The explosion of AI has created some fascinating new creative roles, many of which will be familiar to screenwriters and filmmakers. The creation of AI content is remarkably similar to creating content for film and television.
It’s helpful to delineate these new creative roles more clearly and explore how they might interact within a creative or technical team. Here’s an updated framework for these roles:
1. AI Content Strategist
Responsibilities:
- Develop and implement strategies for using AI in content creation across various media.
- Collaborate with technical and creative teams to integrate AI tools into production pipelines.
- Analyse content performance and adapt strategies to optimise the use of AI in content generation.
Example Industries:
- Digital marketing agencies
- Large technology companies like Google, Amazon, or Facebook
- Media companies like Netflix or The New York Times
Contributions:
- Designing AI-driven campaigns that personalise content to user preferences.
- Leading teams that integrate AI tools for analysing consumer behaviour.
2. Prompt Engineer
Responsibilities:
- Design, test, and refine prompts to guide AI in generating specific and relevant outputs.
- Work closely with AI developers to understand and influence the evolving capabilities of AI models.
- Provide training and best practices for crafting effective prompts to other team members.
Example Industries:
- AI-focused startups
- Companies developing natural language processing tools, such as OpenAI or DeepMind
- E-commerce platforms using AI for product descriptions
Contributions:
- Developing prompts that guide AI in creating engaging and relevant product descriptions.
- Enhancing user interactions with AI-driven customer service chatbots.
3. AI Creative Director
Responsibilities:
- Oversee the creative aspects of AI-driven projects, ensuring that the content aligns with artistic and brand standards.
- Lead the integration of AI in creative processes, fostering innovation in content design and execution.
- Ensure that AI-generated content maintains a high standard of creativity and originality.
Example Industries:
- Advertising firms incorporating AI for creative design
- Entertainment and gaming companies, like Electronic Arts or Warner Bros
Contributions:
- Directing projects that use AI to generate innovative music, art, or interactive content.
- Overseeing AI-driven animation or special effects in films.
4. Machine Learning Editor
Responsibilities:
- Edit and refine AI-generated content to ensure it meets quality and ethical standards.
- Adjust AI models’ parameters to improve content accuracy and appropriateness.
- Work with data scientists to understand content performance metrics and apply insights to content refinement.
Example Industries:
- Publishing houses using AI for manuscript editing
- News organisations experimenting with automated content generation
Contributions:
- Refining AI-generated text to ensure it meets editorial standards.
- Collaborating with data scientists to tailor AI outputs to audience needs.
5. Voice AI Developer
Responsibilities:
- Develop and fine-tune AI-generated audio content, such as voiceovers and synthetic voice applications.
- Collaborate with sound designers and engineers to integrate AI voices into multimedia projects.
- Ensure that AI-generated audio is clear, engaging, and contextually appropriate.
Example Industries:
- Companies developing virtual assistants, like Apple’s Siri or Amazon’s Alexa
- Firms involved in synthetic voice for audiobooks or language learning apps
Contributions:
- Creating more natural and expressive synthetic voices.
- Integrating voice AI into customer service tools to enhance user experience.
6. AI Data Journalist
Responsibilities:
- Use AI to analyse large datasets and extract newsworthy stories and insights.
- Collaborate with editorial teams to develop data-driven reports and articles.
- Ensure the accuracy and integrity of data used in journalistic content.
Example Industries:
- Major news networks and online journalism platforms
- Sports analytics companies
Contributions:
- Using AI to uncover patterns and stories in large datasets about elections, sports statistics, or financial markets.
- Producing compelling, data-driven narratives.
7. AI Video Producer
Responsibilities:
- Use AI tools to assist in various stages of video production, including editing, colour grading, and effects.
- Coordinate with directors and cinematographers to ensure AI tools enhance the artistic vision.
- Monitor the evolving capabilities of AI in video production to stay ahead of industry trends.
Example Industries:
- Film and TV production companies using AI for editing and post-production
- Social media platforms developing AI-driven video content tools
Contributions:
- Automating routine aspects of video production to focus on creative storytelling.
- Enhancing video content personalisation for different audience segments.
8. AI Content Analyst
Responsibilities:
- Monitor and analyse the performance of AI-generated content across platforms.
- Use insights from content analytics to guide content creation and optimisation strategies.
- Collaborate with marketing and analytics teams to enhance content reach and engagement.
Example Industries:
- Social media companies analysing user engagement
- Digital news outlets measuring the impact of different content strategies
Contributions:
- Utilising AI to predict trends and inform content creation.
- Analysing performance data to optimise digital marketing strategies.
Professionals in these roles often share their expertise through industry conferences, white papers, and major projects, making their contributions key to understanding the impact of AI in their respective fields.
New creative roles mean new terms
In the realm of AI, especially with models that involve language processing and generation, several slang terms and jargon are commonly used to describe behaviours, phenomena, or characteristics of the systems. Here are a few of those terms, including “hallucinating”:
Hallucination:
In AI, this refers to when a model generates incorrect or fabricated information. It’s particularly common in language models and image generation where the AI might create plausible but false outputs.
Prompt Engineering:
Of all the new creative roles, this has to be the one tht stands out the most. Crafting the input (or prompt) to an AI in a specific way to achieve the desired output. It’s a bit of an art form, as effective prompts can significantly improve the quality of the AI’s responses.
Fine-tuning:
Refers to adjusting a pre-trained model on a smaller, specific dataset to adapt its responses or outputs to be more relevant to particular tasks or contexts.
Overfitting:
This happens when an AI model learns details and noise from the training data to an extent that it performs well on this data but poorly on new, unseen data.
Underfitting:
Occurs when a model is too simple to learn the underlying pattern of the data, resulting in poor performance on both the training data and new data.
Ground Truth:
The accurate, real-world data used as a benchmark to train or evaluate AI models. It’s essential for training AI systems to make accurate predictions or classifications.
Token:
In NLP, this typically refers to each piece of a text input that an AI model processes, which could be words, parts of words, or punctuation.
Latent Space: A representation of compressed data in which similar data points are closer together. In AI, especially in fields like computer vision, it’s used to describe the abstract, multidimensional space where AI models encode learned representations of the input data.
Activation Function:
In neural networks, this function determines whether a neuron should be activated, influencing how data moves through the model.
Backpropagation:
A method used in artificial neural networks to improve the model by adjusting the weights of neurons based on the error rate obtained in the previous epoch (cycle of processing).
Don’t be frightened by these terms. They are part of the evolving language around AI and reflect both the complexity and the informal approaches that professionals and researchers often adopt when working with these technologies.
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