"Why Tacit Knowledge is More Important Than Deliberate Practice" and "Google sets the bar for AI language models with PaLM"

"Why Tacit Knowledge is More Important Than Deliberate Practice" and "Google sets the bar for AI language models with PaLM"

When it comes to acquiring expertise, there is a common misconception that deliberate practice is the key to success. However, recent research suggests that tacit knowledge plays a much more significant role in skill acquisition. Tacit knowledge refers to the knowledge that is difficult to articulate or explain, but is essential for mastering a particular skill.

Understanding the existence of tacit knowledge opens up new possibilities for individuals looking to improve their skills. Instead of focusing solely on deliberate practice, which involves following a set of procedures blindly, it is more effective to emulate and imitate experts in the field. By observing and copying their actions, individuals can internalize the principles behind those actions and develop their own expertise.

One way to identify tacit knowledge in action is when experts explain their judgments in terms of numerous caveats and exceptions. This indicates that their expertise is based on a deep understanding of the skill, rather than a set of explicit rules or procedures. Tacit knowledge allows experts to make complex judgments and arrive at solutions with confidence and efficiency.

While it may be tempting to try and convert tacit knowledge into explicit knowledge, doing so can hinder the development of expertise. Providing a list of procedures to follow denies individuals the opportunity to build their own expertise and limits their ability to engage in creative problem-solving. Instead, it is more beneficial to focus on gaining tacit knowledge directly through emulation, feedback, and osmosis.

In the field of AI, Google has set a new benchmark with their large language model (LLM) called PaLM (Pathways Language Model). PaLM is part of Google's new AI architecture, Pathways, which aims to handle multiple tasks, learn quickly, and have a better understanding of the world. PaLM 540B, with its 540 billion parameters, is comparable to other leading LLMs in terms of size and performance.

Efficiency is a crucial factor in the training process of LLMs. DeepMind's research suggests that previous training methods for LLMs have been suboptimal in terms of compute usage. PaLM 540B was trained using a combination of model and data parallelism on TPU v4 Pods connected over a data center network. This approach allows for a more efficient use of compute resources.

It's important to consider the sources of training data when evaluating the capabilities of language models like PaLM. While social media conversations are the most prevalent source, they may not capture the full range of language diversity, including casual language, code-switching, or dialectal variations. This limitation may affect PaLM's ability to model non-dominant dialects across English-speaking regions globally.

Google's vision for Pathways is to create an AI system that can generalize across thousands or millions of tasks, understand different types of data, and do so efficiently. PaLM demonstrates the potential to achieve comparable or better performance than existing LLMs while requiring fewer resources and customizations.

In conclusion, tacit knowledge plays a crucial role in skill acquisition, surpassing the importance of deliberate practice. Emulation and imitation of experts, combined with feedback and observation, allow individuals to internalize the principles behind a skill and develop their own expertise. In the field of AI, Google's PaLM sets a new standard for language models, showcasing the potential for more efficient and powerful systems. To pursue expertise, it is essential to focus on gaining tacit knowledge directly and exploring the research surrounding its acquisition.

Actionable advice:

1. Seek opportunities for emulation and imitation in your field of interest. Find mentors or experts who can guide you through their actions and principles.
2. Prioritize feedback and observation in your learning process. Actively observe experts and seek feedback on your own performance to refine your understanding of the skill.
3. Stay informed about the latest research and advancements in your field. By keeping up with the published research on tacit knowledge and skill acquisition, you can gain valuable insights and strategies for improving your expertise.