be deleted, mitigated or substantially reduced as follows: The take-away guideline here is to initially remove from algorithm and data consideration, analysis, and discussion of certain sensitive variables such as age, gender, race, pregnancy, disability, health, religion, politics, sexual orientation, and other similar applicable variables. This is the initial first step in mitigating AI biases. 

2. AI systems typically learn to make their AI decisions (including other similar non-AI decisions) based primarily on big data, which can include pre-existing “baked-in” biased human decisions or reflect historical and social inequities. The take-away guideline here is to be observant, discerning, and aware so as to delete from further algorithm and data consideration any pre-existing “baked-in” biased human decisions or biases that reflect historical and social inequities. 

3. A third takeaway guideline here is for your business leaders to stay up to-date on this fast- moving field of research of AI biases. 

4. Perhaps a fourth takeaway here is to consider using operational practices such as internal review such as : Internal AI Bias Review Teams, “Red Teams” or Third-Party Audits to help mitigate AI biases. 

5. The fifth takeaway is that since algorithms that tend to favor certain male employment applicants (based on the use of seemly innocent words like “executed” or 

“captured” that are more commonly found on men’s resumes) need those words need to be corrected by mitigating/revising such words. 

6. Your sixth takeaway here might be to define and develop a way to measure “fairness” between employment applicants such as using AI models that can be more readily evaluated. 

7. A seventh takeaway here is to consider that AI can improve on traditional human decision-making. Machine learning systems disregard variables that do not accurately predict outcomes (in the data available to them). This is in contrast to humans, who may not even realize the factors that led them to, say, hire or disregard a particular job candidate. 

8. As an eighth takeaway, AI biase mitigation approaches that only look for a one-time “fix” for fairness oversimplifies the complexity of social systems. Within each domain – such as education, healthcare or criminal justice – legacies of bias and movements toward equality have their own histories and practices. Legacies of bias cannot be “solved” without drawing on domain expertise. Addressing fairness meaningfully will require interdisciplinary collaboration and methods of listening across different disciplines. 

The purpose of this article is not to list all of the viable ways to reduce, mitigate, or eliminate AI biases. That could almost be endless for a vast multitude of domains. Rather, the goal of this article is to encourage meaningful and immediate AI bias reduction research and development.

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CONTINUE TO TOP OF 

 COLUMN #3

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WORK IN PROGRESS

"Do it -- Fix It -- Try It"

Chaotic action is preferable to orderly inaction 

This web site will always be a ..."Work In Progress." 

 Changes, updates, additions, .and deletions will be made monthly. The last update to this vertical column of this web site was mad December 8. 2019

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(C) COPYRIGHT JOHN E. MILLER , RUSS PETERMAN, 

AND DR QIN SHENG  2018 - 2019

ALL RIGHTS RESERVED (EXCEPT AS NOTED HEREIN WHERE ANOTHER PARTY OWNS A 

COPYRIGHT INTEREST).