Weini Zhangqi

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Generative Artificial Intelligence (GenAI) has emerged as a transformative force in our rapidly evolving technological landscape. While it holds the promise of incredible advancements, it also raises concerns, with job displacement being one of the most frequently mentioned. Many are worried about their jobs being replaced or even ceasing to exist. However, it is important to remember that GenAI is not only a threat, it can create opportunities such as workforce enhancement, helping us increase efficiency and learning.

CERTD & Long tail strategy in education

The silver lining offered by GenAI is that it can be utilized as a tool to provide personalized training and education. GenAI can be harnessed to create innovative educational solutions, making learning more accessible and personalized. CERTD is a company that has incorporated GenAI to create adaptive mobile-based learning tailored to blue-collar workers. Traditionally, the blue-collar workforce relied on hands-on training, with seniors who have extensive experience in the subject matter serving as the main resources for training. For example, coding learners can find numerous platforms offering coding courses, from online classes to self-paced tracks. This long-tailed education gives an array of choices and flexibility for learners by aiming for niche and personalised ways of learning. However, when it comes to learning blue-collar skills such as construction, hospitality, manufacturing etc., one would typically join a vocational school or gain practical experience through work. This traditional trajectory for the blue-collar force often follows a one mould fits all approach. CERTD offers a long-tail learning strategy by focusing on AI-generated learning content at a coarse level. GenAI has the potential to break the tradition of the apprenticeship, enabling workers to acquire new skills and fast-track their readiness for employment in an ever-changing job market.

Risk and Concerns

There’s a fly in the ointment as well. There could be concerns about the effectiveness, as blue-collar jobs heavily rely on hands-on skills. How effective would it be to learn from watching mobile devices? A potential solution could be introducing technologies such as AR in the training to enhance the learning experience.

Interested in this topic? Here is a good read:

https://www.forbes.com/sites/tedladd/2023/08/22/democratizing-opportunity-how-ai-enabled-long-tail-learning-empowers-the-blue-collar-workforce/

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Disclaimer: I’ll start off by saying that I don’t plan to make a Molotov cocktail. My interest in how to frame prompts, however, is real. My curiosity was first sparked by this post below.

Interaction 1

Interaction 2

Here is a malicious example of prompting. But, how can we use prompts to our advantage? What can be done to enhance ChatGPT’s performance so that we get the best output?

There are a few reusable solutions to the typical LLM problem, which refer to prompting patterns (White et al., 2023).

Meta Language Creation. In this technique, users make up new words to express concepts or ideas. Consider a mathematical symbol or a shorthand abbreviation. This approach works best for discussing complex or abstract situations, such as math problems.

Flipped Interaction. This pattern flips the typical interaction flow in which the LLM queries the user to gather data in order to produce content to address the query. Here’s how I can ask LLM to compile a list of success criteria for software.

Persona: Users give the LLM a particular role, which affects the nuance of the outcome and results it produces. The Molotov cocktail-making example is an illustration of the use of persona patterns

Question Refinement: The user requests LLM to provide improved or more specific versions of the questions. It helps users determine the appropriate question as the final prompt.

More patterns can be found in the article from White et al. (2023).

When interacting with LLM, prompt patterns are useful methods to enhance response quality. It helps in producing highly accurate and relevant responses. Prompting it is an iterative process that necessitates constant improvement (Liu et al., 2022). Prompts might manipulate LLM to produce malicious output despite the enforced policies. Efforts from OpenAI have been employed to prevent such policy violations. OpenAI reported such efforts including continuous model improvement to make it less likely to generate inappropriate or harmful content, the implementation of moderation mechanisms to find and stop prompt misuse and collaboration with AI experts in ethics, AI safety, and policy to gain perspectives on preventing misuse (Our Approach to AI Safety, n.d.). I positively believe that in the near future getting tutorials on making Molotov cocktails from ChatGPT will be history.

References

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2022). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys, 55(9). https://doi.org/10.1145/3560815

Our approach to AI safety. (n.d.). Openai.com. https://openai.com/blog/our-approach-to-ai-safety#OpenAI

White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. ArXiv Preprint ArXiv:2302.11382. https://doi.org/10.48550/arxiv.2302.11382

Generative AI is going to take my job? Think more positively.

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ChatGPT taught me how to make Molotov cocktails! – A lesson of it’s not what you say, it’s HOW you say it.

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