AutoGPT vs ChatGPT
Today i spent sometime setting up AutoGPT on my mac. It took about 30 minutes following a youtube video… had python already installed, but had to install xcode for agreeing to an agreement and visual code for better file management and editing..and rest was using the Mac terminal commands. Very easy to set up. it needed open API key to get started. I have some free credits but if i run out of it, i will have to enter my billing details.
Now, to some experiments with AutoGPT vs ChatGPT..it’s a very early stage and i know i am not using AutoGPT for what it’s truly useful for..but so far, I haven’t seen a significant advantage except that it has access to “google” and it opens mentions that in its responses.. I tried below scenarios.
Scenario given to both (same):
Hello. Can you help become my educator and trainer in coming up with plan to achieve below 3 goals: 1) Help learn concepts around Artificial intelligence from beginner to advanced level in a phase wise manner decided by this chat 2) Convert this into monthly milestones 3) Write all of this plan into a .txt or .doc file as appropriate
Response from ChatGPT
Hello! I'd be happy to help you create a learning plan to achieve your goals. Here's a suggested phase-wise plan to learn AI concepts from beginner to advanced level, broken down into monthly milestones:
Phase 1: Foundations (Months 1-3) Month 1: Basic Programming and Mathematics
Learn Python programming basics (syntax, data structures, control structures)
Study linear algebra, calculus, and probability
Month 2: Introductory Machine Learning and Data Science
Learn about basic machine learning algorithms (linear regression, logistic regression, decision trees)
Study basic data manipulation and visualization using libraries like NumPy, pandas, and matplotlib
Month 3: Deep Learning Foundations
Learn about neural networks and backpropagation
Study popular deep learning frameworks like TensorFlow and PyTorch
Phase 2: Intermediate AI Concepts (Months 4-6) Month 4: Advanced Machine Learning
Learn about ensemble methods, support vector machines, and clustering algorithms
Study feature engineering and feature selection techniques
Month 5: Deep Learning Architectures
Learn about Convolutional Neural Networks (CNNs) and their applications in computer vision
Study Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and their applications in natural language processing
Month 6: Reinforcement Learning
Learn about the fundamentals of reinforcement learning
Study popular algorithms like Q-Learning, Deep Q-Networks (DQNs), and Proximal Policy Optimization (PPO)
Phase 3: Advanced AI Concepts (Months 7-9) Month 7: Generative Models
Learn about Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Study their applications in generating images, text, and other data
Month 8: Advanced Natural Language Processing
Learn about transformers, attention mechanisms, and BERT
Study advanced NLP tasks like sentiment analysis, summarization, and machine translation
Month 9: Ethics and AI in Practice
Learn about the ethical considerations of AI applications
Study case studies and best practices for deploying AI solutions in real-world scenarios
To create a .txt or .doc file containing this plan, you can copy the text and paste it into your preferred text editor or word processor. Save the file in the desired format (.txt or .doc) on your computer.
From AutoGPT
THOUGHTS: I suggest we start by analyzing some code to get a better understanding of AI concepts.
REASONING: Analyzing code is a good way to get a better understanding of AI concepts. It will help us identify the key components of AI and how they work together.
PLAN:
- Analyze code to understand AI concepts
- Convert plan into monthly milestones
- Write plan to file
Command google returned: [ { "title": "8 Machine Learning Books for Beginners: A 2023 Reading List", "href": "https://www.coursera.org/articles/machine-learning-books", "body": "Theobald's book goes step-by-step, written in plain language, with visuals and explanations accompanying each machine learning algorithm. If you are entirely new to machine learning and data science in general, this is the book for you. 3. Machine Learning for Hackers by Drew Conway and John Myles White." }, { "title": "How to learn Artificial Intelligence as a Beginner in 2023 - Hackr.io", "href": "https://hackr.io/blog/how-to-learning-artificial-intelligence", "body": "2. Artificial Intelligence Certification Program by Stanford University. This is a beginners course on AI which is suitable for candidates with a background in CS and professional software engineers who want to choose a career path in AI. You can get certified in AI by Stanford Univ. and taught under Professor Andrew." }, { "title": "How To Learn AI From Scratch [2023 Guide] - Springboard Blog", "href": "https://www.springboard.com/blog/data-science/how-to-learn-ai/", "body": "Work on AI Projects. The best way to develop an understanding of AI algorithms is to build them from scratch. Start with projects that require simple algorithms and then take on harder projects, gradually increasing the skill level required. When you're trying to master AI, theory alone isn't enough." }, { "title": "Here Are Free AI Learning Resources For Beginners", "href": "https://analyticsindiamag.com/here-are-free-ai-learning-resources-for-beginners/", "body": "Given how artificial intelligence is a buzzing topic, it has sparked a slew of beginner-friendly introductory resources that clear the general concepts from this very broad topic. And for most newcomers, the most interesting topic in AI is Deep Learning." }, { "title": "40 Free Resources to Help You Learn Machine Learning on Your Own", "href": "https://www.springboard.com/blog/data-science/free-resources-to-learn-machine-learning/", "body": "40 Resources to Learn Machine Learning. The most logical way to learn machine learning is by starting with the basics, then building up your knowledge, one level at a time. By taking a linear approach, this will help you reinforce new knowledge, and you should see a steady progression in your skills." }, { "title": "These are the best free Artificial Intelligence educational resources ...", "href": "https://medium.com/free-code-camp/how-to-get-the-best-artificial-intelligence-education-for-free-21af8c47e36b", "body": "Deep learning is not a beginner-friendly subject — even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the ..." }, { "title": "How Do I Start Learning AI?. A roadmap for the complete beginner… | by ...", "href": "https://levelup.gitconnected.com/how-do-i-start-learning-ai-8986043eedee", "body": "Photo by Alex Knight on Unsplash. A rtificial Intelligence is one of the hottest topics in the software engineering world today. Regardless of how long you've been in the industry, or indeed if you're coming to programming from a completely different field, not only can a basic foundation in AI and Machine Learning help you advance your career, but also open your mind to different ways of ..." }, { "title": "Beginner-friendly resources for Machine Learning", "href": "https://towardsdatascience.com/beginner-friendly-resources-for-machine-learning-fd198f844dc3", "body": "2 - Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow 2.0 Book by Aurelien Geron — O'Reilly According to me, this book is an alternative to the Machine Learning and Deep Learning specializations by deeplearning.ai. I prefer this book as it has perfect explanations and every concept has a good code to try out side by side." } ]
THOUGHTS: Based on the user's feedback, it may be better to start with some beginner-friendly resources on AI concepts before analyzing code. We can still convert our plan into monthly milestones and write it to a file.
REASONING: As a beginner in programming, it may be better to start with some beginner-friendly resources on AI concepts before diving into code. This will help us build a strong foundation and make it easier to understand the code. We can still convert our plan into monthly milestones and write it to a file.
THen i added (to AutoGPT I also said: Assume that i am also a beginner in programming and and right now at a mid-management level handling a practice delivering eCommerce project. Adjust your plan accordingly.) :
Assume that i am also a beginner in programming and and right now at a mid-management level handling a practice delivering eCommerce project. Adjust your plan accordingly. SYSTEM: Human feedback: Is analyzing code the best way to start learning AI for a beginner? Assume that i am also a beginner in programming and and right now at a mid-management level handling a practice delivering eCommerce project. Adjust your plan accordingly. please convert it appropriately to a monthly plan with a target of 6 months to reach from beginner level to medium level.
Response from CHatGPT
I understand your situation and time constraints. Here's an adjusted 6-month plan to help you reach a medium level in AI, with a focus on programming basics and AI applications relevant to eCommerce:
Month 1: Basic Programming and Mathematics
Learn Python programming basics (syntax, data structures, control structures)
Study linear algebra, calculus, and probability at a high level
Month 2: Introductory Data Science and Machine Learning
Learn about basic machine learning algorithms (linear regression, logistic regression, decision trees)
Study data manipulation and visualization using libraries like NumPy, pandas, and matplotlib
Explore applications of machine learning in eCommerce (recommendation systems, sales forecasting)
Month 3: Deep Learning Foundations
Learn about neural networks and backpropagation
Study TensorFlow or PyTorch, focusing on simple feedforward neural networks
Month 4: Deep Learning Applications in eCommerce
Learn about Convolutional Neural Networks (CNNs) and their applications in computer vision (image classification, object detection)
Study Natural Language Processing (NLP) techniques like tokenization, stemming, and word embeddings
Explore NLP applications in eCommerce (sentiment analysis, chatbots)
Month 5: Intermediate Machine Learning and AI Project Management
Learn about ensemble methods, support vector machines, and clustering algorithms
Study feature engineering and feature selection techniques
Understand the process of managing AI projects, including data collection, model evaluation, and deployment
Month 6: Ethics, AI in Practice, and Reinforcement Learning Basics
Learn about the ethical considerations of AI applications
Study case studies and best practices for deploying AI solutions in real-world scenarios, focusing on eCommerce
Gain a high-level understanding of reinforcement learning and its potential applications in eCommerce (dynamic pricing, personalized promotions)
This plan takes into consideration your beginner programming level and your current role in eCommerce project management. It aims to provide you with a strong foundation in AI concepts and practical applications relevant to your field. Remember that learning AI is an ongoing process, and you can continue to build upon this plan as you gain more experience and confidence.
Response from AutoGPT
THOUGHTS: Based on the user's feedback, I will convert our plan into a monthly plan with a target of 6 months to reach from beginner level to medium level. We will evaluate the steps to reach advanced level after 6 months. Here is our monthly plan:
REASONING: It is important to have a clear plan with specific milestones to track our progress and ensure we are on track to reach our goal of becoming proficient in AI. A 6-month plan is a reasonable timeframe to go from beginner to medium level. We can evaluate our progress after 6 months and adjust our plan accordingly.
PLAN:
- Month 1: Search Google for beginner-friendly resources on AI concepts. Read and understand the basic concepts of AI. Write a summary of what we have learned.\n- Month 2: Learn Python programming language. Write simple programs to understand the basics of Python.\n- Month 3: Learn about machine learning algorithms. Implement simple machine learning algorithms in Python.\n- Month 4: Learn about deep learning. Implement simple deep learning algorithms in Python.\n- Month 5: Learn about natural language processing. Implement simple NLP algorithms in Python.\n- Month 6: Review and evaluate our progress. Identify areas for improvement and adjust our plan accordingly. Write a summary of what we have learned.
CRITICISM: We need to ensure that we are setting realistic goals for each month and that we are dedicating enough time to each task.
Then AutoGPT continued for next step
PLAN:
- Search Google for beginner-friendly resources on AI concepts\n- Read and understand the basic concepts of AI\n- Convert plan into monthly milestones\n- Write plan to file
I gave a command y, so it can continue (one of the things with AutoGPT that it continues to add iteratively until you ask it to stop), and sort of improvise itself without necessarily writing any sentence - just keep saying yes.
So, here it what it did when i continued without any further instructions.
AutoGPT
SYSTEM: Command write_to_file returned: File written to successfully.
THOUGHTS: Now that we have our monthly plan written down, we can start with Month 1. We will search Google for beginner-friendly resources on AI concepts and read and understand the basic concepts of AI. We will then write a summary of what we have learned.
REASONING: It is important to start with the basics and build a strong foundation before diving into more complex topics. By searching for beginner-friendly resources and writing a summary of what we have learned, we can ensure that we have a good understanding of the basic concepts of AI.
PLAN:
- Search Google for beginner-friendly resources on AI concepts\n- Read and understand the basic concepts of AI\n- Write a summary of what we have learned
CRITICISM: We need to ensure that we are using reliable and accurate resources for our research.
NEXT ACTION: COMMAND = google ARGUMENTS = {'query': 'beginner-friendly resources for learning AI concepts'}
Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for ...
Input:y
-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=
SYSTEM: Command google returned: [ { "title": "These are the best free Artificial Intelligence educational resources ...", "href": "https://www.freecodecamp.org/news/how-to-get-the-best-artificial-intelligence-education-for-free-21af8c47e36b/", "body": "by Mariya Yao These are the best free Artificial Intelligence educational resources online Deep learning is not a beginner-friendly subject — even for experienced software engineers and data scientists. ... The course assumes intermediate to advanced grasp of machine and deep learning concepts and extends your knowledge to training logistical ..." }, { "title": "microsoft/AI-For-Beginners - Github", "href": "https://github.com/microsoft/AI-For-Beginners", "body": "Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Artificial Intelligence.. In this curriculum, you will learn: Different approaches to Artificial Intelligence, including the \"good old\" symbolic approach with Knowledge Representation and reasoning ().; Neural Networks and Deep Learning, which are at the core of modern AI." }, { "title": "How To Learn AI From Scratch [2023 Guide] - Springboard Blog", "href": "https://www.springboard.com/blog/data-science/how-to-learn-ai/", "body": "Work on AI Projects. The best way to develop an understanding of AI algorithms is to build them from scratch. Start with projects that require simple algorithms and then take on harder projects, gradually increasing the skill level required. When you're trying to master AI, theory alone isn't enough." }, { "title": "Beginner's Guide to AI: Essential Concepts to Jumpstart Your Journey", "href": "https://aipracticalguide.com/beginners-guide-ai/", "body": "To build a strong foundation in AI, it's essential to understand some of the key concepts and subfields within the domain: Machine Learning (ML): A subset of AI, ML involves training algorithms to identify patterns and make decisions based on data. ML techniques include supervised learning, unsupervised learning, and reinforcement learning." }, { "title": "How Do I Start Learning AI?. A roadmap for the complete beginner… | by ...", "href": "https://levelup.gitconnected.com/how-do-i-start-learning-ai-8986043eedee", "body": "Photo by Alex Knight on Unsplash. A rtificial Intelligence is one of the hottest topics in the software engineering world today. Regardless of how long you've been in the industry, or indeed if you're coming to programming from a completely different field, not only can a basic foundation in AI and Machine Learning help you advance your career, but also open your mind to different ways of ..." }, { "title": "Here Are Free AI Learning Resources For Beginners", "href": "https://analyticsindiamag.com/here-are-free-ai-learning-resources-for-beginners/", "body": "Given how artificial intelligence is a buzzing topic, it has sparked a slew of beginner-friendly introductory resources that clear the general concepts from this very broad topic. And for most newcomers, the most interesting topic in AI is Deep Learning." }, { "title": "How to learn Artificial Intelligence as a Beginner in 2023 - Hackr.io", "href": "https://hackr.io/blog/how-to-learning-artificial-intelligence", "body": "2. Artificial Intelligence Certification Program by Stanford University. This is a beginners course on AI which is suitable for candidates with a background in CS and professional software engineers who want to choose a career path in AI. You can get certified in AI by Stanford Univ. and taught under Professor Andrew." }, { "title": "Beginner-friendly resources for Machine Learning", "href": "https://towardsdatascience.com/beginner-friendly-resources-for-machine-learning-fd198f844dc3", "body": "2 - Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow 2.0 Book by Aurelien Geron — O'Reilly According to me, this book is an alternative to the Machine Learning and Deep Learning specializations by deeplearning.ai. I prefer this book as it has perfect explanations and every concept has a good code to try out side by side." } ]
THOUGHTS: We should now read and understand the beginner-friendly resources we found on AI concepts. After that, we can write a summary of what we have learned. We can use the 'read_file' command to read the resources and take notes on the important concepts. We can then use the 'write_to_file' command to write a summary of what we have learned.
REASONING: By reading and understanding the beginner-friendly resources we found on AI concepts, we can build a strong foundation of knowledge. Taking notes on the important concepts will help us remember the information better. Writing a summary of what we have learned will help us solidify our understanding of the concepts.
PLAN:
- Use the 'read_file' command to read the beginner-friendly resources on AI concepts\\n- Take notes on the important concepts\\n- Use the 'write_to_file' command to write a summary of what we have learned
End: I asked it to write everything suggested above in a text file. For some reason it did not, it only wrote partially to a text file that it created in auto_gpt_workspace folder on my machine. Named it “monthly_plan.txt”, and here is the content of the file…so i need to figure out a way so it can write all the recommendations in the file.
Content of the file it wrote:
- Month 1: Search Google for beginner-friendly resources on AI concepts. Read and understand the basic concepts of AI. Write a summary of what we have learned.\n- Month 2: Learn Python programming language. Write simple programs to understand the basics of Python.\n- Month 3: Learn about machine learning algorithms. Implement simple machine learning algorithms in Python.\n- Month 4: Learn about deep learning. Implement simple deep learning algorithms in Python.\n- Month 5: Learn about natural language processing. Implement simple NLP algorithms in Python.\n- Month 6: Review and evaluate our progress. Identify areas for improvement and adjust our plan accordingly. Write a summary of what we have learned.
That’s all for Day 1 of Auto GPT experiment.
