The Near Future of AI: From Action-Driven Models to Google's Journey

The Near Future of AI: From Action-Driven Models to Google's Journey

The field of artificial intelligence (AI) is rapidly evolving, and one of the most promising developments is the emergence of action-driven models. These models, such as the ReAct model introduced by Yao et al. in 2022, take a three-step approach: Thought, Act, and Observation. By incorporating cognitive assets like search, these models act as agents, making choices and taking actions. This concept brings us closer to the elusive goal of achieving artificial general intelligence (AGI), as action-driven models closely resemble the capabilities of AGI.

One interesting finding is that language models (LLMs) often perform better at question-answering tasks when prompted to "think step by step," as discovered by Kojima et al. in 2022. However, their performance can be further enhanced by leveraging external cognitive assets. By accessing data from external sources, these models can bridge the resource gap and achieve even better results. This highlights the importance of integrating external resources into AI systems, which can significantly improve their performance.

OpenAI's 002-text-davinci model has gained considerable attention, and its success can be attributed to a combination of instruction tuning and Reinforcement Learning from Human Feedback (RLHF). In RLHF, humans rate the success of a given prompt, allowing the model to learn and improve based on this feedback. While this approach has shown promising results, there is a belief that true reinforcement learning, where AI systems are trained to produce better results based on a defined metric, will yield even more impressive outcomes.

In the AI landscape, startups that create powerful feedback loops are likely to thrive. These companies solve customer pain points, collect data on how to improve their solutions, train their models to be more consistent, and iterate. This iterative process allows them to continuously enhance their AI systems and deliver better results. This approach can be seen as a moat in the AI industry, providing a competitive advantage to those who can effectively leverage feedback loops and continuously improve their offerings.

Taking a step back from action-driven models and focusing on the broader picture, it is worth acknowledging the journey of tech giant Google. From its humble beginnings in a Stanford University dorm room in 1995, Google has become a global powerhouse in the technology industry. The company's mission to build technology for everyone has remained constant throughout its growth. Despite its expansion, Google's relentless pursuit of better answers and solutions continues to be the driving force behind its success.

Google's story officially began in August 1998 when Andy Bechtolsheim, co-founder of Sun Microsystems, wrote a $100,000 check to Larry Page and Sergey Brin. This marked the birth of Google Inc. Since then, Google has come a long way, leaving behind its Lego servers and adding more elements to its corporate culture, such as company dogs. However, the passion for building technology that benefits people remains unchanged.

In conclusion, the near future of AI is undoubtedly action-driven, with models like ReAct leading the way. By incorporating external cognitive assets and leveraging reinforcement learning techniques, these models have the potential to achieve AGI-like capabilities. Startups that can create powerful feedback loops and continuously improve their AI systems will enjoy a competitive advantage in the industry. Meanwhile, Google's journey serves as a testament to the power of relentless pursuit and a commitment to building technology for everyone. As AI continues to evolve, the possibilities for automation and new offerings will expand, shaping the future of technology and society.

Actionable Advice:

1. Embrace action-driven models: Explore the potential of action-driven models like ReAct, which can act as agents making choices and taking actions. By leveraging cognitive assets, these models can enhance their performance and bring us closer to AGI-like capabilities.
2. Integrate external resources: Consider incorporating external cognitive assets into AI systems. By accessing data from external sources, AI models can bridge the resource gap and achieve better results.
3. Foster feedback loops: If you're working on an AI startup, focus on creating powerful feedback loops. Solve customer pain points, collect data, train your models, and iterate. This iterative process will help you continuously improve your offerings and stay ahead in the competitive landscape of AI.