The Day The AGI Was Born: Exploring the Future of Artificial General Intelligence


Hatched by Glasp

Sep 12, 2023

4 min read


The Day The AGI Was Born: Exploring the Future of Artificial General Intelligence

In recent years, there has been a significant advancement in the field of Artificial Intelligence (AI). One of the most notable developments is the creation of the "GPT-3.5 series" model, also known as the InstructGPT sibling. This fine-tuned model showcases impressive capabilities, particularly in zero-shot generation of text that follows specific instructions. With a long-term memory of up to 8192 tokens, it outperforms its predecessor, GPT3, by generating output that is twice as long.

However, it is important to note that this advanced model still has its limitations. It falls short when it comes to mathematical calculations, often generates false information about the real world, and produces subpar code. Consequently, it does not pass tests such as Turing, SAT, or IQ. Despite these limitations, there are numerous use cases where this model shines. Its strengths lie in areas where creativity is valued more than precision, such as brainstorming, drafting, and presenting information in unique and creative ways.

One of the fascinating aspects of the AGI discussion is how it relates to the future of search engines. There is an ongoing debate on whether a ChatGPT-like experience could potentially replace Google as the go-to source for information. On one hand, ChatGPT provides direct and legible answers to questions, surpassing the sometimes overwhelming search results provided by Google. On the other hand, these answers are often incorrect and lack proper sourcing. This debate highlights the need for further development and improvement in AI models to ensure accuracy and reliability.

It is essential to acknowledge that the birth of AGI has been accelerated by advancements in Reinforcement Learning via Human Feedback. This newfound knowledge has allowed researchers to make significant progress in a shorter amount of time than previously thought possible. The combination of human feedback and reinforcement learning has proven to be a powerful tool in the development of AGI models.

Looking back, we can draw parallels between the emergence of AGI and the pay-to-surf business model that gained popularity in the late 1990s. Pay-to-surf (PTS) companies promised users a share of advertising revenue in exchange for watching promotional content online. However, this business model faced challenges and a decline following the dot-com crash. Many users attempted to defraud companies out of money, forcing them to terminate accounts frequently due to spammers.

Interestingly, the surviving PTS companies have transitioned to a rewards-based structure. Users now earn reward points by surfing the web, answering marketing emails, or shopping at specific stores. These points can be exchanged for various gifts. This evolution in the pay-to-surf model demonstrates the adaptability and resilience of online business models in the face of challenges.

In recent years, the Brave browser has proposed an alternative compensation method for browsing. Users are rewarded with tokens that hold the promise of eventually being exchangeable for dollars. This approach operates similarly to cryptocurrency, offering a fresh perspective on the pay-to-surf model.

As we contemplate the future of AGI and its potential impact on various industries, it is crucial to consider the following actionable advice:

  • 1. Embrace creativity: AGI models excel in areas that require creative thinking and brainstorming. Explore how these models can enhance your creative processes and improve your output.
  • 2. Verify information: While AGI models like ChatGPT offer quick and legible answers, it is essential to fact-check and verify the information provided. Utilize external sources to ensure accuracy and reliability.
  • 3. Combine AI with human expertise: AGI models can be powerful tools when combined with external assets and human expertise. By leveraging the strengths of both AI and human intelligence, you can achieve more robust and accurate results.

In conclusion, the birth of AGI, exemplified by the advancements in models like the InstructGPT sibling, signifies a significant milestone in the field of AI. While there are still limitations and challenges to overcome, the potential for AGI to revolutionize various industries is undeniable. By understanding its strengths, limitations, and potential use cases, we can harness the power of AGI to drive innovation and creativity in the years to come.

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