The Near Future of AI: Action-Driven Curation and Cognitive Assets


Hatched by Glasp

Aug 05, 2023

4 min read


The Near Future of AI: Action-Driven Curation and Cognitive Assets

In recent years, artificial intelligence (AI) has made significant advancements, particularly in question-answering tasks. One interesting finding is that Language Model Models (LLMs) tend to perform better when they approach problems by thinking step by step. However, researchers have discovered that LLMs can further enhance their performance when they are equipped with external cognitive assets. This concept, known as ReAct (Thought, Act, Observation), involves utilizing cognitive tools such as search engines, code interpreters, and even human interaction to augment the capabilities of AI systems.

The incorporation of external cognitive assets into AI models has the potential to revolutionize the field. By leveraging the power of these tools, LLMs can achieve even more impressive results. However, for these models to excel, they need to understand the capabilities of their own tools and have a clear understanding of what outcomes the user desires. This requires a deep understanding of the task at hand and the ability to accurately assess the effectiveness of different actions.

One intriguing possibility is the application of reinforcement learning to improve AI systems. Reinforcement learning involves training a system to produce better results based on a specific metric of interest. By implementing this approach, AI models can be fine-tuned to deliver optimal outcomes. It is through this iterative process of thought, action, and observation that AI can be elevated to new heights.

On the other hand, the concept of the Network Second Layer, also known as curation, has gained significant attention. The idea behind this concept is that a vast network of sites, like Edu_RSS, should emerge to form a second layer in the network. This second layer would allow the internet to self-organize, clustering information from various sources, interpreting and specializing it, and ultimately producing highly targeted and specific resource feeds.

The potential of the Network Second Layer is immense. Imagine a scenario where information generated from a multitude of sources converges to create curated and highly relevant content. This would not only streamline access to valuable information but also enable users to tap into specialized knowledge and resources. The internet would become a dynamic and adaptive space, catering to the unique needs and interests of individual users.

To realize the full potential of these concepts, certain challenges must be overcome. Task-oriented training, for instance, plays a vital role in ensuring the success of action-driven AI models. Techniques like instruction tuning offer promising avenues for implementation, but the complexity lies in effectively integrating these methods into the training process. Achieving a balance of power between algorithms and consumers is another crucial aspect that needs consideration.

In conclusion, the near future of AI lies in the combination of action-driven curation and the utilization of external cognitive assets. By incorporating these elements into AI models, we can unlock new possibilities and empower users with more personalized and relevant information. To leverage these advances effectively, it is essential to focus on task-oriented training and strike a balance between algorithmic power and user empowerment.

Actionable Advice:

  • 1. Embrace external cognitive assets: Explore the potential of utilizing tools such as search engines, code interpreters, and human interaction to enhance the capabilities of AI systems. By leveraging these assets, AI models can achieve better results and provide more valuable insights.
  • 2. Implement reinforcement learning: Consider incorporating reinforcement learning techniques to train AI systems. By fine-tuning models based on specific metrics of interest, you can improve their performance and optimize outcomes. This iterative process of thought, action, and observation can lead to significant advancements in AI.
  • 3. Foster a network of curated content: Embrace the concept of the Network Second Layer and work towards building a network of curated sites. By clustering and interpreting information from various sources, you can create highly targeted and specialized resource feeds. This approach can revolutionize the way users access and interact with information on the internet.

In summary, the integration of action-driven curation and external cognitive assets holds immense potential for the future of AI. By embracing these concepts and implementing the suggested actionable advice, we can shape a future where AI systems are more powerful, personalized, and user-centric. The path ahead may be challenging, but the rewards are undoubtedly worth the effort.

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