SMPTE/ETC AI/ML Joint Task Force Survey

1. Survey

Foreword
SMPTE (the Society of Motion Picture and Television Engineers) is an internationally-recognized standards developing organization. Headquartered and incorporated in the United States of America, SMPTE has members in over 64 countries on six continents. SMPTE’s Engineering Documents, including Standards, Recommended Practices and Engineering Guidelines, are prepared by SMPTE’s Technology Committees. Participation in these Committees is open to all with a bona fide interest in their work. SMPTE cooperates closely with other standards developing organizations, including ISO, IEC, ITU, ATSC, and SCTE.

The Entertainment Technology Center at the University of Southern California (ETC) is an industry group where the world’s largest media and technology companies research, identify and evaluate emerging technologies for their industry. ETC is part of USC’s prestigious School of Cinematic Arts.

General Information
SMPTE and the ETC have formed a Joint Task Force on AI/ML . This Task Force focuses on studying issues relating to AI/ML that may warrant future standardization. This Questionnaire originates from this Joint Task Force. Please answer as many questions as you can.

What will be done with Responses from this Questionnaire
The information collected in this survey will be presented in a summarized fashion in a report to be issued later by the Joint Task Force. Responses will be treated as anonymous in the report. Response documents will not be made available to the public.

Question Title

* 1. Which of the following best describe your job function?

Executive / managerial

Sales / marketing / business development

Creative

Data scientist / analyst / technologist / algorithm developer

R&D / engineering - hardware

R&D / engineering - software

R&D / engineering - systems

I am currently not employed

Other (please specify)

Question Title

* 2. Are you primarily a provider or user of AI/ML?

Provider

User

Question Title

* 3. What is the size of your organization?

Less than 100

101-500

501-1500

1501+

Question Title

* 4. What is the size of your team?

Less than 10

11-50

51-150

151+

Question Title

* 5. How would you rate your organization's knowledge of AI/ML

Above average

Average

Below average

Question Title

* 6. How many data scientists or AI/ML engineers do you have in your organization?

0-10

11-50

51-250

Don't Know

Question Title

* 7. How many users of AI/ML do you have in your organization?

0-10

11-50

51-250

Don't Know

Question Title

* 8. How many creators of AI/ML do you have in your organization?

0-10

11-50

51-250

Don't Know

Question Title

* 9. In what areas do you use AI/ML?

Production

Asset Management / Archiving

Content or story development

Automated metadata creation

Translation or accessibility

Distribution

Analyzing consumption trends

Consumer marketing (e.g. recommendation engine)

Logging

Editing

Color Correction

Speech to text

Segmentation

None of the above

Other (please specify)

Question Title

* 10. What is your biggest challenge with deploying AI/ML in your organization?

Question Title

* 11. What bottlenecks do you experience in your AI/ML workflows?

Gathering data

Preparing data for processing

Accessing training data

Training compute-related

Hyperparameter optimization

Inference compute-related

Inference latency

Performance optimization

Other (please specify)

Question Title

* 12. What aspects of AI/ML that you use heavily are expensive relative to the value created?

Training models

Retraining or adaptation

Data or media management

Inference deployment

Other (please specify)

Question Title

* 13. Which data handoffs are cumbersome or otherwise problematic?

Pre-processing raw data

Efficient access of training data

Managing large numbers of parameters during training

Deployment and management of models

Model evaluation and feedback

Other (please specify)

Question Title

* 14. What specific types of models do you use, and which present implementation challenges?

	I use this	This presents implementation challenges
Convolution NNs / computer vision	Convolution NNs / computer vision I use this	Convolution NNs / computer vision This presents implementation challenges
Speech recognition	Speech recognition I use this	Speech recognition This presents implementation challenges
NLP / machine translation	NLP / machine translation I use this	NLP / machine translation This presents implementation challenges
Recommendation engine	Recommendation engine I use this	Recommendation engine This presents implementation challenges
Prediction engine	Prediction engine I use this	Prediction engine This presents implementation challenges
Generative models	Generative models I use this	Generative models This presents implementation challenges

Other (please specify)

Question Title

* 15. What specific types of frameworks do you use?

Tensorflow

PyTorch

Caffe

Spark

Keras

Other (please specify)

Question Title

* 16. What are the biggest pain points in your AI/ML related workflows?

Question Title

* 17. Are there handoffs to or from your AI/ML processes where the interfaces or formats are poorly defined or proprietary in nature?

Yes

Question Title

* 18. How would you assess the potential of AI and ML to drive value for your organization?

High

Medium

Low

Question Title

* 19. Please rank the areas in terms of their potential to drive value for your organization

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* 20. What insight about your workflow or your organization do you currently wish you had the most?

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* 21. Please rank the biggest interop challenges you see in the area of AI/ML?

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* 22. What is the bigger interoperability challenge among AI/ML applications?

Cross-vendor Interoperability

Cross-platform Interoperability

Question Title

* 23. What issues exist with AI/ML that do not directly relate to interoperability, but where standardized practices might be useful?

Ethics

Data Privacy

Security

Other (please specify)

Question Title

* 24. What do you see as the biggest obstacle to deploying AI/ML at scale in your line of work?

Too much data

Data is not in a consistent format or is difficult to access

Legal/copyright issues or uncertainty

Lack of expertise or knowledgeable personnel

Too expensive

Models are too inaccurate to be useful

Models are too compute-intensive to be useful

Other (please specify)

Question Title

* 25. In what specific areas might a public data register or registration authority be helpful?

Question Title

* 26. Do you find discovery of available services to be a major challenge in the AI/ML area?

Yes

Question Title

* 27. Would a central register help users to discover available services?

Yes

Question Title

* 28. What opportunities do you see in AI/ML helping to enforce content producers' intellectual property rights?

Identifying leaks of copyrighted media and monitoring dissemination

Automated identification of copyrighted material on public websites or other internet sources

Identifying potentially infringing content (e.g. plagiarism) during creation

Other (please specify)

Question Title

* 29. What aspects of AI ethics are you most worried about?

Question Title

* 30. Are you worried that you and/or your colleagues' jobs would be at risk if AI/ML systems were deployed at scale in your organization?

Yes

SMPTE/ETC AI/ML Joint Task Force Survey

1. Survey

Question Title

* 1. Which of the following best describe your job function?

Question Title

* 2. Are you primarily a provider or user of AI/ML?

Question Title

* 3. What is the size of your organization?

Question Title

* 4. What is the size of your team?

Question Title

* 5. How would you rate your organization's knowledge of AI/ML

Question Title

* 6. How many data scientists or AI/ML engineers do you have in your organization?

Question Title

* 7. How many users of AI/ML do you have in your organization?

Question Title

* 8. How many creators of AI/ML do you have in your organization?

Question Title

* 9. In what areas do you use AI/ML?

Question Title

* 10. What is your biggest challenge with deploying AI/ML in your organization?

Question Title

* 11. What bottlenecks do you experience in your AI/ML workflows?

Question Title

* 12. What aspects of AI/ML that you use heavily are expensive relative to the value created?

Question Title

* 13. Which data handoffs are cumbersome or otherwise problematic?

Question Title

* 14. What specific types of models do you use, and which present implementation challenges?

Question Title

* 15. What specific types of frameworks do you use?

Question Title

* 16. What are the biggest pain points in your AI/ML related workflows?

Question Title

* 17. Are there handoffs to or from your AI/ML processes where the interfaces or formats are poorly defined or proprietary in nature?

Question Title

* 18. How would you assess the potential of AI and ML to drive value for your organization?

Question Title

* 19. Please rank the areas in terms of their potential to drive value for your organization

Question Title

* 20. What insight about your workflow or your organization do you currently wish you had the most?

Question Title

* 21. Please rank the biggest interop challenges you see in the area of AI/ML?

Question Title

* 22. What is the bigger interoperability challenge among AI/ML applications?

Question Title

* 23. What issues exist with AI/ML that do not directly relate to interoperability, but where standardized practices might be useful?

Question Title

* 24. What do you see as the biggest obstacle to deploying AI/ML at scale in your line of work?

Question Title

* 25. In what specific areas might a public data register or registration authority be helpful?

Question Title

* 26. Do you find discovery of available services to be a major challenge in the AI/ML area?

Question Title

* 27. Would a central register help users to discover available services?

Question Title

* 28. What opportunities do you see in AI/ML helping to enforce content producers' intellectual property rights?

Question Title

* 29. What aspects of AI ethics are you most worried about?

Question Title

* 30. Are you worried that you and/or your colleagues' jobs would be at risk if AI/ML systems were deployed at scale in your organization?

Question Title

* 31. If you are willing to participate in a follow-up survey, please provide your email address.