The Near Future of AI is Action-Driven: Incorporating External Cognitive Assets and Task-Oriented Training

The Near Future of AI is Action-Driven: Incorporating External Cognitive Assets and Task-Oriented Training

In recent years, there has been a growing interest in developing AI models that can not only answer questions but also think step by step and take appropriate actions. This approach, known as Action-Driven AI, has shown promising results in various domains, including question-answering tasks (Kojima et al. 2022, arxiv). However, to unlock the full potential of these models, they need to be equipped with external cognitive assets and undergo task-oriented training.

External cognitive assets refer to resources that can enhance the capabilities of AI models. These assets can be in the form of search engines, code interpreters, or even chats with humans. By integrating these assets into the AI model, it gains access to a wealth of information and tools that can aid in decision-making and problem-solving. For example, a language model can use a search engine to gather additional information before providing an answer to a question.

The key to utilizing external cognitive assets effectively lies in the iterative process of Thought, Act, and Observation (ReAct). The AI model first needs to analyze the situation and determine what actions are required. It then selects the most appropriate action from a range of choices. Finally, it observes the outcome of the action and adjusts its approach accordingly. This iterative process allows the model to learn from its actions and improve its performance over time.

One of the challenges in implementing Action-Driven AI is the task-oriented training. To ensure that the AI model performs well in specific tasks, it needs to undergo training that is tailored to those tasks. Techniques like instruction tuning can be used to fine-tune the model's behavior and optimize its performance. However, task-oriented training is not a straightforward process and requires careful consideration of the desired outcomes and metrics of interest.

In addition to external cognitive assets and task-oriented training, reinforcement learning can play a crucial role in improving the performance of AI models. By using a reward-based system, the model can be trained to produce better results based on a metric of interest. This approach allows the model to learn from its own actions and make informed decisions in the future. Incorporating reinforcement learning into Action-Driven AI can lead to significant improvements in performance and decision-making capabilities.

As we move forward, it is essential to rebalance the power of algorithms in favor of the consumer. The integration of external cognitive assets and task-oriented training empowers users to have more control over the AI models they interact with. This shift in power can ensure that AI systems are designed to meet the specific needs and desires of the users.

To fully embrace the potential of Action-Driven AI, here are three actionable pieces of advice:

1. Explore and identify relevant external cognitive assets: Take the time to understand the different resources available that can enhance the capabilities of AI models. Experiment with different tools, such as search engines, code interpreters, or human interactions, and determine which ones are most effective for specific tasks.

2. Invest in task-oriented training: Develop training strategies that are tailored to specific tasks and desired outcomes. Consider techniques like instruction tuning to fine-tune the behavior of AI models and optimize their performance. Continuously evaluate and adjust the training process to ensure that the models are learning and improving over time.

3. Incorporate reinforcement learning: Integrate reinforcement learning into the training process to enable AI models to learn from their own actions. Use a reward-based system to incentivize the models to make better decisions and improve their performance based on specific metrics of interest.

In conclusion, the near future of AI lies in the development of Action-Driven models that can think step by step and take appropriate actions. By incorporating external cognitive assets, undergoing task-oriented training, and leveraging reinforcement learning, AI models can unlock their full potential and provide more personalized and effective solutions. It is crucial to rebalance the power of algorithms in favor of the consumer to ensure that AI systems are designed to meet the specific needs and desires of the users. The possibilities are vast, and with careful development and implementation, Action-Driven AI can revolutionize various industries and improve the overall human-machine interaction.