ARTIFICIAL INTELLIGENCE

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A Look At Artificial Intelligence - And Response

Before writing an article, I will talk to experts and do quite a bit of research. While looking at Artificial Intelligence, I read numerous articles on the subject. I also utilized a program called ChatGPT which is an AI language model developed by OpenAI. This was a fascinating experience.

Artificial Intelligence replicates human intelligence processes with a computer or software that can perform tasks. AI systems are designed to analyze data, adapt to new information, and improve their performance over time, often mimicking human cognitive functions.

At this point, AI still needs human intelligence, such as understanding natural language, recognizing patterns, making decisions, and learning from experience.

Funding, digital infrastructure, hardware and program accessibility, community, responsibilities, accountability, addressing human growth and development, and transmitting societal norms must all be addressed. What role will AI play moving forward—especially in education?

AI is categorized into two main types.

1) Narrow/Weak AI: This AI type is designed and trained for a specific range of tasks. It operates within predefined boundaries and lacks general intelligence. Examples include virtual personal assistants like Siri and Alexa, recommendation algorithms on streaming platforms, and image recognition systems.

2) General/Strong AI: This type can understand, learn, and apply knowledge across a wide range of tasks—like human intelligence. General AI remains more of a theoretical concept and has not been achieved.

AI techniques can be classified into the following categories:

Machine Learning: This involves training algorithms to recognize patterns and make predictions or decisions based on data. It includes techniques like supervised learning, unsupervised learning, and reinforcement learning.

Deep Learning: This utilizes artificial neural networks to analyze and process data. It has been particularly successful in tasks like image and speech recognition.

Natural Language Processing (NLP): NLP focuses on enabling machines to understand, interpret, and generate human language. This technology underlies applications like language translation, sentiment analysis, and chatbots.

Computer Vision: This field involves teaching machines to interpret and understand visual information from the world, such as images and videos. It is used in facial recognition, object detection, and autonomous driving tasks.

Robotics: AI-driven robots are designed to perform tasks autonomously or semi-autonomously. These tasks can range from manufacturing and assembly to exploration and medical procedures.

Expert Systems: These mimic the decision-making abilities of a human expert in a specific domain. They use a knowledge base to provide advice or make decisions based on user input.

As AI technology advances, we will continue to see the development of innovative applications across various industries, including education, healthcare, finance, transportation, entertainment, and more. Yet, challenges and ethical considerations, such as bias in algorithms, data privacy, and the impact on employment, also must be considered with the emergence of Artificial Intelligence.

AI has the potential to significantly impact education in various ways, transforming how students learn, teachers instruct, and educational institutions operate. We need a better understanding of AI, and the potential uses and possible dangers.

JC BowmanExecutive director of Professional Educators of Tennessee

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 "AI has the potential to significantly impact education in various ways, transforming how students learn, teachers instruct, and educational institutions operate. We need a better understanding of AI, and the potential uses and possible dangers."     

Potential? Shouldn't a NASA like program be implemented, ASAP, to, at least, use AI to tutor students in basic math skills, English skills, financial decision making, computer coding, use of spreadsheets, etc.?

AI should (could) provide 1-1 tutoring that schools can't afford to do, let alone find qualified tutors. Monitoring of student progress would be incredibly accurate unlike "standard" exams. E.G. Duolino app.

I'd like to have Mr Bowman address these points. 

P.S. As a former college computer engineering professor, I have tested AI in that field and it has done an impressive job of creating C and HTML code. It is currently (not in the future, Mr. Bowman) making experienced programmers far more efficient, but concerns are for junior coders and their opportunities. Nonetheless, grade school 'graduates' not reading at the third grade level, being able to do financial calculations, and not having a real world work ethic is beyond tragic.

Robert Dreyer

Artificial General Intelligence (AGI): Definition, How It Works, And Examples

What Is Artificial General Intelligence (AGI)? Artificial general intelligence (AGI) is a branch of theoretical artificial intelligence (AI) research working to develop AI with a human level of cognitive function, including the ability to self-teach. However, not all AI researchers believe that it is even possible to develop an AGI system, and the field is divided on what factors constitute and can accurately measure "intelligence." Other terms for AGI include strong AI or general AI. These theoretical forms of AI stand in contrast to weak AI, or narrow AI, which are able to perform only specific or specialized tasks within a predefined set of parameters. AGI would be able to autonomously solve a variety of complex problems across different domains of knowledge. Key Takeaways Artificial general intelligence (AGI) is a theoretical pursuit in the field of artificial intelligence (AI) research that is working to develop AI with a human level of cognition.  AGI is considered strong AI (compared to weak AI, which can function only within a specific set of parameters). Strong AI, like AGI, would theoretically be self-teaching and able to carry out a general range of tasks autonomously. AGI research is still evolving, and researchers are divided on both the approach(es) necessary to achieve AGI and the predicted timeline for its eventual creation. How Artificial General Intelligence (AGI) Works Given that AGI remains a theoretical concept, opinions differ as to how it might eventually be realized. According to AI researchers Ben Goertzel and Cassio Pennachin, "'general intelligence' does not mean exactly the same thing to all researchers." However, "loosely speaking," AGI refers to "AI systems that possess a reasonable degree of self-understanding and autonomous self-control, and have the ability to solve a variety of complex problems in a variety of contexts, and to learn to solve new problems that they didn't know about at the time of their creation." Because of the nebulous and evolving nature of both AI research and the concept of AGI, there are different theoretical approaches to how it could be created. Some of these include techniques such as neural networks and deep learning, while other methods propose creating large-scale simulations of the human brain using computational neuroscience. Artificial General Intelligence (AGI) vs. Artificial Intelligence (AI) While artificial intelligence (AI) currently encompasses a vast range of technologies and research avenues that deal with machine and computer cognition, artificial general intelligence (AGI), or AI with a level of intelligence equal to that of a human, remains a theoretical concept and research goal.  AI researcher Peter Voss defines general intelligence as having "the ability to learn anything (in principle)." Under his criteria, AGI's learning ability would need to be "autonomous, goal-directed, and highly adaptive." AGI is generally conceptualized as being AI that has the ability to match the cognitive capacity of humans, and is categorized under the label of strong AI. (Artificial super intelligence [ASI] also sits under the strong AI category; however, it refers to the concept of AI that surpasses the function of the human brain.) In comparison, most of the AI available at this point would be categorized as weak AI, or narrow AI, as it has been developed to focus on specific tasks and applications. It's worth noting, however, that these AI systems can still be incredibly powerful and complex, with applications ranging from autonomous vehicle systems to voice-activated virtual assistants; they merely rely on some level of human programming for training and accuracy. Examples of Artificial General Intelligence (AGI) Because AGI remains a developing concept and field, it is debatable whether any current examples of AGI exist. Researchers from Microsoft, in tandem with OpenAI, claim that GPT-4 "could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system." This is due to its "mastery of language" and its ability to "solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting" with capabilities that are "strikingly close to human-level performance." However, Sam Altman, CEO of ChatGPT, says that ChatGPT is not even close to an AGI model. In the future, examples of AGI applications might include advanced chatbots and autonomous vehicles, both domains in which a high level of reasoning and autonomous decision making would be required. Types of Artificial General Intelligence (AGI) Research Computer scientists and artificial intelligence researchers continue to develop theoretical frameworks and work on the unsolved problem of AGI. Goertzel has defined several high-level approaches that have emerged in the field of AGI research and categorizes them as follows: Symbolic: A symbolic approach to AGI holds the belief that symbolic thought is "the crux of human general intelligence" and "precisely what lets us generalize most broadly." Emergentist: An emergentist approach to AGI focuses on the idea that the human brain is essentially a set of simple elements (neurons) that self-organize complexly in reaction to the experience of the body. In turn, it might follow that a similar type of intelligence might emerge from re-creating a similar structure. Hybrid: As the name suggests, a hybrid approach to AGI sees the brain as a hybrid system in which many different parts and principles work together to create something in which the whole is greater than the sum of its parts. By nature, hybrid AGI research varies widely in its approaches. Universalist: A universalist approach to AGI centers on "the mathematical essence of general intelligence" and the idea that once AGI is solved in the theoretical realm, the principles used to solve it can be scaled down and used to create it in reality. The Future of Artificial General Intelligence (AGI) The year when we will be able to achieve AGI (or whether we will even be able to create it at all) is a topic of much debate. Several notable computer scientists and entrepreneurs believe that AGI will be created within the next few decades: Louis Rosenberg, CEO and chief scientist of Unanimous AI, predicted in 2020 that AGI would be achieved by 2030. Ray Kurzweil, Google's director of engineering and a pioneer of pattern recognition technology, believes that AI will reach "human levels of intelligence" in 2029 and surpass human intelligence by 2045. Jürgen Schmidhuber, co-founder and chief scientist at NNAISENSE and director of Swiss AI lab IDSIA, estimates AGI by around 2050. However, the future of AGI remains an open-ended question and an ongoing research pursuit, with some scholars even arguing that AGI cannot and will never be realized. AI researcher Goertzel has explained that it's difficult to objectively measure the progress toward AGI, as "there are many different routes to AGI, involving integration of different sorts of subsystems" and there is no "thorough and systematic theory of AGI." Rather, it's a "patchwork of overlapping concepts, frameworks, and hypotheses" that are "often synergistic and sometimes mutually contradictory." In an interview on the topic of AGI's future, Sara Hooker of research lab Cohere for AI said, "It really is a philosophical question. So, in some ways, it's a very hard time to be in this field, because we're a scientific field." What Is an Example of Artificial General Intelligence (AGI)? Researchers from Microsoft and OpenAI claim that GPT-4 could be an early but incomplete example of AGI. As AGI has not yet been fully achieved, future examples of its application might include situations that require a high level of cognitive function, such as autonomous vehicle systems and advanced chatbots. How Far Off Is Artificial General Intelligence (AGI)? Because artificial general intelligence (AGI) is still a theoretical concept, estimations as to when it might be realized vary. Some AI researchers believe that it is impossible, while others assert that it is only a matter of decades before AGI becomes a reality. What Is the Difference Between Artificial Intelligence (AI) and Artificial General Intelligence (AGI)? AI encompasses a wide range of current technologies and research avenues in the field of computer science, mostly considered to be weak AI or narrow AI. Conversely, researchers in the field of AGI are working on developing strong AI, which can match the intelligence of humans. Is Artificial General Intelligence (AGI) Smarter than Humans? Most researchers define AGI as having a level of intelligence that is equal to the capacity of the human brain, while artificial super intelligence (ASI) is a term ascribed to AI that can surpass human intelligence. What Year Will AGI Be Fully Developed? Researchers have differing opinions regarding when they believe AGI can be achieved, with some predicting its creation as soon as 2030 to 2050, and some believing that it is downright impossible. The Bottom Line The concepts of AI and AGI have long captured the human imagination, and explorations of the ideas abound in stories and science fiction. Recently, scholars have argued that even mythology dating from as far back as ancient Greece can be seen to reflect our fascination with artificial life and intelligence. There are currently many different approaches toward creating AI that can think and learn for itself and apply its intelligence outside the bounds of a previously specified range of tasks. Due to the theoretical and multifaceted nature of this research, it is difficult to say if and when AGI might be achieved. However, if it does become a reality, one thing is certain: It will have fundamental and wide-ranging impacts across our technologies, systems, and industries.

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