The Near Future of AI: Action-Driven and the Decline of Newspapers

The Near Future of AI: Action-Driven and the Decline of Newspapers

In recent years, the field of artificial intelligence (AI) has made significant strides, and researchers are constantly exploring new ways to push the boundaries of what AI can achieve. One promising development is the concept of action-driven AI, which aims to create models that can act as agents, making choices and taking actions based on their understanding of the world.

The ReAct model, introduced by Yao et al. in 2022, takes a three-step iterative approach: Thought, Act, and Observation. This model allows AI systems to think about what is needed, choose an action to address the need, and then observe the outcome of that action. By incorporating cognitive assets like search, these action-driven models have the potential to revolutionize the way AI operates.

This concept of action-driven AI aligns closely with the idea of artificial general intelligence (AGI), which is a system that can perform any intellectual task that a human being can do. While there is ongoing debate among academics about the true definition of AGI, it is clear that an action-driven language model (LLM) shares many similarities with AGI. In fact, LLMs have shown improved performance in question-answering tasks when prompted to "think step by step" (Kojima et al., 2022).

However, the true power of action-driven AI lies in its ability to leverage external cognitive assets. By accessing external resources and data, AI models can bridge the resource gap and achieve even better results. OpenAI's 002-text-davinci model has demonstrated this capability through a combination of instruction tuning and Reinforcement Learning from Human Feedback (RLHF). This approach involves humans rating the success of a given prompt, allowing the model to learn and improve iteratively.

In the future, we can expect to see startups capitalizing on the potential of action-driven AI by creating powerful feedback loops. These companies will identify customer pain points, develop simple solutions to address them, collect data on how to improve those solutions, and train their models to deliver more consistent results. This iterative process will create a competitive advantage for these startups and pave the way for AI-driven moats in various industries.

While the rise of action-driven AI presents exciting possibilities, it is essential to consider its implications in the broader context of technological advancements. Just as newspapers have experienced a decline in the face of changing communication dynamics, AI-powered systems may disrupt traditional industries and shift the landscape of business and information dissemination.

The decline of newspapers can be mapped against the evolution of communication on the internet. Stage 1 involved the migration of offline content to online platforms, allowing users to access the objectively best content, such as that offered by renowned publications like the New York Times. This shift had a significant impact on local newspapers, which struggled to compete with the accessibility and quality of online content.

Stage 2 marked the introduction of user-generated content and social media platforms. This development dramatically increased the range of available content while enabling users to find content that was subjectively better suited to their preferences. The democratization of content creation and distribution further disrupted the traditional newspaper industry.

Stage 3, the mobile and contextual stage, has been characterized by the rise of messaging and personalized content delivery. Today, users expect content that is not only personalized but also contextually relevant to their specific situation. This shift has further challenged traditional news outlets, as AI-powered algorithms and recommendation systems have become adept at delivering tailored content experiences.

As AI-driven systems become more domain-general and capable of performing a wide range of tasks, the potential for automation and new offerings will continue to expand. However, it is crucial to approach these advancements with caution and consider the ethical implications and potential consequences of relying heavily on AI-powered systems.

In conclusion, the near future of AI is action-driven, with models like ReAct paving the way for AGI-like capabilities. Leveraging external cognitive assets and incorporating reinforcement learning will be key to unlocking the full potential of AI systems. Startups that embrace action-driven AI and create feedback loops will have a competitive advantage in the AI landscape. However, it is essential to recognize the broader implications of AI advancements and consider the lessons learned from the decline of newspapers in the face of technological disruption.

Actionable Advice:

1. Embrace action-driven AI: Explore how your business or industry can benefit from AI models that can think, act, and observe outcomes. Identify pain points and develop simple solutions that can be iteratively improved with the help of AI technologies.
2. Leverage external cognitive assets: Consider how external resources and data can enhance the capabilities of your AI models. Look for opportunities to fetch information from external spaces to bridge resource gaps and improve performance.
3. Stay adaptable and contextually relevant: As AI-driven systems continue to evolve, focus on delivering personalized and contextually appropriate content or solutions. Embrace mobile and messaging platforms to meet the changing expectations of users.

By understanding the potential of action-driven AI and learning from the decline of newspapers, we can navigate the evolving technological landscape and harness the power of AI for positive and impactful outcomes.