GenLens: Demystifying Artificial General Intelligence
Welcome everyone to the first edition of GenLens, a series in which we will explore the world of General Intelligence and its various sub-divisions. This series aims to create an awareness of general discussions and topics in the modern AI landscape and where it is eventually leading.
We will discuss Artificial General Intelligence, the broader application of Artificial Intelligence in a wide spectrum of human cognitive tasks and abilities.
The Quest for General Intelligence
General Intelligence defines the ability to understand, learn, and apply knowledge over many tasks and objectives. In a more human sense, General Intelligence encompasses reasoning, problem-solving, autonomous adaptability to our environment, and other tasks associated with Human Cognitive operations.
The quest for general intelligence in AI is not something that has been recently thought of but an idea that has been living over the years through fiction and practice. AGI is considered the holy grail in the AI development sector, and here are some reasons why:
- Versatility: A theoretically perfect AGI will be as versatile as a human at learning and performing various tasks.
- Autonomy: This AGI would be able to learn and reason independently without relying on a human for greenlights, making processes much more autonomous.
- Problem Solving: AGI allows models to peruse petabytes of data to solve complex problems in fields such as healthcare and mathematics.
The implications for such an AGI are broad, presumptuous, and vastly dependent on the guardrails and thresholds we allow such AGI to exist and act upon.
Challenges and Hurdles
When developing something genuinely aware, some various challenges and hurdles come from the vast amount of information that we intend the final product to be able to respond to and act upon dynamically. Let us list down some such complexities that are stopping us from achieving AGI:
- Complexity of Generalization: It is difficult to Develop Systems that are able to generalize specific instances and requirements from a given niche stimulant to a broader category, so solving the problem is difficult.
- The Problem of Cognition: Replicating human-like understanding and fixation to a specific type of stimulant, such as perception, reasoning, or decision making, is a problem that requires numerous different sensors and sub-systems, which are difficult to integrate as well as a sentient being.
- Explainability and Accountability: Thanks to fiction, trust will be the key to developing and deploying such a system. Flags and guardrails will pinpoint potential lapses in transparency and biases in the decision-making for a safer deployment.
However, We have overgeneralized the potential challenges of such a system, and we will revisit this topic in some upcoming editions. Considering what we have already discussed, let us examine the road ahead.
The Road Ahead
The future of AGI may involve a symbiotic relationship between such systems and human beings for it to be truly beneficial to society as a whole rather than just some cash-hungry organizations. As we move forward, we will be required to implement a combination of various strategies and policies to ensure the safe and effective development of such systems.
Techniques such as more collaborative interdisciplinary involvement of various stakeholders and knowledge need to exist so that people set aside their momentary gains by withholding their contributions to these potentially sentient systems that we intend to exist down the line. A deeper understanding of Neuromorphic Computing or replicating the very architecture and functionality of the human brain (much like the inspiration for the first neural network) is the way where we can hope to invent perfect AGI and, along the way, maybe expand our spiritual knowledge of what makes us conscious.
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