Project Alpha examines the potential of Generative Pre-trained Transformers (GPTs) in education, with a specific focus on the AlphaGPT series and its innovative educational applications. Global educational challenges worsened by the COVID-19 pandemic have created a need for personalized and effective learning. The AlphaGPT series, with advanced neural networks and large language models (LLMs), and educational Alpha iOS apps offer an innovative way to address these needs. This paper provides a detailed description of the AlphaGPT series, from the simple AlphaGPT 0.01 to the complex AlphaGPT 0.04, and the advanced AlphaGPT 0.2, reflecting their architecture, uses, and potential to revolutionize educational content creation and delivery.

An analysis of my novel artificial intelligence (AI) tutor, AlphaGPT, with a final total tested accuracy of 99.17%, against other large language models, such as OpenAI's GPT series and Meta's LLAMA2 models, was used to assess each model’s performance in generating and interpreting educational content. The research then introduces Project Alpha's innovative applications, which are AlphaGPT, and my AI powered iOS apps Alpha Words, and Alpha Math & Science. Each application is designed to enhance the educational experience and outcomes for K-12 students of different ages and educational backgrounds.

The project highlights the significant role that AI and GPTs can play in overcoming educational challenges and creating personalized learning. The findings of this study show the potential of AlphaGPT and the innovative educational Alpha iOS apps in creating a more engaging, safe, and personalized learning experience for students worldwide.

Machine learning and artificial intelligence (AI) can be used to revolutionize education by creating innovative educational solutions. This research paper goes into the revolutionary potential of Generative Pre-trained Transformers (GPTs) in the education field, particularly focusing on the AlphaGPT series and its innovative educational applications. GPTs have been common in recent advancements in natural language processing (NLP), revealing new capabilities in generating coherent and relevant text. The AlphaGPT series, a set of neural networks and large language models, and the innovative educational Alpha iOS apps represent a significant leap towards the education revolution, aiming to transform educational content creation, customization, and delivery.

The arrival of GPTs has unlocked new possibilities in creating interactive and adaptive learning environments. From generating educational content to providing detailed analyses, these models have the power to reshape the way educators teach and how students engage with material. This paper shows the fundamental concepts of GPTs and their relevance in the educational sector. The paper dives into a detailed exploration of the AlphaGPT neural networks, starting from the simple AlphaGPT 0.01 to the more complex AlphaGPT 0.04 and finally the sophisticated AlphaGPT 0.2. Each model is explained in detail for a deep understanding of their architecture and functionality.

Next, this paper presents an analysis of several large language models, including OpenAI’s GPT series and Meta’s LLAMA2 models, to assess their performance in interpreting and generating educational content. This analysis is important to understand the strengths and limitations of each model and how they can be fine-tuned and integrated into educational settings to enhance learning experiences.

Through this research, Project Alpha aims to provide educators and students with a deep and detailed understanding of the potential of GPTs in education. The project goal is to create innovative applications of AI to create more engaging, personalized, and effective learning environments. The AlphaGPT series and the iOS apps Alpha Words and Alpha Math & Science are a starting point toward a future of personalized learning. 

Transformer Models in Deep Learning

Attention Is All You Need (Vaswani et al.)[5]: 

This paper introduced the Transformer model, revolutionizing sequence modeling tasks like translation. The model is known for its reliance solely on attention mechanisms, it changed the previous norms of recurrent or convolutional layers in neural networks. Its main innovations included:

❖ A Self-Attention Mechanism, which allows the model to weigh the significance of different parts of the input data differently.

❖ Multi-Head Attention, which improves the model's ability to focus on different positions, letting it capture various contextual relationships.

❖ Scalability and Efficiency, due to parallelization advantages, which makes the model faster and more efficient to train.

The Transformer model showed superior performance in machine translation tasks, achieving record results in English-to-German and English-to-French translation tasks.

Scaling Language Models

LLaMA: Open and Efficient Foundation Language Models (Touvron et al.)[6]: 

This paper focused on large-scale language models (LLMs) and the efficiency and its accessibility challenges. The paper proposed LLaMA, a LLM that balances performance with efficiency. Its key differences were:

❖ Efficient Training, which addressed the computational demands of training large models and showed how LLaMA offered a more resource-efficient approach.

❖ Open Accessibility, which made large language modeling more accessible to a broader research and experimental community.

❖ A Foundation Model Concept, which showed that LLaMA was made to be a foundational model, capable of being fine-tuned for various tasks.

Both of these papers represent significant advancements in neural network architectures and language modeling, allowing for new innovations in the AI field. Project Alpha builds on these ideas, applying and extending these concepts to new innovative areas and addressing challenges in machine learning and AI.

OECD Reading, Math, Science Performance (PISA) Diagram, using the Python library Matplotlib.

The “AlphaGPT 0.01” neural network defines a simple character-based neural network for generating city names. It has 4,356 parameters. It starts by loading a dataset of city names and creating a bigram model as a baseline. The script then prepares data for a PyTorch [10] model by creating a matrix using character bigrams. The model is a basic neural network without hidden layers, and it uses one-hot encoded inputs with a single linear layer with 4,356 weights and no biases. The model's loss function is calculated, and training is performed with 10,000 iterations. The neural network includes code for generating city names using the trained model and visualizes both the loss during training and a heatmap of character transitions. This version is a simple model with a focus on character-level transitions.

The “AlphaGPT 0.02” neural network enhances the previous model by introducing hidden layers and embedding layers. It has 24,976 parameters, meaning this model is much larger than the one before it. The architecture includes an embedding layer, two linear layers with weights W1 and W2, and biases b1 and b2. The model uses blockSize = 3 characters, with numHidden = 250 neurons in the hidden layer. The training loop runs for 10,000 iterations, and the script includes a city name generation procedure like the previous version. There are additional visualizations for weights and biases. This model is more complex than the first, with an added non-linear function to capture more intricate patterns in the data.

The “AlphaGPT 0.03” neural network is further advanced in complexity compared to the previous versions. It has 51,958 parameters, more than double the parameters of the previous network. The architecture is deeper with multiple hidden layers, each using batch normalization and a Tanh activation function. The model is trained for 200,000 iterations, which shows a more extensive training process than before. This neural network includes visualizations for loss while training along with weights and biases histograms. Like the previous models, it features generation of city names but also includes the evaluation of losses for the train, develop, and test sets. This model is designed to find more complex patterns and dependencies in the dataset, with a focus on deeper learning.

The “AlphaGPT 0.04” neural network is the most advanced model among the four. It has an architecture similar to the last version but includes increased complexity from its layer depth and size. It has 1,191,682 parameters and 1,602 neurons. The model includes an Embedding layer, Combine layers, multiple Linear layers, Batch Normalization, and Tanh activation functions. It uses a larger training scale with 200,000 iterations with extensive training for better performance. This model offers the best loss and generation of city names. It includes visualizations for training loss and weights and biases histograms.

Final Comparison of the 4 AlphaGPT Models (0.01, 0.02, 0.03, 0.04), using the Python library Matplotlib.

ChatGPT 4 answered each question and provided an explanation of how the answer was reached. Although ChatGPT 4 missed one of the questions on the physics test, ChatGPT 4 got the correct answer when asked the question a second time. In contrast, ChatGPT 3.5 only provided the answer to the question with no explanation with a total accuracy of 215% out of 300% (71.67%).

The LLAMA2 7B and 70B models only provided the answer to the questions, but the LLAMA2 70B model performed much better than the 7B model on the Science STAAR 8th Grade [25] and New York Physics [26] tests. LLAMA2 7B was more accurate than LLAMA2 70B on the New York Chemistry test [27].

The LLAMA2 13B model responded with the answer and an explanation for the chemistry test and responded with the answer and the question for the physics test, showing the qualities of a good AI tutor, however, the model performed poorly on the tests and tied at 20% with LLAMA2 70B for the New York Chemistry test.

Mistral 7B only responded with the answer to each question for all the tests, but still performed better than LLAMA2 7B on the New York Physics and New York Chemistry tests, also beating LLAMA2 13B and 70B on the New York Chemistry test.

Based on these results, I decided to build AlphaGPT, a helpful, supportive AI tutor for grades K-12 and coding assistant. AlphaGPT is using ChatGPT4 in the backend. Then I used AlphaGPT to develop two iOS apps, Alpha Words, a multi-language vocabulary and creative writing platform including the Latin and Mandarin languages. And, Alpha Math & Science covering mental math, K-12 science topics, and best-selling book summaries.

I also went back and tested AlphaGPT with the same three standard tests [25][26][27] and it achieved 297.5% out of the 300% (99.17%) for all three tests, slightly better than ChatGPT4. AlphaGPT answered each question and provided an explanation of how the answer was reached. AlphaGPT reported the incorrect answer for one of the physics test questions but then went on to state the correct answer when explaining. This was due to the fact that GPT models do not think, they simply just predict the next token in a sequence and they cannot go back and rethink their answer.

I developed three different innovative applications to revolutionize education using the power of Deep Machine Learning (DML) and Artificial Intelligence (AI).

AlphaGPT

AlphaGPT is an artificial intelligence model using OpenAI’s GPT 4 [19] in the backend, and it is specifically fine-tuned for educational purposes in grades K-12. Although GPT4 has been trained on a large amount of the internet, AlphaGPT has strict guidelines to ensure students receive age-appropriate responses and moves inappropriate conversations towards constructive and educational topics. It is designed to engage students in a supportive way, provide highly accurate educational information, respond while keeping in mind a student’s age and knowledge, and encourage curiosity. AlphaGPT also uses advanced natural language processing to create positive educational outcomes, emphasize safety, and personalize the education of every student.

This research has explored the revolutionary potential of Generative Pre-trained Transformers (GPTs) within education, specifically looking at the AlphaGPT series and its innovative applications of AlphaGPT and the Alpha iOS apps. The paper dove into the architectures and functionalities of several neural network models, from the rudimentary AlphaGPT 0.01 to the more complex AlphaGPT 0.04, moving to the highly sophisticated AlphaGPT 0.2. These models have established an important milestone to understand and generate educational text, showing how machine learning (ML) and artificial intelligence (AI) can potentially create safe, engaging, and personalized learning environments.

The comparative analysis of AlphaGPT against other large language models (LLMs) including OpenAI's GPTs and Meta's LLAMA2 models, provided important information about their performance in understanding and generating educational content. AlphaGPT had 99.17% accuracy in all tested scenarios, highlighting its potential as a powerful tool for educational purposes.

The paper addressed important educational challenges, emphasized by the United Nations' alarming statistics on learning losses and the United States’ educational lag behind other developed countries. Through Project Alpha, innovative applications like AlphaGPT, Alpha Words, and Alpha Math & Science were developed, each contributing uniquely to revolutionizing the education field. These applications leverage the capabilities of GPTs to provide safe, engaging, and personalized learning experiences, emphasizing the transformative impact of AI in education.

Project Alpha is a significant stride towards using ML and AI to revolutionize education. The AlphaGPT series and its innovative applications offer a promising pathway towards addressing current educational challenges, promoting personalized learning, and inspiring continuous innovation in the education field. The next steps of Project Alpha will focus on refining and creating more of these applications, ensuring that the benefits of personalized education are accessible to all.

There are several different next steps for Project Alpha and each requires future funding, moving from very high cost to lower cost:

• One direction is to build a better Transformer architecture that does not require hundreds of parallel GPU hours to train and train it on selective high quality educational datasets.

• Another direction is to enhance LLAMA2 13B and train it with high quality educational datasets to improve its accuracy, which will cost less than option 1 because LLAMA2 13B is already pre-trained on 2 trillion tokens.

• A third direction is to continue working with OpenAI’s high accuracy models and utilize them to produce more educational content for education apps including Alpha Math & Science and Alpha Words, which will have the per token charge price from OpenAI’s API.