AI vs AGI - We are Super AI

Sep 5, 2023


Ever since the launch of ChatGPT in November 2022, AI has been trending all over the globe. Every influencer, tech enthusiast, or even mediocre college kid talks about AI in all corners of the world. But what happened there that was so unusual from before? ChatGPT showed the power of artificial intelligence to the entire common population like never before. Till then what was to be just a concept for sci-fi movies and topics discussed just by a small group of AI enthusiasts now became a common tea table conversation topic. Every person started using and witnessing AI’s power thanks to its simple interface. So what's next? Super AI or AGI!

In this blog, we delve into the differences between AI and AGI, exploring what sets them apart and why AGI is considered the next frontier of artificial intelligence. Artificial generative intelligence is the far-advanced version of AI that has been showcased in sci-fi films and conceptualized alone to date. The major difference between an AGI and AI is the ability of the software to learn any task or concept and perform on its own like a human being, while AI can just perform specified tasks that have been pre-programmed to it. 

Characteristics of AI:

  • Narrow Focus: AI systems excel in performing specific tasks, such as image recognition, language translation, or playing chess.

  • Limited Learning: They rely on supervised or unsupervised learning techniques to improve performance within their predefined domains.

  • Lacks Generalization: AI lacks the ability to transfer knowledge or skills from one domain to another.

Example: Siri, Alexa, etc.

Characteristics of AGI:

  • Broad Capabilities: AGI systems can learn and apply knowledge across diverse domains, much like the general intelligence exhibited by humans.

  • Continuous Learning: They possess the capacity to learn from experiences, adapt to new situations, and apply existing knowledge creatively.

  • Cross-Domain Transfer: AGI systems can generalize skills and insights, making them adaptable to various tasks without extensive reprogramming.

So where do we stand?

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