** The following summary was mainly generated by the GPT-4-1106-preview model in CotranslatorAI.
About the Zoom Call
Question 1 (40 responses): Please indicate your primary field of activity:
Question 2 (36 responses): What information/insights were you expecting to get from the call?
Respondents were anticipating a variety of insights from the call, primarily centered around the integration and impact of artificial intelligence (AI) in the translation industry. Key expectations included:
1. Updates on AI translation tools: Participants were looking forward to learning about the latest advancements in AI translation technologies, such as Cotranslator AI, OpenAI, and specifically the developments in GPT-4 and other generative AI models.
2. Experiences and applications: There was a strong interest in hearing about the practical experiences of fellow translators with AI tools, including how these technologies are currently being used in the industry and the new opportunities they present.
3. Industry impact: Respondents were curious about the broader effects of AI on the translation market, including the potential for AI to change current workflows, create new job opportunities, or even pose a threat to traditional translation roles.
4. Professional development: Many sought advice from more experienced translators on how to adapt to the evolving landscape, with a focus on understanding the challenges, limitations, and best practices for using AI in translation.
5. Future outlook: There was a desire to gain insights into the future possibilities that AI might offer to translators, including how it could transform the profession and what new skills might be required.
6. Knowledge sharing: The call was seen as an opportunity for an open exchange of ideas and experiences, with some participants looking for a general overview due to their limited experience with AI tools.
7. Curiosity and learning: A few respondents had no specific expectations but attended out of curiosity and a desire to stay informed about the current status and potential of AI in translation.
Overall, the respondents showed a keen interest in understanding the evolving role of AI in translation, seeking both strategic insights and practical knowledge to navigate the changes in their profession.
Question 3 (35 responses): To what extent did you get what you expected from the call?
Based on the responses to the additional question regarding the extent to which participants’ expectations were met during the Zoom call, the following executive summary can be provided:
1. Exceeded Expectations: A number of respondents felt that the call not only met but exceeded their expectations, providing additional insights and comments they had not anticipated.
2. High Satisfaction: Many participants reported a high degree of satisfaction, with some indicating a 100% fulfillment of their expectations.
3. Comprehensive Coverage: Several respondents appreciated that all topics were thoroughly examined, suggesting a well-structured and comprehensive discussion.
4. Interest in Follow-up: At least one participant expressed interest in scheduling another session to continue the conversation, indicating the value and engagement the call provided.
5. Diverse Perspectives: The call was noted for presenting a variety of viewpoints, which participants found very interesting and informative.
6. Practical Demonstrations: The live walkthroughs, particularly of GPT models, were highlighted as particularly helpful, suggesting that practical demonstrations were a valuable component of the call.
7. API Platform Consideration: Insights into the API platform led at least one participant to consider its use more extensively, indicating the call’s influence on future professional decisions.
8. Changed Perceptions: The discussion helped to shift some participants’ views on AI, with at least one no longer seeing AI as a job killer but rather as a prompt for continuous learning.
9. Peer Reassurance: Hearing from colleagues about their challenges with adapting to new technologies provided reassurance to some participants, highlighting the importance of community support.
10. Mixed Engagement: While many respondents were fully engaged, a few noted limitations such as missing parts of the call or technical difficulties, suggesting areas for improvement in accessibility and communication.
Overall, the call was well-received with participants gaining valuable insights, though some experienced challenges in participation or had unmet expectations.
Question 4 (33 responses): How could we have improved the call?
1. Time Management: Several participants suggested setting a clear end time for the call and possibly shortening its duration to maintain engagement and prevent fatigue.
2. Content Depth: There was interest in delving deeper into specific topics, such as the technical aspects of AI in translation and discussing practical success cases.
3. Future Roadmap: Participants desired more information on the development trajectory of AI translation technologies and emerging industry trends.
4. Preparedness: Some respondents would have appreciated receiving specific topics and questions beforehand to better prepare for meaningful contributions.
5. Recording Access: Providing a recording of the meeting was mentioned as a way to accommodate those who could not attend or wished to review the content.
6. Meeting Structure: Suggestions included having a rough agenda with a mix of presentations and open discussions, as well as better crowd control and moderation to ensure a focused and orderly conversation.
7. Participant Engagement: Encouraging quieter attendees to participate and clarifying the process for input, such as using the “raise hand” feature, could improve interaction.
8. Breaks: Implementing longer breaks during the session was recommended to maintain attendee comfort and attentiveness.
9. Spontaneity vs. Structure: While the spontaneity of the discussion was appreciated, some suggested that having a few prepared topics could enhance the call without sacrificing the dynamic nature of the conversation.
10. Discipline: A call for stricter discipline was noted, particularly to manage disruptive behavior and ensure a respectful environment for all participants.
Overall, while many found the call satisfactory, these points reflect areas where participants felt improvements could enhance the experience and effectiveness of future calls.
Question 5 (41 responses): If we schedule another call next month, how likely would you be to attend the live call or watch the recording?
Question 6 (31 responses): If we schedule another call next month, what would you like the topic to be?
1. AI Empowerment: Participants expressed a desire to learn how to ensure AI works for them, rather than the other way around, emphasizing the need for control and effective utilization of AI in their work.
2. AI Developments: There was interest in continuing discussions on AI advancements, with a focus on any significant changes that occur within the next month, particularly in the context of translation.
3. Comparative Analysis: Some respondents requested a deeper comparison between the outputs of Large Language Models (LLMs) and standard Neural Machine Translation (NMT) models to better understand their respective capabilities.
4. Future Roadmaps: There was a call for more information on the future development of AI translation technologies, including emerging trends and the strategic direction of tools like CotranslatorAI.
5. Pricing Strategies: Respondents were interested in discussing how translators can maintain or increase their rates in the face of AI-driven price pressures, including justifying the value of AI tools like ChatGPT and Cotranslator.
6. Practical Application: A number of participants sought more practical advice on using CotranslatorAI, with some new users requesting a focus on hands-on tips and demonstrations.
7. AI Updates: There was a request for updates on the situation with ChatGPT and other AI tools, recognizing that the landscape can change rapidly.
8. Efficiency and Speed: Participants were interested in discussing how AI can save time in their daily workflows, with a desire for use cases and discussions on the speed of AI translation.
9. Sharing Best Practices: Respondents wanted to share and discuss effective prompts and strategies for using AI tools, learning from each other’s experiences.
10. Industry Impact and Human Value: There was a clear interest in exploring the future of the translation business in the age of AI, including how to demonstrate the added value of human translators to clients and integrate AI with existing tools like CAT (Computer-Assisted Translation).
Overall, the respondents are looking for a blend of strategic insights, practical knowledge, and community discussion to better understand and leverage AI in their profession.
Question 7 (14 responses): If you have any other feedback about the call, please leave it here.
1. Appreciation for the Host: Respondents expressed gratitude towards Steven for his efforts in organizing the call, highlighting his competence and courteous moderation, as well as his passion for the translation profession.
2. Positive Reception of the Session: The call was well-received, with participants finding it engaging and informative, even those who were initially weary of AI-related discussions.
3. Suggestions for Improvement: Some feedback suggested that future sessions could benefit from more structured interaction, such as live survey questions, to ensure broader participation, especially from those who are less inclined to speak up or be on camera.
4. Desire for Focused Discussion: A recommendation was made to keep the conversation more directed, avoiding tangential discussions that may not be as relevant to the core topic of AI in translation.
5. Acknowledgment of Contributions: Attendees appreciated the contributions from other participants, which provided valuable insights, particularly for those new to using AI in translation.
6. Recognition of Tools and Contributors: There was a specific acknowledgment for Mr. Okhvat and the use of TransTools, indicating the practical value of tools and their creators in the translation industry.
7. Enjoyment and Fun: The call was not only educational but also enjoyable, indicating that the balance between learning and engagement was well-maintained.
8. Encouragement for Continuation: The overall positive feedback and thanks suggest that participants would welcome similar future events.
In summary, the Zoom call was a success, with participants valuing the knowledge shared, the hosting style, and the interactive format. They offered constructive suggestions for enhancing participant engagement and maintaining focus during discussions.
Question 8 (40 responses): Are you a CotranslatorAI user?
Question 9 (20 responses): If you don’t use CotranslatorAI or don’t use it very much, what is keeping you from using it or using it more?
1. Preference for Competing Tools: Some respondents prefer using other AI translation tools like DeepL, particularly for specific language pairs such as English to Russian, due to perceived superior output quality.
2. Limited Use Cases: A few participants indicated that their current roles, which focus more on reviewing or editing, do not necessitate extensive use of CotranslatorAI or similar tools.
3. Technical Complexity: The technical nature of CotranslatorAI is a barrier for some, who find it overly complex and challenging to integrate into their workflow.
4. Learning Curve: Respondents expressed a need for time to learn and adapt to the tool, with some having only recently become aware of it or having difficulty finding time to engage with it.
5. Client Restrictions: Certain translators are restricted by their clients’ policies, which do not allow the use of machine translation (MT) and AI tools.
6. Quality and Speed Concerns: Some users have reservations about the speed and quality of translations produced by AI engines, including CotranslatorAI, although it is acknowledged as a good software in itself.
7. Connectivity and Responsiveness: Issues with slower connection speeds and less satisfactory interactions compared to other AI services like ChatGPT have been noted.
8. Industry Relevance: One respondent mentioned a lack of translation work as a reason for not using the tool, implying that the relevance of such tools may vary based on individual circumstances within the industry.
9. Integration Challenges: There is a lack of understanding on how to effectively integrate AI translation tools into standard translation processes, which acts as a deterrent for some translators.
10. Potential and Anonymity: Despite the challenges, there is recognition of the potential benefits of using CotranslatorAI, especially appreciating the anonymity feature when submitting text via the API.
Overall, while there is interest and acknowledgment of the potential benefits of AI in translation, barriers such as preference for other tools, technical complexity, learning requirements, client restrictions, and integration challenges are preventing some translators from fully embracing CotranslatorAI.
Question 10 (32 responses): If you do use CotranslatorAI, what use cases do you use it for?
Question 11 (23 responses): What do you think we should prioritize to improve CotranslatorAI itself or other aspects of the user experience?
1. Integration with CAT Tools: Respondents expressed a desire for CotranslatorAI to be available as an add-in or plug-in for Computer-Assisted Translation (CAT) tools like Trados and MemoQ, facilitating a more seamless workflow.
2. Handling Layouts: There is a need for CotranslatorAI to automatically handle document layouts, particularly for formats like HTML, to save time and streamline the translation process.
3. Prompt Library and Customization: The creation of a prompt library and the ability to customize prompts with shortcuts were suggested to enhance efficiency and user experience.
4. Automation and Interaction: Users are looking for ways to automate interactions with other software and databases, reducing manual copying and improving the translation process.
5. Speed and Quality: Improvements in the speed of translation without compromising quality were mentioned, with a comparison to competitors like DeepL.
6. Subtitling Adaptation: There is interest in adapting CotranslatorAI for specific translation fields such as subtitling, indicating a demand for AI tools in niche markets.
7. Knowledge Base and Learning: The ability to upload files to construct a personalized knowledge base and the provision of a written manual or suggested workflows were highlighted as areas for improvement.
8. AI Assistant and OCR: Integrating an AI assistant within the app and adding Optical Character Recognition (OCR) capabilities for PDFs or images were suggested to enhance functionality.
9. User Interface Enhancements: Suggestions for user interface improvements included adding numbering to prompts for easy reference and predefined prompt shortcuts.
10. Comprehensive Accessibility: A general desire for rolling all features into a single, accessible program with a simplified user interface was noted, along with the need for more space for adjusted prompts.
Overall, respondents are looking for a more integrated, automated, and user-friendly experience with CotranslatorAI, emphasizing the importance of quality translations and efficient workflows.
Question 12: If you use CotranslatorAI, would you be willing to leave a named testimonial that we can use in our marketing? If so, please enter it below, along with your name, job title, language pair, etc.
Thank you to those who provided testimonials!
Question 13 (13 responses): If you have any other feedback about CotranslatorAI, please leave it here.
1. Positive Reception: Users expressed gratitude and appreciation for CotranslatorAI, highlighting its usefulness in their translation work and its role in encouraging them to stay current with technological advancements.
2. Acknowledgment of Effort: There was a recognition of the effort and time invested by the developers, particularly Steven and Stanislav, and gratitude for making the software available for free.
3. Platform Availability: A request was made for the expansion of the software’s availability, specifically the creation of a version compatible with Mac operating systems.
4. Community Engagement: Respondents valued the opportunity for community engagement and knowledge exchange provided by the project and the call, with some expressing interest in contributing to future discussions.
5. User Interface Suggestions: Feedback included a suggestion to improve the user interface, such as simplifying the token counter and adding a feature to directly add prompts to a folder.
6. Communication and Updates: Users appreciated the proactive communication from the developers, including emails with information and updates about the software.
7. No Additional Feedback: Some respondents did not have further feedback at the time, indicating either satisfaction with the tool as it is or a lack of specific comments.
8. Desire for Continued Development: The overall sentiment was one of encouragement for the continued development and improvement of CotranslatorAI, with users looking forward to future enhancements.
This summary reflects the respondents’ overall positive experience with CotranslatorAI, their appreciation for the developers’ efforts, and their desire for continued progress and community involvement.