The Near Future of AI is Action-Driven: Incorporating External Cognitive Assets and Reinforcement Learning

The Near Future of AI is Action-Driven: Incorporating External Cognitive Assets and Reinforcement Learning

Artificial intelligence (AI) continues to advance at a rapid pace, with new developments and applications emerging every day. One of the key trends in the near future of AI is the shift towards action-driven systems. This approach involves enabling AI models to not only think step by step but also take action and observe the outcomes of those actions.

A recent study by Kojima et al. (2022) found that Language Model-based systems (LLMs) perform better at question-answering tasks when prompted to "think step by step." However, their performance can be further enhanced when they are provided with external cognitive assets. These assets can be any function that takes text as an input and provides text as an output, such as searches, code interpreters, or even chats with humans.

To implement this action-driven approach, a three-step process called ReAct is proposed. The first step is Thought, where the AI model considers what is needed to solve a given problem. The second step is Act, where the model chooses a suitable action to address the problem, utilizing cognitive assets like search functions. Finally, the model enters the Observation step, where it can see the outcome of the action taken.

The key to the success of this approach lies in the AI model's understanding of its own tools and the desired outcomes of the user. By training the model to recognize the power of its cognitive assets and align its actions with user expectations, better results can be achieved. This can be accomplished through reinforcement learning, where the system is trained to produce improved outcomes based on a defined metric of interest.

On the left side of this action-driven approach, we have the External Cognitive Assets that can greatly enhance the power of an AI model. These assets can range from simple search functions to complex code interpreters or even interactions with human experts. By incorporating these external resources, AI models can access a wealth of knowledge and information, enabling them to make more informed decisions.

On the right side, we have the task-oriented training that is essential for the success of this approach. Implementing techniques like instruction tuning can help fine-tune the model's understanding and response to user needs. However, developing effective training methods remains a challenge and an area that requires further exploration.

Despite the promising potential of this action-driven approach, there are still uncertainties and questions. How can we ensure a fair balance of power between algorithms and consumers? How can we address potential biases or ethical concerns that may arise from relying on external cognitive assets? These are important considerations that need to be addressed as AI continues to evolve.

Looking ahead, the future of AI is undoubtedly exciting. As spending on AI solutions is projected to double in the United States by 2025, reaching $120 billion (according to an IDC Spending Guide), we can expect even greater advancements in the field. Retail and banking are predicted to be the leading industries in AI spending, with professional services, media, and securities and investment services showing the fastest growth rates.

In conclusion, the near future of AI is action-driven, with a focus on incorporating external cognitive assets and reinforcement learning. By enabling AI models to not only think step by step but also take action and observe outcomes, we can unlock their true potential. However, as this field continues to evolve, it is crucial to address issues of fairness, bias, and ethics.

To harness the power of action-driven AI, here are three actionable pieces of advice:

1. Understand the capabilities of external cognitive assets: Familiarize yourself with the various resources that can enhance the performance of AI models, such as search functions, code interpreters, or human interactions. By leveraging these assets effectively, you can empower AI systems to provide more accurate and valuable insights.

2. Define clear metrics for reinforcement learning: When training AI models to produce better results through reinforcement learning, it is essential to establish clear metrics of interest. By defining what constitutes improved outcomes, you can guide the training process and ensure that AI systems align their actions with user expectations.

3. Foster a balanced and ethical AI ecosystem: As AI becomes increasingly integrated into various industries, it is crucial to foster a balanced and ethical ecosystem. This involves addressing issues of fairness, bias, and accountability. By actively promoting transparency and ethical practices, we can ensure that AI benefits both consumers and society as a whole.

In the near future, AI will continue to shape our lives and transform industries. By embracing an action-driven approach and harnessing the power of external cognitive assets, we can unlock new possibilities and drive innovation forward. However, it is essential to remain vigilant and address the challenges and ethical considerations that arise along the way. Only then can we truly maximize the potential of AI for the benefit of all.