Many media houses already use ChatGPT, especially for tasks such as summarisation, ideation, and assistance.

The success behind it is based in the human-like language — what you would use to communicate with friends. That makes it powerful; no coding and deep understanding of technology is necessary.

What ChatGPT is still lacking is deeper autonomous operations. For that, Auto-GPT comes into play.

Auto-GPT and ChatGPT are built on the same technology, but they differ significantly in their functionality. The primary difference between the two is that Auto-GPT can function autonomously without the need for human agents, whereas ChatGPT requires human prompts to operate.

Auto-GPT achieves a given goal by breaking it into sub-tasks and using the Internet and other tools in an automatic loop. It can create its own prompts, browse the Internet, summarise information, and offer information in a structured format.

Auto-GPT uses in the newsroom

1. A posting assistant: Auto-GPT tools will revolutionise the news industry by automating various editorial tasks and streamlining content generation. Algorithms can summarise articles, find good SEO titles, measure performance and update it, and automatically create a post once an article is finished.
2. Fact checking: Auto-GPT offers a valuable solution for enhancing the accuracy and credibility of news reporting. An automated fact-checking system can scrape verified sources in real time, flagging inconsistencies and helping journalists maintain high standards of integrity. By supplementing human efforts, AI can significantly reduce the time and effort required for fact checking, allowing journalists to focus on providing in-depth analysis and contextual information.
3. Data activation in journalism: Auto-GPT can enhance data-driven journalism by automating the process of sifting through vast amounts of information and identifying patterns and trends. This enables journalists to uncover hidden stories and provide readers with valuable insights based on data analysis. Without too much coding, Auto-GPT might also boost investigative journalism.

Minimal viable AI team

For most publishers, investing in AI is becoming a necessity. Starting your journey of AI experimentation can also be challenging.

To help get you started, I suggest considering the following key roles to form your minimal viable AI team:

• Web developer (preferably full stack): This individual is responsible for presenting the AI solution in a user-friendly manner to ensure target users can easily interact with it. They should also possess an understanding of the underlying infrastructure.
• Data scientist (or data analyst): This team member possesses an in-depth understanding of the data provided by publishers and their corresponding structures. They are responsible for fine-tuning the AI solution as needed.
• AI strategist (or AI product manager): This role involves defining the objectives and strategies that the AI team aims to achieve. They are responsible for outlining processes, workflows, and designing the AI solution. Additionally, they must effectively communicate the value of the solution to the newsroom and stakeholders.

AI privacy

We live in the age where AI systems are developing very quickly. The apps that would have taken years to develop are now being created at a speed never seen before.

However, we also need to be concerned about the potential negative consequences. For instance, Auto-GPT could be used to generate hoaxes by scraping trending topics and posting manipulative information on social media. Therefore, it is essential to create laws to regulate the use of AI now more than ever.

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