The Future of Streaming: AI’s Transformative Role

AI is changing the landscape of every application we commonly use in our day-to-day lives. We all know it is just the beginning. AI gives everyone the ability to utilize the content as they would like. Entertainment apps are one area that can benefit from AI as an industry. They can start by reaching out to a wide range of audiences in more countries. They can also satisfy more audiences in a never-before-imagined way. AI would redefine our streaming experiences.

Let’s get deeper into how AI can elevate our interactions with video and music streaming platforms based on the below factors –

a) Feature: The specific capability or functionality that enhances the user experience within an application.
b) Model: The underlying AI algorithm or architecture that enables the feature to function.
c) Tech: The broader set of technologies and tools used to implement and support the AI model and feature.
d) On-device vs. Cloud: The decision on where the AI processing occurs, either locally on the user’s device or remotely on cloud servers.

1. Video Streaming Apps Reimagined

• Breaking Language Barriers with Auto Translation:
  Feature: Imagine watching a captivating Korean drama without the need for subtitles. AI-powered translation will not only provide accurate real-time subtitles but also dub the audio seamlessly into your preferred language. This opens a world of content, making foreign films, documentaries, and series easily accessible to everyone.
  Model: Transformer models.
  Tech: Speech-to-text, machine translation, text-to-speech.
  On-Device vs. Cloud: Cloud-based for computationally intensive translation models, but on-device for real-time transcription and potentially for common language pairs to reduce latency and bandwidth usage.
• Content Rating Filtering for a Safe Viewing Experience:
  Feature: With AI, you’ll have complete control over the content you and your family consume. The app will intelligently analyze each video, filtering out scenes that don’t align with your chosen content rating. This ensures a safe and age-appropriate viewing experience for everyone, eliminating the worry of encountering inappropriate content.
  Model: Computer vision models (CNNs).
  Tech: Object detection, scene recognition, action recognition.
  On-Device vs. Cloud: Hybrid approach. On-device models for basic filtering and quick decisions, with cloud support for more complex scenes and nuanced content analysis.
• Localization: A More Immersive Experience:
  Feature: Beyond just language, AI can localize other elements within videos, such as cultural references, humor, and even product placement. This deeper level of adaptation creates a more immersive and culturally relevant experience, making you feel truly connected to the content.
  Model: NLP models.
  Tech: Machine translation, sentiment analysis, named entity recognition.
  On-Device vs. Cloud: Primarily cloud-based for handling diverse languages and cultural adaptations, but on-device for common translations and localized UI elements.
• Scene Search: Your Personal Video Navigator:
  Feature: Remember that hilarious scene from your favorite sitcom? AI will make it effortless to find. Describe the scene, and the app will pinpoint its exact location within the video. This feature is also great for sharing memorable moments with friends or quickly revisiting your favorite parts.
  Model: NLU and scene understanding models.
  Tech: Video indexing, semantic analysis, information retrieval.
  On-Device vs. Cloud: Cloud-based for complex scene understanding and search across large video libraries, but on-device for basic scene recognition and local video search.
• Personalized Editing: Curate Your Own Viewing Experience:
  Feature: Gone are the days of skipping through boring parts or enduring lengthy intros. AI-powered editing allows you to create personalized cuts of videos. Focus on the scenes you love, compile highlights, or even rearrange the sequence for a unique viewing experience tailored to your preferences.
  Model: Video summarization and highlight detection models.
  Tech: Scene segmentation, object tracking, shot boundary detection.
  On-Device vs. Cloud: Hybrid approach. On-device for basic editing and trimming, with cloud support for more advanced features like automated highlight reels and scene rearrangement.
• Content-Aware Recommendations: Discover Hidden Gems:
  Feature: AI will go beyond simple genre-based recommendations. By analyzing the nuances of the content you watch, the algorithm will uncover hidden gems you might otherwise miss. Discover new movies, documentaries, or series that genuinely resonate with your interests.
  Model: Collaborative filtering, content-based filtering, deep learning models.
  Tech: User behavior analysis, embedding techniques, recommendation algorithms.
  On-Device vs. Cloud: Primarily cloud-based for leveraging large user data and complex recommendation models, but on-device for basic recommendations based on local viewing history.

2. Music Streaming Apps: A Symphony of AI Innovation

• Track Infusion: Unleash Your Inner DJ:
  Feature: Ever wanted to blend your favorite songs into unique mashups? AI will make it possible. Mix and match tracks, experiment with different genres, and create personalized music experiences that reflect your creativity.
  Model: Audio source separation and music generation models.
  Tech: Signal processing, music information retrieval, audio mixing.
  On-Device vs. Cloud: Cloud-based for computationally intensive source separation and music generation tasks, but on-device for basic mixing and effects.
• Custom Lyrics: Express Yourself Through Music:
  Feature: Add your own lyrics to existing songs, transforming them into personalized anthems. Or, collaborate with AI to compose entirely new songs from scratch. This feature opens doors for aspiring songwriters and music enthusiasts to express themselves like never before.
  Model: Language models (GPT-3 or similar).
  Tech: NLG, sentiment analysis, rhyme detection.
  On-Device vs. Cloud: Cloud-based for leveraging the power of large language models, but on-device for basic lyric generation and suggestions.
• Mood-Based Playlists: The Soundtrack to Your Emotions:
  Feature: AI will curate playlists that perfectly match your current mood. Whether you need upbeat tunes for a workout or soothing melodies for relaxation, the app will adapt to your emotional state, creating a dynamic and personalized listening experience.
  Model: Emotion recognition and content-based filtering models.
  Tech: Sentiment analysis, music tagging, recommendation algorithms.
  On-Device vs. Cloud: Hybrid approach. On-device for basic mood detection and local music library analysis, with cloud support for advanced emotion recognition and personalized playlist generation.
• AI-Powered DJ: Seamless Transitions and Endless Mixes:
  Feature: Say goodbye to awkward song transitions. AI will act as your personal DJ, mixing and blending tracks seamlessly for a continuous and captivating listening experience. Let the AI curate an endless stream of music tailored to your taste.
  Model: Music analysis and beatmatching algorithms.
  Tech: Audio feature extraction, tempo analysis, key detection.
  On-Device vs. Cloud: Primarily on-device for real-time music analysis and mixing, but cloud support for advanced music understanding and personalized DJing.
• Music Production Assistance: Empowering the Next Generation of Musicians:
  Feature: AI-powered tools will democratize music creation. From composing melodies to arranging harmonies and even mastering tracks, AI will guide and assist aspiring musicians, making music production accessible to everyone.
  Model: Generative models and AI-powered mastering tools.
  Tech: Music composition tools, DAWs, audio processing.
  On-Device vs. Cloud: Hybrid approach. On-device for basic music creation and editing, with cloud support for computationally intensive generative tasks and advanced mastering features.

The integration of AI into video and music streaming apps is set to revolutionize in a new era of personalized and interactive entertainment. These intelligent features will not only make our streaming experiences more enjoyable but also empower us to explore and create in ways we never thought possible. The future of streaming is bright, and AI is leading the way.

AI usefulness in streaming

What are the limitations to roll out these features today?

Apart from the common limitations regarding trust in AI itself, I see two other major factors in rolling out these features:

1. Existing hardware infrastructure to run models on-device:

• TVs: Most smart TVs, especially older models, lack the processing power and memory required to run complex AI models locally. This severely limits their ability to handle real-time features like advanced scene recognition or sophisticated content filtering.
• Mobile devices: While newer mobile phones have significantly more powerful processors and GPUs, they still face limitations in terms of battery life and thermal management when running resource-intensive AI tasks.

2. The FinOps factor involved in running models on the cloud:

• Cost: Cloud computing, especially for large-scale AI inference, can incur significant costs. Processing vast amounts of audio and video data from millions of users can quickly become expensive.
• Scalability: Cloud infrastructure needs to be highly scalable to handle peak usage times and ensure smooth user experiences. This requires careful planning and management to avoid performance bottlenecks and additional costs.
• Latency: Depending on the user’s location and network conditions, cloud-based AI processing can introduce noticeable latency, affecting real-time features like translation and content filtering.

These limitations present significant challenges to widespread adoption of these advanced AI-powered features in streaming platforms today. Addressing them will require a combination of hardware advancements, optimized model architectures, and efficient cloud infrastructure management. Over time, as these technologies evolve and mature, we can expect to see these limitations diminish, making AI-powered streaming experiences more accessible and seamless for users worldwide.

Share your idea on how AI can improve the streaming experience.
Happy learning!

Originally published at http://shankarkumarasamy.blog on September 29, 2024.