When I was developing Intranets more than 20 years ago, I hired a lot of engineers: brilliant technicians capable of building websites, designing databases and connecting them to back-office systems like Oracle or SAP.
But when I needed experts to structure the content flows of the Intranets, to give advice about interfaces and user experience or to manage, query and facilitate content, engineers proved pretty useless.
I discovered that trained librarians really stood out in the complex intellectual task of massaging content into knowledge, and making that accessible to users.
One of the principal responsibilities of librarians had been to categorize, catalog, and organize vast amounts of information. They were extremely adept at conducting research and helping patrons find information efficiently. And they often had a broad knowledge of cultural and societal contexts due to their exposure to diverse literature and resources.
Today, I’ve become highly fascinated with the connection between generative AI, LLMs and organizations. Where companies now have a data science department and data scientists, I believe they will also need to build content science departments and hire content scientists who understand how to work with unstructured data in strategic ways. And that’s where these ‘Conan the Librarians’ will come to play a pivotal role.
Certainly, the capability to assess vast amounts of information will be crucial for content scientists who will need to structure and curate digital content effectively.
In the age of generative AI, we will also need skills in matters of privacy, censorship, and information access. These ethical considerations align with the challenges content scientists face, especially in an era of misinformation, data privacy concerns, and digital rights.
They will also need to find ways to avoid model collapse. That is what happens if we feed the synthetic data - the huge amount of ‘new’ and sometimes hallucinated data that is being generated by generative AI chatbots like OpenAI’s ChatGPT, Google’s Bard, Anthropic’s Claude or Baidu’s Ernie Bot - back into the LLMs. These large language models feeding on data that was generated by (other) large language models could very well result in contaminated data and untrustworthy data and a poorly functioning model. And that will become a really interesting challenge for content scientists.
As the digital content ecosystem becomes more intricate and intertwined with technology, content scientists will play a pivotal role. Cultivating this blend of creative, technical, and analytical skills will position them at the forefront of unlocking the potential of generative AI tools.
But we may need even more. Just as the world of Data has produced not just Data Scientists, but also Data Engineers and Data Analysts. In a very similar way, the field of Content Science will not just require Content Scientist, but equally Content Engineers and Content Analysts as well.
For me, it's clear: this is a brand new and exciting field, and very different from the world of structured data, and the field of data science.
Read all about this and more in my new and free guide about generative AI and LLMs for companies, “The CEO’s Guide To Content Science”. Download it here for free.
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