AI and ML for Entertainment Globalization | Challenges & Solutions

The Challenges of International Content Localization

Recent inaccuracies in translations for popular shows like “Squid Game” and other films have brought to light the difficulties inherent in utilizing technology for global content distribution.

Each year, the global media and entertainment sector releases tens of thousands of films and television episodes across numerous streaming services.

The aim is to reach an audience of 7.2 billion people spread across almost 200 countries. However, fluency in the approximately 7,000 recognized languages is not widespread.

The Traditional Localization Process

To achieve international reach, subtitles and audio dubs are essential for global distribution.

For decades, this process – known as “localization” – has been primarily human-driven. A translator with deep linguistic knowledge analyzes the screen dialogue.

They review the original content, if accessible, and create a script for the audio dub. This stage can often require several weeks per language to complete.

Following translation, voice actors perform the script, striving to synchronize their delivery with the on-screen action and lip movements.

Audio dubs are created first, and then subtitles are generated from these dubs. Each stage of linguistic adaptation introduces potential for error or alteration of the original narrative.

Cultural Nuances and Director Involvement

Effective localization incorporates a degree of cultural sensitivity, recognizing that direct translations of words, actions, or contexts aren't always possible.

Bong Joon-ho, director of the Academy Award-winning “Parasite,” provided his translation team with comprehensive notes prior to their work.

He, along with others in the industry, emphasizes that constraints like time, limited subtitle space, and the need for cultural understanding add complexity.

Despite these challenges, skillful localization significantly enhances the viewing experience.

The Rise of AI and Machine Learning

The rapid expansion of distribution platforms and the constant influx of new content are driving the search for faster, more accurate translation methods.

Artificial intelligence (AI) and machine learning (ML) are viewed as potential solutions, but currently, they haven’t reached a level capable of fully replacing human localization experts.

Directors of prominent titles like “Squid Game” and “Parasite” remain hesitant to fully embrace these technologies.

The inherent complexities of language and culture still require the nuanced understanding that only a human translator can provide.

Maintaining the integrity of the original story and ensuring accurate conveyance of meaning remain paramount concerns.

The Significance of Cultural Context

A direct, word-for-word translation often fails to fully convey the intended meaning of a text. This is because it struggles to capture the subtle linguistic, cultural, and contextual nuances embedded within the original script, vocal delivery, or visual elements.

Even the developers of artificial intelligence acknowledge these inherent limitations. Machine-based translations are frequently described as being akin to dictionaries rather than true translators.

Computers, they emphasize, can only execute the tasks they are programmed to perform and currently lack genuine comprehension.

Illustrative Example: "Squid Game"

Consider the first episode of “Squid Game.” While its English title is “Red Light, Green Light,” referencing the children’s game featured, the original Korean title, “무궁화 꽃이 피던 날” (“Mugunghwa Kkoch-I Pideon Nal”), translates to “The Day the Mugunghwa Bloomed.”

This Korean title bears no explicit connection to the game itself.

Within Korean culture, the title represents the concept of new beginnings. This symbolizes the potential for a fresh start offered to the game’s participants, a core theme of the series.

Although “Red Light, Green Light” is relevant to the episode’s events, it overlooks the deeper cultural resonance of the original title – a promised opportunity for those facing hardship.

The decision to prioritize the game’s name over the cultural metaphor may seem minor to some, but it represents a significant loss of meaning.

The Challenge for AI

If human translators sometimes miss these crucial connections, how can we realistically expect to develop machines capable of autonomously recognizing and applying such nuanced cultural understanding?

The ability to accurately interpret and convey meaning requires a level of contextual awareness that currently exceeds the capabilities of artificial intelligence.

• Cultural nuances are often lost in direct translation.
• AI translations are often described as being similar to dictionaries.
• Computers lack the comprehension needed for accurate interpretation.

Successfully bridging linguistic and cultural gaps necessitates a deeper understanding than simply converting words from one language to another.

Understanding the Difference Between Knowing and Knowledge

The ability of a computer to perform language translation from Korean to English is demonstrable. However, grasping the nuanced relationship dynamics depicted in a series like “Squid Game” – such as those between immigrants and citizens, acquaintances and relatives, or superiors and subordinates – and understanding their impact on the narrative represents a significantly greater challenge.

Integrating cultural comprehension and emotional intelligence into AI systems is a complex undertaking, particularly when emotions are conveyed nonverbally, through facial expressions. Predicting emotional responses, which can vary across cultures, further complicates the process.

Currently, AI development is ongoing in areas of explainability, interpretability, and the mitigation of algorithmic bias. The notion of machines achieving self-directed learning remains distant, considering the current state of the AI/ML industry.

The Importance of Context in Media and Entertainment

For creative industries like media and entertainment, where content is paramount, context is crucial. This encompasses both the creator’s intended context and the audience’s interpretation of it.

When considering global distribution, context is inextricably linked to culture. A truly advanced system would be capable of orchestrating and anticipating not only the audio, video, and textual elements but also the intricate layers of cultural nuance present in each frame, scene, theme, and genre.

Fundamentally, achieving this requires high-quality training data – prioritizing a data-centric approach over a model-centric one.

Challenges in Content Moderation

Recent data suggests that platforms like Facebook identify only 3% to 5% of problematic content. Despite substantial investment in development, programming AI to accurately discern context and intent remains exceptionally difficult.

While fully autonomous translation solutions are not yet feasible, AI/ML can still significantly alleviate workload pressures today.

A Hybrid Approach to Culturalization

By analyzing millions of films and television shows, combined with the cultural insights of individuals from nearly 200 countries, a combined human and AI/ML process can deliver the detailed analysis needed to pinpoint content potentially considered objectionable in any given country or culture.

This “culturalization” process generates a cultural roadmap used during localization to maintain narrative consistency, prevent cultural errors, and secure appropriate global age ratings – ultimately reducing post-production time and costs while minimizing regulatory risks.

Reaching a Global Audience

Today’s viewers have unprecedented access to a vast array of content. Success in the global marketplace demands that content creators prioritize understanding their audience, both domestically and internationally.

The most effective strategy for content creators and streaming services is to collaborate with organizations possessing a deep understanding of local audiences and their preferences, ensuring their content resonates and avoids misinterpretation.