Understanding Intelligence: From Weak AI to Strong AI
Reviewing Model Functionality in the AI Marathon
The rapid onset of artificial intelligence since the release of OpenAI’s generative text application ChatGPT has been like Pandora’s Box — once opened, it can never be closed, and an unstoppable force is unleashed. AI hasn’t unleashed an endless force of destruction and chaos like Pandora, but instead a force of innovation and creativity.
Today, we will break down AI’s progress and discuss where that progress is headed — from Narrow AI to General AI and eventually to Super AI — as well as AI’s capabilities and functions at its current level of progress. Let’s dive in!
Artificial Narrow Intelligence (ANI): The Now
ANI, referred to as “Weak AI,” makes up the current phase of AI progress; anything else is theoretical. We are in a transitory period between ANI and Artificial General Intelligence (AGI).
The inception of AI Agents and Multi-Agent Systems (we will come back to this later) marked a turning point in the progress of AI, landing humanity somewhere towards AGI, but we are still in the era of Weak AI.
Designed to perform specific functions, weak AI is precisely that, dogmatic in its capabilities. ANI is used for simple tasks in everyday life and takes the form of image recognition software, language translation tools, or virtual assistants. Examples of ANI include:
- Google Translate — released in 2006
- Facebook’s Tag Suggestion feature — a facial recognition tool released in 2010
- Apple’s voice assistant Siri — released in 2011
- Google Goggles — an image recognition feature released in 2011
- Amazon’s voice recognition software, Alexa, was released in 2014
ANI models are highly specialized in the functions they can perform. Designed for one specific task or a narrow set of related tasks, they lack the general reasoning abilities to perform functions outside of their programming. Classic ANI is deterministic—these models are intelligent enough to execute their predefined tasks, but do not possess contextual awareness to understand why or how their tasks are being performed.
ANI models commonly combine two functional types of artificial intelligence: Reactive AI and Limited Memory AI. Reactive AI is based on present input and can analyze vast amounts of data to produce an intelligent output. Limited Memory AI can recall past events and monitor current situations over time. Limited Memory AI can be trained on both past and present datasets to generate an output, and as it trains on more data, it can reduce future performance issues.
Reactive AI and Limited Memory AI are used together in ANI models, enabling them to react to a current situation while recalling past events to provide context for generating accurate outputs.
Common Types of ANI Models:
- Machine Learning (ML) — an algorithmic ANI model used to build systems that adapt to and learn from new datasets, improving their function performance over time without reprogramming.
- Deep Learning (DL) is a specialized subset of ML. DL models feature multi-layered architectures for advanced pattern recognition. They identify patterns, extract metadata, and learn high-level insights from large, unstructured, and complex datasets.
- Neural Network — comprised of an input layer and output layer, which are connected by any number of hidden layers, neural networks execute advanced computations to mimic how the human brain processes and learns from new information.
Neural networks comprise interconnected artificial neurons that transmit input signals between hidden layers. After input signals are transmitted through each hidden layer, revealing connections in the data, the model has fully processed and distilled its received input, from which it can generate a more accurate output.
- Large Language Model (LLM) — a type of generative AI built using DL, LLMs are trained on vast amounts of text data that they reference to generate context-aware, human-like text by predicting word sequences within natural conversations.
- Natural Language Processing (NLP) — ANI that focuses on the interaction between computers and human language. NLP allows AI to understand and interpret human language and make sense of text or speech, generating human-like responses.
Artificial General Intelligence (AGI): The Near Future
AGI — strong AI — represents the next phase of AI progress. AGI can recall previous events and leverage past learnings to complete new tasks in a different context without requiring human training. AGI will possess reasoning abilities to learn how to perform new tasks independently.
The rise of agentic systems, where individual autonomous AI agents perform tasks, make decisions, and interact with their environments, marks a significant step towards AGI. There are cases now of AI agents running X accounts and creating their content.
Multi-agent systems (MAS) are the future of agentic computing and allow swarms of AI agents to work collaboratively on tasks without human facilitation. So, say multiple agents are separately working on related tasks within a MAS. These agents can reason about how to interact to create cohesion between the tasks and achieve a common goal.
However, with more advanced reasoning capabilities than typical ANI models, AI agents are still not AGI. AI agents represent a step towards AGI, but can only complete specific predetermined functions through human programming. Agents do not have enough reasoning capabilities to execute new tasks individually.
The inception of AGI will likely form around emotion AI, where models can analyze voices, videos, and images to understand and respond to human thoughts and feelings. Emotion AI models could infer human motives to personalize interactions based on an individual’s unique emotional needs.
Artificial Super Intelligence (ASI): The Distant Future
Straight out of a sci-fi movie, ASI is defined by AI models that are completely self-aware, possessing enough reasoning capabilities to decide on their functions. ASI models will be able to reason enough to choose the tasks they want to complete, how to execute them, and why they want to perform them. Additionally, ASI models will be able to understand their unique internal conditions, resulting in individual sets of emotions and beliefs — an actual opening of Pandora’s box.
Conclusion
AI rapidly evolves from narrow models performing specific, predetermined tasks to sophisticated, context-aware systems capable of interpreting vast amounts of complex data to analyze human meaning and autonomously complete assigned tasks.
Though we are still in the era of ANI and slowly progressing towards AGI, the emergence of MAS signals a steady trajectory towards more advanced AI reasoning. Looking ahead, ASI represents genuine AI autonomy and self-awareness while remaining strictly theoretical in today’s landscape.
To immerse yourself in ANI today, check out the InFlux Technologies suite of intelligent solutions — FluxAI — and follow us on X for all the latest ecosystem updates!
