What is Artificial General Intelligence?
Artificial general intelligence (AGI) is a system of generalized, humanlike cognitive capabilities designed to learn and complete a vast range of tasks.
AGI is also called strong AI or deep AI. Strong AI uses the theory of mind AI framework not to replicate or simulate, it’s about training machines to understand humans to differentiate needs, emotions, beliefs, and thought processes. The core idea behind mind-level AI is to teach machines how to mimic human behavior and comprehend consciousness. AGI can gain cognitive abilities, make judgments, handle uncertain situations, use prior knowledge in decision-making.
Strong AI and Weak AI
Strong AI, also known as AGI, is a form of AI whereby a machine would require an intelligence equal to humans. It would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. The approach to achieving strong AI is associated with symbolic AI, in which the machine forms an internal symbolic representation of the physical and abstract world, so it can apply reasoning for further learning and decision-making.
Compared to strong AI, weak or narrow AI is not intended to have general cognitive abilities, meaning they are programs designed to solve only one problem and do not have consciousness. The approach to achieving weak AI has typically revolved around the use of artificial neural networks.
AGI Capabilities
AGI will be capable of performing any task that the human brain is capable of. It also has near-instant memorization and split-second number computing skills. What skills will AGI have?
Sensory perception
There are specific types of sensory perception that AGI can understand, such as color recognition, depth perception, 3D static images and determine the spatial characteristics of an environment from sound.
Social and emotional engagement
Similar to sensory perception, AGI will be designed to recognize emotions in facial expressions, vocal tones, and body language.
Fine motor skills
AGI is projected to complete tasks that would normally require fine motor skills such as grabbing keys from a pocket or solving a Rubik’s cube.
Natural language understanding
Currently, AI can be programmed to present relevant information upon request. This does not show AI comprehension or an understanding of context, which, alternatively, is something that AGI would understand.
Cause and effect
AGI must understand reactions to certain actions, which will allow AI to understand cause and effect.
Problem solving
AGI should be able to diagnose problems, adapt and solve them. While AI offers similar capabilities, it requires training and parameters to be met in order to solve the problem.
Creativity
AGI creativity is defined as the model being able to rewrite its own code. It must understand a huge amount of code and identify new ways to improve it. This will result in improvements on understanding and reacting to situations.
Approach to Teaching
Multiple approaches have been tried and tested to achieve human-like intelligence. Listed below are some of the core approaches to AGI.
Symbolic Approach
In this approach, we operate with symbols — they can represent the fundamental elements of the physical world. The method imitates the more sophisticated levels of human thought.
The symbolic approach is able to develop logic and thinking, but it does not cope with the task of teaching perception. For example, if an object has a distorted feature, the AI may misunderstand what kind of object it is.
Connectionism Approach
The connectionism approach, a sub-symbolic technique, builds general intelligence using designs that resemble the human brain (such as neural nets). The method consists in the fact that the combination of simple objects can form complex systems with completely different behavior that was not originally planned.
Hybrid Approach
The hybrid approach combines various methods, thus dividing intelligence into some modules.
Whole Organism Architecture
An AGI should have a physical body and gain knowledge via interacting with people physically.
Conscious
Can programmable computers ever be conscious? It’s very difficult to find an objective definition that captures the essence of consciousness. Some say consciousness is that state that we all feel when we are awake as opposed to when we are sleeping but this won’t do because dreaming is a state of consciousness. Furthermore, this is too narrow a definition because it says nothing about what happens to us all every minute of our lives. We all know from our own experience what consciousness is but it’s very difficult to define.
Artificial Consciousness is one step above artificial general intelligence and implies more than just intelligence — it implies intelligence and self-awareness.
Aspects of consciousness
There are various aspects of consciousness that are generally considered necessary for a machine to be artificially conscious. Here are a few:
Awareness
Awareness is an aspect that is required for a machine to be conscious. The results of experiments of neuro scanning on monkeys suggest that a process — not only a state or an object — activates neurons.
Making such models that have awareness demands a lot of flexibility, modeling of the physical world, modeling of one’s own internal states and processes, and modeling of other conscious entities.
Awareness itself has three categorisations: agency awareness, goal awareness, and sensorimotor awareness.
- Agency awareness: When one is aware of a certain action that one has performed or did not perform.
- Goal awareness: The motive that drives the action one takes. For example, searching for a lost object.
- Sensorimotor awareness: The knowledge or recognition one has when physically engaging in an action. For instance, being aware when one’s hand rests on a hot or cold object.
Learning
Learning is considered important for artificial consciousness. According to Axel Cleeremans and Luis Jiménez, learning is defined as “a set of phylogenetically advanced adaptation processes that critically depend on an evolved sensitivity to subjective experience so as to enable agents to afford flexible control over their actions in complex, unpredictable environments”.
Anticipation
This aspect is also considered important because it is the ability to predict foreseeable events. Anticipation helps predict the consequences of one’s own actions and those of other entities or objects. Hence, it is crucial that an artificially conscious machine should be able to anticipate events correctly so that it can respond to it when it occurs or take some action.
The Future of AGI
At the moment, with conventional AI, changes in our lives are already visible. But the development of AGI is complicated by the current stage of technological development, lack of knowledge in the field of brain and consciousness.
Our team believes in a world where AGI exists as friendly animals along with nature and humans, AI will help end boring work by allowing us to perform higher-level tasks. This will unlock the human potential and allow us to focus on studying the brain and the most interesting mysteries of humanity.
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