Machine learning and artificial intelligence systems are becoming increasingly important in our day-to-day lives. We have become accustomed to asking our smart-home system to turn the light on, ordering delivery food with the tips of our fingers, and having our fit watch tell us how many steps we have taken that day. With technology advancing faster than ever, it is crucial that we are able to differentiate between the various types of artificial intelligence, as well as how each one builds upon one another.
Artificial intelligence as a concept is a science – just like computer science, neuroscience, or mathematics. What is interesting about artificial intelligence, though, is that it encompasses all three of said sciences and more. These sciences are often referred to as the cognitive sciences, as they all focus on the human brain and mind in relation to its implementation into technology. Its implementation into technology requires a fundamental understanding of machine learning, deep learning, and neural networking.
Those seeking a foundational understanding will be pleased to find out that there are three main types and categories of artificial intelligence that vary in their goals as systems. Similarly, there are three subcategories to artificial intelligence that help us delve deeper into what being artificially intelligent truly means in the context of intelligent beings. Thus, artificial intelligence is the external and all-encompassing category, whereas machine learning is a subcategory. Machine learning, alongside artificial neural networking and deep representational learning, is at the core of artificial intelligence and is crucial to its success. Machine learning, however, is philosophically complex to a much greater extent than basic artificial intelligence.
The Three Categories of Artificial Intelligence
The three main categories or types of artificial intelligence are often referred to as artificial intelligence’s three different ‘faces’ – two of which you might have heard famously uttered by one of the founders of computer science, Alan Turing: “strong” and “weak” artificial intelligence. Weak artificial intelligence is also commonly referred to as GOFAI (Good Old Fashioned Artificial Intelligence), with algorithms that simulate phenomenal consciousness and thought without actually possessing phenomenal consciousness and thought.
Weak Artificial Intelligence
Weak artificial intelligence exists today, and has existed for many decades now. It is the kind of artificially intelligent system that has become woven into the fabric of our everyday lives. Weak artificial intelligence tends to algorithmically focus on solving a specific problem within a specific domain, such as what moves to play to win a game of Chess.
Weak artificial intelligence is weak in that it is limited in its capabilities; it can only perform one task or function at a time. Furthermore, weak artificial intelligence often relies on a human to set its parameters. This means that weak artificial intelligence has no learning algorithms beyond what the human has coded it to do. Essentially, weak artificial intelligence is simulating phenomenal consciousness without actually being phenomenally conscious. The human behind any given weak artificially intelligent system may be making one believe it is phenomenally conscious based on its actions or output. All it is doing in actuality is relaying information to the necessary processes, and repeatedly completing the algorithms coded into it. You have most likely interacted with a piece of weak artificial intelligence, whether it be an online chatbot or virtual talking assistant (such as Apple’s Siri).
Strong Artificial Intelligence
The second category is strong artificial intelligence. Strong artificial intelligence aims to replicate the intelligence of a human-being, alongside the multi-tasking ability of a human; in that it can understand, learn, and perform a variety of problems and solutions that builds upon and strengthens itself over time. The gradual self-evolvement of strong artificially intelligent systems based on experiences means that it is teaching itself new things based on experience, much like a human would! Some argue that strong artificial intelligence has yet to even exist. While this may hold some truth, it is important to note that complex algorithms such as predictive algorithms on human behaviour (such as those seen on Amazon that are used to calculate what you may like your next purchase to be) are a sign that strong artificial intelligence is on its way or already here.
The third remaining category is superintelligence, a form of even stronger-than-strong artificial intelligence that we have yet to create as humans. To artificial intelligence experts, this is the final form of artificial intelligence that would essentially replicate us humans and our mind’s capabilities wholly and identically. Some argue superintelligence would surpass human intelligence. It is the robot utopia (or dystopia) you have always heard and read about, or perhaps even imagined yourself. Artificial superintelligence would combine all subcategories of artificial intelligence, including machine-learning algorithms, neural networks, machine reasoning, and robotics – all into one giant artificially intelligent super-system that would not only match humans in their reasoning and consciousness, but surpass them. Cognitive science aims to naturalize and formalize the human mind into a machine, and this has yet to be done. Thus, superintelligence has yet to exist in our present, modern-day world.
The Subcategories of Machine Learning
With the basics and potentials of artificially intelligent systems covered, let us think back to the basics of strong versus weak artificial intelligence. It was briefly touched upon that in weak artificial intelligence, humans often set the parameters and they do not learn beyond whatever the human codes them to do. Machine learning is therefore a subset of artificial intelligence, and a smaller one at that, with relations directly to strong artificially intelligent systems. Machine learning exclusively focuses on teaching computers how to learn and evolve, without the help of a human essentially doing the learning for them and having it coded in. Machine learning itself is a powerful tool and used by many industries.
READ MORE: What Is Machine Learning Used For?
Machine learning as a subset is typically broken down into three of its own subcategories: algorithms, datasets, and features.
Algorithms are at the core of machine learning, because the algorithm does not have to be done a particular way. Algorithms are unique in that each algorithm will solve a problem or produce an output with varying accuracy and speeds. In order to use algorithms to help computers learn, modify, and grow, we must combine many different algorithms that, when completed, would produce another algorithm all by itself that is new and different. An even deeper subset of machine learning is deep learning that exclusively focuses on algorithms similar to the human brain and our thought structure. What is so complex about deep learning algorithms in machine learning, however, is that even the most basic algorithms influenced by the structure of the human mind, such as common sense, are substantially more complex than they first appear to be. Naturalizing, replicating, and formalizing such an algorithm would take a lot of philosophical and neuro-scientific discussion and thought.
If one is familiar with computer science theory or even the fundamental basics of coding, then one will know that datasets are an important part of any machine and its ability to not only produce results, but to evolve as they should in machine learning. Datasets can take the form of dictionaries, lists, sets, etc. Each dataset contains important information, often vital to the completion, performance, and efficiency of algorithms. Datasets can include anything and everything from numbers, to pictures, to text.
In machine learning, features are the keys that unlock the door to the solution. They are vital pieces of data that tell the machine what to focus on. Features in machine learning often take the form of mathematical formulas that can be used to compare solutions to other solutions. This not only results in the most efficient solution, but a solution that can be analyzed as an experience to learn from when a new and comparable problem arises. This is similar to what a phenomenally conscious human would do; take the problems and solutions from a particular instance and subconsciously build upon it when a similar, correlated problem occurs. This is one step closer to strong artificial intelligence and a core component of it!
Machine learning is a vital component of artificial intelligence. They are, however, not to be confused with one another. Artificial intelligence is a massive category that encompasses every aspect of artificial intelligence science and cognitive science. This spans out into a category that is innumerably large and intricate. Machine-language learning, while just as complex as artificial intelligence, does not encompass the whole that artificial intelligence does. Machine learning is simply a subcategory of artificial intelligence that defines its inner workings. Machine learning is also not “better” than artificial intelligence, as it is simply a part of it.
We would not have certain kinds of powerful artificial intelligence without machine learning, such as self-driving cars or speech/pattern recognition. These are types of artificial intelligence that rely on machine learning to sustain itself by using machine learning’s classic formula: input, feature extraction, classification, and output. Machine learning and machine language learning in conversational artificial intelligence, alongside deep learning, are at the core of artificial intelligence. They are essential.
The eventual deepening of our understanding in machine learning will hopefully lead us to a time and place where artificial intelligence and cognitive scientists are comfortable with machines’ potential and ability to outperform humans. In some cases, machines already are outperforming humans. This is because computing simple algorithms are far faster and more reliable than a human brain computing the same simple algorithm. This ultimately begs the question: is the human brain just a machine? We have yet to find out.