How many algorithmic intelligences are there and what are their limitations?
There are several types of artificial intelligence (AI) and algorithms used in its development, including machine learning, deep learning, neural networks, genetic algorithms, search algorithms, among others. Each has its own characteristics and is used in specific applications. Therefore, there is no fixed number of algorithmic intelligences.
The types of artificial intelligence bring different ways of trying to make machines think and act like humans. Throughout history, that idea was seen as extremely futuristic. However, we already have highly capable machines running our routines, even if we don’t know it.
Automation is one of the main gains made with artificial intelligence, yet let’s look at some of the AIs that are being applied and understand how each of them works in broad strokes with some examples:
-Artificial Narrow Intelligence (ANI)
-Artificial General Intelligence (AGI)
-Artificial Superintelligence (ASI)
-Reactive Machines
-Limited Memory
-Theory of Mind
-Self-Awareness
Artificial Narrow Intelligence (ANI)
Artificial Narrow Intelligence (ANI) is a form of artificial intelligence that is capable of performing a specific task with a high level of competence. For example, a speech recognition program is an example of ANI, as it is able to recognise and respond to specific voice commands, but does not have the capability of general understanding or continuous learning.
Typically, ANI is used in functions such as:
virtual assistants (Siri, Alexa, Cortana, among others);
facial recognition;
spam filters in emails;
autonomous vehicle systems.
Artificial General Intelligence (AGI)
Among the types of artificial intelligence, AGI is considered strong and deep, as a machine capable of mimicking human intelligence and with a vast capacity for action.
In its behaviour, it can learn and, based on that, replicate attitudes to solve different issues. This is what makes it one of the most versatile models available today.
AGI has the role of thinking, which leads to a unique and not completely robotic understanding. Thus, for each scenario it has to deal with, the proposed solution is different.
This ability to adapt to different scenarios means that she has a resolution activity that is very close to the human mind. This is precisely why it is considered a much deeper intelligence.
One of the bases of AGI is its theoretical structure. This means that it has the ability to evaluate and detect different needs, processes and even emotions in order to act correctly. That is a unique feature when compared to other types of artificial intelligence.
In practice, its learning capacity and cognitive level are very high. This makes it possible, for example, to shape a company’s service according to the most common doubts and needs of the brand owner.
It is very common to work with machines that are able to replicate human actions, which in itself is very beneficial. However, AGI is a system capable of studying and understanding humans and dealing accurately with user interactions and behaviours.
Artificial Superintelligence (ASI)
Artificial Superintelligence (ASI) is a term used to describe an artificial intelligence that is significantly more advanced than human intelligence in one or more areas. This could include an ability to learn, solve problems, make decisions and adapt to new situations more efficiently than humans. ASI is a topic of great interest and concern in the AI research community, as its development could have significant implications for society. Some experts believe that ASI could lead to a revolution in technology and economics, while others fear that it could create significant ethical and security issues. However, some AI scientists believe that the development of superintelligence is difficult to achieve in the near future due to the complexity of the human brain and the lack of complete understanding of intelligence.
Some examples of tasks that could be considered superintelligence include:
Information processing: A superintelligence could process information at a much faster speed and scale than humans, allowing it to analyse large amounts of data in a matter of seconds.
Problem solving: A superintelligence could solve complex problems much more efficiently than humans, using advanced algorithms and machine learning processes.
Decision-making: A superintelligence could make decisions based on a more complete and accurate analysis of available information, which could lead to more accurate and efficient decisions.
Self-improving: A superintelligence could be able to improve itself autonomously, which would make it smarter as time goes by.
However, it is important to mention that these examples are theoretical and there is no scientific consensus on whether these levels of intelligence are achievable in practice.
Reactive machines
Reactive machines are a type of artificial intelligence that are characterised by their ability to respond quickly and efficiently to events in the environment. These machines are designed to react to stimuli in the environment rather than plan or make long-term decisions.
An example of a reactive machine would be a robot that is used to navigate an unfamiliar environment. The robot would be equipped with sensors that allow it to detect objects and obstacles in its path, and a control system that allows it to avoid them quickly and efficiently. However, this machine does not have the ability to plan its path, it only reacts to what it sees at that moment.
Another example would be an air traffic control system, which has the ability to detect and react quickly to changes in the position of aircraft, thus avoiding potential collisions.
In general, reactive machines are useful in situations where a quick and accurate response to changes in the environment is required, but there is no need for long-term planning or decision-making.
Limited Memory
Limited memory refers to the ability of an artificial intelligence system to store and access information only for a limited period of time. This means that a system with limited memory can only remember information relevant to the current moment and cannot access previously stored information once it has been forgotten.
An example of a system with limited memory could be a reinforcement learning agent that is training to complete a specific task. The agent might only have access to information relevant to the current task and would not have the ability to recall information from previous tasks once it has completed the task.
Another example would be an air traffic control system that only has access to information from aircraft in its control zone and does not have the ability to recall information from aircraft outside its zone.
Memory-limited systems are common in artificial intelligence applications, especially in machine learning systems. However, they can also have disadvantages as they cannot remember past information and cannot apply what they have learned in similar situations.
Theory of Mind
Theory of mind refers to the ability of a being to understand that other individuals have minds of their own, with their own thoughts, emotions and desires. Theory of mind is especially important in social interaction as it allows us to understand and predict the behaviour of others. Theory of mind has been studied mainly in the field of psychology and neuroscience, and has been found to be a skill that develops throughout childhood and is related to social and cognitive development. In the context of artificial intelligence, theory of mind refers to the ability of an artificial intelligence system to understand and model the minds of humans and other agents. This could include the ability to understand and predict the behaviour of people, as well as the ability to interact effectively with them. Although theory of mind is a complex and developing topic in AI research, it is considered crucial for the development of natural and interactive AI systems.
An example of an application of theory of mind in artificial intelligence could be a customer service system that uses natural language processing techniques to understand customer intentions and needs. The system could use a theory of mind model to infer the customer’s emotions and mental states from their language and behaviour, and adapt its response accordingly.
Another example could be a game agent that uses theory of mind techniques to infer the intentions and strategies of human players. The agent could use this information to adapt its own strategy and make more informed decisions in the game.
Another example could be a virtual assistant that has the ability to understand and respond to a user’s emotion and state of mind. The assistant could use natural language processing and emotion analysis techniques to infer the user’s emotional state and respond appropriately.
These are just a few examples, theory of mind is a developing field in AI and is expected to be applied in a variety of fields and applications in the future.
Self-awareness
Self-awareness is just an idea, i.e. a concept that guides the development of artificial intelligence.
If we first define self-awareness, it refers to the ability of a being to be aware of itself and its own mental states. It is the ability to recognise oneself in a mirror or a video, to know that one has one’s own thoughts, emotions and desires, to understand that one is an individual distinct from others.
In the context of artificial intelligence, self-awareness refers to the ability of an AI system to be aware of its own existence and state. However, self-awareness is a highly debated topic and there is no scientific agreement on whether it is possible to develop self-awareness in an AI system. Many AI researchers believe it is difficult to achieve due to the complexity of the human brain and the lack of complete understanding of consciousness. However, some researchers are working on projects related to self-awareness, such as developing systems that can evaluate their own performance and make decisions to improve it. Research is also being done on techniques to develop AI systems that can be aware of their environment and adapt to it autonomously.
Reflection:
At the current point we are at with the development of AI I wonder what are the limitations of ( little in comparison to human and a lot of… ) Artificial Intelligence?
There are several limitations at present:
Lack of understanding: While AI systems can perform complex tasks, they often lack the deep understanding and context needed to make decisions and solve problems in a human-like manner.
Limitations in machine learning: Machine learning-based AI systems require large amounts of data to train and can be prone to making mistakes when faced with new situations.
Lack of flexibility: Many AI systems are designed to perform specific tasks and may have difficulty adapting to new environments or tasks.
Ethical and privacy issues: AI can raise ethical and privacy concerns, such as data discrimination, privacy of personal data and automated decision-making.
Data dependency: The quality of an AI system’s output is closely related to the quality of the data it is trained on, meaning that systems can be prone to errors if trained on incomplete or biased data.
Security issues: AI systems may also be vulnerable to cyber-attacks and other forms of exploitation, which could have serious consequences for users’ privacy and security.
Difficulty in interpreting results: AI systems can generate results that are difficult to interpret, especially in complex tasks.
It is important to mention that these limitations are current and it is expected that with the advancement of AI research some of them will be overcome. However, it is important to keep them in mind when developing and using AI systems to ensure that they are used ethically and safely.
Although technology offers us an infinite number of practical aspects, it fails to achieve human abilities such as having a deep self-awareness, having emotions such as being able to feel, discerning between right and wrong in the ethical sense of the concept, etc. Therefore, the reflexive use of self-awareness and being able to project oneself into transcendental philosophical aspects is a frontier that has not been reached by AI and I would dare to say that if this AI is based on our own limitation in the structural knowledge of how our mind works organically, psychologically and how it is influenced by the new tendencies of quantum physics, the result of AI will also be the same.
