Introduction

We take our ability to think and reach correct conclusions for granted. In everyday life, we rely on our cognitive abilities to make the right decisions and find the right answers. This trust, however, is based on the assumption that our thoughts are clear and our reasoning is sound.

However, clear thinking is not an automatic process which happens on its own. Instead, it requires deliberate effort and a structured approach. We must actively work to organize our thoughts, critically evaluate the information we receive, and apply logical principles to ensure that our conclusions are valid and reliable. Without this conscious effort, our thinking becomes muddled, leading to errors in judgment and poor decision-making.

To better understand how to achieve clear thinking, we can turn to the insights of René Descartes, a pivotal figure in philosophy who created a framework for structuring thought and reason.

Descartes’ Clear and Distinct Ideas

René Descartes was a French philosopher, mathematician and scientist. He was born in 1596 and is often referred to as the father of modern philosophy. His work laid the foundation for much of Western philosophy, and he is best known for his statement “Cogito, ergo sum” (“I think, therefore I am”).

In Principles of Philosophy (1:45) Descartes says:

I call a perception “clear” when it is present and accessible to the attentive mind – just as we say that we see something clearly when it is present to the eye’s gaze and stimulates it with a sufficient degree of strength and accessibility. I call a perception “distinct” if, as well as being clear, it is so sharply separated from all other perceptions that it contains within itself only what is clear.

Also, in Meditations on First Philosophy, Descartes discusses the idea of distinct ideas (Sixth Meditation):

The fact that I can clearly and distinctly understand one thing apart from another is enough to make me certain that the two things are distinct, since they are capable of being separated.

This means that:

An idea is clear when you can think about it as “one thing”, and not as a collection of facts.
An idea is different to other ideas if it can be understood on its own merits without background noise.
Two similar ideas are separate when each of the ideas passes Rule 2.

For example, consider the clarity and distinctness required in scientific observation, a point highlighted by Bertrand Russell. When a scientist observes a phenomenon, the observation must be both clear (unambiguous and direct) and distinct (free from confounding variables and extraneous details) for it to serve as a reliable basis for further inquiry.

Unstructured Thinking

In contrast with structured thinking, unstructured thinking involves processing information in a haphazard manner, without following a logical sequence or framework. This approach results in an amalgamation of disparate pieces of information which may look like something new, but in fact contains no more knowledge than the original facts.

The results of unstructured thinking are:

Unclear: The mixed information does not form a clear, understandable concept; each piece of data is not distinct or logically connected to others.
Inconsistent: Without a structured approach, the resulting amalgamation of ideas may contain conflicting or redundant information.
Non-actionable: Because the information is not organized into a coherent unit, it is useless for further thought, decision-making or action.

For example, consider an unstructured approach to learning about a new topic. If you randomly read articles, watch videos, and listen to podcasts without a plan, you might gather a lot of information but struggle to form a clear understanding. The information might be interesting, but without coherence, it remains fragmented and irrelevant.

Units of Thought

To think clearly, it’s helpful to break down complex ideas into smaller, more manageable units of thought. These units should be distinct, logically connected, and built sequentially to form a coherent whole. This method ensures that each step in our reasoning is solid and reliable, like building a house where each well-seated brick supports the next and so creates the overall structure.

By organizing our thoughts into coherent units, we create a framework that allows for progression, reliability, and effective communication. This approach aids in understanding and solving problems and also facilitates the sharing and dissemination of knowledge, since the units of thought can be easily understood on their own merits.

New, coherent units of thought allow:

Progression: Each unit serves as a foundation for further learning and development, for example:
- Knowing how to bake a cake allows you to experiment with different flavors or techniques.
- Understanding basic maths concepts allows you to combine these concepts in understanding higher level maths constructs.
Reliability: Coherent units of thought become “tried and tested ideas”, making them reliable building blocks for future problem scenarios.
Communication: Clear and coherent units of thought can be easily shared and understood by others.

A Clear Thinking GPT

This GPT – Clear Thinking – is configured to apply Descartes’ principles of clear thinking. Let’s see how it performs when asked for advice about self study.

Question: Design a plan of self study to learn about how LLM’s such as ChatGPT work. Explain how my understanding will develop at each stage, leading onto the next stage of study.

Using ChatGPT 4:

Designing a comprehensive self-study plan to understand how large language models (LLMs) like ChatGPT work involves a structured approach, starting from fundamental concepts and progressing to more complex ideas. Here’s a step-by-step plan:

Stage 1: Foundational Knowledge

1.1 Introduction to Machine Learning and AI

Objectives: Understand the basics of machine learning (ML) and artificial intelligence (AI).
Resources:
- Online courses: Coursera, edX, Udacity (e.g., “Introduction to Machine Learning” by Andrew Ng).
- Books: “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig.
Outcome: Grasp the general concepts of ML, types of learning (supervised, unsupervised, reinforcement), and the role of data in training models.

1.2 Basics of Natural Language Processing (NLP)

Objectives: Learn the fundamentals of NLP, including text preprocessing, tokenization, and basic algorithms.
Resources:
- Online courses: “Natural Language Processing with Python” on Coursera or edX.
- Books: “Speech and Language Processing” by Daniel Jurafsky and James H. Martin.
Outcome: Understand how text data is processed and the foundational techniques in NLP.

Stage 2: Intermediate Concepts

2.1 Deep Learning Foundations

Objectives: Dive into deep learning, focusing on neural networks, backpropagation, and optimization techniques.
Resources:
- Online courses: “Deep Learning Specialization” by Andrew Ng on Coursera.
- Books: “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Outcome: Gain a solid understanding of neural networks, their architectures, and how they are trained.

2.2 Sequence Models and Transformers

Objectives: Learn about sequence models (RNNs, LSTMs) and the transformer architecture.
Resources:
- Online courses: “Sequence Models” in the Deep Learning Specialization by Andrew Ng.
- Research papers: “Attention is All You Need” by Vaswani et al.
Outcome: Understand the evolution of sequence models and the significance of transformers in NLP.

Stage 3: Advanced Concepts

3.1 In-Depth Study of Transformers and BERT

Objectives: Delve deeper into transformers, including key models like BERT, GPT, and their variants.
Resources:
- Research papers: “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” “Language Models are Few-Shot Learners” by Brown et al.
- Tutorials: Hugging Face’s Transformers library tutorials.
Outcome: Understand the mechanisms of transformers, attention mechanisms, and pre-training/fine-tuning strategies.

3.2 Practical Implementation

Objectives: Implement and experiment with LLMs using popular frameworks and libraries.
Resources:
- Libraries: TensorFlow, PyTorch, Hugging Face Transformers.
- Tutorials and projects: Kaggle competitions, GitHub repositories.
Outcome: Gain hands-on experience in training, fine-tuning, and deploying LLMs.

Stage 4: Specialized Topics

4.1 Fine-Tuning and Transfer Learning

Objectives: Explore techniques for fine-tuning pre-trained models for specific tasks.
Resources:
- Tutorials: Hugging Face’s fine-tuning guides.
- Case studies and projects.
Outcome: Learn how to adapt pre-trained models for custom applications effectively.

4.2 Ethics and Bias in AI

Objectives: Study the ethical implications and potential biases in LLMs.
Resources:
- Articles and papers on AI ethics and bias.
- Courses on AI ethics (e.g., “AI For Everyone” by Andrew Ng).
Outcome: Develop an understanding of the ethical considerations and challenges in deploying LLMs.

Stage 5: Research and Development

5.1 Current Trends and Research

Objectives: Stay updated with the latest research, trends, and innovations in the field of LLMs.
Resources:
- Academic conferences: NeurIPS, ACL, EMNLP.
- Research platforms: arXiv, Google Scholar.
Outcome: Keep abreast of cutting-edge developments and emerging technologies in the field.

5.2 Contributing to the Field

Objectives: Engage in research, contribute to open-source projects, or develop novel applications.
Resources:
- Collaboration platforms: GitHub, research groups.
- Publication venues: Journals, conferences.
Outcome: Apply your knowledge to real-world problems, contribute to the academic community, and innovate within the field.

Additional Tips:

Consistency: Set aside regular study time and stick to a schedule.
Community Engagement: Join online forums, study groups, and attend webinars to enhance learning.
Hands-On Practice: Continuously work on practical projects to solidify your understanding.
Mentorship: Seek guidance from experts or mentors in the field to navigate complex topics.

By following this structured plan, your understanding will evolve from foundational knowledge to advanced expertise, enabling you to grasp how LLMs like ChatGPT work and how to effectively utilize and innovate with these powerful tools.

Using the Clear Thinking GPT:

Clear Idea: Understanding the Basics of Large Language Models (LLMs)

To effectively study how LLMs like ChatGPT work, you need to build a strong foundation in the key areas of machine learning, natural language processing, and neural networks, progressing step-by-step from basic concepts to advanced techniques.

Study Plan

1. Introduction to Machine Learning and Neural Networks

Objective: Grasp the basic concepts of machine learning and neural networks.
Topics:
- Basics of machine learning: supervised vs. unsupervised learning
- Introduction to neural networks: perceptrons, activation functions, and basic architectures
Resources:
- Online courses (Coursera, edX) on machine learning
- “Machine Learning Yearning” by Andrew Ng

2. Deep Learning Fundamentals

Objective: Understand the principles of deep learning and its applications.
Topics:
- Deep neural networks: layers, forward and backward propagation
- Training deep learning models: gradient descent, loss functions
- Introduction to frameworks (TensorFlow, PyTorch)
Resources:
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Practical tutorials on TensorFlow and PyTorch

3. Natural Language Processing (NLP) Basics

Objective: Learn the fundamental techniques in NLP.
Topics:
- Text preprocessing: tokenization, stemming, lemmatization
- Basic NLP tasks: sentiment analysis, part-of-speech tagging
- Word embeddings: Word2Vec, GloVe
Resources:
- “Speech and Language Processing” by Daniel Jurafsky and James H. Martin
- NLP courses on platforms like Coursera or Udacity

4. Advanced NLP and Transformer Models

Objective: Dive into advanced NLP techniques and the architecture of transformer models.
Topics:
- Advanced NLP tasks: named entity recognition, machine translation
- Transformers: attention mechanisms, architecture, and training
- Key models: BERT, GPT-2, GPT-3
Resources:
- Research papers: “Attention Is All You Need” by Vaswani et al.
- Transformer model tutorials (Hugging Face documentation)

5. Practical Implementation and Experimentation

Objective: Apply your knowledge by implementing and experimenting with LLMs.
Activities:
- Build and fine-tune a simple transformer model
- Experiment with pre-trained models from libraries like Hugging Face’s Transformers
- Engage in hands-on projects, such as chatbot development or text generation
Resources:
- Jupyter notebooks and code repositories
- Online forums and communities (Kaggle, GitHub)

Developmental Understanding

Foundation: By understanding basic machine learning and neural networks, you will develop the groundwork needed to comprehend more complex models.
Deep Learning Knowledge: Building on your foundation, deep learning concepts will allow you to grasp how large-scale models are structured and trained.
NLP Proficiency: Knowledge of NLP techniques will provide context on how language models process and generate text.
Advanced Techniques: Learning about transformers and state-of-the-art models will enable you to understand the specific mechanisms and innovations behind LLMs.
Practical Skills: Implementing and experimenting with models will solidify your understanding and allow you to see theory in action, making you proficient in the practical aspects of working with LLMs.

By following this structured plan, you will progress from foundational knowledge to advanced understanding and practical expertise in how LLMs like ChatGPT operate.

In comparison:

The custom GPT answer:

Has an introduction and central clear idea. In this way it sets a clear goal for the study plan right from the beginning.
Offers a concise, structured study plan: The study plan is divided into distinct, logical stages, each building on the previous one. Also, each stage clearly states the objective, topics to be covered, and resources needed.
Ensures developmental understanding: It explains how understanding will develop at each stage and the importance of each step in building a comprehensive understanding of LLMs. This ensures the reader knows the progression and interconnection of concepts.
Includes practical implementation and experimentation: The custom GPT makes practical experimentation an integral part of the learning process, reinforcing the theoretical knowledge. This makes the learning process engaging and effective.

On the other hand, the generic ChatGPT answer:

Is detailed but overwhelming: It provides an exhaustive list of resources and steps, this makes it difficult to follow the main points.
Lacks clear developmental steps: Although the stages are numbered, the generic answer does not clearly explain how understanding will develop at each stage. Also, it jumps into listing resources and outcomes without showing the logical progression from one stage to the next.
Is missing practical integration: While it mentions practical implementation, the generic answer does not integrate this aspect as seamlessly as the custom GPT.

Conclusion

Just as we have to use principles of clear thinking to get to a useful result, so too when we use AI, we must apply structured and deliberate methods to achieve optimal outcomes.

By following Descartes’ principles of clear and distinct thinking, we can enhance our use of AI and achieve more coherent, logically organized, and aligned answers.

This approach allows us to use AI in a way that is more accurate, reliable, capable of addressing complex problems and generating valuable insights.

Clear Thinking in AI