DTTP AI PM

Access AI

This product introduces AI-powered speech-to text and content discovery features for the YouTube Web Platform.

Pain Points 

How can we increase YouTube's total hours of content  watched by educational content viewers and ad revenue by  using AI to enhance accessibility and usability through text-to speech features to exceed 70% accuracy of auto-generated  captions and transcripts? 

Problem Background 

YouTube currently holds 52% of the video content market  with 5.2 billion monthly users, competing with platforms like  TikTok, Instagram, Facebook, and Netflix. While YouTube  offers auto-generated captions and transcripts, their accuracy  is only around 60-70%. Enhancing its speech-to-text AI could  help YouTube stand out, particularly in the educational  

In performing a detailed user  survey of 13 participants, we  cited two key pain points: 

Youtube’s Excessive forced Ad  Experience significantly detracts  from the viewing experience. 

Youtube’s existing Speech-to Text features of auto-generated  captions and as-spoken  transcripts have been cited to be  incomplete, inaccurate or  inconsistent transcriptions.  

Supporting Data 

Sometimes the auto-generated  captions aren't accurate so I turn  it off and make the volume higher  to listen carefully 

I watch a lot of international  content and oftentimes there are  no translations available or the  one provided is not accurate  

the transcript is not complete, it  broke the sentences and does not  separate the speech of speakers 

content space, competing with platforms like Kajabi, Udemy,  and Skillshare. Other platforms have integrated accessibility  features like auto-captioning and multilingual subtitles.

 

Prioritized Features 

Prioritized Features for Development 

Based on the user persona analysis, prioritization metrics, and  pain points identified, I have considered the following three  use cases as the prioritized focus of the AI feature: 

1. AI-powered Speech-to-text transcription feature for both  content creators and viewers  

Search suggestion based on history  

Integrated speech-to-text translations of foreign  languages 

75% of people we spoke to mentioned they were unhappy  with the Youtube captioning feature due to inaccurate,  incomplete or inconsistent transcripts and captions. 

Feedback 

The preliminary user research analysis to validate this problem  revealed that video captioning and transcripts affect a subset  of users, educational content viewers and creators. This subset  amounts for 86% of viewers within the United States and has  become the second most popular genre on the platform.  

Future Steps 

• Continued training and development of AI model to  integrate diverse dialects, accents and subject matter from  niche or technical topics to improve the accuracy of AI  speech-to-text features.  

• Expand multi-lingual capabilities beyond Spanish and French to target South Asian demographic

Learnings

Product Manager Learnings:

RAHUL LOHANA

Designer Learnings:

Designer Learnings:

Jo Sturdivant

  1. Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
  2. Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
  3. Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.

Developer Learnings:

Developer Learnings:

Vanady Beard

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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.

Developer Learnings:

Stephen Asiedu

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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.

Developer Learnings:

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Developer Learnings:

Maurquise Williams

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  1. Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
  2. Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
  3. Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.

Developer Learnings:

Jeremiah Williams

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All in all this experience was very awesome I learned that in coding with others being transparent is key

Developers Learnings:

Justin Farley

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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.

Full Team Learning