"The Near Future of AI: Action-Driven Growth and Disruptive Innovations"

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Jul 02, 2023
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"The Near Future of AI: Action-Driven Growth and Disruptive Innovations"
In recent years, the field of artificial intelligence (AI) has seen significant advancements. Researchers have been exploring ways to develop models that are not only capable of processing information but also taking action based on that information. This idea of action-driven AI is gaining traction, and it holds immense potential for the future of technology.
One such model that embodies the concept of action-driven AI is the ReAct model, as proposed by Yao et al. in their 2022 paper. The ReAct model operates in three iterative steps: Thought, Act, and Observation. It involves thinking about what actions are needed, choosing the appropriate action, and then observing the outcome of that action. By incorporating cognitive assets like search, the ReAct model enables AI to act as an agent, making decisions and taking actions.
What makes action-driven AI truly exciting is its resemblance to artificial general intelligence (AGI). While the definition of AGI may be subject to debate among academics, an action-driven Language Model (LLM) closely aligns with the characteristics of AGI. LLMs, such as OpenAI's 002-text-davinci model, have demonstrated superior performance in question-answering tasks when prompted to "think step by step," as highlighted by Kojima et al. in their 2022 paper. However, these models can achieve even better results when equipped with external cognitive assets.
The utilization of external cognitive assets, such as fetching data from external sources, bridges the resource gap for LLMs. This approach allows the models to access a wealth of information that enhances their understanding and decision-making capabilities. The combination of instruction tuning and reinforcement learning from human feedback (RLHF) has proven successful in achieving remarkable outcomes with the 002-text-davinci model, as explained in an OpenAI blog post. By training the model to produce better results based on human feedback, a reinforcement learning approach can further elevate the performance of action-driven AI.
The potential for growth and disruptive innovations in the AI field is immense. Startups that focus on creating powerful feedback loops have the opportunity to become successful by addressing customer pain points and continuously improving their offerings. This iterative process involves solving a specific problem, collecting data on how to solve it better, training AI models to be more consistent, and iterating. This approach can serve as a moat in the AI industry, at least for now, allowing companies to gain a competitive edge by continuously improving their AI systems.
To understand where growth comes from in the broader context, we can turn to the insights of Clayton Christensen, a renowned Harvard Business School professor and author. In his talk at Google, Christensen introduces the concept of four types of innovation: Potential, Sustaining, Disruptive, and Efficiency. These types of innovation serve as catalysts for growth within organizations. Christensen emphasizes that disruption is often built within the business model itself, rather than solely relying on technological advancements.
Christensen further explores the concept of "Jobs to be done" and highlights the importance of understanding the needs in a workflow rather than focusing solely on users' demographics. He uses the example of McDonald's milkshake to illustrate how identifying the job to be done, in terms of functional, emotional, and social aspects, can drive innovation and customer satisfaction. This approach entails understanding the experience to provide, integrating various elements, and applying the appropriate brand strategy.
In measuring success and happiness in life, Christensen challenges the common metric of achievement. He suggests that true fulfillment comes from seeking immediate evidence of happiness and well-being. This perspective aligns with the idea of long-term satisfaction rather than short-term accomplishments.
In conclusion, the near future of AI lies in the development of action-driven models that can think, act, and observe. The ReAct model exemplifies this approach, demonstrating how AI can function as an agent by making decisions and taking actions based on cognitive assets. Incorporating external resources and implementing reinforcement learning techniques can significantly enhance the capabilities of AI systems. Startups that embrace the feedback loop approach have the potential to create disruptive innovations and establish a competitive advantage in the AI industry. Additionally, insights from Clayton Christensen shed light on the different types of innovation and the importance of understanding customer needs and experiences. By combining these concepts, we can shape a future where AI-driven growth and disruptive innovations thrive.
Actionable Advice:
- 1. Embrace the feedback loop: Continuously collect data and feedback to improve your AI models and offerings. Iterate and refine based on the insights gained from customer interactions.
- 2. Identify the "Jobs to be done": Understand the needs and workflows of your target audience. Focus on providing a comprehensive experience that addresses functional, emotional, and social aspects.
- 3. Measure long-term satisfaction: Look beyond short-term achievements and focus on personal fulfillment and well-being. Seek immediate evidence of happiness in your life and make choices that align with your long-term goals and values.
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