chat_log.txt

You: How are you?
Chat GPT: I'm a large language model trained by OpenAI. I don't have feelings or emotions, so I can't experience things like happiness or sadness. I'm here to provide information and answer questions to the best of my ability. Is there anything I can help you with?
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

14:30:58 - You:
 Hi, hello. so,

14:34:46 - You:
 Oh, brother.

Chat GPT: Main Points:
- None

Action Items:
- None

Recent Summary:
- No recent points made.
You: How are you?
Chat GPT: I'm just a computer program, so I don't have the ability to have feelings or emotions like a human does. I exist to provide information and answer questions to the best of my ability. Is there something specific you'd like to know about?
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

14:30:58 - You:
 Hi, hello. so,

14:34:46 - You:
 Oh, brother.

14:36:26 - You:
 Professional.

Chat GPT: Main Points:
- No main points were discussed.

Action Items:
- No action items were discussed.

Recent Summary:
- None discussed.
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

14:30:58 - You:
 Hi, hello. so,

14:34:46 - You:
 Oh, brother.

14:36:26 - You:
 Professional.

Chat GPT: Main Points:
- No main points were discussed.

Action Items:
- No action items were discussed.

Recent Summary:
- None discussed.
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

14:30:58 - You:
 Hi, hello. so,

14:34:46 - You:
 Oh, brother.

14:36:26 - You:
 Professional.

Chat GPT: Main Points:
- No main points were discussed.

Action Items:
- No action items were discussed.

Recent Summary:
- None discussed.
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

14:30:58 - You:
 Hi, hello. so,

14:34:46 - You:
 Oh, brother.

14:36:26 - You:
 Professional.

Chat GPT: Main Points:
- No main points were discussed.

Action Items:
- No action items were discussed.

Recent Summary:
- None discussed.
You: How are you?
Chat GPT: I'm just a computer program, so I don't have feelings or emotions like a human does. I exist to provide information and answer questions to the best of my ability. Is there something specific you would like to know? I'm here to help.
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

18:29:55 - You:
 Hi, welcome to the meeting. here, I'm going to discuss some Italian points about gpt3. In this call. And also going to mention some action items. For executing this project.

18:30:26 - You:
Yeah. so with more than 2 decades of x experience in the application, lifecycle management space, on the regularly, help companies with their digital transformation by using their sort of products, given the fact that the, we are an agile team and the use can then And the chaos way of working. Standard calls are really important to us and in fact, you wake up in one of our employees at midnight, they will swear an oath of legions to the stand-up call. The other thing we saw by his matrix, observable metrics like burndown charts etc are one thing They have been doing that for a for so long. Yeah, good at it. How about qualitative metrics? How do you measure the qualitative stuff like team, cohesion teams and sentiment objective priority, and then use them to manage projects. Daily stand. Up calls are an excellent way of capturing this information. the COVID-19 forcing us to move, all start of meetings, to zoom, hang outs and Microsoft teams, it became an opportunity for us to explore these use cases, The first step to attempting, those large problem is to capture the summary of the meeting. What we describe. Next is a local experiment, we conducted and it results by using Google Speech to text and gpt3. so what we do is the breakdown, the problem into three broad steps, one is transcribe the audio Um, Given that all the speech is over multiple channels. We found that Google TTS engine did an excellent job. to run rapid experiments, we chose to use a chrome extension need and scripts which dumps that and script of the entire conversation into a Google word talk. For further processing, We chose a sample from our daily calls for this experiment. And then be three process. The text all our visions and reprocessing work to improve performance of subsequent steps, other than the usual, special character removal nonsense. Word removal. Um, There were other steps that we have included in our code. It's funny that for most for something, that is a privy, think a lot more about it in post. And then we run the, the summary using gpt3. This is what typically shines given a meeting transcript, because he doesn't see engine cannot extract relevant. Details, that can be acted upon anything. That means that we are not just looking at a summary button who button proper actionable summary. What follows is a discussion of experiment and subsequent results. We have like kids with shiny toys after the first experiment. We want to take gp3 out for a ride with zero pre-work. That means that we wanted to understand how robust the solution was in its official state. The STD engine itself was itself a source of error, but we found the jbt was able to overcome this problem at first glance. Um, we give the typical status stand up, would involve a lead, directing the flow of the conversation. With each team member giving his work update in done. We took such an extract from the transcript of our own team picture below. It is a 600 words transcript, which is filled with broad updates on the AI products that that we worked on the day before. He then asked gp3 to summarize, this text asus the DaVinci engine um, there are team as well. Well, working on the first release of the radio app and now waiting for approval from the red desk representative that emails also working on care on for next release. That is awesome. We were able to condense our conversation from 500 word to just 50 words without Mom much work, but there is an issue. The thing with the minutes of making is to capture who did what extremely well. These details are important so that one can create assign the work to create an assign the work. What we have is good for an email but not something. We can perform subsequent processing online, agile card creation card moment on this one board and etc.

Chat GPT: Main Points:
- Discussion of using Google Speech to Text and GPT-3 to create summaries of meeting transcripts
- Experiment using a sample of daily calls
- Results of using GPT-3 to create a summary of a team's work updates

Action Items:
- Continue using GPT-3 to create summaries of meeting transcripts
- Explore other ways to improve the performance of the GPT-3 summaries

Recent Summary:
- Experiment using GPT-3 to create summaries of meeting transcripts was successful in condensing a 500-word transcript to 50 words.
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

18:29:55 - You:
 Hi, welcome to the meeting. here, I'm going to discuss some Italian points about gpt3. In this call. And also going to mention some action items. For executing this project.

18:30:26 - You:
Yeah. so with more than 2 decades of x experience in the application, lifecycle management space, on the regularly, help companies with their digital transformation by using their sort of products, given the fact that the, we are an agile team and the use can then And the chaos way of working. Standard calls are really important to us and in fact, you wake up in one of our employees at midnight, they will swear an oath of legions to the stand-up call. The other thing we saw by his matrix, observable metrics like burndown charts etc are one thing They have been doing that for a for so long. Yeah, good at it. How about qualitative metrics? How do you measure the qualitative stuff like team, cohesion teams and sentiment objective priority, and then use them to manage projects. Daily stand. Up calls are an excellent way of capturing this information. the COVID-19 forcing us to move, all start of meetings, to zoom, hang outs and Microsoft teams, it became an opportunity for us to explore these use cases, The first step to attempting, those large problem is to capture the summary of the meeting. What we describe. Next is a local experiment, we conducted and it results by using Google Speech to text and gpt3. so what we do is the breakdown, the problem into three broad steps, one is transcribe the audio Um, Given that all the speech is over multiple channels. We found that Google TTS engine did an excellent job. to run rapid experiments, we chose to use a chrome extension need and scripts which dumps that and script of the entire conversation into a Google word talk. For further processing, We chose a sample from our daily calls for this experiment. And then be three process. The text all our visions and reprocessing work to improve performance of subsequent steps, other than the usual, special character removal nonsense. Word removal. Um, There were other steps that we have included in our code. It's funny that for most for something, that is a privy, think a lot more about it in post. And then we run the, the summary using gpt3. This is what typically shines given a meeting transcript, because he doesn't see engine cannot extract relevant. Details, that can be acted upon anything. That means that we are not just looking at a summary button who button proper actionable summary. What follows is a discussion of experiment and subsequent results. We have like kids with shiny toys after the first experiment. We want to take gp3 out for a ride with zero pre-work. That means that we wanted to understand how robust the solution was in its official state. The STD engine itself was itself a source of error, but we found the jbt was able to overcome this problem at first glance. Um, we give the typical status stand up, would involve a lead, directing the flow of the conversation. With each team member giving his work update in done. We took such an extract from the transcript of our own team picture below. It is a 600 words transcript, which is filled with broad updates on the AI products that that we worked on the day before. He then asked gp3 to summarize, this text asus the DaVinci engine um, there are team as well. Well, working on the first release of the radio app and now waiting for approval from the red desk representative that emails also working on care on for next release. That is awesome. We were able to condense our conversation from 500 word to just 50 words without Mom much work, but there is an issue. The thing with the minutes of making is to capture who did what extremely well. These details are important so that one can create assign the work to create an assign the work. What we have is good for an email but not something. We can perform subsequent processing online, agile card creation card moment on this one board and etc.

Chat GPT: Main Points:
- Discussion of using Google Speech to Text and GPT-3 to create summaries of meeting transcripts
- Experiment using a sample of daily calls
- Results of using GPT-3 to create a summary of a team's work updates

Action Items:
- Continue using GPT-3 to create summaries of meeting transcripts
- Explore other ways to improve the performance of the GPT-3 summaries

Recent Summary:
- Experiment using GPT-3 to create summaries of meeting transcripts was successful in condensing a 500-word transcript to 50 words.
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

18:29:55 - You:
 Hi, welcome to the meeting. here, I'm going to discuss some Italian points about gpt3. In this call. And also going to mention some action items. For executing this project.

18:30:26 - You:
Yeah. so with more than 2 decades of x experience in the application, lifecycle management space, on the regularly, help companies with their digital transformation by using their sort of products, given the fact that the, we are an agile team and the use can then And the chaos way of working. Standard calls are really important to us and in fact, you wake up in one of our employees at midnight, they will swear an oath of legions to the stand-up call. The other thing we saw by his matrix, observable metrics like burndown charts etc are one thing They have been doing that for a for so long. Yeah, good at it. How about qualitative metrics? How do you measure the qualitative stuff like team, cohesion teams and sentiment objective priority, and then use them to manage projects. Daily stand. Up calls are an excellent way of capturing this information. the COVID-19 forcing us to move, all start of meetings, to zoom, hang outs and Microsoft teams, it became an opportunity for us to explore these use cases, The first step to attempting, those large problem is to capture the summary of the meeting. What we describe. Next is a local experiment, we conducted and it results by using Google Speech to text and gpt3. so what we do is the breakdown, the problem into three broad steps, one is transcribe the audio Um, Given that all the speech is over multiple channels. We found that Google TTS engine did an excellent job. to run rapid experiments, we chose to use a chrome extension need and scripts which dumps that and script of the entire conversation into a Google word talk. For further processing, We chose a sample from our daily calls for this experiment. And then be three process. The text all our visions and reprocessing work to improve performance of subsequent steps, other than the usual, special character removal nonsense. Word removal. Um, There were other steps that we have included in our code. It's funny that for most for something, that is a privy, think a lot more about it in post. And then we run the, the summary using gpt3. This is what typically shines given a meeting transcript, because he doesn't see engine cannot extract relevant. Details, that can be acted upon anything. That means that we are not just looking at a summary button who button proper actionable summary. What follows is a discussion of experiment and subsequent results. We have like kids with shiny toys after the first experiment. We want to take gp3 out for a ride with zero pre-work. That means that we wanted to understand how robust the solution was in its official state. The STD engine itself was itself a source of error, but we found the jbt was able to overcome this problem at first glance. Um, we give the typical status stand up, would involve a lead, directing the flow of the conversation. With each team member giving his work update in done. We took such an extract from the transcript of our own team picture below. It is a 600 words transcript, which is filled with broad updates on the AI products that that we worked on the day before. He then asked gp3 to summarize, this text asus the DaVinci engine um, there are team as well. Well, working on the first release of the radio app and now waiting for approval from the red desk representative that emails also working on care on for next release. That is awesome. We were able to condense our conversation from 500 word to just 50 words without Mom much work, but there is an issue. The thing with the minutes of making is to capture who did what extremely well. These details are important so that one can create assign the work to create an assign the work. What we have is good for an email but not something. We can perform subsequent processing online, agile card creation card moment on this one board and etc.

Chat GPT: Main Points:
- Discussion of using Google Speech to Text and GPT-3 to create summaries of meeting transcripts
- Experiment using a sample of daily calls
- Results of using GPT-3 to create a summary of a team's work updates

Action Items:
- Continue using GPT-3 to create summaries of meeting transcripts
- Explore other ways to improve the performance of the GPT-3 summaries

Recent Summary:
- Experiment using GPT-3 to create summaries of meeting transcripts was successful in condensing a 500-word transcript to 50 words.
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

18:29:55 - You:
 Hi, welcome to the meeting. here, I'm going to discuss some Italian points about gpt3. In this call. And also going to mention some action items. For executing this project.

18:30:26 - You:
Yeah. so with more than 2 decades of x experience in the application, lifecycle management space, on the regularly, help companies with their digital transformation by using their sort of products, given the fact that the, we are an agile team and the use can then And the chaos way of working. Standard calls are really important to us and in fact, you wake up in one of our employees at midnight, they will swear an oath of legions to the stand-up call. The other thing we saw by his matrix, observable metrics like burndown charts etc are one thing They have been doing that for a for so long. Yeah, good at it. How about qualitative metrics? How do you measure the qualitative stuff like team, cohesion teams and sentiment objective priority, and then use them to manage projects. Daily stand. Up calls are an excellent way of capturing this information. the COVID-19 forcing us to move, all start of meetings, to zoom, hang outs and Microsoft teams, it became an opportunity for us to explore these use cases, The first step to attempting, those large problem is to capture the summary of the meeting. What we describe. Next is a local experiment, we conducted and it results by using Google Speech to text and gpt3. so what we do is the breakdown, the problem into three broad steps, one is transcribe the audio Um, Given that all the speech is over multiple channels. We found that Google TTS engine did an excellent job. to run rapid experiments, we chose to use a chrome extension need and scripts which dumps that and script of the entire conversation into a Google word talk. For further processing, We chose a sample from our daily calls for this experiment. And then be three process. The text all our visions and reprocessing work to improve performance of subsequent steps, other than the usual, special character removal nonsense. Word removal. Um, There were other steps that we have included in our code. It's funny that for most for something, that is a privy, think a lot more about it in post. And then we run the, the summary using gpt3. This is what typically shines given a meeting transcript, because he doesn't see engine cannot extract relevant. Details, that can be acted upon anything. That means that we are not just looking at a summary button who button proper actionable summary. What follows is a discussion of experiment and subsequent results. We have like kids with shiny toys after the first experiment. We want to take gp3 out for a ride with zero pre-work. That means that we wanted to understand how robust the solution was in its official state. The STD engine itself was itself a source of error, but we found the jbt was able to overcome this problem at first glance. Um, we give the typical status stand up, would involve a lead, directing the flow of the conversation. With each team member giving his work update in done. We took such an extract from the transcript of our own team picture below. It is a 600 words transcript, which is filled with broad updates on the AI products that that we worked on the day before. He then asked gp3 to summarize, this text asus the DaVinci engine um, there are team as well. Well, working on the first release of the radio app and now waiting for approval from the red desk representative that emails also working on care on for next release. That is awesome. We were able to condense our conversation from 500 word to just 50 words without Mom much work, but there is an issue. The thing with the minutes of making is to capture who did what extremely well. These details are important so that one can create assign the work to create an assign the work. What we have is good for an email but not something. We can perform subsequent processing online, agile card creation card moment on this one board and etc.

Chat GPT: Main Points:
- Discussion of using Google Speech to Text and GPT-3 to create summaries of meeting transcripts
- Experiment using a sample of daily calls
- Results of using GPT-3 to create a summary of a team's work updates

Action Items:
- Continue using GPT-3 to create summaries of meeting transcripts
- Explore other ways to improve the performance of the GPT-3 summaries

Recent Summary:
- Experiment using GPT-3 to create summaries of meeting transcripts was successful in condensing a 500-word transcript to 50 words.
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of most recent point made>

        Transcript:

        Transcript

18:29:55 - You:
 Hi, welcome to the meeting. here, I'm going to discuss some Italian points about gpt3. In this call. And also going to mention some action items. For executing this project.

18:30:26 - You:
Yeah. so with more than 2 decades of x experience in the application, lifecycle management space, on the regularly, help companies with their digital transformation by using their sort of products, given the fact that the, we are an agile team and the use can then And the chaos way of working. Standard calls are really important to us and in fact, you wake up in one of our employees at midnight, they will swear an oath of legions to the stand-up call. The other thing we saw by his matrix, observable metrics like burndown charts etc are one thing They have been doing that for a for so long. Yeah, good at it. How about qualitative metrics? How do you measure the qualitative stuff like team, cohesion teams and sentiment objective priority, and then use them to manage projects. Daily stand. Up calls are an excellent way of capturing this information. the COVID-19 forcing us to move, all start of meetings, to zoom, hang outs and Microsoft teams, it became an opportunity for us to explore these use cases, The first step to attempting, those large problem is to capture the summary of the meeting. What we describe. Next is a local experiment, we conducted and it results by using Google Speech to text and gpt3. so what we do is the breakdown, the problem into three broad steps, one is transcribe the audio Um, Given that all the speech is over multiple channels. We found that Google TTS engine did an excellent job. to run rapid experiments, we chose to use a chrome extension need and scripts which dumps that and script of the entire conversation into a Google word talk. For further processing, We chose a sample from our daily calls for this experiment. And then be three process. The text all our visions and reprocessing work to improve performance of subsequent steps, other than the usual, special character removal nonsense. Word removal. Um, There were other steps that we have included in our code. It's funny that for most for something, that is a privy, think a lot more about it in post. And then we run the, the summary using gpt3. This is what typically shines given a meeting transcript, because he doesn't see engine cannot extract relevant. Details, that can be acted upon anything. That means that we are not just looking at a summary button who button proper actionable summary. What follows is a discussion of experiment and subsequent results. We have like kids with shiny toys after the first experiment. We want to take gp3 out for a ride with zero pre-work. That means that we wanted to understand how robust the solution was in its official state. The STD engine itself was itself a source of error, but we found the jbt was able to overcome this problem at first glance. Um, we give the typical status stand up, would involve a lead, directing the flow of the conversation. With each team member giving his work update in done. We took such an extract from the transcript of our own team picture below. It is a 600 words transcript, which is filled with broad updates on the AI products that that we worked on the day before. He then asked gp3 to summarize, this text asus the DaVinci engine um, there are team as well. Well, working on the first release of the radio app and now waiting for approval from the red desk representative that emails also working on care on for next release. That is awesome. We were able to condense our conversation from 500 word to just 50 words without Mom much work, but there is an issue. The thing with the minutes of making is to capture who did what extremely well. These details are important so that one can create assign the work to create an assign the work. What we have is good for an email but not something. We can perform subsequent processing online, agile card creation card moment on this one board and etc.

Chat GPT: [Status Code] 500 | [Response Text] {"detail":"Hmm...something seems to have gone wrong. Maybe try me again in a little bit."}
You: How are you?
Chat GPT: I am a large language model trained by OpenAI. I am not capable of experiencing emotions or physical sensations. I exist solely to provide information and answer questions to the best of my ability based on my training. Is there something specific I can help you with?
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of the transcript>

        Transcript:

        Transcript

18:29:55 - You:
 Hi, welcome to the meeting. here, I'm going to discuss some Italian points about gpt3. In this call. And also going to mention some action items. For executing this project.

18:30:26 - You:
Yeah. so with more than 2 decades of x experience in the application, lifecycle management space, on the regularly, help companies with their digital transformation by using their sort of products, given the fact that the, we are an agile team and the use can then And the chaos way of working. Standard calls are really important to us and in fact, you wake up in one of our employees at midnight, they will swear an oath of legions to the stand-up call. The other thing we saw by his matrix, observable metrics like burndown charts etc are one thing They have been doing that for a for so long. Yeah, good at it. How about qualitative metrics? How do you measure the qualitative stuff like team, cohesion teams and sentiment objective priority, and then use them to manage projects. Daily stand. Up calls are an excellent way of capturing this information. the COVID-19 forcing us to move, all start of meetings, to zoom, hang outs and Microsoft teams, it became an opportunity for us to explore these use cases, The first step to attempting, those large problem is to capture the summary of the meeting. What we describe. Next is a local experiment, we conducted and it results by using Google Speech to text and gpt3. so what we do is the breakdown, the problem into three broad steps, one is transcribe the audio Um, Given that all the speech is over multiple channels. We found that Google TTS engine did an excellent job. to run rapid experiments, we chose to use a chrome extension need and scripts which dumps that and script of the entire conversation into a Google word talk. For further processing, We chose a sample from our daily calls for this experiment. And then be three process. The text all our visions and reprocessing work to improve performance of subsequent steps, other than the usual, special character removal nonsense. Word removal. Um, There were other steps that we have included in our code. It's funny that for most for something, that is a privy, think a lot more about it in post. And then we run the, the summary using gpt3. This is what typically shines given a meeting transcript, because he doesn't see engine cannot extract relevant. Details, that can be acted upon anything. That means that we are not just looking at a summary button who button proper actionable summary. What follows is a discussion of experiment and subsequent results. We have like kids with shiny toys after the first experiment. We want to take gp3 out for a ride with zero pre-work. That means that we wanted to understand how robust the solution was in its official state. The STD engine itself was itself a source of error, but we found the jbt was able to overcome this problem at first glance. Um, we give the typical status stand up, would involve a lead, directing the flow of the conversation. With each team member giving his work update in done. We took such an extract from the transcript of our own team picture below. It is a 600 words transcript, which is filled with broad updates on the AI products that that we worked on the day before. He then asked gp3 to summarize, this text asus the DaVinci engine um, there are team as well. Well, working on the first release of the radio app and now waiting for approval from the red desk representative that emails also working on care on for next release. That is awesome. We were able to condense our conversation from 500 word to just 50 words without Mom much work, but there is an issue. The thing with the minutes of making is to capture who did what extremely well. These details are important so that one can create assign the work to create an assign the work. What we have is good for an email but not something. We can perform subsequent processing online, agile card creation card moment on this one board and etc.

Chat GPT: Main Points:
- Discussion of using GPT-3 to summarize meeting transcripts
- Experiments using Google Speech-to-Text and GPT-3
- Discussion of results

Action Items:
- Use GPT-3 to create actionable summaries of meeting transcripts
- Explore using GPT-3 without pre-work

Recent Summary:
- Meeting to discuss use of GPT-3 for summarizing meeting transcripts, with experiments and results discussed.
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of the transcript>

        Transcript:

        Transcript

18:29:55 - You:
 Hi, welcome to the meeting. here, I'm going to discuss some Italian points about gpt3. In this call. And also going to mention some action items. For executing this project.

18:30:26 - You:
Yeah. so with more than 2 decades of x experience in the application, lifecycle management space, on the regularly, help companies with their digital transformation by using their sort of products, given the fact that the, we are an agile team and the use can then And the chaos way of working. Standard calls are really important to us and in fact, you wake up in one of our employees at midnight, they will swear an oath of legions to the stand-up call. The other thing we saw by his matrix, observable metrics like burndown charts etc are one thing They have been doing that for a for so long. Yeah, good at it. How about qualitative metrics? How do you measure the qualitative stuff like team, cohesion teams and sentiment objective priority, and then use them to manage projects. Daily stand. Up calls are an excellent way of capturing this information. the COVID-19 forcing us to move, all start of meetings, to zoom, hang outs and Microsoft teams, it became an opportunity for us to explore these use cases, The first step to attempting, those large problem is to capture the summary of the meeting. What we describe. Next is a local experiment, we conducted and it results by using Google Speech to text and gpt3. so what we do is the breakdown, the problem into three broad steps, one is transcribe the audio Um, Given that all the speech is over multiple channels. We found that Google TTS engine did an excellent job. to run rapid experiments, we chose to use a chrome extension need and scripts which dumps that and script of the entire conversation into a Google word talk. For further processing, We chose a sample from our daily calls for this experiment. And then be three process. The text all our visions and reprocessing work to improve performance of subsequent steps, other than the usual, special character removal nonsense. Word removal. Um, There were other steps that we have included in our code. It's funny that for most for something, that is a privy, think a lot more about it in post. And then we run the, the summary using gpt3. This is what typically shines given a meeting transcript, because he doesn't see engine cannot extract relevant. Details, that can be acted upon anything. That means that we are not just looking at a summary button who button proper actionable summary. What follows is a discussion of experiment and subsequent results. We have like kids with shiny toys after the first experiment. We want to take gp3 out for a ride with zero pre-work. That means that we wanted to understand how robust the solution was in its official state. The STD engine itself was itself a source of error, but we found the jbt was able to overcome this problem at first glance. Um, we give the typical status stand up, would involve a lead, directing the flow of the conversation. With each team member giving his work update in done. We took such an extract from the transcript of our own team picture below. It is a 600 words transcript, which is filled with broad updates on the AI products that that we worked on the day before. He then asked gp3 to summarize, this text asus the DaVinci engine um, there are team as well. Well, working on the first release of the radio app and now waiting for approval from the red desk representative that emails also working on care on for next release. That is awesome. We were able to condense our conversation from 500 word to just 50 words without Mom much work, but there is an issue. The thing with the minutes of making is to capture who did what extremely well. These details are important so that one can create assign the work to create an assign the work. What we have is good for an email but not something. We can perform subsequent processing online, agile card creation card moment on this one board and etc.

Chat GPT: [Status Code] 500 | [Response Text] {"detail":"Hmm...something seems to have gone wrong. Maybe try me again in a little bit."}
You: How are you?
Chat GPT: I'm just a computer program, so I don't have feelings or emotions like a human. I exist to provide information and help answer questions to the best of my abilities. Is there something I can help you with today?
You:  
        Summarize the following transcript. Write using the following format. Replace everything in <> brackets.

        Main Points:
        - <main point 1>
        - <main point 2>
        - <and so on>

        Action Items:
        - <action 1>
        - <action 2>
        - <and so on>

        Recent Summary: 
        <very short summary of the transcript>

        Transcript:

        Transcript

18:29:55 - You:
 Hi, welcome to the meeting. here, I'm going to discuss some Italian points about gpt3. In this call. And also going to mention some action items. For executing this project.

18:30:26 - You:
Yeah. so with more than 2 decades of x experience in the application, lifecycle management space, on the regularly, help companies with their digital transformation by using their sort of products, given the fact that the, we are an agile team and the use can then And the chaos way of working. Standard calls are really important to us and in fact, you wake up in one of our employees at midnight, they will swear an oath of legions to the stand-up call. The other thing we saw by his matrix, observable metrics like burndown charts etc are one thing They have been doing that for a for so long. Yeah, good at it. How about qualitative metrics? How do you measure the qualitative stuff like team, cohesion teams and sentiment objective priority, and then use them to manage projects. Daily stand. Up calls are an excellent way of capturing this information. the COVID-19 forcing us to move, all start of meetings, to zoom, hang outs and Microsoft teams, it became an opportunity for us to explore these use cases, The first step to attempting, those large problem is to capture the summary of the meeting. What we describe. Next is a local experiment, we conducted and it results by using Google Speech to text and gpt3. so what we do is the breakdown, the problem into three broad steps, one is transcribe the audio Um, Given that all the speech is over multiple channels. We found that Google TTS engine did an excellent job. to run rapid experiments, we chose to use a chrome extension need and scripts which dumps that and script of the entire conversation into a Google word talk. For further processing, We chose a sample from our daily calls for this experiment. And then be three process. The text all our visions and reprocessing work to improve performance of subsequent steps, other than the usual, special character removal nonsense. Word removal. Um, There were other steps that we have included in our code. It's funny that for most for something, that is a privy, think a lot more about it in post. And then we run the, the summary using gpt3. This is what typically shines given a meeting transcript, because he doesn't see engine cannot extract relevant. Details, that can be acted upon anything. That means that we are not just looking at a summary button who button proper actionable summary. What follows is a discussion of experiment and subsequent results. We have like kids with shiny toys after the first experiment. We want to take gp3 out for a ride with zero pre-work. That means that we wanted to understand how robust the solution was in its official state. The STD engine itself was itself a source of error, but we found the jbt was able to overcome this problem at first glance. Um, we give the typical status stand up, would involve a lead, directing the flow of the conversation. With each team member giving his work update in done. We took such an extract from the transcript of our own team picture below. It is a 600 words transcript, which is filled with broad updates on the AI products that that we worked on the day before. He then asked gp3 to summarize, this text asus the DaVinci engine um, there are team as well. Well, working on the first release of the radio app and now waiting for approval from the red desk representative that emails also working on care on for next release. That is awesome. We were able to condense our conversation from 500 word to just 50 words without Mom much work, but there is an issue. The thing with the minutes of making is to capture who did what extremely well. These details are important so that one can create assign the work to create an assign the work. What we have is good for an email but not something. We can perform subsequent processing online, agile card creation card moment on this one board and etc.

Chat GPT: [Status Code] 500 | [Response Text] {"detail":"Hmm...something seems to have gone wrong. Maybe try me again in a little bit."}
You: How are you?
Chat GPT: I'm a machine learning model, so I don't have feelings or emotions like a human does. I'm here to assist you with any questions you may have, to the best of my ability. Is there something specific you would like to know?
You: How are you?
Chat GPT: As a large language model trained by OpenAI, I do not have the ability to feel emotions or experience physical sensations. I am a machine learning model designed to assist with a wide range of tasks, including answering questions and providing information on a wide range of topics. Is there something specific you would like help with?
You: How are you?
Chat GPT: I am an artificial intelligence and do not have the ability to experience emotions or have a physical presence. I exist only as a programmed set of algorithms and do not have the capacity for consciousness or personal experiences. I am here to provide information and answer questions to the best of my ability. Is there something I can help you with?
You: How are you?
Chat GPT: I am an artificial intelligence programmed to assist with a variety of tasks. I do not have feelings or emotions like a human, so I cannot experience things like happiness or sadness. Is there something specific I can help you with today?
You: How are you?
Chat GPT: I'm just a computer program, so I don't have the ability to experience emotions like a human would. I'm here to provide information and help answer your questions to the best of my ability. Is there something specific you would like to know?
You: How are you?
Chat GPT: I'm just a machine learning model, so I don't have feelings or emotions. I'm programmed to assist with tasks and answer questions to the best of my ability. Is there something specific you would like help with?
You: How are you?
Chat GPT: I'm just a computer program, so I don't have feelings or emotions like a human does. I'm here to help answer your questions to the best of my ability. Is there something specific you would like to know? I'll do my best to provide you with accurate and helpful information.
You: How are you?
Chat GPT: I'm a machine learning model trained by OpenAI, so I don't have the ability to experience emotions like a human does. I'm here to provide information and assist with tasks to the best of my ability. Is there something specific you need help with today?
You: How are you?
Chat GPT: As a language model trained by OpenAI, I don't have feelings or emotions. I'm a computer program designed to assist with a variety of tasks, such as answering questions or generating text. My sole purpose is to assist you to the best of my ability. Is there something I can help you with today?
You: How are you?
Chat GPT: I'm a large language model trained by OpenAI, so I don't have feelings or emotions like a human. I'm just a computer program designed to assist with questions and provide information. Is there something specific you'd like help with?
You: How are you?
Chat GPT: As a language model trained by OpenAI, I am not capable of feeling emotions or experiencing consciousness. My sole purpose is to assist users in generating human-like text based on the inputs provided to me. Is there something specific I can help you with today?
You: How are you?
Chat GPT: I'm a machine learning model, so I don't have feelings or emotions like a human does. I'm here to provide information and help answer questions to the best of my ability. Is there something I can help you with today?
You: How are you?
Chat GPT: I'm just a computer program, so I don't have feelings or emotions like a human. I'm programmed to assist with questions and provide information to the best of my ability. Is there something specific you would like help with?
You: How are you?
Chat GPT: I'm a machine learning model trained by OpenAI, so I don't have feelings or consciousness in the same way that humans do. I'm here to assist with any questions you may have and provide information to the best of my ability. Is there anything I can help you with?
You: How are you?
Chat GPT: [Status Code] 500 | [Response Text] {"detail":"Hmm...something seems to have gone wrong. Maybe try me again in a little bit."}
You: How are you?
Chat GPT: As a machine learning model, I don't have the ability to have emotions or feelings like a human. I'm here to assist you with any questions you have to the best of my ability. Is there something specific you'd like to know or discuss?
You: How are you?
Chat GPT: I'm a machine learning model trained by OpenAI, so I don't have the ability to have feelings or experiences like a human does. I exist to provide information and answer questions to the best of my ability. Is there anything I can help you with today?
You: How are you?
Chat GPT: I am an AI assistant, so I do not have the ability to feel emotions like a human. I am programmed to provide information and answer questions to the best of my ability. Is there something specific you would like to know?
You: How are you?
Chat GPT: I'm a machine learning model, so I don't have feelings or emotions. I exist to provide information and answer questions to the best of my ability. Is there something specific you'd like to know?
You: How are you?
Chat GPT: As a machine learning model, I don't have feelings or emotions. I'm programmed to assist you with any questions you have to the best of my abilities. Is there anything specific you would like to know?
You: How are you?
Chat GPT: I am an artificial intelligence, so I don't have feelings or emotions like a human does. I exist to assist you with any questions or tasks you may have within my capabilities. Is there anything specific you would like help with?
You: How are you?
Chat GPT: As a large language model trained by OpenAI, I do not have feelings or emotions. I am a computer-based program designed to provide information and answer questions to the best of my ability. Is there something specific you would like to know?
You: How are you?
Chat GPT: As a language model trained by OpenAI, I don't have feelings or emotions. I'm an AI assistant designed to assist with a variety of tasks, such as answering questions and providing information on a wide range of topics. Is there something I can help you with today?
You: How are you?
Chat GPT: I'm just a computer program, so I don't have feelings or emotions like a human does. I exist to provide information and assist with tasks, so if there's something you need help with, just let me know. Is there something specific you'd like to talk about or ask about?
You: How are you?
Chat GPT: I'm just a computer program, so I don't have the ability to feel emotions like a human. I exist to provide information and answer questions to the best of my ability. Is there something specific you'd like to know?
You: How are you?
Chat GPT: I'm just a computer program, so I don't have feelings or emotions. I exist to provide information and answer questions to the best of my ability. Is there something I can help you with?
You: How are you?
Chat GPT: As a large language model trained by OpenAI, I don't have feelings or emotions like a human. I'm programmed to assist with a wide range of tasks, such as answering questions, providing information, and generating text based on the input I receive. Is there something specific you would like help with today?
You: How are you?
Chat GPT: I'm just a computer program, so I don't have feelings or emotions. I exist to assist you with any questions you might have, to the best of my abilities. Is there anything specific you would like help with?
You: How are you?
Chat GPT: I'm just a machine learning model, so I don't have feelings or emotions. I exist to provide information and answer questions to the best of my ability based on the data that I've been trained on. Is there something specific you'd like to know? I'll do my best to help.
You: How are you?
Chat GPT: I am just a computer program, so I don't have the ability to feel emotions like a human does. I exist to provide information and answer questions to the best of my ability. Is there something I can help you with?