Evolwe AI and consciousness
Artificial consciousness (AC), also known as machine consciousness or synthetic consciousness, is a field related to artificial intelligence and cognitive robotics. The aim of the theory of artificial consciousness is to “Define that which would have to be synthesized were consciousness to be found in an engineered artifact” .
Neuroscience hypothesizes that consciousness is generated by the interoperation of various parts of the brain, called the neural correlates of consciousness or NCC, though there are challenges to that perspective. Proponents of AC believe it is possible to construct systems (e.g., computer systems) that can emulate this NCC interoperation.
Aspects of consciousness
We used this approach to train Evolwe AI. There are various aspects of consciousness generally deemed necessary for a machine to be artificially conscious. A variety of functions in which consciousness plays a role were suggested by Bernard Baars in 1988. The functions of consciousness suggested by Bernard Baars are Definition and Context Setting, Adaptation and Learning, Editing, Flagging and Debugging, Recruiting and Control, Prioritizing and Access-Control, Decision-making or Executive Function, Analogy-forming Function, Metacognitive and Self-monitoring Function, and Autoprogramming and Self-maintenance Function. Igor Aleksander suggested 12 principles for artificial consciousness and these are: The Brain is a State Machine, Inner Neuron Partitioning, Conscious and Unconscious States, Perceptual Learning and Memory, Prediction, The Awareness of Self, Representation of Meaning, Learning Utterances, Learning Language, Will, Instinct, and Emotion. The aim of AC is to define whether and how these and other aspects of consciousness can be synthesized in Evolwe AI platform. This list is not exhaustive; there are many others not covered.
Awareness could be one required aspect, but there are many problems with the exact definition of awareness. The results of the experiments of neuroscanning on monkeys suggest that a process, not only a state or object, activates neurons. Awareness includes creating and testing alternative models of each process based on the information received through the senses or imagined, and is also useful for making predictions. Such modeling needs a lot of flexibility. Creating such a model includes modeling of the physical world, modeling of one’s own internal states and processes, and modeling of other conscious entities.
There are at least three types of awareness: agency awareness, goal awareness, and sensorimotor awareness, which may also be conscious or not. For example, in agency awareness, you may be aware that you performed a certain action yesterday, but are not now conscious of it. In goal awareness, you may be aware that you must search for a lost object, but are not now conscious of it. In sensorimotor awareness, you may be aware that your hand is resting on an object, but are not now conscious of it.
Al Byrd, the author of Superhuman Creators, defines consciousness, for animals, humans and artificial agents, as the effect of integrating and filtering many different types of affordance awareness; that is, awareness of the action possibilities in an environment. According to this definition, all agents that can perceive and act on affordances are conscious to some extent.
Because objects of awareness are often conscious, the distinction between awareness and consciousness is frequently blurred or they are used as synonyms.
Conscious events interact with memory systems in learning, rehearsal, and retrieval. The IDA model elucidates the role of consciousness in the updating of perceptual memory, transient episodic memory, and procedural memory. Transient episodic and declarative memories have distributed representations in IDA, there is evidence that this is also the case in the nervous system. In IDA, these two memories are implemented computationally using a modified version of Kanerva’s Sparse distributed memory architecture.
Learning is also considered necessary for AC. By Bernard Baars, conscious experience is needed to represent and adapt to novel and significant events. 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”.
The ability to predict (or anticipate) foreseeable events is considered important for AC. The emergentist multiple drafts principle proposed by Daniel Dennett in Consciousness Explained may be useful for prediction: it involves the evaluation and selection of the most appropriate “draft” to fit the current environment. Anticipation includes prediction of consequences of one’s own proposed actions and prediction of consequences of probable actions by other entities.
Relationships between real world states are mirrored in the state structure of a conscious organism enabling the organism to predict events. An artificially conscious machine should be able to anticipate events correctly in order to be ready to respond to them when they occur or to take preemptive action to avert anticipated events. The implication here is that the machine needs flexible, real-time components that build spatial, dynamic, statistical, functional, and cause-effect models of the real world and predicted worlds, making it possible to demonstrate that it possesses artificial consciousness in the present and future and not only in the past. In order to do this, a conscious machine should make coherent predictions and contingency plans, not only in worlds with fixed rules like a chess board, but also for novel environments that may change, to be executed only when appropriate to simulate and control the real world.
Subjective experiences or qualia are widely considered to be the hard problem of consciousness. Indeed, it is held to pose a challenge to physicalism, let alone computationalism. On the other hand, there are problems in other fields of science that limit what we can observe, such as the uncertainty principle in physics, which have not made the research in these fields of science impossible.
So these are fundamental principles for our team when we started to build Evolwe AI, the first conscious AI.