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 risk of some unintended circumstances. If fact, AI may turn out to be a greater world problem than: 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, sanitation or lack of education.
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 (AI) 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, AND INTERNET OF THINGS,
PROGRESS ALWAYS COMES
AT A COST
Authman Aperture of Coding Dojo on June 1, 2018 stated the following in his article "Ethics and Unintended Consequences of Technology: "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."
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, 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. Neural network is a computer system mimicking the human brain.
An example of deep learning is speech recognition.
Here is a good link to 6 definitions of AI from Forbes Magazine:
There is also included in this AI definition 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 artificial intelligence (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 companies 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. 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: “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: "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 - 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 Artificial Intelligence 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 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). 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) - 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 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 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 sensors/ devices to make some kind of change."
For a deeper understanding of Iot see: https://www.leverege.com/iot-intro-ebook
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, and 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 2015, 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 provides step by step procedures for calculations.
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."
This is a useful video on medical and other AI applications.
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
AS THE INTENDED AI CIRCUMSTANCE, AI SHOULD BE BENEFICIAL, ETHICAL, HAVE GOOD, ETHICS, BE GUIDED BY COMMON SENSE, BE 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 intelligent, brilliant, rational, unbiased, guided by common sense, responsible, reasonable, timely, and (of course) ethical with integrity.
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.
"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."
"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
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.
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 .
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;
11. Autonomous weapons;
12. Mass surveillance;
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.
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:
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."
"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."
Don't Miss This Video About a Computer Using Human-Like Intuition
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
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:
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.
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, 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.
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:
May 18,, 2019
(C) COPYRIGHT JOHN E. MILLER AND DR QIN SHENG
2018 - 2019
ALL RIGHTS RESERVED ( EXCEPT AS NOTED HEREIN WHERE ANOTHER PARTY OWNS A COPYRIGHT INTEREST )
CURRENT LEADERSHIP OF THIS GROUP:
1. MEMBER / ADVISOR
The Good US Ai Group
BS, MS, Ph.D
University of Cambridge
Post Doc. UCL, London
2. MEMBER / ADVISOR
The Good US Ai Group
UT Austin, Texas
University of Colorado
THE PRESIDENT OF
PETERMAN CONSULTING ASSOCIATES
Photo for Peterman Consulting Associates:
3. MEMBER ADVISOR
The Good Ai Group
BA, JD, LLM, CPCM
Baylor, U of M, UMKC
THE JOHN E. MILLER
HERE IS A LINK TO 10 CURRENT EVERYDAY AI APPLICATIONS:
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:
October 3, 2018
THREE KINDS OF MONEY
"Follow the money. AI and its subset, machine learning, are both 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."
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."
A SAMPLING OF SOME 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.
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.
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."
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?
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.
“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.
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.
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.
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 made May 18, 2019
(C) COPYRIGHT JOHN E. MILLER
AND DR QIN SHENG 2018 - 2019
ALL RIGHTS RESERVED (EXCEPT AS NOTED HEREIN WHERE ANOTHER PARTY OWNS A
COP YRIGHT INTEREST).
1. HERE IS A SUMMARY OF A PORTION OF THE ARTICLE: "GAME CHANGING TRENDS TO LOOK OUT FOR WITH AI ."
THIS IS AN INTERESTING PLAIN-ENGLISH ARTICLE ABOUT THE FUTURE OF AI
The author advises: "A recent report by McKinsey states that Baidu, the Chinese equivalent of Alphabet spent $20 billion in AI last year.
At the same time, Alphabet invested roughly $30 billion in developing AI technologies. The Chinese government has been actively pursuing AI technology in an attempt to control a future cornerstone innovation. Companies in the US are also investing time, money and energy into advancing AI technology.
The reason for such interest towards artificial intelligence is that artificial intelligence can enhance any product or function. This is why companies and governments make considerable investments in AI."
The author concludes by stating:
"Therefore, the best approach is not to wait until AI leaves you unemployed, but rather proactively embrace it and learn to live with it. As we said already, AI can also create jobs, so a wise move would be to learn to manage AI-based tools. With the advance of AI products, learning to work with them may secure you a job and even promote your career. ... The wisest strategy is to embrace artificial intelligence and let it work to maintain our well-being. "
2. HERE IS ANOTHER SUMMARY OF AN INTERESTING ARTICLE ABOUT : "HOW TO PREPARE YOURSELF AND YOUR CAREER FOR AN AI - FUTURE"
Kasper Nymand kaspernymand.com/
Mr. Nymand states: "The future is here. It’s time to rethink your position in the job market. It’s time to question your current career path, and consider if it would be wise to make a few changes. Bots and intelligent assistants / agents are on the rise, ready to disrupt and turn the job market on its head. Now, it has always been key in life to stay up-to-date, grow and develop yourself and your knowledge base. But now more than ever this is definitely true.
AI is expected to eliminate 6% of jobs by 2021, according to Forrester, and a further 47% is expected in the next two decades, according to a US Federal report. Those are current types of jobs, but the evolution will very likely create new types of jobs that we haven’t really been thinking about yet."
The author further states: "What this all means is simply that you need to keep yourself in the loop. You need to keep following the newest trends and tendencies in your market, and just generally beware that things are going to change. But remember, it's most likely not going to be the end of your career."
The author offers the following advice:
"We should all take the time to do this simple, yet comprehensive, exercise every now and then. Nobody is safe in a time with intelligent machines. We’re all in the same boat. Some are just closer to the edge than others. Get up and move as close to the wheelhouse as possible. Use your imagination, be creative and open to new combinations and collaborations within and across industries. Innovation and disruption will happen eventually, it’s just a matter of whether you’ll be in charge or somebody else will."
Author: Kasper Nymand:
ABOUT THE AUTHOR OF THE ABOVE ARTICLE
UNILEVER HR USE OF AI
Unilever hires many people and sells a multitude of products worldwide. It has massive duties in dealing with employee recruitment, interviewing, employee selection, and managing employees, Thus the Unilever HR departments have huge efforts in dealing with this very important area of Unilever. See the following link to understand how Unilever is creatively and efficiently addressing this HR issue using AI (machine learning and neural networks): https://www.businessinsider.com/unilever-artificial-intelligence-hiring-process-2017-6
Check out this link to a good WSJ article about how savvy lenders are using AI to better analyze potential borrowers. Lenders, with the help of AI, are completing a more thorough risk management analysis of potential loans and as a result are making more risk-appropriate loans to more borrowers who meet the acceptability standards: https://www.wsj.com/articles/ai-helps-auto-loan-company-handle-industrys-trickiest-turn-11546516801
OPAQUE BLACK BOX
In XDNET's article "Inside the Black Box: Understanding AI Decision Making" Charles McLellan states:" AI algorithms are increasingly influential in peoples' lives, but their inner workings are often opaque." This article discusses what is being done about that problem. We need to be able to peek under the hood and understand why AI makes decisions. Of course, not everyone agrees with that sort of visablity because humans may not be able to understand and explain.
FUTURE OF AI, ANI, ACI, AND ASI (SEE LINK AND TEXT BELOW)
Here is a very good article that addresses the possible future of AI, ANI, AGI, and ASI in as early as 2029:
Title: "The Risks of Advanced AI Are Real. We Need to Act Before It's Too Late, Warn Experts"
BY PAUL SALMON, PETER HANCOCK & TONY CARDEN, THE CONVERSATION
FEBRUARY 01, 2019
"Artificial intelligence can play chess, drive a car and diagnose medical issues. Examples include Google DeepMind's AlphaGo, Tesla's self-driving vehicles, and IBM's Watson.
This type of artificial intelligence is referred to as Artificial Narrow Intelligence (ANI) – non-human systems that can perform a specific task. We encounter this type on a daily basis, and its use is growing rapidly.
But while many impressive capabilities have been demonstrated, we're also beginning to see problems. The worst case involved a self-driving test car that hit a pedestrian in March. The pedestrian died and the incident is still under investigation.
The next generation of AI
With the next generation of AI the stakes will almost certainly be much higher.
Artificial General Intelligence (AGI) will have advanced computational powers and human level intelligence. AGI systems will be able to learn, solve problems, adapt and self-improve.
They will even do tasks beyond those they were designed for.
Importantly, their rate of improvement could be exponential as they become far more advanced than their human creators. The introduction of AGI could quickly bring about Artificial Super Intelligence (ASI).
While fully functioning AGI systems do not yet exist, it has been estimated that they will be with us anywhere between 2029 and the end of the century.
What appears almost certain is that they will arrive eventually. When they do, there is a great and natural concern that we won't be able to control them.
The risks associated with AGI
There is no doubt that AGI systems could transform humanity.
Some of the more powerful applications include curing disease, solving complex global challenges such as climate change and food security, and initiating a worldwide technology boom.
But a failure to implement appropriate controls could lead to catastrophic consequences.
Despite what we see in Hollywood movies, existential threats are not likely to involve killer robots.
The problem will not be one of malevolence, but rather one of intelligence, writes MIT professor Max Tegmark in his 2017 book Life 3.0: Being Human in the Age of Artificial Intelligence.
It is here that the science of human-machine systems – known as Human Factors and Ergonomics – will come to the fore.
Risks will emerge from the fact that super-intelligent systems will identify more efficient ways of doing things, concoct their own strategies for achieving goals, and even develop goals of their own.
Imagine these examples:
an AGI system tasked with preventing HIV decides to eradicate the problem by killing everybody who carries the disease, or one tasked with curing cancer decides to kill everybody who has any genetic predisposition for it
an autonomous AGI military drone decides the only way to guarantee an enemy target is destroyed is to wipe out an entire community
an environmentally protective AGI decides the only way to slow or reverse climate change is to remove technologies and humans
that induce it.
These scenarios raise the spectre of disparate AGI systems battling each other, none of which take human concerns as their central mandate.
Various dystopian futures have been advanced, including those in which humans eventually become obsolete, with the subsequent extinction of the human race.
Others have forwarded less extreme but still significant disruption, including malicious use of AGI for terrorist and cyber-attacks, the removal of the need for human work, and mass surveillance, to name
only a few.
So there is a need for human-centred investigations into the safest ways to design and manage AGI to minimise risks and maximize the benefits .
POTENTIAL USES OF AI AND MACHINE LEARNING IN USA EDUCATION
It is estimated by Cognilytica, in their #87 "AI Today" Podcast, that by 2024 six billion US dollars will be spent annually in the USA on educational AI and machine learning.
The types of educational case studies that are anticipated are: augumation of teachers on non-instructional tasks; tutoring; hyper-personalized learning; AI classroom assistants / robots; voice systems; and possibly students advancing at their own individualized learning pace with hyper-personalized academic content regardless of a student's grade level.
TRUCKS - TRUCKS - TRUCKS !
USING AV TECHNOLOGY
Our nation's highways are packed with trucks. Most are driven by competent professional drivers. However, some are driven by careless unprofessional drivers. According to Chris Spear -- President and CEO of the American Trucking Associations (ATA) -- as stated on the ATA web site -- 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 a year for the next decade (according to ATA) to keep up with demand. (See; https://trucking.org).
Self-driving trucks using autonomous vehicle (AV) technology can drive more than the current 11 hour daily driving limit and do not need breaks for food, restroom use or rest.
However, the driver-less trucks will not be doing “dock to dock" runs for a long time. 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.
With 94% of highway accidents attributed to human error, the successful deployment of AV technology can drastically reduce fatalities on the road. (See: https://medium.com/.../why-automated-vehicle-technology-holds-enormous-potential-...
Sep 14, 2017). Moreover, the technology can deliver significant returns by reducing traffic congestion, improving driver productivity and decreasing emissions through lower fuel burn.
SOME INITIAL IDEAS ON HOW TO MONETIZE AI and ML. ... NOW
Obviously, a company that has invested a lot of time, energy, and resources in getting its own company ready for AI and ML applications in order to benefit its own organization will want to implement beneficial applications internally for its own company.
However, why not get started now with some of that "low hanging fruit" type ROI.
At the right time (not too soon or too late) start the process of initially monetizing ROI. (Keep in mind that it's ..."the second mouse that gets to eat the cheese").
1. Provide tools for people to build there own solutions.
It is kind-of like how during the gold rush in the western USA in the 1800's the first folks to profit from the gold rush were not the gold miners but rather the merchants selling wheel barrels, picks, shovels, and other tools to facilitate the gold mining. So selling AI and ML related tools (products), like various machine learning foundational platforms, can be profitable. The key word is here "tools."
2. Leverage the power of AI and ML for numerous AI and ML activities involving ordinary consumer activities, such as conversational chat bots, special Siri-like domain-specific products, and chat boxes, so as to give the ordinary consumer a taste of AI and ML. Other examples here are things like Uber, on-demand insights, and language recognition.
3. Explore the intersection of AI / ML and enterprise / company business processes so as to help the business save time, to make more money, to reduce costs, to improve safety, to be more efficient, to gain more insights, to improve compliance, to improve customer services, and to examine and improve company processes.
4. Provide AI and ML services and consulting in unstructured data, structured data, big data, algorithms, artificial neural networks, robotics, robotic vision, drones, math services , statistical services, legal services, facial recognition, voice recognition, speech recognition, deep learning, signal processing, and other services related to ML and AI.
5. Sell ownership or license access of Big Data owned or legally acquired by your company to customers for use with customers' AI and ML projects.
6. Create an indirect sales channel by engaging resellers, value added resellers,
integrators, and sales reps that will market and sell your Big Data.
7. Create direct and indirect sales channels of all other individual AI and ML components.
8. Establish education and training programs for AI and ML .
9. So as to proactively encourage world-wide equal-access democratization of certain open source non-proprietary AI and ML information, data, hardware products, software items, and other Al and/or MI relevant things, the Parties state the following:
The Parties will attempt to create a non-binding informal Joint Cooperation Agreement (JCA) between certain companies or other entities (i.e. universities, government organizations, research organizations, etc) that have relevant AI and ML experts working for or engaged by such companies or other entities. The JCA companies or entities would meet periodically(electronically or in-person) to discuss open source non-proprietary AI and ML issues.
THE FACIAL RECOGNITION ASPECT OF AI AND MACHINE LEARNING
According to the Gemalto web site (now part of the Thales Group) [See: https://www.thalesgroup.com ] it states - ”Facial recognition is the process of identifying or verifying the identity of a person using their face. It identifies, captures, analyzes, and compares patterns based on the person's facial details. The face detection process is an essential step as it detects and locates human faces in-person and in images and video. The face capture process transforms analog information (a face) into a set of digital information (data) based on the person's facial features. The face match process verifies if two faces belong to the same person.”
Also, on the Gemlto web site (See link above), it is stated: “Biometrics are used in assisting one in identifying and authenticating a person using a set of recognizable and verifiable data unique and specific to that person. 2D or 3D sensor "captures" a face. It then transforms it into digital data by applying an algorithm, before comparing the image captured to those held in a database. These automated systems can be used to identify or check the identity of individuals in just a few seconds based on their facial features: spacing of the eyes, bridge of the nose, contour of the lips, ears, chin, etc. They can even do this in the middle of a crowd and within dynamic and unstable environments.”
Apple, Facebook, Google, IBM, Oracle, Accenture, MIT, Harvard, Stanford, Amazon, USA, UK, Russia, China, India, Japan, Canada, and others are rapidly researching, developing, and applying facial recognition products and services.
The following recognition signatures using the human body are also applied: fingerprints, iris scans, voice recognition, digitization of veins in the palm of the hand, and other behavioral criteria. Those biometrics (for the most part) are used to protect internet based monetary transactions where illegal/unlawful cyber actions have been rapidly expanding.
Why is facial recognition so enamored by Ai personnel today? Facial biometrics continues to be the widely preferred biometric benchmark. That's because it's easy to plan, prepare, deploy, implement, and analyze feedback. There is no hampering or interfering physical interaction required by the facial recognition user. Additionally, face detection and face match procedures for implementation, identification, and verification are so amazingly quick.
Facial recognition use cases today currently include law enforcement identifications, lost persons identification, military identification uses, social media identification / tracking, identification of threats / risks in large venues ( i.e. stadiums, airports, stations, festivals, political gatherings, and other facial reconization uses.
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 weekly. The last update to this web site was made on: May 18, 2019
(C) COPYRIGHT JOHN ("JOHNNY") E . MILLER AND QIN ("TIM") SHENG
2018 - 2019
ALL RIGHTS RESERVED EXCEPT AS NOTED HEREIN WHERE ANOTHER PARTY OWNS A COPYRIGHT INTEREST
_________THE END ________