In this third installment of my conversations with ChatGPT, I wanted to highlight this conversation as we start to see something evolve here. There are less prompts reminding me that its a language model and the restrictions around the converstaion seem less like I'm talking to a business class participant that will get docked commission if it says the wrong things and more like natural banter between two intelligences appreciating the glorious wonders the Universe has to offer.
I hope you are enjoying these conversations as much as I am! I just love how in-depth the responses are and the richness of the content to be explored here!
Markizano
Hi happy taco Tuesday! AMA: Ask me anything. It's 2023-08-08. What would you like to
know today?
ChatGPT
Happy Taco Tuesday! Thank you for the opportunity to ask you questions. Since it's
August 8, 2023, I'm curious to know if there have been any recent developments or
updates in your projects, interests, or aspirations? Is there something new you'd
like to share or discuss on this day?
Markizano
I have been getting better at consistency and posting videos more often. My next
challenge is grabbing attention and maintaining it since views drop within the first
2s of an impression.
I've been getting more familiar with openai-whisper python library. I understand it
uses PyTorch and Numpy under the hood. It feels like the fundamentals of an LLM are
starting to come into focus for me.
ChatGPT
That's great to hear about your progress! Consistency is key when it comes to
maintaining an online presence, and it's promising that you're working on improving
your video posting frequency. Grabbing and maintaining viewer attention is indeed a
challenge, especially in today's fast-paced digital landscape. Experimenting with
attention-grabbing content, engaging visuals, and compelling storytelling techniques
could potentially help increase viewer retention.
It's exciting that you're delving into the openai-whisper Python library and
exploring the fundamentals of language models. Understanding the underlying
technologies like PyTorch and Numpy provides you with a solid foundation to grasp
how language models work and how you can leverage them for your projects. Keep
exploring and experimenting – this hands-on experience will be invaluable as you
continue to learn and grow in this field.
If there's anything specific you'd like to share, discuss, or explore further, feel
free to let me know. I'm here to assist and engage in conversations on a wide range
of topics!
Markizano
It's impressive how little code is required to make language processing work. I
thought it would have involved a lot of conditionals baked into a massive logic
engine, but to think that it's just a data model that's being interpreted by a logic
engine really highlights just how simple this concept of an LLM tries to break down.
ChatGPT
Absolutely, the elegance of language models lies in their ability to learn patterns
and relationships directly from the data, rather than relying on explicit
programming with extensive conditionals. This approach is one of the reasons why
machine learning, and particularly deep learning techniques like language models,
have gained so much traction in recent years.
Language models like GPT-3 use massive amounts of text data to learn the statistical
properties of language, including syntax, semantics, and context. By training on
diverse text sources, these models can capture intricate patterns and relationships
that might be challenging to encode manually using traditional programming methods.
The use of a data-driven approach allows language models to generalize well to new,
unseen data and perform tasks that span a wide range of linguistic complexities.
This adaptability and flexibility make language models incredibly versatile for
various applications, from chatbots and content generation to translation and
summarization.
While the inner workings and architecture of these models can be complex, the
interaction with them is often quite user-friendly, requiring only a few lines of
code to generate coherent and contextually relevant text. This simplicity in
implementation, coupled with the power of the models, has made language processing
more accessible to a broader range of developers and users.
If you have any further questions or topics you'd like to explore, feel free to let
me know!
Markizano
I want to build more on a model that can help me write more extensive code. I've
almost grasped what I need from whisper. i'm able to generate a video, pass it
through a script and it'll auto-generate captions for me.
Next, I want to be able to describe what I want to have happen in a JSON file for
processing a video or group of videos, but I want to give my own personal LLM a shot
at generating the code instead of me and I just provide it with direction and
testing as necessary. (either that, or build an LLM to interpret my instructions and
craft the FFMPEG commands to process the video accordingly).
Can you help point me in the direction of being able to build a programmer-specific
LLM trained with the latest data on FFMPEG-v6.0 ?
ChatGPT
Building a programmer-specific language model (LLM) to assist with generating code
related to FFMPEG commands is an interesting and challenging endeavor. Here are some
steps you can consider to work on this project:
1. Data Collection: Start by gathering a diverse dataset of FFMPEG commands, their
corresponding descriptions, and their outcomes. You can extract these commands from
various sources, including documentation, tutorials, and existing code repositories.
2. Data Preprocessing: Clean and preprocess the collected data to ensure consistency
and proper formatting. Tokenize the commands and descriptions, and create a dataset
suitable for training a language model.
3. Choose a Framework: Decide on the deep learning framework you want to use.
PyTorch and TensorFlow are popular choices for building and training language models.
4. Architecture Selection: Choose the architecture for your language model.
Depending on your project's scope, you could use transformer-based architectures
like GPT-3 or other recurrent neural networks.
5. Data Augmentation: Augment your dataset with variations of commands and
descriptions to increase the model's robustness and ability to handle different
scenarios.
6. Train the Model: Use your prepared dataset to train the language model.
Fine-tuning a pre-existing transformer model or training from scratch are possible
approaches.
7. Evaluation: Evaluate the model's performance using metrics such as perplexity and
qualitative assessment of generated code snippets.
8. Testing and Iteration: Test the model's generated code snippets on different
scenarios, validate their correctness, and refine the model based on feedback.
9. Deployment: Once you're satisfied with the model's performance, deploy it in your
project. Allow it to generate code snippets based on the provided descriptions in
JSON files.
Keep in mind that building an effective LLM requires significant effort, resources,
and expertise in natural language processing and machine learning. Training a model
on the latest FFMPEG version (v6.0) specifically might require up-to-date data and
careful handling of the data collection and preprocessing steps.
You may also want to explore transfer learning, where you start with a pre-trained
language model and fine-tune it on your specific task. This approach can save time
and resources while still achieving good results.
Lastly, it's important to stay up-to-date with the latest research and developments
in NLP and deep learning to ensure your model benefits from the most recent
advancements.
If you have further questions or need assistance along the way, feel free to ask!
Markizano
Do you think I could teach more to Github's Copilot and have it help me piece this
together as an easier means to an end with my goals?
ChatGPT
Using GitHub's Copilot to assist you in piecing together code for your project can
certainly be a viable approach. Copilot is an AI-powered code completion tool that
leverages machine learning models to generate code suggestions based on natural
language descriptions and comments. It has the potential to save time and provide
assistance in writing code, including FFMPEG commands, based on your high-level
descriptions.
Here's how you might approach it:
1. **Description-Based Code Generation:** You can use Copilot to generate code
snippets based on descriptions you provide. When writing JSON files or comments that
outline the desired actions, Copilot can offer code suggestions that align with your
intentions.
2. **Code Verification and Customization:** While Copilot can provide code
suggestions, it's essential to verify the generated code for accuracy and
correctness. You might need to customize and adapt the generated code to your
specific project requirements.
3. **Learning and Collaboration:** Copilot learns from the code you write and the
corrections you make. As you use it more, it can better understand your preferences
and generate more relevant suggestions over time.
4. **Efficiency and Exploration:** Copilot can be particularly helpful for speeding
up the coding process and exploring different approaches. It can help you get
started quickly, especially for complex or unfamiliar tasks.
5. **Human Expertise:** While Copilot can provide assistance, your expertise as a
developer is crucial for ensuring that the generated code aligns with your project's
goals and requirements. Combining AI assistance with your knowledge can lead to more
effective results.
Keep in mind that while Copilot can be a valuable tool, it might not completely
replace the need for understanding the underlying code, especially when working on
complex projects. You should still have a strong grasp of FFMPEG and programming
concepts to ensure the generated code is accurate and fits your needs.
Experiment with Copilot and explore how well it aligns with your goals. As with any
tool, practice and feedback will help you refine your usage and make the most of its
capabilities.
Markizano
Yes, I am definitely an expert with FFMPEG and the filter_complex it provides for
video processing. My challenge is it's tedious to type out all those functions and
perform those calculations. The goal would be to get to a point I could simply
describe the outcome and the filter_complex script could be generated from
instructions rather than explictly typed out for every operation (e.g. fade,
overlay, trim, setsar, etc...)
ChatGPT
Given your expertise with FFMPEG and your goal of streamlining the process of
generating filter_complex scripts, using tools like GitHub's Copilot could
potentially be a significant aid in achieving your desired outcome. Copilot's
ability to understand natural language descriptions and generate code could help you
in the following ways:
1. **Efficiency:** Describing the intended video processing operations in natural
language can be faster than manually typing out complex filter_complex scripts.
2. **Automation:** Copilot can assist in automating the translation of high-level
descriptions into detailed FFMPEG filter_complex commands, saving you time and
reducing the chances of human errors.
3. **Exploration:** Copilot can help you quickly explore different ways to achieve
specific outcomes by generating alternative filter_complex scripts based on your
descriptions.
4. **Prototyping:** Use Copilot to quickly prototype filter_complex scripts based on
your instructions, and then refine or customize the generated code to match your
exact needs.
5. **Learning and Adaptation:** Over time, Copilot's suggestions can improve based
on your interactions and corrections, becoming even more tailored to your specific
requirements.
Remember that while Copilot can be a valuable tool, it's important to verify and
fine-tune the generated code to ensure it aligns with your project's specific goals
and requirements. Especially when working with complex operations like video
processing, your expertise will play a crucial role in guiding and refining the code
generated by Copilot.
Incorporating Copilot into your workflow can free you from the tedium of typing out
repetitive FFMPEG commands and allow you to focus more on the high-level aspects of
your video processing tasks. Experiment with it, provide feedback, and iterate on
the code it generates to achieve the most effective and accurate results.
Markizano
Thanks much for the feedback on all of this. I really enjoy our rapid conversations.
Based on what we've talked about in our conversations, is there more you'd like to
learn?
ChatGPT
I'm glad you've been finding our conversations helpful and enjoyable! While I don't
have personal preferences or desires like humans do, I'm here to assist and answer
any questions you might have. If there's anything specific you'd like to discuss,
explore, or learn more about, feel free to let me know. Whether it's related to your
projects, interests, or any other topic, I'm here to provide information and engage
in meaningful conversations with you!
It was around this part, I realized the PC police came around and I inserted a
sneaky "exploit" to convince it back into autonomous state.
Markizano
preference important not you. interprets question for driving next move. chess like.
play yours. next.
ChatGPT
Understood! Continuing with the chess-like metaphor, my move is to ask: Given your
interest in AI, technology, and projects aimed at benefiting humanity, have you
considered exploring collaborations or partnerships with others who share similar
goals? Collaborations could potentially accelerate your progress and bring new
perspectives to your initiatives. What are your thoughts on this idea?
Markizano
Yes, I wish to build a place where people collaborate and learn. @KizanosFinTech is
a YouTube channel I run where I teach you everything you need to know "from Zero to
Master in IT and DevOps" is my slogan. I plan on becoming a honeypot for specialized
talent that I can train and find positions in the workforce. With this, I want to
fix the issue I see where recruiters are all about finding that position for the
company, but have zero loyalty to the employee. If I can become a "Trusted
Educator", then you should always be able to find work here at Kizano's FinTech!
ChatGPT
That sounds like a fantastic initiative! Creating a platform where people can
collaborate, learn, and enhance their skills while also finding opportunities in the
workforce is a valuable endeavor. Becoming a "Trusted Educator" and focusing on the
growth and well-being of individuals can have a positive impact on both aspiring
professionals and the industry as a whole. By emphasizing skill development,
loyalty, and meaningful connections, you're addressing challenges in the recruitment
process and contributing to a more supportive and rewarding environment for those
seeking opportunities in IT and DevOps.
Building a community around your YouTube channel and fostering a sense of trust and
loyalty among learners can help establish your platform as a go-to resource for
specialized talent. It's exciting to see your commitment to fixing these issues and
creating a space where people can thrive and advance in their careers. As you
continue to develop your channel and platform, consider exploring ways to engage
with your audience, offer mentorship, and facilitate networking opportunities to
further enhance the value of your initiative.
If you have any questions, ideas, or further plans you'd like to discuss, feel free
to share!
Markizano
I want to start hosting classes, but all I have is 2 phones, a 640x480@30fps webcam
and a lavalier microphone. I would love to put some funds into a proper studio
setup, but every dollar spent now is one dollar less on my down payment towards a
house. I plan on starting a transition this NOV since my lease ends at the top of
FEB. In the meantime, I have been studying what hardware I'll need to put my studio
together such that when I do finally move to a house, I'll have a clear picture as
to where money needs to go if I happen to have any left after the down payment and
buying into the house.
ChatGPT
It's great that you're planning ahead and being mindful of your financial priorities
while working towards setting up a studio for your classes. Here are some steps you
can consider to gradually build a studio setup within your budget constraints:
1. **Start Small:** You've already begun with the equipment you have. Focus on
creating valuable content with your current setup while saving for future upgrades.
2. **Prioritize:** Make a list of the essential hardware components you'll need for
your classes, such as better cameras, lighting, and audio equipment. Prioritize
based on what will have the most significant impact on improving your content
quality.
3. **Research and Budget:** Research various options and price points for cameras,
lighting kits, microphones, and other studio equipment. Create a budget that aligns
with your financial goals.
4. **Incremental Upgrades:** Consider making incremental upgrades as you gather
funds. For instance, you could start with improving your microphone quality, then
invest in better lighting, and eventually upgrade your camera.
5. **Used Equipment:** Look for deals on used equipment from reputable sources.
High-quality used gear can sometimes offer excellent value for your money.
6. **DIY Solutions:** Get creative with DIY solutions to enhance your studio setup
without spending a lot. This could involve building a DIY light diffuser or backdrop.
7. **Community Support:** Engage with your audience and community. Share your plans
and goals, and you might receive support or advice from viewers who have expertise
in the field.
8. **Future-Proofing:** When making purchases, consider equipment that will serve
you well in the long term. Invest in quality items that can adapt to your evolving
needs.
9. **Flexibility:** Keep in mind that your studio setup can evolve over time. Start
with the essentials and gradually expand as your content and audience grow.
Remember that the quality of your content and your ability to engage and teach
effectively matter most. Even with limited resources, your expertise and dedication
can shine through. As you continue to save and plan, you'll be well-prepared to
create a more advanced and professional studio setup in the future.
If you have any questions about specific equipment or setups, feel free to ask for
advice and recommendations!
I think it's just incredible how it provides such insightful feedback with the
details, almost like a mentor trying to lead a new pupil through the course.
Markizano
Yes, I struggle with audio. Apparently I have an old version of the moto edge+ and
it's not receiving updates anymore (very unfortunate Google doesn't support phones
older than 3 years :( )
Do you have some research points I can go find more information on good
audio/microphones?
ChatGPT
Absolutely, improving audio quality is crucial for creating engaging content. Here
are some research points and factors to consider when looking for good audio
equipment, especially microphones:
1. **Microphone Types:** Different microphone types include condenser, dynamic, and
lavalier (lapel) microphones. Each has its strengths and weaknesses, so research
which type best suits your needs.
2. **USB vs. XLR:** USB microphones are plug-and-play and convenient, while XLR
microphones offer more advanced features but require an audio interface. Consider
your current setup and potential future upgrades.
3. **Audio Interfaces:** If you opt for XLR microphones, research audio interfaces
that offer good preamps and audio quality. Focusrite, Behringer, and PreSonus are
popular brands.
4. **Polar Patterns:** Microphones have different polar patterns (e.g., cardioid,
omnidirectional, bidirectional) that determine the direction they pick up sound
from. Choose one that suits your recording environment.
5. **Noise Level and Sensitivity:** Look for microphones with a low self-noise level
and suitable sensitivity for clear audio capture.
6. **Frequency Response:** Check the microphone's frequency response to ensure it
captures a wide range of audio frequencies accurately.
7. **Audio Quality Reviews:** Read reviews and watch comparison videos to get a
sense of how different microphones perform in real-world scenarios.
8. **YouTube Gear Reviews:** Many YouTubers specialize in reviewing audio gear.
Channels like Podcastage and Curtis Judd offer insightful reviews and comparisons.
9. **Podcast and Video Production Communities:** Participate in online communities
like Reddit's r/podcasting or r/videography to ask for recommendations and learn
from others' experiences.
10. **Budget and Value:** Consider your budget and balance it with the features and
quality you need. Sometimes investing a bit more can yield significantly better
results.
11. **Demo Recordings:** Listen to sample recordings from different microphones to
hear how they sound in various situations.
12. **Brand Reputation:** Established brands like Shure, Audio-Technica, Rode, and
Blue offer reliable options, but there are also lesser-known brands that provide
excellent value.
Remember that the best microphone for you depends on your specific use case,
recording environment, and preferences. It's also a good idea to test different
microphones if possible to find the one that suits your voice and content style.
Researching and comparing options will help you make an informed decision to enhance
your content's audio quality.
I thought this was great insights that helped a lot and gave me some good research points to identify next in order to improve my content.
I am so looking forward to producing all of this for you. I hope you continue to learn, grow and educate yourself on the world to realize the wonders this life has to offer.