Last week, over 210,000 Forbes readers in just over 4 days engaged with an article on AI titled “Meet The Future Of AI. Itās Not ChatGPT, Bard AI, Or Code Whisperer. Itās A Texas A&M Second Year Undergraduate Named Hannah.ā If you type the term “AI” into the news section of Google, you will notice that news about AI has surged by 450% over the last twelve weeks. The surge is not merely a passing trend but rather the tipping point that annual predictions (2021, 2022, and 2023) had forewarned about ā the imminent widespread influence of AI, accessible to all, not just a select few. It appears that the third time is indeed the charm for AI, and we find ourselves here, a mere eight months into the year, with the AI revolution unfolding for everyone.
Scrolling down to the end of the article reveals how Hannah, the promising figure in AI’s future, accomplished this feat.
Every CEO views AI as a defining moment, a time to put down their metaphorical pens, not because they have to do everything themselves, but due to the swift results AI can produce. However, it necessitates a collective effort to set things in motion. The challenge, or perhaps the psychological hurdle, lies in the perception that AI is complex. Yet, it does not have to be. The key is to approach it in a simplified manner. Hannah, a second-year undergraduate studying Geo Sciences at Texas A&M, exemplified this. Her story perfectly illustrates how Generation Z is already shaping the work landscape for the next decade and beyond. AI prompting could become the hottest job market for Gen Z once they leave college. It’s a harmonious blend of human insight, the expansive capacity of the cloud, and its ability to learn from data. Excelling in this role entails not only worldly experience but also the aptitude to play, construct frameworks for analysis, nurture curiosity, and derive insights from raw data.
This lesson imparts three crucial takeaways about the power of AI:
- Acquire Quality Data and Experiment: To make AI work, a substantial amount of data is essential. In this case, Hannahs work involved analyzing content from 120 Forbes visionary podcasts, which amounted to four days of continuous conversation. This equated to seven hundred thousand words, composed of intricate statements and nuanced language frequently embedded in ordinary, everyday discourse. Only three English-language novels contain more words, and each of them focuses on a single concept. Once a significant volume of data is available, AI engines can comprehensively process the information. Natural language engines require ample data to train themselves and provide meaningful insights. The magic of AI prompting comes from refining and manipulating the data.
- Shift Perspectives on Data, Themes, and Streams: Surprises make up over 30% of the value Hannah derived from using ChatGPT. Being receptive to these surprises, embracing them, and delving deeper to validate or dismiss them is essential. This mindset diverges significantly from the era of scarce data, where any information was better than none. The concept of themes and streams lies at the core of “The Digital Helix,” a Wall Street Journal bestseller. Data can hold value across multiple themes or areas of interest. For instance, an interview with the CEO of a cloning company like Viagen might intersect intriguingly with the CEO of a firm specializing in printing human organs, such as Prellis Biologics.
- Design for Value, Not Just Discovery: Reducing massive data volumes into more digestible and hopefully more valuable insights requires discipline. While revelations are exciting, they don’t inherently translate into value. The paradox of all AI lies in the human value it contributes, focusing on intelligent conclusions and well-reasoned recommendations regarding what actions to initiate, cease, modify, or continue based on the data. Designing for value is a crucial aspect of adopting this new perspective. Gen Z, with their fresh minds and emphasis on essence and value, seem particularly suited for this approach.
AI at this level is here to stay. Locking it away and discarding the key is not an option. The best way to embrace it is the path Hannah, a future AI prompter, embarked upon. The next global workforce will possess a fundamental understanding of this era of what is considered AI.
If organizations aim to harness their potential in transforming a pre-AI era establishment, they should begin now. Hiring Gen Z, providing internships, establishing ethical guidelines, and allowing them to explore is the way forward.
Here is how Hannah achieved it: She initially organized the data in a specific format (word documents). Then, developed four straightforward processes and filters: an extractive summary, an abstraction summary, and pre-trained models like BERT (Bidirectional Encoder Representations from Transformers), GPT, or T5 (Text-to-Text Transfer Transformer). These models could be fine-tuned for summarization tasks. Since these models were trained on extensive text data (including books, articles, and websites), they proved effective in generating summaries. Finally, she prompted ChatGPT to “summarize individual content,” “identify repeating words,” “highlight the most relevant phrases,” and “present impactful quotes.” After aggregating the interview data sets, combinations were clustered to identify cross-trends, terms, and ideas that could be applied across vastly different technology, science, biology, society, sports, and entertainment sectors. The possibilities are nearly endless, as ChatGPT can handle up to five combinations at once. For those curious, there is a simple tool to calculate the number of combinations using the formula nCr (non-repeated combinations). AI prompters are poised to deliver far more value than we can presently conceive. In this case, the figure is 190,578,024. No human could complete this task; even if each combination took 30 minutes to review, it would take over ten thousand years.