< prev | next >

Artificial General Intelligence

One of the key goals of AI is to develop Artificial General Intelligence (AGI) systems that are capable of reasoning, learning, and adapting to a wide range of tasks and environments, much like humans do. AGI can also be thought of as Human Level Intelligence.

AGI on the AI Growth Curve

AGI follows Artificial Narrow Intelligence (ANI) and precedes Artificial Superintelligence (ASI) on the AI exponential growth curve.

AGI Capabilities

There are many and varying definitions of Artificial General Intelligence (AGI) capabilities. These include:

  • All the capabilities of Artificial Narrow Intelligence (ANI)

  • Abstract thinking

  • Adaptation to new environments

  • Application of common sense knowledge

  • Autonomy (ability to function safely without human intervention)

  • Common sense

  • Complex reasoning

  • Creativity

  • Empathy

  • Goal-directedness (ability to learn towards achieving specific goals)

  • Handling uncertain situations

  • Integration of prior knowledge in decision making

  • Learning from past experiences

  • Recall and reliving memories

  • Planning for the future

  • Reasoning

  • Self-awareness

AGI Potential Technology Architectures

There are a few key architectures and frameworks being explored and utilized for developing artificial general intelligence (AGI) capabilities. While neural networks remain the predominant architecture type, the search results indicate that novel hybrid, open-source, multi-agent, and multimodal architectural approaches are being actively explored by major tech companies and AI research groups in their pursuit of realizing artificial general intelligence capabilities.

Neural Network Architectures

  • Deep Neural Networks and Deep Learning frameworks like TensorFlow, PyTorch, and Keras are being widely used as foundational architectures for building more advanced AGI models.

  • Transformer-based models like BERT and GPT have shown state-of-the-art performance and are being adapted for AGI research.

  • Novel neural network architectures inspired by the human brain, such as Yann LeCun's proposals for a "world model" and "configurator" networks integrated into a cognitive architecture, are being investigated for AGI development at Meta.

Hybrid Symbolic/Sub-symbolic Architectures

  • Combining neural networks (sub-symbolic) with symbolic AI techniques like knowledge representation, reasoning, and planning is an approach being explored for achieving more general and human-like intelligence.

  • Integrating multiple neural network components with different capabilities (perception, reasoning, learning, etc.) into unified cognitive architectures is a direction being pursued.

Open-Source and Collaborative Architectures

  • Meta has pledged to make its AGI developments open-source, contrasting with the typically proprietary approaches of other tech giants. This could accelerate innovation through open collaboration.

  • Frameworks that enable combining and orchestrating multiple AI models/agents in a collaborative multi-agent architecture are being researched for realizing AGI.

Multimodal Architectures

  • Integrating different modalities like language, vision, and robotics into unified AI systems is seen as a key requirement for achieving general intelligence.

  • Architectures that can process and reason across multimodal inputs in a human-like manner are an active area of AGI research.

AGI Timeframe

Based on the information provided in search results, there is no consensus on a precise timeframe for developing AGI. However, recent developments in models such as OpenAI GPT-4o, AGI may arrive more quickly than previously thought … possibly within a few months to a few years. One such prediction is from Elon Musk.

One challenge in predicting this timeframe is the lack of a precise definition of AGI. What may happen is that we experience an incremental, staged deployment of AGI as it progressively includes an increasing number of the AGI characteristics, such as those listed above.

AI Capabilities Relative to Human Performance

Per the chart below, AI models are rapidly approaching and exceeding human performance in a number of dimensions.