Hello, you either spend your next 8 minutes a few seconds reading this article or spend the whole week reading 15 encyclopedia text.
Without further ado, here is the list of things to cover to ensure a solid foundation knowledge in getting started with Artificial Intelligence and Artificial General Intelligence.
For brevity sake, let's delve straight to the point on the essential factors in this introduction guide, and this includes:
The goal of this introductory guide is to be brief as possible and to try and minimize the amount of complexity behind understanding some fundamental structure of getting started with AGI; therefore, only the first five bullet points feature in this article while the others are to be expected covered in the next article.
The common word between these two terms is 'Intelligence,' but, one is driving for a higher level of intelligence similar to ours, which is human level like.
Artificial Intelligence commonly referred to as AI is the use of software to solve problems and accomplish tasks. However, this level of intelligence is heavily reliant on data to process and achieve an outstanding result, hence the reason for it being called weak or narrow AI.
Artificial General Intelligence, on the other hand, aims at learning and thinking independently without necessarily relying on data being fed to it frequently, merely human-level intelligence, however, this is yet to be fully accomplished, but we are getting closer to building this.
AGI is commonly referred to as Strong AI relative to Weak AI, and it is a machine but capable of equipping itself with information, thereby posing a level of general intelligence.
If you discerned closely as to how the word intelligence is applied in the last two paragraphs, they seem somewhat different but close to contradicting one another, and this is because there is no one true definition on intelligence. However, research poses that to achieve intelligence, a machine must have some of the following characteristics:
Amongst others are the above characteristics, such machine must beget to exhibit intelligent behavior.
With a rough idea on AI and AGI is and what it indeed aims to be, let's delve in further to examine what we can do with the AI of today, to give us an idea as to all the good AI has done, and a fragmentary view as to what AGI should be able to do.
There seems to be a considerable amount of criticisms as to the cost of AI in our daily lives. However, much emphasis has been placed on the disadvantages of AI and not the benefits, specifically the good it has done. Without little or no doubt, every technology has its pros and cons, so does AI.
Weak AI is in trend in various sectors of the economy, and Strong AI is yet to have a firm ground in the ecosystem. Specific tasks are completed with the use of this technology with activities ranging from robot control, robot sensing, electronic trading platforms, and many more.
In a review of some of the areas in which AI is applied, the following sectors will be examined:
Purportedly, AI is utilized in numerous fields, but the above primary sectors are discussed.
Given the growth rate of the world population, the agricultural sector is one of the primary sectors that require development in terms of output to meet up with the growing demand for food.
AI is used in Agricultural predictive analytics to predict the time it takes for a crop to plump. It is also used in crop and soil monitoring to manage and track the health of a crop, making it easier and more sustainable for farmers. The goal is to increase the production of healthy food in a short amount of time.
The Future of AI in the classroom is bright.
The innovation of AI in the educational sector has been a game-changer to how learning is accosted, one of which is personal AI and Tutor for students to ensure personalized learning in substitute of generalized learning and this could do some real change in the educational sector of developed and most importantly, the developing countries.
Lastly, AI is also used in gathering data on the performance of students in retrospect of various learning techniques to determine what works best for an individual student, and this shows the lot on the good AI has done and will do to education but let's keep it this way.
This section goes to the traders and financial institutions of Wall Street.
You probably have heard of trading bots, yeah thanks to AI you can have algorithmic trading systems in place to carry out market orders while making the job more comfortable for you, trading now requires low maintenance with the aid of AI.
AI also aids in personal finance, trying not to promote any specific products of AI. Consumer spending can now be properly managed and optimized with the use of AI; and should you consider this if you want to increase your savings, you might be astounded as to how your spending far outweigh what you have left for savings despite the increase in your income.
Out of curiosity, do you still consider AI harmful to humans in spite of the fact as to how many problems it solves? You should consider giving it a chance.
The world and the labor force specifically are pessimistic about AI, and now that the world is working on having a higher level of AI, this technology, however, seems to pose a threat to the labor force which leaves the questions for humans as to what happens when AGI does all the work.
Industries incur high-end costs in automation, and there has been increasing demand for it, especially in world economies like Korea, Japan, Germany, China, and the United States. With the robot to workers ratio snowballing, the cost of implementing more robots in the manufacturing industries has shrunk over time.
Given the fact that this technology is wholly limited to developed countries, how does this affect developing countries labor force tend to react to this:
Fact check, China has one of the world highest population of consumer base and labor force, but with AGI acclimatization into production, over 77% jobs are at risk in a developed country, while countries like Ethiopia and Nepal have over 80% job at risk and globally on an average over 55% of the world population jobs are at risk.
This technology developed by advanced countries doesn't place them in an ideal situation to continue through in the search for a Strong AI. Profoundly, AGI doesn't just end with the manufacturing sector but expands its effects on other job classification such as construction, insurance underwriter, fast food cook, and many more.
The question remains the same: what are the plans in place to take care of our workers when there isn't any work for them? UBI has been one of the schemes.
Universal Basic Income is perceived as a way to diminish the effect of unemployment as a consequence of exponential advancement in technology, but given the ratio of unemployed and expected unemployed workers, do countries have what it takes to cover the cost of giving monthly benefit to its citizens and still strive in carrying out economic functions.
Consciousness and Mind, however, intertwined is one of the significant thesis of AGI. There are controversies as to how possible this is or not. However, an argument has been in place, stating the fact that a computer executing a program can not be said to have a mind regardless of how efficient it is.
According to John Searle, he expressed AGI (Strong AI) as an appropriately programmed computer with the right inputs and outputs that would thereby have a mind in precisely the same way human beings have minds. It is avered that by accurately describing the functions relating to each other that form a mind, a computer program can accurately represent this functional relationship and can have mental phenomena if it runs the program accurately.
Searle furthers his argument by illustrating that the Mind is barely composed of some mechanical process but also composed of biological process thereby amplifying that the event to drive consciousness through neuroscience is a mere technical process, thus for a computer to have a mind, some sense of dualism must occur between the mechanical and biological process of an AGI.
In truth, certain conclusions have been made about the Chinese room that achieving consciousness is merely impossible. However, the goal of this article is not to discuss the Chinese room argument and the Turin test to determine if a computer can think indifferently from a man but to understand some of the underlying factors relating to Strong AI.
Building a machine that knows what to do without external influence and interference is one of the critical features of AGI. How AGI can inaugurate the action by itself for the sake of satisfying itself rather than satisfying external goals. This process is attributed to the self-determination theory.
The self-determination theory stated that there are three primary intrinsic needs involved in carrying out an action based on one's will, and these needs are:
According to Deci, giving instantaneous feedback on a task increases people's intrinsic motivation to do it, meaning that this was because the positive feedback was fulfilling people's need for competence. Giving positive feedback on a task served only to increase people's intrinsic motivation and decreased extrinsic motivation for the job.
The will to interact, be connected to, and to experience caring for others also motivates people to make individualistic choices.
Autonomy is another critical factor to take into consideration. Situations where freedom seems to be given increases the individual intrinsic motivation to do something that they want to without any form of restriction and control.
When you make a keyword search on AGI, the top result, you find is how the world is going to end with AGI being the sole crucial factor.
It is okay to feel the worse of new technologies but rather than being pessimistic about the capabilities of AGI, how about putting measures in place to ensure AGI does not have such prospects as we humans expect.
However, specific theories exist as to how we can avoid this catastrophe and how much damage AGI can do to the world.
I hate to wrap this first section of an introduction to getting started with AI and AGI, but what to expect in the second part is I delving in-depth on the following: