Introduction
In this tutorial, we'll explore how to create a basic AI agent that mimics some of the capabilities demonstrated by DeepMind's Aletheia. While we won't build a full research assistant, we'll create a simple system that can generate, verify, and revise mathematical proofs using natural language processing. This hands-on approach will help you understand the foundational concepts behind autonomous AI research agents.
Prerequisites
Before starting this tutorial, you'll need:
- A computer with internet access
- Python 3.8 or higher installed
- Basic understanding of Python programming
- Basic knowledge of mathematical concepts (proofs, theorems)
We'll use several Python libraries that will be installed via pip. No prior experience with AI research systems is required.
Step-by-Step Instructions
1. Install Required Libraries
First, we need to install the necessary Python libraries. Open your terminal or command prompt and run:
pip install transformers torch sympyWhy? The transformers library gives us access to pre-trained language models, torch provides deep learning capabilities, and sympy helps with symbolic mathematics.
2. Create a New Python File
Create a new file called aletheia_agent.py. This will be our main file for building the AI agent.
3. Import Required Modules
At the top of your aletheia_agent.py file, add:
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import sympy as sp
import randomWhy? These imports give us access to the language model for text generation, mathematical tools for proof verification, and random number generation for simulating the revision process.
4. Initialize the Language Model
Add the following code to load a pre-trained language model:
# Load a pre-trained model for text generation
model_name = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Create a text generation pipeline
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)Why? We're using a conversational model that can generate human-like text. This simulates how Aletheia might generate mathematical reasoning and proofs.
5. Create a Simple Proof Generator
Now, let's create a function that generates a simple mathematical proof:
def generate_proof(problem):
prompt = f"Generate a mathematical proof for the following theorem: {problem}"
# Generate text using our language model
output = generator(prompt, max_length=200, num_return_sequences=1,
do_sample=True, temperature=0.7)
return output[0]['generated_text']Why? This function takes a mathematical problem and asks our AI to generate a proof. The parameters control how creative and diverse the output will be.
6. Create a Proof Verification Function
Next, we'll build a basic verification function:
def verify_proof(proof):
# Simple verification - check if proof contains key mathematical terms
key_terms = ['theorem', 'proof', 'assume', 'therefore', 'thus', 'hence']
proof_lower = proof.lower()
found_terms = [term for term in key_terms if term in proof_lower]
# Simple check - if we find 3 or more key terms, we consider it a valid structure
return len(found_terms) >= 3Why? This is a simplified verification system. In a real implementation, this would involve complex mathematical checking, but for our tutorial, we're looking for structural elements that indicate a proper proof format.
7. Implement the Revision Loop
Now, let's create the iterative revision process:
def revise_proof(problem, initial_proof, max_iterations=3):
current_proof = initial_proof
for i in range(max_iterations):
print(f"\nIteration {i+1}:")
print(current_proof)
# Simulate revision by asking for improvement
revision_prompt = f"Improve this proof of '{problem}':\n{current_proof}"
# Generate a revised version
revision_output = generator(revision_prompt, max_length=300,
num_return_sequences=1, do_sample=True, temperature=0.8)
current_proof = revision_output[0]['generated_text']
# Simple check to see if we're making progress
if i < max_iterations - 1:
print("\n--- Revision Complete ---\n")
return current_proofWhy? This function simulates how Aletheia would iteratively improve its work. Each iteration takes the previous output and tries to make it better, mimicking the research process.
8. Put It All Together
Add this code to run our AI agent:
def main():
# Define a mathematical problem
problem = "The sum of two odd numbers is even"
print("AI Research Agent - Aletheia Simulation")
print("=======================================")
print(f"Problem: {problem}\n")
# Step 1: Generate initial proof
print("Step 1: Generating initial proof...")
initial_proof = generate_proof(problem)
print(initial_proof)
# Step 2: Verify the proof
print("\nStep 2: Verifying proof structure...")
is_valid = verify_proof(initial_proof)
print(f"Proof structure valid: {is_valid}")
# Step 3: Revise the proof
print("\nStep 3: Iteratively revising proof...")
final_proof = revise_proof(problem, initial_proof)
print("\nFinal Revised Proof:")
print(final_proof)
if __name__ == "__main__":
main()Why? This main function ties everything together, simulating the complete process from problem definition to final output.
9. Run the Agent
Save your file and run it:
python aletheia_agent.pyWhy? This executes your AI research agent, showing how it would work with a mathematical problem.
Summary
In this tutorial, we've created a simplified version of an AI research agent similar to DeepMind's Aletheia. We built a system that can:
- Generate mathematical proofs using language models
- Verify the structure of generated proofs
- Iteratively revise and improve its outputs
While our implementation is basic compared to Aletheia's capabilities, it demonstrates the core concepts of autonomous research: problem understanding, generation, verification, and revision. As you continue learning, you can expand this system by adding more sophisticated verification methods, integrating with mathematical libraries, or connecting to research databases.
