Home News AI Consciousness: An Exploration of Possibility, Theoretical Frameworks & Challenges

AI Consciousness: An Exploration of Possibility, Theoretical Frameworks & Challenges

AI Consciousness: An Exploration of Possibility, Theoretical Frameworks & Challenges

AI consciousness is a fancy and engaging concept that has captured the interest of researchers, scientists, philosophers, and the general public. As AI continues to evolve, the query inevitably arises:

With the emergence of Large Language Models (LLMs) and Generative AI, the road to achieving the replication of human consciousness can also be becoming possible.

Or is it?

A former Google AI engineer Blake Lemoine recently propagated the speculation that Google’s language model LaMDA is sentient i.e., shows human-like consciousness during conversations. Since then, he has been fired and Google has called his claims “wholly unfounded”.

Given how rapidly technology is evolving, we may only be a number of a long time away from achieving AI consciousness. Theoretical frameworks corresponding to Integrated Information Theory (IIT), Global Workspace Theory (GWT), and Artificial General Intelligence (AGI) provide a frame of reference for the way AI consciousness may be achieved.

Before we explore these frameworks further, let’s try to grasp consciousness.

What Is Consciousness?

Nonetheless, the subtleties and intricacies of consciousness make it a fancy, multi-faceted concept that is still enigmatic, despite exhaustive study in neuroscience, philosophy, and psychology.

David Chalmers, philosopher and cognitive scientist, mentions the complex phenomenon of consciousness as follows:

It will be significant to notice that consciousness is a subject of intense study in AI since AI plays a big role within the exploration and understanding of consciousness. An easy search on Google Scholar returns about 2 million research papers, articles, thesis, conference papers, etc., on AI consciousness.

The Current State of AI: Non-conscious Entities

AI today has shown remarkable advancements in specific domains. AI models are extremely good at solving narrow problems, corresponding to image classification, natural language processing, speech recognition, etc., but they don’t possess consciousness.

They lack subjective experience, self-consciousness, or an understanding of context beyond what they’ve been trained to process. They’ll manifest intelligent behavior with none sense of what these actions mean, which is entirely different from human consciousness.

Nonetheless, researchers are attempting to take a step towards a human-like mind by adding a memory aspect to neural networks. Researchers were capable of develop a model that adapts to its environment by examining its own memories and learning from them.

Theoretical Frameworks for AI Consciousness

1. Integrated Information Theory (IIT)

Integrated Information Theory is a theoretical framework proposed by neuroscientist and psychiatrist Giulio Tononi to elucidate the character of consciousness.

AI models have gotten more complex, with billions of parameters able to processing and integrating large volumes of knowledge. In keeping with IIT, these systems may develop consciousness.

Nonetheless, it’s essential to think about that IIT is a theoretical framework, and there continues to be much debate about its validity and applicability to AI consciousness.

2. Global Workspace Theory (GWT)

Global Workspace Theory is a cognitive architecture and theory of consciousness developed by cognitive psychologist Bernard J. Baars. .

The “stage” of consciousness can only hold a limited amount of knowledge at a given time, and this information is broadcast to a “global workspace” – a distributed network of unconscious processes or modules within the brain.

Applying GWT to AI suggests that, theoretically, if an AI were designed with an identical “global workspace,” it might be able to a type of consciousness.

It doesn’t necessarily mean the AI would experience consciousness as humans do. Still, it might have a process for selective attention and knowledge integration, key elements of human consciousness.

3. Artificial General Intelligence (AGI)

AGI contrasts with Narrow AI systems, designed to perform specific tasks, like voice recognition or chess playing, that currently constitute the majority of AI applications.

By way of consciousness, AGI has been considered a prerequisite for manifesting consciousness in a man-made system. Nonetheless, AI shouldn’t be yet advanced enough to be regarded as intelligent as humans.

Challenges in Achieving Artificial Consciousness

1. Computational Challenges

The Computational Theory of Mind (CTM) considers the human brain a physically implemented computational system. The proponents of this theory imagine that to create a conscious entity, we’d like to develop a system with cognitive architectures just like our brains.

However the human brain consists of 100 billion neurons, so replicating such a  complex system would require exhaustive computational resources. Furthermore, understanding the dynamic nature of consciousness is beyond the boundaries of the present technological ecosystem.

Lastly, the roadmap to achieving AI consciousness will remain unclear even when we resolve the computational challenge. There are challenges to the epistemology of CTM, and this raises the query:

2. The Hard Problem of Consciousness

The “hard problem of consciousness” is a crucial issue within the study of consciousness, particularly when considering its replication in AI systems.

The hard problem signifies the subjective experience of consciousness, the qualia (phenomenal experience), or “what it’s like” to have subjective experiences.

Within the context of AI, the hard problem raises fundamental questions on whether it is feasible to create machines that not only manifest intelligent behavior but additionally possess subjective awareness and consciousness.

Philosophers Nicholas Boltuc and Piotr Boltuc, while providing an analogy for the hard problem of consciousness in AI, say:

However the essential problem is that we don’t clearly understand consciousness. Researchers say that our understanding and the literature built around consciousness are unsatisfactory.

3. Ethical Dilemma

Ethical considerations around AI consciousness add one other layer of complexity and ambiguity to this ambitious quest. Artificial consciousness raises some ethical questions:

  1. If an AI can understand, learn, and adapt to the extent of humans, should or not it’s given rights?
  2. If a conscious AI commits against the law, who’s held accountable?
  3. If a conscious AI is destroyed, is that considered damage to property or something just like murder?

Progress in neuroscience and advances in machine learning algorithms can create the potential of broader Artificial General Intelligence. Artificial consciousness, nevertheless, will remain an enigma and a subject of debate amongst researchers, tech leaders, and philosophers for a while. AI systems becoming conscious comes with various risks that should be thoroughly studied.

For more AI-related content, visit unite.ai.


Please enter your comment!
Please enter your name here