What are Machine Intelligence, Artificial Intelligence, Machine Learning and Data Analytics?

True and Real Artificial Intelligence (AI) is what is designed as Real Man-Machine Superintelligence (RSI) Platform, the CyberEngine of the Metaverse.

Today's AI is the statistic ML & DL & ANNs, involving big data, statistic learning theory, optimization, data science and analytics, automated software, GPUs.

After 70-years trials and errors with symbolic, statistic, narrow, general and supreme AI, there emerges a real, true, genuine AI as Machine Intelligence and Learning (MIL), or Man-Machine Superintelligence.

Man-Machine MetaIntelligence = Human Intelligence + Artificial Intelligence + Machine Learning + Deep Learning + Data Analytics

ML [DNNs = DL < ML] + AI [NAI < AGI < ASI] + DA =MIL = Global AI =Real AI = Real Man-Machine Superintelligence

The RSI will allow computers to effectively and sustainably interact with the world taking in all of the world’s information to solve any possible problems and come up with any possible solutions.

Towards a True and Real Machine Intelligence and Learning

Artificial Intelligence (AI) is defined as a single and consolidated transdisciplinary science and engineering which is aimed to build a Trans-AI combining a wide range of intelligences, capacities, abilities and skills in one entity, as a single integrated?system/network/platform of man-machine superintelligence.?

The Trans-AI features a unifying world metamodel (global ontology), with a unifying reasoning and learning framework (master algorithm), to effectively interact with the world by intelligent processing its data,?from the real-world?data to the web data.

It is proved that the whole idea?of mainstream AI to mimic human?brains/mind/intelligence/behavior is deeply non-scientific, if not humanly amoral.

Today's big data-driven AI/ML/DL/ANNs are very different, having?little to do with simulating human?cognition and intelligence or brains, if only somehow inspired.?

Today's AI is mathematical?algorithms and statistical rules and predictive analytics, which excel?humans in many specific areas, such as judging, strategic games, algorithmic trading, self-driving, diagnosing, computing, measuring, recognising objects, characters, faces, human speech, or translating languages.

Such “narrow” AIs have superhuman capabilities, but only in their specific areas of dominance, much outsmarting humans in doing specific?tasks, jobs and works.?

Presently, there are a?numberless number of possible technologies, techniques, methods, models, algorithms and applications looking for a single transdisciplinary AI foundation:

Artificial narrow intelligence, artificial general intelligence, machine consciousness, intelligent agents, logic programming, machine learning, deep?learning,?artificial neural networks, artificial vision, computational discovery, computational creativity, self-aware systems, pattern matching, pattern?recognition,?knowledge representation and reasoning,?automatic?reasoning,?expert?systems, information extraction, data analytics, data mining,?question answering, text mining, natural language processing, large language models, etc.

https://www.dhirubhai.net/pulse/real-superintelligence-rsi-vs-artificial-asi-why-musk-abdoullaev/?fbclid=IwAR3PvNbDfY5vO5czKRHzwOnSq8HmD12-IyOlSSW8hWciw13eBlLglfZCMa0

Machine Intelligence Renaissance

Machine Intelligence, Artificial Intelligence, Data Analytics, Machine Learning, Deep Learning, and Predictive Analytics are all techniques that could radically change our world.

Nevertheless, designating the pillars of AI industry, these terms are liberally used and rarely well-defined and explained.

MI is even living without its home in all-knowing Wikipedia, if only mentioned by chance. It is generally used as a synonym of AI as?the simulation of human intelligence processes by machines, computer systems, with specific applications as expert systems, natural language processing, speech recognition and machine vision.

Again, machine learning with data analytics are often used interchangeably ?with artificial intelligence.

Data Analytics?requires the collection, storage, and categorization of large quantities of data to identify patterns and relationships in data, including the use of fundamental statistical analysis. Its Predictive Analytics, which through data analysis?predicting?an outcome, relationship, trend, model, rule, etc., is widely impersonated as Machine Learning.

AI is generally reduced to artificial human intelligence (AHI), with the mass of innumerable consequences, as AI risks, AI transparency, AI trust, AI explainability, AI bias, AI responsibility, or AI ethics.

It is well reflected as the EU regulations on AI which could be found in the combination of the European Commission’s?AI proposal?published on April 21, 2021, and the?White Paper?‘On Artificial Intelligence — A European approach to excellence and trust’ published on February 2, 2021.

And the need for responsible AHI has becoming a life-and-death question for human beings. Gartner identified “Smarter, Responsible and Scalable AI” as the?No. 1 market trend for data and analytics in 2021.

This big semantic chaos undermines the whole idea of MI/AI, corrupting the entire AI pipeline, the AI principles, design, research and development processes, deployment, and maintenance.

To upright things and provide transparency, here’s a?very basic overview of these technologies and how they are converging into a single machine intelligence as true artificial intelligence.

What is the definition of AHI?

There is no?accepted definition to what AHI is. Different sides have different opinions on what AI is, and what the definition of it is. Some of them are listed below.

Dictionary.com online?dictionary:

1. the ability of a computer, robot, or other programmed mechanical device to perform operations and tasks analogous to human learning and decision-making, such as speech recognition or answering questions;
2. a computer, robot or other mechanical device programmed with this human capability;
3. the branch of computer science involved in the design of computers or other mechanical devices programmed with the ability to mimic human intelligence and thought;

IBM

• Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

Oracle

• Artificial Intelligence refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.

Accenture

• Artificial intelligence is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence

SAS

• Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.

Encyclopedia Britannica

• Artificial Intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

Amazon AWS

• Artificial Intelligence is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.

European Parliament

• AI is the ability of a machine to display human-like capabilities such as reasoning, learning, planning and creativity.

Qualcomm

• AI is an umbrella term representing a range of techniques that allow machines to mimic or exceed human intelligence.

What is definition of Real AI?

Professor John McCarthy

• Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Wikipedia

• Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.

Deloitte

• AI refers to a broad field of science encompassing not only computer science but also psychology, philosophy, linguistics, and other areas.

EIS

• Real AI/MI is about Total Environment/Everything/Reality/World/Causality/Mentality Simulation Modeling to emerge as Real [Man-Machine] Superintelligence to effectively, efficiently and sustainable interact with any environment, physical, mental, digital or virtual

So, as you can see, there are many different definitions and versions for the definition of Artificial Intelligence, there is no one definition for AI. The Fake AI is an attempt to make computers as intelligent, or even more intelligent than human beings, giving computers human-like behaviors, thought processes, and reasoning skills. And the consequences of it are as heavy as follows.

1. AHI Causes Massive Technological Unemployment.

• AI is replacing routine and repetitious, boring and monotonous jobs across various fields such as manufacturing, healthcare, security, privacy, customer service and many more.

Human Workers-?Time Consuming, Boring, Efficiency Reduces Periodically, Slow. The Workers need raises, holidays etc., which slows down the process.

AI-?Fast, No Concept of Boring, Efficiency is increased significantly, Fast or Slow depending upon the need, we don’t give AI raises and Holidays.

Myths about FAI. It will add 97 million new jobs by 2025, and $30 Trillion to the World’s GDP by 2030.

2. AHI is smarter than Humans in parts and narrow tasks

3. We cannot Trust AHI for the following reasons

• AI is a “black box,” i.e. a mysterious system that generated output without providing insights into what the underlying algorithm did and why.
• Fitness of data we use to train machine learning models.
• The risk of amplifying human bias and discrimination.
• Explainability of predictions and decisions made with algorithms.
• The role of human oversight in monitoring the fairness and transparency of AI applications.

4. AHI is Complicated and Hard to Understand.

• AI and AI systems are hard to understand, complicated, and hard to understand.
• Explainable AI is in need to improve the transparency of decision making, to comprehend and trust the results and output created by machine learning algorithms.

5. AHI will lead to Destruction and Slavery.

• Hollywood Movies as ‘2001: Space Odessey’, ‘I Robot’, ‘The Matrix’, etc., have tather ealistic and sometimes scary/frightening graphics.
• Because of graphics like these, people tend to refuse Sci-Fi Robots and AI Development.

6. You need to be proficient in AI to use it.

• You need to be an AI or Data Science Professional to use and harness the power of FAI.

7. Machine Learning using Artificial ‘Neural Nets’ means that machines and computers can learn and understand like the human brain can.

• While the technology of Machine Learning Neural Nets is powerful and useful, it is not yet competent with the Neural Nets of the human brain. Therefore, Machine Learning Neural Nets cannot match the complexity and originality of the human brain.
• While neural nets can solve many types of problems, they are not capable of enabling the creative synthesis of diverse concepts and information sources that are characteristic of human thinking.
• The human brain contains more 200 Billion neurons, with each neuron connecting with as many as 10,000 other neurons through synapses.
• The largest neural nets in the world, GPT-3 has 175 Billion Neural Nets.
• By one estimate, a human brain has more switches than all the computers, routers, and internet connections on Earth.
• So, naturally, it is not possible soon for the FAI as Artificial Neural Nets (ANN)s to take over and compete with the human brain.

Such AHI examples are automation/personalized gadgets?like coupon generators; Chatbots, automated software agents having fixed responses, as ‘I’m sorry, I couldn’t understand what you are trying to say’ or ‘I’m sorry, I don’t understand what you are saying, would you like me to forward this conversation to a live agent?’; IoT applications collecting and analyzing user information, rather than to make a consumer IoT device work better; ?automation programs, bots, machines, etc. which can be doing the tasks of humans, but this does not make them ‘intelligent.’ They are simply doing exactly what they are programmed to do. For Instance, the software that underpins blood testing is doing the analysis work instead of a doctor, but it is not intelligent. What is AI Exactly?

AHI Predictions 2021

"Too many AI investments end up as “pretty shiny objects” that don’t pay off. Most companies have yet to adapt talent strategies, organizational structures, business strategies, development methodologies and risk mitigation for a world that moves at AI speed.

So there’s work to be done, but the reward can be concrete benefits today and the foundation for success tomorrow. As we’ve done for the last four years, we’ve made key predictions informed by our survey of more than 1,000 executives (including over 200 CEOs) at US companies that are using or considering AI. Together, these insights should help your company navigate the top AI trends it will face in 2021 and beyond.

PwC’s annual AI Predictions survey, now in its fourth year, explores the activities and attitudes of US business and technology executives who are involved in their organization’s AI strategies. Among this year’s 1,032 survey respondents, 71% have C-suite titles and 25% are from companies with revenues of $5 billion and up. They represent industrial products (20%), consumer markets (20%), financial services (18%), tech, media and telecommunications (17%), health industries (17%), and energy, utilities and mining (8%). The survey was conducted by PwC Research, PwC’s global Center of Excellence for market research and insight, in October 2020". AI Predictions 2021

Among other AHI predictions, we have to note the following:

An NLP model with over one trillion parameters will be built.

A political deepfake will go mainstream in the U.S., fueling widespread confusion and misinformation.

AI will become an important part of the narrative in regulators’ antitrust efforts against big tech companies. 10 AI Predictions For 2021

Machine Intelligence is to take over Artificial Intelligence and Machine Learning and Deep Learning

Machine Intelligence is going to embrace Machine Learning and Artificial Intelligence as Machine Intelligence and Learning (MIL).

There are good and bad news for all who is to learn MIL from scratch.

The good news is, no need to study standard AI textbooks, as far its subject rendered obsolete by a current statistic ML.

The so-called "Classic/symbolic/logical AI" is dead due to the large-scale AI projects, as GOFAI, CYC, Soar, Japan's 5th Generation CI, US SCI, WBE, failed and closed or failing.

The bad news is that the whole construct of AI, be it weak AI or strong AI, full AI, or HL AI, is turned speculative due to its failed program of simulating human reasoning by formal logical means.

Deep Learning and AGI. Part I: Computer Vision

Today's AI is the statistic ML & DL & ANNs, involving big data, statistic learning theory, optimization, data science and analytics, automated software, GPUs.

After 70-years trials and errors with symbolic, statistic, narrow, general and supreme AI, there emerges a real, true, genuine AI as Machine Intelligence and Learning (MIL).

Machine Intelligence = Artificial Intelligence + Machine Learning + Deep Learning + Data Analytics

ML [DNNs = DL < ML] + AI [NAI < AGI < ASI] + DA =MIL = Global AI =Real AI = Real Man-Machine Superintelligence

MI will allow computers to?effectively and sustainably interact with the world taking in?all of the world’s information to solve any possible problems and come up with any possible solutions.?

What is AI, Really?

Real AI or Machine Intelligence and Learning embraces:

Big Data Analytics/Information Science

Logic/Symbol manipulation

Mathematics and Statistics

Neural Networks/Artificial Brains

Psychology/Mental models

Programming/Algorithm/Software/Hardware/Computer Science

Linguistics/Language models/NLP/NLU

Science/World’s Knowledge/

Engineering/Robotics/Automation

Philosophy/Ontology/Epistemology/Ethics/Principles

Real MIL is not a “branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence”.

It is the science and engineering of making intelligent machines…

AI Science and Engineering is NOT monodisciplinary, interdisciplinary, or multidisciplinary.

AI/MI is totally transdisciplinary, being about the world/reality/causality/mentality, its digital representation, modeling and simulation, processing, inference and interaction.

Resources

TRANSDISCIPLINARY ARTIFICIAL INTELLIGENCE AS FUTURE INTELLIGENCE: The Trans-AI Platform of AI/ML/DL/NNs

https://www.dhirubhai.net/pulse/whats-fundamentally-wrong-ai-azamat-abdoullaev/?published=t

https://futurium.ec.europa.eu/en/european-ai-alliance/posts/whats-fundamentally-wrong-ai-artificial-intelligence-vs-machine-intelligence

The First GLOBAL AI Company: EIS Encyclopedic Intelligent Systems ltd