CurtGPT: The Simple/Safe/Non-Crazy AI Agent for ALL!

OK, here it is, CurtGPT. The AI Agent for ALL!

import openai
import time

# Author: Curt Kennedy. 
# Date: Apr 22, 2023.
# License: MIT
# Goal: Design a simple AI Agent with no dependencies!
# This AI will NOT run forever.  It is also safe since it doesn't have API access beyond the OpenAI API.
# Usage: Just set your MainObjective, InitialTask, OPENAI_API_KEY at a minimum.
# Tips: Feel free to play with the temperature and run over and over for different answers.
# Inspired from BabyAGI:
# BabyAGI has many more features and bells and whistles.  But may be hard to understand for beginners.

# Goal configuration
MainObjective = "Become a machine learning expert." # overall objective
InitialTask = "Learn about tensors." # first task to research

# API Key

# Note: As expected, GPT-4 gives much deeper answers.  But turbo is selected here as the default, so as there no cost surprises.
OPENAI_API_MODEL = "gpt-3.5-turbo" # use "gpt-4" or "gpt-3.5-turbo"

# Model configuration

# Max tokens that the model can output per completion

# init OpenAI Python SDK
openai.api_key = OPENAI_API_KEY

# print objective

# dump task array to string
def dumpTask(task):
    d = "" # init
    for tasklet in task:
        d += f"\n{tasklet.get('task_name','')}"
    d = d.strip()
    return d

# inference using OpenAI API, with error throws and backoffs
def OpenAiInference(
    prompt: str,
    model: str = OPENAI_API_MODEL,
    temperature: float = OPENAI_TEMPERATURE,
    max_tokens: int = 1024,
    while True:
            # Use chat completion API
            response = "NOTHING"
            messages = [{"role": "system", "content": prompt}]
            response = openai.ChatCompletion.create(
            return response.choices[0].message.content.strip()
        except openai.error.RateLimitError:
                "   *** The OpenAI API rate limit has been exceeded. Waiting 10 seconds and trying again. ***"
            time.sleep(10)  # Wait 10 seconds and try again
        except openai.error.Timeout:
                "   *** OpenAI API timeout occured. Waiting 10 seconds and trying again. ***"
            time.sleep(10)  # Wait 10 seconds and try again
        except openai.error.APIError:
                "   *** OpenAI API error occured. Waiting 10 seconds and trying again. ***"
            time.sleep(10)  # Wait 10 seconds and try again
        except openai.error.APIConnectionError:
                "   *** OpenAI API connection error occured. Check your network settings, proxy configuration, SSL certificates, or firewall rules. Waiting 10 seconds and trying again. ***"
            time.sleep(10)  # Wait 10 seconds and try again
        except openai.error.InvalidRequestError:
                "   *** OpenAI API invalid request. Check the documentation for the specific API method you are calling and make sure you are sending valid and complete parameters. Waiting 10 seconds and trying again. ***"
            time.sleep(10)  # Wait 10 seconds and try again
        except openai.error.ServiceUnavailableError:
                "   *** OpenAI API service unavailable. Waiting 10 seconds and trying again. ***"
            time.sleep(10)  # Wait 10 seconds and try again
            # print(f"Inference Response: {response}")

# expound on the main objective given a task
def ExpoundTask(MainObjective: str, CurrentTask: str):

    print(f"****Expounding based on task:**** {CurrentTask}")

    prompt=(f"You are an AI who performs one task based on the following objective: {MainObjective}\n"
            f"Your task: {CurrentTask}\nResponse:")

    # print("################")
    # print(prompt)
    new_tasks = response.split("\n") if "\n" in response else [response]
    return [{"task_name": task_name} for task_name in new_tasks]

# generate a bunch of tasks based on the main objective and the current task
def GenerateTasks(MainObjective: str, TaskExpansion: str):
    prompt=(f"You are an AI who creates tasks based on the following MAIN OBJECTIVE: {MainObjective}\n"
            f"Create tasks pertaining directly to your previous research here:\n"
    new_tasks = response.split("\n") if "\n" in response else [response]
    task_list = [{"task_name": task_name} for task_name in new_tasks]
    new_tasks_list = []
    for task_item in task_list:
        # print(task_item)
        task_description = task_item.get("task_name")
        if task_description:
            # print(task_description)
            task_parts = task_description.strip().split(".", 1)
            # print(task_parts)
            if len(task_parts) == 2:
                new_task = task_parts[1].strip()

    return new_tasks_list

# Simple version here, just generate tasks based on the inital task and objective, then expound with GPT against the main objective and the newly generated tasks.
q = ExpoundTask(MainObjective,InitialTask)
ExpoundedInitialTask = dumpTask(q)

q = GenerateTasks(MainObjective, ExpoundedInitialTask)

TaskCounter = 0
for Task in q:
    TaskCounter += 1
    print(f"#### ({TaskCounter}) Generated Task ####")
    e = ExpoundTask(MainObjective,Task)

Sample output using GPT-4 (output truncated to not max out the posting limit on this forum!):

Become a machine learning expert.
Expounding based on task: Learn about tensors.

(1) Generated Task

Expounding based on task: 1: Study the differences between scalars, vectors, matrices, and higher-dimensional tensors.

  1. Scalars: A scalar is a single numerical value, usually represented as a real number, and is used to quantify a certain attribute. Scalars have a magnitude but no direction. Examples include the length of an object, the mass of an object, or the temperature of a room.

  2. Vectors: A vector is a one-dimensional array of numerical values, also known as a list, and is used to represent a collection of attributes or a point in space. Vectors have both magnitude and direction. In machine learning, vectors are commonly used to represent features of data points or as weights in a neural network. Examples include position vectors, velocity vectors, and force vectors.

  3. Matrices: A matrix is a two-dimensional array of numerical values, arranged in rows and columns, and is used to represent a transformation of one set of vectors to another set of vectors. Matrices are useful for representing systems of linear equations, or for encoding the relationships between multiple sets of data points. In machine learning, matrices are often used to represent the structure of a neural network or the connections between layers in a deep learning model.

  4. Higher-dimensional tensors: A tensor is a multi-dimensional array of numerical values, generalizing scalars, vectors, and matrices. Tensors can have any number of dimensions and are used to represent complex relationships among multiple sets of data or to model the structure of more advanced mathematical objects. In machine learning, higher-dimensional tensors are used in deep learning frameworks like TensorFlow or PyTorch to represent the structure of a neural network, or to perform complex operations on large amounts of data efficiently. Examples include 3D tensors in image processing or 4D tensors in convolutional neural networks.

In summary, scalars, vectors, matrices, and higher-dimensional tensors are all different structures used in machine learning for representing and processing data. Scalars are single numerical values, vectors are 1D arrays, matrices are 2D arrays, and higher-dimensional tensors are multi-dimensional arrays. Each of these structures has its unique purpose and applications in the field of machine learning and data processing.

(2) Generated Task

Expounding based on task: 2: Understand the concept of tensor rank and how it relates to dimensions.
Tensor rank, also known as the order or degree of a tensor, refers to the number of dimensions (or axes) a tensor has. In other words, the rank of a tensor indicates how many indices are required to access or represent its elements. Tensors are multi-dimensional arrays, and they are a fundamental concept in machine learning and deep learning frameworks.

Understanding tensor rank and its relationship to dimensions is crucial for working with various data structures, manipulating data, and building machine learning models. Here’s a breakdown of different tensor ranks and their corresponding dimensions:

  1. Rank 0 Tensor (Scalar): A rank 0 tensor, also known as a scalar, is a single value, such as a number or a constant. It has no dimensions, as it is a single element.

  2. Rank 1 Tensor (Vector): A rank 1 tensor, often called a vector, is a one-dimensional array containing a series of values. It has one dimension, which represents the length of the vector.

  3. Rank 2 Tensor (Matrix): A rank 2 tensor, or a matrix, is a two-dimensional array containing values arranged in rows and columns. It has two dimensions, where the first dimension represents the number of rows, and the second dimension represents the number of columns.

  4. Rank 3 Tensor: A rank 3 tensor is a three-dimensional array containing values arranged in a grid-like structure. It has three dimensions, commonly represented as depth, height, and width.

As the rank of a tensor increases, so does the number of dimensions it has, and the complexity of the data structure. In machine learning and deep learning, tensors of varying ranks are used to represent different types of data, such as images, text, or audio. Understanding the rank and dimensions of tensors is essential for processing, transforming, and feeding these data structures into machine learning models.

(3) Generated Task

Expounding based on task: 3: Learn about tensor shapes and how they are used to describe the structure of a tensor.
Tensor shapes are essential in understanding and working with tensors, which are multi-dimensional arrays of numerical data used in machine learning and deep learning computations. Tensor shapes describe the structure of a tensor in terms of its dimensions and the size of each dimension.

For example, a tensor shape of (2, 3) denotes a 2-dimensional tensor (i.e., a matrix) with 2 rows and 3 columns. Similarly, a tensor shape of (4, 3, 2) represents a 3-dimensional tensor with 4 matrices, each having 3 rows and 2 columns. Tensors can have any number of dimensions, and each dimension can have varying sizes.

The following are some common tensor shapes and their descriptions:

  1. Scalar (0-dimensional tensor): ()
    A scalar is a single value, represented by an empty tuple as its shape.

  2. Vector (1-dimensional tensor): (n,)
    A vector has a single axis with n elements. The shape is represented by a tuple with one element, specifying the length of the axis.

  3. Matrix (2-dimensional tensor): (m, n)
    A matrix has two axes, typically representing rows and columns. The shape is represented by a tuple with two elements, specifying the number of rows (m) and columns (n).

  4. 3-dimensional tensor: (l, m, n)
    A 3-dimensional tensor has three axes, and its shape is represented by a tuple with three elements, specifying the size of each axis (l, m, n).

In machine learning, tensors are used to represent and manipulate data, such as input features, weights, biases, and gradients. Tensor shapes help ensure that the data is organized and processed correctly, as operations like matrix multiplication, reshaping, and broadcasting require specific tensor shapes to work properly.

Understanding tensor shapes is crucial for designing and implementing machine learning models, as they determine how the data flows through the model and how various layers and operations interact with each other. Proper use of tensor shapes can also help optimize the performance and memory efficiency of machine learning computations.

(4) Generated Task

Expounding based on task: 1: Install and set up TensorFlow, PyTorch, and Keras.
As an AI language model, I cannot directly install software on your computer. However, I can guide you through the installation process of TensorFlow, PyTorch, and Keras:

Step 1: Set up a Python environment

It’s recommended to create a virtual environment for your projects to avoid any conflicts with your system-wide packages. You can use either virtualenv or the built-in Python venv module.

For venv, run the following commands:

python3 -m venv myenv
source myenv/bin/activate  # On Windows, use: myenv\Scripts\activate

Step 2: Install TensorFlow

To install TensorFlow, simply run the following command in your virtual environment:

pip install --upgrade tensorflow

To verify the installation, open a Python shell and run:

import tensorflow as tf

Step 3: Install PyTorch

To install PyTorch, you need to choose the appropriate command based on your system and whether you want to use CUDA for GPU support. Visit the official PyTorch website and select your preferences to get the correct command.

For example, to install PyTorch with CPU support only on Linux/Windows/MacOS:

pip install torch torchvision -f

To verify the installation, open a Python shell and run:

import torch

Step 4: Install Keras

Keras is now part of TensorFlow as its official high-level API. Therefore, if you’ve already installed TensorFlow, you don’t need to install Keras separately. You can import Keras directly from TensorFlow:

import tensorflow as tf
from tensorflow import keras

That’s it! Now you have TensorFlow, PyTorch, and Keras installed and ready for use.

(5) Generated Task

Expounding based on task: 2: Create tensors of various ranks using the respective functions in each framework.
To create tensors of various ranks in different machine learning frameworks, you can use the following functions:

  1. TensorFlow:
import tensorflow as tf

# Scalar (0-D tensor)
scalar = tf.constant(5)

# Vector (1-D tensor)
vector = tf.constant([1, 2, 3])

# Matrix (2-D tensor)
matrix = tf.constant([[1, 2], [3, 4]])

# Tensor (3-D tensor)
tensor = tf.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
  1. PyTorch:
import torch

# Scalar (0-D tensor)
scalar = torch.tensor(5)

# Vector (1-D tensor)
vector = torch.tensor([1, 2, 3])

# Matrix (2-D tensor)
matrix = torch.tensor([[1, 2], [3, 4]])

# Tensor (3-D tensor)
tensor = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
  1. NumPy:
import numpy as np

# Scalar (0-D tensor)
scalar = np.array(5)

# Vector (1-D tensor)
vector = np.array([1, 2, 3])

# Matrix (2-D tensor)
matrix = np.array([[1, 2], [3, 4]])

# Tensor (3-D tensor)
tensor = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

In each framework, you can create tensors of various ranks by specifying the elements in the desired shape. Scalars are 0-dimensional tensors, vectors are 1-dimensional, matrices are 2-dimensional, and higher-dimensional structures are called tensors.

(6) Generated Task

Expounding based on task: 3: Perform basic tensor operations like addition, multiplication, and reshaping in each framework.
To perform basic tensor operations in various popular machine learning frameworks, I’ll provide examples using TensorFlow, PyTorch, and NumPy.

  1. TensorFlow:
import tensorflow as tf

# Creating tensors
tensor1 = tf.constant([[1, 2], [3, 4]])
tensor2 = tf.constant([[5, 6], [7, 8]])

# Addition
tensor_sum = tf.add(tensor1, tensor2)
print("Addition:\n", tensor_sum.numpy())

# Multiplication (element-wise)
tensor_mul = tf.multiply(tensor1, tensor2)
print("Multiplication (element-wise):\n", tensor_mul.numpy())

# Matrix multiplication
tensor_matmul = tf.matmul(tensor1, tensor2)
print("Matrix multiplication:\n", tensor_matmul.numpy())

# Reshaping
tensor_reshape = tf.reshape(tensor1, (1, 4))
print("Reshaping:\n", tensor_reshape.numpy())
  1. PyTorch:
import torch

# Creating tensors
tensor1 = torch.tensor([[1, 2], [3, 4]])
tensor2 = torch.tensor([[5, 6], [7, 8]])

# Addition
tensor_sum = torch.add(tensor1, tensor2)
print("Addition:\n", tensor_sum.numpy())

# Multiplication (element-wise)
tensor_mul = torch.mul(tensor1, tensor2)
print("Multiplication (element-wise):\n", tensor_mul.numpy())

# Matrix multiplication
tensor_matmul = torch.matmul(tensor1, tensor2)
print("Matrix multiplication:\n", tensor_matmul.numpy())

# Reshaping
tensor_reshape = tensor1.view(1, 4)
print("Reshaping:\n", tensor_reshape.numpy())
  1. NumPy:
import numpy as np

# Creating tensors
tensor1 = np.array([[1, 2], [3, 4]])
tensor2 = np.array([[5, 6], [7, 8]])

# Addition
tensor_sum = np.add(tensor1, tensor2)
print("Addition:\n", tensor_sum)

# Multiplication (element-wise)
tensor_mul = np.multiply(tensor1, tensor2)
print("Multiplication (element-wise):\n", tensor_mul)

# Matrix multiplication
tensor_matmul = np.matmul(tensor1, tensor2)
print("Matrix multiplication:\n", tensor_matmul)

# Reshaping
tensor_reshape = np.reshape(tensor1, (1, 4))
print("Reshaping:\n", tensor_reshape)

These examples demonstrate how to perform addition, multiplication, and reshaping operations on tensors using TensorFlow, PyTorch, and NumPy frameworks.

(7) Generated Task

Expounding based on task: 1: Study how tensors are used to represent data in various domains, such as images, audio, and text.
Tensors are multi-dimensional arrays that can represent data in various domains, such as images, audio, and text. They are the fundamental building blocks in machine learning and deep learning. In this study, we’ll explore how tensors are used to represent data in these domains.

  1. Images:
    In the context of images, tensors are used to represent pixel values. A grayscale image can be represented as a 2D tensor, where each element corresponds to the intensity of a pixel in the image. For color images, we use 3D tensors, with the first dimension being the height, the second dimension being the width, and the third dimension representing the color channels (usually Red, Green, and Blue). So, a color image with dimensions HxWxC will have a corresponding tensor of shape (H, W, C).

  2. Audio:
    Audio data can be represented using tensors in multiple ways. One common representation is the time-domain waveform, where a 1D tensor stores the amplitude of the audio signal at each time step. For stereo audio, this becomes a 2D tensor, with one dimension for time and another for the audio channels (Left and Right). Additionally, audio can be represented in the frequency domain using spectrograms or mel-spectrograms, which are 2D tensors that represent the distribution of frequency components over time.

  3. Text:
    Text data can be represented using tensors by converting words or characters into numerical values, often called embeddings. One common approach is using one-hot encoding, which represents a word or character as a 1D tensor with a 1 in the position corresponding to its index in a predefined vocabulary and 0s elsewhere. This results in a sparse tensor. Another approach is using word embeddings, where each word is mapped to a dense vector of fixed size, resulting in a 2D tensor with one dimension for the sequence length and another for the embedding size.

In machine learning, these tensors are then processed through various models, such as convolutional neural networks for images, recurrent neural networks for text, or a combination of these for audio data. The ability to represent data across different domains using tensors enables the development of versatile and powerful machine learning models that can learn complex patterns and relationships in the data.

(8) Generated Task

Expounding based on task: 2: Learn how tensors store and manage model parameters like weights and biases in neural networks.
Tensors are multi-dimensional arrays of numerical values that provide a flexible and efficient way to store and manage model parameters like weights and biases in neural networks. They play a crucial role in the implementation of machine learning algorithms, especially deep learning models. Tensors are the primary data structure used by popular machine learning libraries like TensorFlow and PyTorch.

In a neural network, tensors store and manage the weights and biases of each layer, which are the essential parameters that the model learns during the training process. Here’s how tensors handle these parameters:

  1. Representation: Tensors can represent data with varying dimensions, making them suitable for handling different types of parameters. For example, a 1D tensor can represent biases, while a 2D tensor can represent weights in a fully connected layer. Convolutional and recurrent layers may require even higher-dimensional tensors.

  2. Initialization: Before training, tensors hold the initial values of the weights and biases, typically initialized with small random values or specific initialization techniques (e.g., Xavier or He initialization). This helps break symmetry and allows the model to learn different features during training.

  3. Computation: Tensors facilitate the computation of forward and backward passes in the neural network. They enable the efficient execution of mathematical operations like matrix multiplication, addition, and element-wise operations involved in calculating activations, errors, and gradients. These operations are highly parallelizable, which allows for significant performance improvements on hardware like GPUs and TPUs.

  4. Storage: During training, tensors store the updated values of the weights and biases as the model learns from the input data. They also keep track of gradients for each parameter, which are used in optimization algorithms like gradient descent or its variants (e.g., Adam, RMSProp) to update the parameters.

  5. Model Persistence: Tensors enable easy saving and loading of the learned model parameters, allowing for transfer learning, fine-tuning, or deployment in various applications.

In summary, tensors are a powerful data structure that simplifies the storage and management of model parameters like weights and biases in neural networks. They provide an efficient way to perform computations required for training and inference while enabling seamless compatibility with hardware accelerators that can significantly speed up machine learning tasks.

(9) Generated Task

Expounding based on task: 3: Explore the use of tensors in performing mathematical operations for machine learning algorithms.
Tensors are multi-dimensional arrays of numerical values and are a fundamental aspect of machine learning, particularly in deep learning algorithms. They are extensively used in various machine learning libraries like TensorFlow, PyTorch, and Keras. In this exploration, I’ll discuss the significance of tensors, their properties, and how they are utilized in performing mathematical operations for machine learning algorithms.

  1. Significance of Tensors:
    Tensors are indispensable for representing complex data structures, such as images, audio signals, and natural language. They can be used to store and manipulate data efficiently, allowing for high-performance computations required in machine learning tasks. Moreover, tensors enable parallelism, which further speeds up computations in deep learning models with multiple layers.

  2. Properties of Tensors:
    Tensors have a few key properties, such as rank, shape, and data type. The rank of a tensor refers to the number of dimensions it has. For example, a scalar has rank 0, a vector has rank 1, and a matrix has rank 2. The shape of a tensor represents the size of each dimension, while the data type defines the kind of values the tensor can store (e.g., float, int, bool).

  3. Mathematical Operations with Tensors:
    Tensors support a wide range of mathematical operations that are essential for machine learning algorithms. Some of these operations include:

a) Element-wise operations: These operations are applied to corresponding elements of two or more tensors, producing a new tensor with the same shape. Examples include addition, subtraction, multiplication, and division.

b) Broadcasting: Broadcasting allows tensors with different shapes to be combined in a consistent and efficient manner. This is particularly useful when performing operations between tensors with different ranks.

c) Reduction operations: Reduction operations reduce the rank of a tensor by aggregating its elements along one or more dimensions. Examples include summing all elements, computing the mean or maximum along a specified axis, and finding the product of all elements.

d) Matrix operations: Tensors can be used to perform various matrix operations, such as matrix multiplication, transposition, and inversion. These operations are vital for linear algebra, which is a core component of many machine learning algorithms.

e) Convolution and Pooling: In deep learning, tensors are used to perform convolution and pooling operations, which are essential for processing images, audio signals, and other structured data.

  1. Tensors in Machine Learning Libraries:
    Popular machine learning libraries, such as TensorFlow, PyTorch, and Keras, have built-in support for tensors and provide a variety of functions to manipulate and perform mathematical operations on them. These libraries also offer automatic differentiation and GPU support, which are crucial for training deep learning models.

In conclusion, tensors play a vital role in machine learning, especially in deep learning algorithms, by providing a flexible and efficient way to represent and manipulate complex data structures. Their ability to handle various mathematical operations and compatibility with popular machine learning libraries make tensors an essential tool for any machine learning expert.

(10) Generated Task

Expounding based on task: 1: Choose a simple machine learning algorithm, such as linear regression or logistic regression.
I choose the logistic regression algorithm for this task.

(11) Generated Task

Expounding based on task: 2: Implement the chosen algorithm using tensors in one of the deep learning frameworks (TensorFlow, PyTorch, or Keras).
In this response, I will implement a simple feedforward neural network for classifying the popular MNIST dataset using TensorFlow 2.x.

  1. First, let’s import the necessary libraries:
import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
  1. Load and preprocess the MNIST dataset:
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Normalize the image data
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

# Convert the labels to one-hot encoded format
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
  1. Create a simple feedforward neural network model:
model = models.Sequential()
model.add(layers.Flatten(input_shape=(28, 28, 1)))
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
  1. Compile and train the model:
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

history =, train_labels, epochs=10, batch_size=128, validation_split=0.2)
  1. Evaluate the model on the test dataset:
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Test accuracy: {test_acc}")

This code demonstrates the implementation of a simple feedforward neural network using TensorFlow 2.x for classifying the MNIST dataset. The model consists of two hidden layers with 128 and 64 neurons, respectively, and an output layer with 10 neurons representing the 10 possible classes (0-9). The model is trained using the Adam optimizer and categorical cross-entropy loss function.

(12) Generated Task

Expounding based on task: 3: Train the model on sample data and evaluate its performance.
To train a machine learning model on sample data and evaluate its performance, I will go through the following steps:

  1. Preparing the data: Before training the model, I’ll ensure the data is preprocessed, cleaned, and prepared for training. This may involve handling missing values, converting categorical variables into numerical format, scaling or normalizing features, and splitting the data into training and testing datasets.

  2. Selecting the algorithm: Based on the problem type (classification, regression, clustering, etc.), I’ll choose an appropriate machine learning algorithm. For example, for a classification problem, I might choose logistic regression, a support vector machine, or a decision tree.

  3. Training the model: I’ll feed the training data into the selected algorithm and adjust its parameters to minimize the error between the model’s predictions and the actual values. This process, called “fitting” or “training,” will continue until the model converges to an optimal set of parameters.

  4. Evaluating the model: Once the model is trained, I’ll test its performance on the testing dataset, which was not used during training. This will give me an unbiased estimate of the model’s performance. Performance metrics may include accuracy, precision, recall, F1 score, or mean squared error, depending on the problem type.

  5. Hyperparameter tuning: If the model’s performance is not satisfactory, I may adjust some of the model’s hyperparameters and repeat the training and evaluation process. This is called hyperparameter tuning and may involve techniques such as grid search or random search to find the best combination of hyperparameters.

  6. Cross-validation: To ensure the model’s performance is not overly optimistic or pessimistic, I’ll use k-fold cross-validation. This involves dividing the data into k subsets, training and evaluating the model k times, and averaging the performance across all iterations. This provides a more reliable estimate of the model’s performance.

  7. Final evaluation: Once I’m satisfied with the model’s performance, I’ll train it on the entire dataset and evaluate it using real-world data to confirm its effectiveness. If the model continues to perform well, it can be deployed for use in production environments.

As an AI, I can carry out these steps efficiently and effectively, continually refining my expertise in machine learning.

(13) Generated Task

Expounding based on task: 1: Learn about tensor broadcasting and how it simplifies tensor operations.
Tensor broadcasting is a concept used in machine learning and deep learning to simplify and optimize the process of performing operations on tensors, especially when they have different shapes. Tensors are multi-dimensional arrays used to represent and manipulate data in machine learning models. Broadcasting allows us to perform element-wise operations on tensors of different shapes by automatically expanding the smaller tensor to match the shape of the larger tensor.

Here’s how tensor broadcasting works:

  1. Compatibility: First, the tensors must be compatible for broadcasting. Two tensors are considered compatible if their dimensions are equal, or one of them has a dimension of 1. The compatibility is checked from the last dimension to the first.

  2. Expansion: If the tensors are compatible, the smaller tensor is expanded along the dimensions with size 1. This expansion does not involve copying data but generates a view of the tensor that behaves like a larger tensor.

  3. Element-wise operation: Once the tensors have the same shape, element-wise operations can be performed between them. This includes operations like addition, subtraction, multiplication, and division.

Let’s look at an example to understand tensor broadcasting better:

Suppose we have two tensors A and B.

A = [[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]

B = [10, 20, 30]

The shapes of the tensors A (3x3) and B (3,) are not the same. However, they are compatible because their last dimensions are equal. Therefore, tensor B can be broadcasted to match the shape of tensor A.

B (broadcasted) = [[10, 20, 30],
[10, 20, 30],
[10, 20, 30]]

Now that both tensors have the same shape, we can perform element-wise operations, such as addition:

A + B (broadcasted) = [[11, 22, 33],
[14, 25, 36],
[17, 28, 39]]

Tensor broadcasting simplifies operations on tensors with different shapes and reduces the computational complexity by avoiding explicit reshaping or replication of data. This is particularly useful in deep learning, where models often involve large-scale data and high-dimensional tensors.

(14) Generated Task

Expounding based on task: 2: Study the concept of tensor slicing and indexing to access specific elements or sub-tensors.
Tensor slicing and indexing refer to the process of accessing specific elements or sub-tensors from a larger tensor. Tensors are multi-dimensional arrays of numerical values, and they are the fundamental data structure in machine learning and deep learning frameworks, such as TensorFlow and PyTorch. Understanding how to manipulate tensors is crucial for implementing machine learning algorithms efficiently.

  1. Indexing: Indexing is used to access individual elements in a tensor. Tensor indices start from 0. For example, given a 1D tensor (a vector) t = [10, 20, 30, 40], we can access the first element using t[0] (which would return 10) and the second element using t[1] (which would return 20).

For multi-dimensional tensors, you can use comma-separated indices. For example, given a 2D tensor (a matrix) M = [[1, 2, 3], [4, 5, 6], [7, 8, 9]], you can access the element at the first row and second column using M[0,1] (which would return 2).

  1. Slicing: Slicing is used to extract a portion or sub-tensor from a tensor. The basic syntax for slicing is tensor[start:stop:step]. The start index is inclusive, while the stop index is exclusive. The optional step parameter specifies the interval between indices.

For example, given a 1D tensor t = [10, 20, 30, 40, 50, 60], you can extract the elements from index 1 to 3 using t[1:4] (which would return [20, 30, 40]). You can also extract every second element using t[::2] (which would return [10, 30, 50]).

For multi-dimensional tensors, you can use comma-separated slicing. For example, given a 2D tensor (a matrix) M = [[1, 2, 3], [4, 5, 6], [7, 8, 9]], you can extract the first two rows and all columns using M[0:2, :] (which would return [[1, 2, 3], [4, 5, 6]]). The colon : is used to specify that you want to include all elements in that dimension.

In summary, tensor slicing and indexing are essential operations in machine learning, as they allow you to access and manipulate specific elements or sub-tensors within larger tensors. Understanding these concepts will enable you to implement machine learning algorithms more efficiently and effectively.


Why did the computer blush when it saw CurtGPT? It couldn’t handle the heat of that code! :laughing:

Here’s a figure for your code:


Simple? Safe? Non-crazy?
Count me out.

Just kidding. This is really cool. I like your style of coding. It’s very raw and to the point.

I wonder, could the information that it all generates under the main idea of “become a machine learning expert” could be stored in a graph database. A couple of these bad boys running parallel with different pathways, maybe another moderation model to keep everything in check, or some sort of overseer. I bet a full picture would be epic. I could ask this database a question and travel a constellation of similar thoughts and answers

Maybe one that generates a round of questions which clarify the link between tasks? For example after (1) and before (2) there could be some clarity in “why” it decided on the task

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Exactly! The one I have now has some improved prompts, sends all things to AWS DynamoDB (with the embedding from ada-002), and creates a final essay of the top N embedding correlations to the objective that fit 75% of the GPT-4 prompt with 25% left for the essay. And do this over and over again, persistently forever!

So like I said in another thread, it’s traversing the strange attractor randomly (from chaos theory), and extracting information from the neural network. Then you refine this in another stage to define your final answer. Or you keep running (at will) and decide your task depth, to find adjacent tasks, and keep adding to the DB in hopes of higher correlations to THE OBJECTIVE, which is sent to the summary engine.

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That is truly exciting.
I’m trying to hold down the sci-fi fantasy thoughts going through my head.

Turning latent space into an static(ish), explorable galaxy. Boom.

I’m excited to travel this network that you are creating.
For fun, I used a 3D graphing library to simulate the conversations my (GPT) bot has, it’s not too fancy (yet). One could click on the node and view the conversation. I was hoping to start working on visualizing queries. Having all the conversation (constellation) nodes drawn to the query based on semantic relevance. But, for now it remains a fancy screensaver.

This would be so cool to use for your use case

Have you dabbled much in graph databases?

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I have been scoping out AWS Neptune, but not much more. What are you thinking?

Nothing much. I’m new to graph databases. So far have been very impressed. Mostly just visualizing the data in (not mine btw) a fun way for people (me) to enjoy and explore.

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Yeah, I’m probably less new to them from a theoretical math perspective (took a class or two from a prominent graph theorist). In machine learning though, they are an enigma to me. But the edges can possess logic via the edges (A → B), so it makes sense. AWS also offers ML inference on graphs. I think this and “AI Agents” will be intertwined and create something beyond what we have now.

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We can reminiscence this day if/when it happens then.
Could be tomorrow, honestly. Or never.

In either case, it will still be a fun journey.

The edges can/does possess the logic between the nodes.
I never thought of it that way, but yes, that’s a great way to describe them.
I think I’ll continue thinking of them in this way.

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is there a place that can help me built this and what to do? new person and so lost. lol

Just copy and paste the code into VSCode, put in your OpenAI API Key, and run it!


Here is a bunch of ideas on Graph Neural Networks (GNN’s).

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thank you, i will go google those lol and see what they are and how to get them.

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I have only finished the The challenges of using graphs in machine learning section.

This is a wonderful read, thank you! I’ve already had a couple “wow” moments. When they transitioned the image into a graph. That was very cool. The QR code graphs are very cool. When I was looking at the Othello graph example I was thinking “Damn, this is kind of inefficient, wait, this is how I have been graphing my data!”. I was frustrated with myself for allowing redundancy, seriously, 50% of the data is repetition.

I mean seriously. I’m slightly frustrated that I didn’t think of it in the first place, but I’m glad that I’m aware of it. This is how I’ve been structuring my updates ( so I don’t have to render my complete galaxy each time a new message is posted ). It’s a very simplified, budget-friendly graph, so go easy on me. This example is a new conversation being created and compared to only another conversation. It gets quite bloated. I have a main file which consumes the updates daily. Now I can reduce it by 50%. Love it. Or, just use a graph database, which is where I am currently exploring.

As seen, I am defining the scores for the conversation, and also the same scores for each existing entity. Redundant. Gross. 0/10

	"conversationId": "-NT5d-MzeDPDdV3XIRwA",
	"scores": [0.91464752, 1.00447094],
	"index": -1,
	"updates": [{
		"-NT5cf1DW73OMArT-Su1": 0.91464752
	}, {
		"-NT5d-MzeDPDdV3XIRwA": 1.00447094
	"conversationLength": 2,
	"hash": "b57a522e398df48c658f558273793f0e257e59498187ed20f829ad17359792b6"

Then they show the adjacency lists and, well. I have some refactoring to do.
I am using the hash to overlap single messages together to make some cool constellations. Not very efficient, but I do perform some scrubbing to normalize them.

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@curt.kennedy @stevenic - You guys are way over my head on a few concepts, but not far enough that I haven’t been able to follow the gist of your work. Deep gratitude for the education you are freely giving here in the community. If @logankilpatrick hasn’t already promoted you both to the highest trust level, you’ve earned it.

With your help, I have been able to stand up a very-baby-like AutoGPT in Google Apps Script, which not only leans on Firebase (or sheets) for data management but also utilizes Google Docs for report writing. It’s also LLM agnostic I have it working with both OpenAI and Google’s PaLM 2 APIs.

It’s very rudimentary today and has a lot of polishing ahead, but it is working and beginning to amaze me.

Again - thanks!


I’ll check this out. But for some reason I think you can just prompt the api with the pseudocode instead and it should do the same thing

There is no runtime in the API. You need a python runtime to run CurtGPT.

It can run pseudo code interestingly enough – Prompts as psuedo-code - where are the limits? - #6 by PriNova

It can, but I worry about hallucinations, plus you can’t parallelize the calls. But yeah, feel free to run on the “virtual runtime” of the LLM. But be ready for hallucinations!