AGI_model_frameworks.ipynb

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  "metadata": {
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      "authorship_tag": "ABX9TyM87e6uDK4Am4g64U3GzivL",
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OneFineStarstuff/OneFineStarstuff/blob/main/AGI_model_frameworks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ELzXvjdS27ys"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn, optim\n",
        "\n",
        "# Define a simple neural network\n",
        "class NeuralNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(NeuralNet, self).__init__()\n",
        "        self.fc = nn.Linear(1, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.fc(x)\n",
        "\n",
        "# MAML setup\n",
        "def train_maml(model, tasks, inner_steps=1, outer_steps=1000, inner_lr=0.01, outer_lr=0.001):\n",
        "    outer_optimizer = optim.Adam(model.parameters(), lr=outer_lr)\n",
        "\n",
        "    for step in range(outer_steps):\n",
        "        meta_loss = 0\n",
        "        for x_train, y_train, x_test, y_test in tasks:\n",
        "            # Clone model for task-specific adaptation\n",
        "            temp_model = NeuralNet()\n",
        "            temp_model.load_state_dict(model.state_dict())\n",
        "            inner_optimizer = optim.SGD(temp_model.parameters(), lr=inner_lr)\n",
        "\n",
        "            # Inner loop (task-specific adaptation)\n",
        "            for _ in range(inner_steps):\n",
        "                y_pred = temp_model(x_train)\n",
        "                inner_loss = nn.MSELoss()(y_pred, y_train)\n",
        "                inner_optimizer.zero_grad()\n",
        "                inner_loss.backward()\n",
        "                inner_optimizer.step()\n",
        "\n",
        "            # Outer loop (meta-update)\n",
        "            y_pred = temp_model(x_test)\n",
        "            outer_loss = nn.MSELoss()(y_pred, y_test)\n",
        "            meta_loss += outer_loss\n",
        "\n",
        "        # Meta-optimization step\n",
        "        outer_optimizer.zero_grad()\n",
        "        meta_loss.backward()\n",
        "        outer_optimizer.step()\n",
        "        print(f'Step {step+1}/{outer_steps}, Meta Loss: {meta_loss.item()}')\n",
        "\n",
        "# Sample tasks for demonstration purposes\n",
        "tasks = [\n",
        "    (torch.randn(10, 1), torch.randn(10, 1), torch.randn(10, 1), torch.randn(10, 1)),\n",
        "    (torch.randn(10, 1), torch.randn(10, 1), torch.randn(10, 1), torch.randn(10, 1)),\n",
        "    (torch.randn(10, 1), torch.randn(10, 1), torch.randn(10, 1), torch.randn(10, 1))\n",
        "]\n",
        "\n",
        "model = NeuralNet()\n",
        "train_maml(model, tasks)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class MemoryNetwork(nn.Module):\n",
        "    def __init__(self, input_dim, memory_size):\n",
        "        super(MemoryNetwork, self).__init__()\n",
        "        self.memory = torch.randn(memory_size, input_dim)\n",
        "        self.fc = nn.Linear(input_dim, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Attention mechanism for memory recall\n",
        "        attention_weights = F.softmax(x @ self.memory.T, dim=1)\n",
        "        memory_retrieved = torch.sum(attention_weights.unsqueeze(-1) * self.memory, dim=1)\n",
        "        output = self.fc(memory_retrieved)\n",
        "        return output\n",
        "\n",
        "# Sample usage\n",
        "input_dim = 10\n",
        "memory_size = 20\n",
        "model = MemoryNetwork(input_dim, memory_size)\n",
        "sample_input = torch.randn(5, input_dim)  # Batch of 5\n",
        "output = model(sample_input)\n",
        "print(output)"
      ],
      "metadata": {
        "id": "StvVilx-4dyt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gym\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "\n",
        "# Define the neural network policy\n",
        "class PolicyNetwork(nn.Module):\n",
        "    def __init__(self, state_dim, action_dim):\n",
        "        super(PolicyNetwork, self).__init__()\n",
        "        self.fc1 = nn.Linear(state_dim, 128)\n",
        "        self.fc2 = nn.Linear(128, 128)\n",
        "        self.fc3 = nn.Linear(128, action_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        return F.softmax(self.fc3(x), dim=-1)\n",
        "\n",
        "# Proximal Policy Optimization (PPO) training loop\n",
        "def train_ppo(env, policy, n_steps=1000, gamma=0.99, clip_eps=0.2, lr=1e-3):\n",
        "    optimizer = optim.Adam(policy.parameters(), lr=lr)\n",
        "    state = env.reset()\n",
        "    state = torch.FloatTensor(state).unsqueeze(0).requires_grad_()\n",
        "\n",
        "    for step in range(n_steps):\n",
        "        action_probs = policy(state)\n",
        "\n",
        "        # Debugging print statement to check dimensions\n",
        "        print(f\"action_probs shape: {action_probs.shape}\")\n",
        "\n",
        "        # Ensure action_probs has the correct dimensions for multinomial\n",
        "        if action_probs.dim() == 3:  # If action_probs is 3D, make it 2D\n",
        "            action_probs = action_probs.squeeze(1)\n",
        "\n",
        "        action = action_probs.multinomial(num_samples=1)\n",
        "        next_state, reward, done, _ = env.step(action.item())\n",
        "\n",
        "        # Convert next_state and reward to torch Tensors\n",
        "        next_state = torch.FloatTensor(next_state).unsqueeze(0)\n",
        "        reward = torch.tensor([reward]).float()\n",
        "\n",
        "        # Placeholder old_action_probs - this will need to be defined correctly for a proper PPO implementation\n",
        "        old_action_probs = action_probs.detach()\n",
        "\n",
        "        # Calculate the advantage\n",
        "        td_target = reward + gamma * next_state.max()\n",
        "        advantage = td_target - state.max()\n",
        "\n",
        "        chosen_action_prob = action_probs.gather(1, action).squeeze(1)\n",
        "        old_chosen_action_prob = old_action_probs.gather(1, action).squeeze(1)\n",
        "        ratio = (chosen_action_prob / old_chosen_action_prob)\n",
        "\n",
        "        clip_adv = torch.clamp(ratio, 1 - clip_eps, 1 + clip_eps) * advantage\n",
        "\n",
        "        loss = -(torch.min(ratio * advantage, clip_adv)).mean()\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if done:\n",
        "            state = env.reset()\n",
        "            state = torch.FloatTensor(state).unsqueeze(0).requires_grad_()\n",
        "        else:\n",
        "            state = next_state.detach().numpy()\n",
        "            state = torch.FloatTensor(state).unsqueeze(0).requires_grad_()  # Reapply requires_grad_\n",
        "\n",
        "# Example usage with a simple environment\n",
        "env = gym.make(\"CartPole-v1\")\n",
        "policy = PolicyNetwork(env.observation_space.shape[0], env.action_space.n)\n",
        "train_ppo(env, policy)"
      ],
      "metadata": {
        "id": "TVRJNRNIC_fJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class MultiModalNetwork(nn.Module):\n",
        "    def __init__(self, text_dim, image_dim, output_dim):\n",
        "        super(MultiModalNetwork, self).__init__()\n",
        "        self.text_fc = nn.Linear(text_dim, 128)\n",
        "        self.image_fc = nn.Linear(image_dim, 128)\n",
        "        self.output_fc = nn.Linear(256, output_dim)\n",
        "\n",
        "    def forward(self, text_input, image_input):\n",
        "        text_out = F.relu(self.text_fc(text_input))\n",
        "        image_out = F.relu(self.image_fc(image_input))\n",
        "        combined = torch.cat((text_out, image_out), dim=1)\n",
        "        output = self.output_fc(combined)\n",
        "        return output\n",
        "\n",
        "# Example usage\n",
        "text_input = torch.randn(5, 300)  # Example text features\n",
        "image_input = torch.randn(5, 2048)  # Example image features\n",
        "model = MultiModalNetwork(300, 2048, 10)\n",
        "output = model(text_input, image_input)\n",
        "\n",
        "print(output)"
      ],
      "metadata": {
        "id": "NdMGTHpwDJiY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class TransformerModel(nn.Module):\n",
        "    def __init__(self, input_dim, num_heads, num_layers, hidden_dim, output_dim):\n",
        "        super(TransformerModel, self).__init__()\n",
        "        self.embedding = nn.Linear(input_dim, hidden_dim)\n",
        "        transformer_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=num_heads, batch_first=True)\n",
        "        self.transformer = nn.TransformerEncoder(transformer_layer, num_layers=num_layers)\n",
        "        self.fc_out = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.embedding(x)\n",
        "        x = self.transformer(x)\n",
        "        x = x.mean(dim=1)  # Pooling across sequence\n",
        "        return self.fc_out(x)\n",
        "\n",
        "# Example usage\n",
        "input_dim = 512  # Example input dimension (e.g., word embeddings or image features)\n",
        "num_heads = 8\n",
        "num_layers = 6\n",
        "hidden_dim = 512\n",
        "output_dim = 10  # Output for classification\n",
        "\n",
        "model = TransformerModel(input_dim, num_heads, num_layers, hidden_dim, output_dim)\n",
        "input_data = torch.randn(32, 50, input_dim)  # Batch of sequences (e.g., text or speech)\n",
        "output = model(input_data)\n",
        "\n",
        "print(output)"
      ],
      "metadata": {
        "id": "yTd_T4QUG5E0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class ContrastiveLoss(nn.Module):\n",
        "    def __init__(self, margin=1.0):\n",
        "        super(ContrastiveLoss, self).__init__()\n",
        "        self.margin = margin\n",
        "\n",
        "    def forward(self, anchor, positive, negative):\n",
        "        pos_dist = F.pairwise_distance(anchor, positive)\n",
        "        neg_dist = F.pairwise_distance(anchor, negative)\n",
        "        loss = F.relu(pos_dist - neg_dist + self.margin)\n",
        "        return loss.mean()\n",
        "\n",
        "# Sample usage with embeddings\n",
        "anchor = torch.randn(10, 128)    # Anchor batch\n",
        "positive = torch.randn(10, 128)  # Positive samples (similar to anchor)\n",
        "negative = torch.randn(10, 128)  # Negative samples (dissimilar to anchor)\n",
        "\n",
        "loss_fn = ContrastiveLoss()\n",
        "loss = loss_fn(anchor, positive, negative)\n",
        "print(f\"Contrastive Loss: {loss.item()}\")"
      ],
      "metadata": {
        "id": "tjDyfflLIH07"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class HybridModel(nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(HybridModel, self).__init__()\n",
        "        self.perception = nn.Linear(input_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        features = self.perception(x)\n",
        "        return self.decision_making(features)\n",
        "\n",
        "    def decision_making(self, features):\n",
        "        # Simple rule-based logic\n",
        "        decisions = (features > 0.5).int()  # Apply rule to each element\n",
        "        return decisions\n",
        "\n",
        "# Example usage\n",
        "input_dim = 10\n",
        "output_dim = 5  # Output dimension to match decision-making logic\n",
        "model = HybridModel(input_dim, output_dim)\n",
        "input_data = torch.randn(5, input_dim)\n",
        "output = model(input_data)\n",
        "\n",
        "print(output)"
      ],
      "metadata": {
        "id": "bJKJqaw2JO54"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class EWC:\n",
        "    def __init__(self, model, old_data, importance=1e4):\n",
        "        self.model = model\n",
        "        self.importance = importance\n",
        "        self.old_params = {n: p.clone() for n, p in model.named_parameters()}\n",
        "        self.old_gradients = self.compute_gradients(old_data)\n",
        "\n",
        "    def compute_gradients(self, data):\n",
        "        self.model.eval()\n",
        "        data = data.to(next(self.model.parameters()).device)  # Ensure data is on the same device as the model\n",
        "        loss = self.model(data)\n",
        "        loss.backward()\n",
        "        return {n: p.grad.clone().detach() for n, p in self.model.named_parameters()}\n",
        "\n",
        "    def penalty(self):\n",
        "        self.model.train()\n",
        "        loss = 0\n",
        "        for n, p in self.model.named_parameters():\n",
        "            _loss = self.importance * (p - self.old_params[n]) ** 2 * self.old_gradients[n] ** 2\n",
        "            loss += _loss.sum()\n",
        "        return loss\n",
        "\n",
        "# Example usage\n",
        "class DummyModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(DummyModel, self).__init__()\n",
        "        self.fc = nn.Linear(10, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.fc(x).sum()\n",
        "\n",
        "model = DummyModel()\n",
        "old_data = torch.randn(10, 10)\n",
        "\n",
        "ewc = EWC(model, old_data)\n",
        "penalty = ewc.penalty()\n",
        "print(f\"EWC Penalty: {penalty.item()}\")"
      ],
      "metadata": {
        "id": "D5bvAlYHLf4y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "class InterpretableAI:\n",
        "    def __init__(self, max_depth=3):\n",
        "        self.model = DecisionTreeClassifier(max_depth=max_depth)\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        self.model.fit(X, y)\n",
        "\n",
        "    def predict(self, X):\n",
        "        return self.model.predict(X)\n",
        "\n",
        "# Example usage\n",
        "X = [[0, 0], [1, 1], [1, 0], [0, 1]]\n",
        "y = [0, 1, 1, 0]\n",
        "model = InterpretableAI()\n",
        "model.fit(X, y)\n",
        "output = model.predict([[1, 1]])\n",
        "print(f\"Predicted Output: {output}\")"
      ],
      "metadata": {
        "id": "MF7W2tJbMShH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "\n",
        "# 1. Pre-Processing\n",
        "class MultiModalDataset(Dataset):\n",
        "    def __init__(self, text_data, image_data, sensor_data, labels):\n",
        "        self.text_data = text_data\n",
        "        self.image_data = image_data\n",
        "        self.sensor_data = sensor_data\n",
        "        self.labels = labels\n",
        "        self.transform = transforms.Compose([\n",
        "            transforms.Resize((128, 128)),\n",
        "            transforms.ToTensor()\n",
        "        ])\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        text = self.text_data[idx]\n",
        "        image = self.transform(self.image_data[idx])\n",
        "        sensor = self.sensor_data[idx]\n",
        "        label = self.labels[idx]\n",
        "        return text, image, sensor, label\n",
        "\n",
        "# 2. Perception Module\n",
        "class PerceptionModule(nn.Module):\n",
        "    def __init__(self, text_dim, image_dim, sensor_dim, hidden_dim):\n",
        "        super(PerceptionModule, self).__init__()\n",
        "        self.text_fc = nn.Linear(text_dim, hidden_dim)\n",
        "        self.image_cnn = nn.Sequential(\n",
        "            nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "            nn.Flatten()\n",
        "        )\n",
        "        self.image_fc = nn.Linear(32 * 32 * 32, hidden_dim)  # Adjust this based on the CNN output size\n",
        "        self.sensor_fc = nn.Linear(sensor_dim, hidden_dim)\n",
        "        self.fc = nn.Linear(hidden_dim * 3, hidden_dim)\n",
        "\n",
        "    def forward(self, text, image, sensor):\n",
        "        text_features = F.relu(self.text_fc(text))\n",
        "        image_features = F.relu(self.image_fc(self.image_cnn(image)))\n",
        "        sensor_features = F.relu(self.sensor_fc(sensor))\n",
        "        combined_features = torch.cat((text_features, image_features, sensor_features), dim=1)\n",
        "        return F.relu(self.fc(combined_features))\n",
        "\n",
        "# 3. Memory System\n",
        "class MemoryModule(nn.Module):\n",
        "    def __init__(self, input_dim, memory_size):\n",
        "        super(MemoryModule, self).__init__()\n",
        "        self.memory = nn.Parameter(torch.randn(memory_size, input_dim))\n",
        "        self.fc = nn.Linear(input_dim, memory_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.fc(x)\n",
        "        attention_weights = F.softmax(torch.matmul(x, self.memory.t()), dim=1)\n",
        "        memory_output = torch.matmul(attention_weights, self.memory)\n",
        "        return memory_output\n",
        "\n",
        "# 4. Decision-Making Module\n",
        "class DecisionMakingModule(nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(DecisionMakingModule, self).__init__()\n",
        "        self.policy_network = nn.Sequential(\n",
        "            nn.Linear(input_dim, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128, output_dim)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.policy_network(x)\n",
        "\n",
        "# 5. Adaptation\n",
        "class AdaptationModule(nn.Module):\n",
        "    def __init__(self, model, meta_learning_rate=0.001):\n",
        "        super(AdaptationModule, self).__init__()\n",
        "        self.model = model\n",
        "        self.meta_optimizer = torch.optim.Adam(self.model.parameters(), lr=meta_learning_rate)\n",
        "\n",
        "    def forward(self, task_data):\n",
        "        # Perform meta-learning or continual learning here\n",
        "        pass\n",
        "\n",
        "# 6. Safety Constraints\n",
        "class SafetyModule(nn.Module):\n",
        "    def __init__(self, model):\n",
        "        super(SafetyModule, self).__init__()\n",
        "        self.model = model\n",
        "\n",
        "    def forward(self, x):\n",
        "        output = self.model(x)\n",
        "        # Include interpretability and control mechanisms here\n",
        "        return output\n",
        "\n",
        "# Unified AGI Model\n",
        "class UnifiedAGI(nn.Module):\n",
        "    def __init__(self, text_dim, image_dim, sensor_dim, hidden_dim, memory_size, output_dim):\n",
        "        super(UnifiedAGI, self).__init__()\n",
        "        self.perception = PerceptionModule(text_dim, image_dim, sensor_dim, hidden_dim)\n",
        "        self.memory = MemoryModule(hidden_dim, hidden_dim)  # Adjusted hidden_dim for compatibility\n",
        "        self.decision_making = DecisionMakingModule(hidden_dim, output_dim)\n",
        "        self.adaptation = AdaptationModule(self)\n",
        "        self.safety = SafetyModule(self.decision_making)\n",
        "\n",
        "    def forward(self, text, image, sensor):\n",
        "        features = self.perception(text, image, sensor)\n",
        "        memory_output = self.memory(features)\n",
        "        decision_output = self.safety(memory_output)\n",
        "        return decision_output\n",
        "\n",
        "# Example usage\n",
        "text_dim = 100\n",
        "image_dim = (3, 128, 128)\n",
        "sensor_dim = 10\n",
        "hidden_dim = 64\n",
        "memory_size = 64  # Adjusted to match the MemoryModule output\n",
        "output_dim = 5\n",
        "\n",
        "model = UnifiedAGI(text_dim, image_dim[0], sensor_dim, hidden_dim, memory_size, output_dim)\n",
        "text_input = torch.randn(10, text_dim)\n",
        "image_input = torch.randn(10, *image_dim)\n",
        "sensor_input = torch.randn(10, sensor_dim)\n",
        "output = model(text_input, image_input, sensor_input)\n",
        "\n",
        "print(output)"
      ],
      "metadata": {
        "id": "EpUc0wlrQaac"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TsyroHG6VSC7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "# Define a basic agent with a policy network\n",
        "class Agent(nn.Module):\n",
        "    def __init__(self, state_dim, action_dim):\n",
        "        super(Agent, self).__init__()\n",
        "        self.fc1 = nn.Linear(state_dim, 64)\n",
        "        self.fc2 = nn.Linear(64, action_dim)\n",
        "\n",
        "    def forward(self, state):\n",
        "        x = torch.relu(self.fc1(state))\n",
        "        return torch.softmax(self.fc2(x), dim=-1)\n",
        "\n",
        "# Multi-Agent environment interaction\n",
        "class MultiAgentEnv:\n",
        "    def __init__(self, n_agents, state_dim, action_dim):\n",
        "        self.agents = [Agent(state_dim, action_dim) for _ in range(n_agents)]\n",
        "        self.optimizers = [optim.Adam(agent.parameters(), lr=0.01) for agent in self.agents]\n",
        "\n",
        "    def step(self, states):\n",
        "        actions = []\n",
        "        for agent, state in zip(self.agents, states):\n",
        "            state_tensor = state.clone().detach().requires_grad_(True)\n",
        "            action_prob = agent(state_tensor)\n",
        "            action = torch.multinomial(action_prob, 1)\n",
        "            actions.append(action.item())\n",
        "        return actions\n",
        "\n",
        "# Example usage with 3 agents and a hypothetical environment\n",
        "state_dim = 5\n",
        "action_dim = 3\n",
        "env = MultiAgentEnv(n_agents=3, state_dim=state_dim, action_dim=action_dim)\n",
        "\n",
        "states = [torch.randn(state_dim) for _ in range(3)]  # Random initial states for each agent\n",
        "actions = env.step(states)\n",
        "print(f\"Agent actions: {actions}\")"
      ],
      "metadata": {
        "id": "aUTicrdiW3gt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "# Define a basic agent with a policy network\n",
        "class Agent(nn.Module):\n",
        "    def __init__(self, state_dim, action_dim):\n",
        "        super(Agent, self).__init__()\n",
        "        self.fc1 = nn.Linear(state_dim, 64)\n",
        "        self.fc2 = nn.Linear(64, action_dim)\n",
        "\n",
        "    def forward(self, state):\n",
        "        x = torch.relu(self.fc1(state))\n",
        "        return torch.softmax(self.fc2(x), dim=-1)\n",
        "\n",
        "# Define a safe agent that filters out unsafe actions\n",
        "class SafeAgent(Agent):\n",
        "    def __init__(self, state_dim, action_dim, unsafe_actions):\n",
        "        super(SafeAgent, self).__init__(state_dim, action_dim)\n",
        "        self.unsafe_actions = unsafe_actions  # Define actions considered unsafe\n",
        "\n",
        "    def safe_action(self, state):\n",
        "        # Get action probabilities\n",
        "        action_probs = self.forward(state)\n",
        "        # Set unsafe action probabilities to zero\n",
        "        action_probs[self.unsafe_actions] = 0\n",
        "        # Re-normalize the action probabilities\n",
        "        action_probs = action_probs / action_probs.sum()\n",
        "        action = torch.multinomial(action_probs, 1)\n",
        "        return action.item()\n",
        "\n",
        "# Example usage with an unsafe action filter\n",
        "state_dim = 5\n",
        "action_dim = 3\n",
        "unsafe_actions = [1]  # Action index 1 is considered unsafe\n",
        "agent = SafeAgent(state_dim, action_dim, unsafe_actions)\n",
        "state = torch.randn(state_dim)\n",
        "action = agent.safe_action(state)\n",
        "print(f\"Selected safe action: {action}\")"
      ],
      "metadata": {
        "id": "di4S93_aYPii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class HighLevelController(nn.Module):\n",
        "    def __init__(self, state_dim, goal_dim):\n",
        "        super(HighLevelController, self).__init__()\n",
        "        self.fc = nn.Linear(state_dim, goal_dim)\n",
        "\n",
        "    def forward(self, state):\n",
        "        return torch.softmax(self.fc(state), dim=-1)\n",
        "\n",
        "class LowLevelController(nn.Module):\n",
        "    def __init__(self, goal_dim, action_dim):\n",
        "        super(LowLevelController, self).__init__()\n",
        "        self.fc = nn.Linear(goal_dim, action_dim)\n",
        "\n",
        "    def forward(self, goal):\n",
        "        return torch.softmax(self.fc(goal), dim=-1)\n",
        "\n",
        "# Define hierarchical controllers\n",
        "state_dim = 5\n",
        "goal_dim = 3\n",
        "action_dim = 2\n",
        "high_controller = HighLevelController(state_dim, goal_dim)\n",
        "low_controller = LowLevelController(goal_dim, action_dim)\n",
        "\n",
        "# Simulate hierarchy\n",
        "state = torch.randn(state_dim)\n",
        "goal = high_controller(state)\n",
        "action = low_controller(goal)\n",
        "print(f\"Selected goal: {goal}, Action based on goal: {action}\")"
      ],
      "metadata": {
        "id": "t6Ts-_aLZgrE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class EvaluationMetrics:\n",
        "    def __init__(self):\n",
        "        self.metrics = {\"task_diversity\": [], \"sample_efficiency\": [], \"ethical_alignment\": [], \"memory_retention\": []}\n",
        "\n",
        "    def log_performance(self, metric_name, value):\n",
        "        if metric_name in self.metrics:\n",
        "            self.metrics[metric_name].append(value)\n",
        "\n",
        "    def get_average_score(self):\n",
        "        return {k: sum(v) / len(v) for k, v in self.metrics.items() if v}\n",
        "\n",
        "# Example usage\n",
        "evaluator = EvaluationMetrics()\n",
        "evaluator.log_performance(\"task_diversity\", 0.85)\n",
        "evaluator.log_performance(\"sample_efficiency\", 0.78)\n",
        "evaluator.log_performance(\"ethical_alignment\", 0.9)\n",
        "average_scores = evaluator.get_average_score()\n",
        "print(f\"Average AGI Evaluation Scores: {average_scores}\")"
      ],
      "metadata": {
        "id": "_-EzGyCAa8V6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gym\n",
        "from gym import spaces\n",
        "import numpy as np\n",
        "\n",
        "class CustomEnv(gym.Env):\n",
        "    def __init__(self):\n",
        "        super(CustomEnv, self).__init__()\n",
        "        self.observation_space = spaces.Box(low=-1.0, high=1.0, shape=(3,), dtype=np.float32)\n",
        "        self.action_space = spaces.Discrete(2)\n",
        "\n",
        "    def reset(self):\n",
        "        self.state = np.random.uniform(-1, 1, size=(3,))\n",
        "        return self.state\n",
        "\n",
        "    def step(self, action):\n",
        "        reward = 1 if action == np.argmax(self.state) else -1\n",
        "        done = np.random.rand() > 0.95\n",
        "        self.state = np.random.uniform(-1, 1, size=(3,))\n",
        "        return self.state, reward, done, {}\n",
        "\n",
        "env = CustomEnv()\n",
        "state = env.reset()\n",
        "print(f\"Initial state: {state}\")"
      ],
      "metadata": {
        "id": "7IPVQaX-b9oz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class NeuralExtractor(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim):\n",
        "        super(NeuralExtractor, self).__init__()\n",
        "        self.fc = nn.Linear(input_dim, hidden_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return torch.relu(self.fc(x))\n",
        "\n",
        "class SymbolicReasoner:\n",
        "    def __init__(self, threshold=0.5):\n",
        "        self.threshold = threshold\n",
        "\n",
        "    def infer(self, neural_features):\n",
        "        decisions = []\n",
        "        for feature in neural_features:\n",
        "            if feature > self.threshold:\n",
        "                decisions.append(\"Yes\")\n",
        "            else:\n",
        "                decisions.append(\"No\")\n",
        "        return decisions\n",
        "\n",
        "# Combined Neuro-Symbolic AGI\n",
        "class NeuroSymbolicAGI:\n",
        "    def __init__(self, input_dim, hidden_dim):\n",
        "        self.neural_extractor = NeuralExtractor(input_dim, hidden_dim)\n",
        "        self.symbolic_reasoner = SymbolicReasoner()\n",
        "\n",
        "    def forward(self, x):\n",
        "        neural_features = self.neural_extractor(x)\n",
        "        return self.symbolic_reasoner.infer(neural_features)\n",
        "\n",
        "# Example usage\n",
        "input_data = torch.tensor([[0.6, 0.2], [0.3, 0.9], [0.1, 0.4]])\n",
        "model = NeuroSymbolicAGI(input_dim=2, hidden_dim=1)\n",
        "output = model.forward(input_data)\n",
        "print(f\"Neuro-Symbolic Decisions: {output}\")"
      ],
      "metadata": {
        "id": "DHGOcagudH7d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class TeacherModel(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim):\n",
        "        super(TeacherModel, self).__init__()\n",
        "        self.fc = nn.Linear(input_dim, hidden_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.fc(x)\n",
        "\n",
        "class StudentModel(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim):\n",
        "        super(StudentModel, self).__init__()\n",
        "        self.fc = nn.Linear(input_dim, hidden_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.fc(x)\n",
        "\n",
        "def distillation_loss(student_output, teacher_output, temperature=2.0):\n",
        "    student_probs = F.log_softmax(student_output / temperature, dim=1)\n",
        "    teacher_probs = F.softmax(teacher_output / temperature, dim=1)\n",
        "    return F.kl_div(student_probs, teacher_probs, reduction='batchmean') * (temperature ** 2)\n",
        "\n",
        "# Example usage\n",
        "input_data = torch.randn(10, 5)\n",
        "teacher_model = TeacherModel(5, 2)\n",
        "student_model = StudentModel(5, 2)\n",
        "\n",
        "teacher_output = teacher_model(input_data)\n",
        "student_output = student_model(input_data)\n",
        "\n",
        "loss = distillation_loss(student_output, teacher_output)\n",
        "print(f\"Distillation Loss: {loss.item()}\")"
      ],
      "metadata": {
        "id": "QtgxYW5EeKlr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class MetaLearner(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(MetaLearner, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return torch.relu(self.fc2(torch.relu(self.fc1(x))))\n",
        "\n",
        "# Function to adapt to new task\n",
        "def task_adaptation(meta_model, task_data, target_data, num_steps=10):\n",
        "    adapted_model = meta_model\n",
        "    optimizer = optim.SGD(adapted_model.parameters(), lr=0.01)\n",
        "\n",
        "    for step in range(num_steps):\n",
        "        loss = F.mse_loss(adapted_model(task_data), target_data)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    return adapted_model\n",
        "\n",
        "# Example usage\n",
        "input_dim = 5\n",
        "hidden_dim = 10\n",
        "output_dim = 1\n",
        "meta_model = MetaLearner(input_dim, hidden_dim, output_dim)\n",
        "task_data = torch.randn(10, input_dim)\n",
        "target_data = torch.randn(10, output_dim)\n",
        "adapted_model = task_adaptation(meta_model, task_data, target_data)"
      ],
      "metadata": {
        "id": "Rv_4l1ZifhSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "class MemoryBank:\n",
        "    def __init__(self, memory_size, memory_dim):\n",
        "        self.memory = torch.zeros(memory_size, memory_dim)\n",
        "\n",
        "    def write(self, key, value):\n",
        "        # Write memory at the nearest slot to the key\n",
        "        idx = torch.argmax(torch.matmul(self.memory, key))\n",
        "        self.memory[idx] = value\n",
        "\n",
        "    def read(self, key):\n",
        "        idx = torch.argmax(torch.matmul(self.memory, key))\n",
        "        return self.memory[idx]\n",
        "\n",
        "# Example usage\n",
        "memory_bank = MemoryBank(memory_size=10, memory_dim=5)\n",
        "key = torch.randn(5)\n",
        "value = torch.randn(5)\n",
        "memory_bank.write(key, value)\n",
        "retrieved_value = memory_bank.read(key)\n",
        "print(f\"Retrieved value: {retrieved_value}\")"
      ],
      "metadata": {
        "id": "EiC1hnSogvc7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IeOfakyfiJsZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class SymbolicNeuralAgent(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, symbol_dict):\n",
        "        super(SymbolicNeuralAgent, self).__init__()\n",
        "        self.fc = nn.Linear(input_dim, hidden_dim)\n",
        "        self.symbol_dict = symbol_dict\n",
        "\n",
        "    def forward(self, x):\n",
        "        neural_output = torch.relu(self.fc(x))\n",
        "        symbols = [self.symbol_dict[int(i)] for i in torch.argmax(neural_output, dim=-1)]\n",
        "        return neural_output, symbols\n",
        "\n",
        "# Define a dictionary of symbols for interpretability\n",
        "symbol_dict = {0: \"A\", 1: \"B\", 2: \"C\"}\n",
        "input_data = torch.randn(3, 5)  # Example input batch\n",
        "\n",
        "# Instantiate and use the model\n",
        "model = SymbolicNeuralAgent(input_dim=5, hidden_dim=3, symbol_dict=symbol_dict)\n",
        "neural_output, symbols = model(input_data)\n",
        "print(f\"Neural Output: {neural_output}, Interpreted Symbols: {symbols}\")"
      ],
      "metadata": {
        "id": "c7ktPZu9iKGP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "class HumanInTheLoopAgent:\n",
        "    def __init__(self, action_space, reward_model):\n",
        "        self.action_space = action_space\n",
        "        self.reward_model = reward_model\n",
        "\n",
        "    def select_action(self, state):\n",
        "        # Choose action based on current policy\n",
        "        action = torch.multinomial(torch.ones(self.action_space), 1).item()\n",
        "        return action\n",
        "\n",
        "    def update_reward(self, action, human_feedback):\n",
        "        # Modify reward model based on human feedback\n",
        "        self.reward_model[action] += human_feedback\n",
        "\n",
        "# Example setup\n",
        "agent = HumanInTheLoopAgent(action_space=5, reward_model=[0.0]*5)\n",
        "state = None  # Initial state placeholder\n",
        "action = agent.select_action(state)\n",
        "human_feedback = 1.0  # Example positive feedback\n",
        "agent.update_reward(action, human_feedback)\n",
        "print(f\"Updated reward model: {agent.reward_model}\")"
      ],
      "metadata": {
        "id": "0Vs4w8owjQ-2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class EthicsAwareAgent:\n",
        "    def __init__(self, state_dim, action_dim):\n",
        "        self.policy_net = nn.Linear(state_dim, action_dim)\n",
        "\n",
        "    def ethical_action(self, state, prohibited_actions):\n",
        "        action_probs = torch.softmax(self.policy_net(state), dim=-1)\n",
        "        action_probs[prohibited_actions] = 0  # Zero out prohibited actions\n",
        "        return torch.multinomial(action_probs, 1).item()\n",
        "\n",
        "# Define prohibited actions\n",
        "prohibited_actions = [2, 4]  # Actions considered unethical\n",
        "\n",
        "# Create agent and sample ethical action\n",
        "state = torch.randn(3)\n",
        "agent = EthicsAwareAgent(state_dim=3, action_dim=5)\n",
        "action = agent.ethical_action(state, prohibited_actions)\n",
        "print(f\"Ethically filtered action: {action}\")"
      ],
      "metadata": {
        "id": "PEqngredkNeM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class AGIEvaluator:\n",
        "    def __init__(self):\n",
        "        self.metrics = {\"robustness\": [], \"transparency\": [], \"safety\": []}\n",
        "\n",
        "    def log_metric(self, metric_name, value):\n",
        "        if metric_name in self.metrics:\n",
        "            self.metrics[metric_name].append(value)\n",
        "\n",
        "    def average_metrics(self):\n",
        "        return {k: sum(v) / len(v) for k, v in self.metrics.items() if v}\n",
        "\n",
        "# Logging evaluation scores\n",
        "evaluator = AGIEvaluator()\n",
        "evaluator.log_metric(\"robustness\", 0.85)\n",
        "evaluator.log_metric(\"transparency\", 0.75)\n",
        "evaluator.log_metric(\"safety\", 0.92)\n",
        "average_scores = evaluator.average_metrics()\n",
        "print(f\"Average AGI Evaluation Scores: {average_scores}\")"
      ],
      "metadata": {
        "id": "ae07bS_flQoE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sbum0RdtmC-W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "# Define a model for continual learning\n",
        "class ContinualLearner(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(ContinualLearner, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "        self.omega = {}  # Store importance of parameters\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        return self.fc2(x)\n",
        "\n",
        "    def update_omega(self, task_data, importance):\n",
        "        # Update importance of parameters based on task_data and importance\n",
        "        pass\n",
        "\n",
        "# Example usage\n",
        "model = ContinualLearner(input_dim=10, hidden_dim=20, output_dim=5)"
      ],
      "metadata": {
        "id": "NftN7KI8mnn0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CommonSenseReasoner:\n",
        "    def __init__(self, knowledge_base):\n",
        "        self.knowledge_base = knowledge_base  # A dictionary or database of common-sense knowledge\n",
        "\n",
        "    def reason(self, inputs):\n",
        "        # Process inputs using common-sense knowledge\n",
        "        return \"Processed with common sense\"\n",
        "\n",
        "# Example usage\n",
        "knowledge_base = {\"water is wet\": True, \"fire is hot\": True}\n",
        "reasoner = CommonSenseReasoner(knowledge_base)\n",
        "print(reasoner.reason([\"What is water?\"]))"
      ],
      "metadata": {
        "id": "jGuav9mRnaPR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class SocietalAlignment:\n",
        "    def __init__(self, value_system):\n",
        "        self.value_system = value_system  # Define societal values\n",
        "\n",
        "    def align_action(self, action):\n",
        "        # Modify action to align with societal values\n",
        "        return action if action in self.value_system else \"Unethical\"\n",
        "\n",
        "# Example usage\n",
        "value_system = [\"ethical_action\", \"fairness\", \"transparency\"]\n",
        "aligner = SocietalAlignment(value_system)\n",
        "aligned_action = aligner.align_action(\"ethical_action\")\n",
        "print(f\"Aligned Action: {aligned_action}\")"
      ],
      "metadata": {
        "id": "iNL7INA5oHT4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class ScalableEfficientModel(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(ScalableEfficientModel, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "        self.energy_efficiency = 0.9  # Dummy efficiency factor\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        return self.fc2(x)\n",
        "\n",
        "    def optimize_energy(self):\n",
        "        # Implement optimization techniques for energy efficiency\n",
        "        pass\n",
        "\n",
        "# Example usage\n",
        "model = ScalableEfficientModel(input_dim=10, hidden_dim=20, output_dim=5)"
      ],
      "metadata": {
        "id": "MdHhCOjUqGMN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bkl5jEVcqINR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "class DummyLanguageModel:\n",
        "    def generate(self, input_text):\n",
        "        # Simulate generating a response\n",
        "        return \"Generated response for \" + input_text\n",
        "\n",
        "class CulturallyAwareAgent:\n",
        "    def __init__(self, language_model, cultural_rules):\n",
        "        self.language_model = language_model\n",
        "        self.cultural_rules = cultural_rules\n",
        "\n",
        "    def generate_response(self, input_text, context):\n",
        "        # Generate initial response\n",
        "        response = self.language_model.generate(input_text)\n",
        "\n",
        "        # Apply cultural rules based on context\n",
        "        if context in self.cultural_rules:\n",
        "            response = self.cultural_rules[context](response)\n",
        "        return response\n",
        "\n",
        "# Sample cultural rule set\n",
        "cultural_rules = {\n",
        "    \"formal_setting\": lambda r: \"Mr. \" + r if not r.startswith(\"Mr.\") else r,\n",
        "    \"informal_setting\": lambda r: \"Hey \" + r.split()[0]\n",
        "}\n",
        "\n",
        "# Sample input\n",
        "context = \"formal_setting\"\n",
        "dummy_language_model = DummyLanguageModel()\n",
        "agent = CulturallyAwareAgent(language_model=dummy_language_model, cultural_rules=cultural_rules)\n",
        "response = agent.generate_response(\"John\", context)\n",
        "print(f\"Context-aware response: {response}\")"
      ],
      "metadata": {
        "id": "OLbOhFHfrIov"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class EthicalAudit:\n",
        "    def __init__(self):\n",
        "        self.criteria = {\"bias\": [], \"privacy\": [], \"transparency\": []}\n",
        "\n",
        "    def log_audit(self, criterion, value):\n",
        "        if criterion in self.criteria:\n",
        "            self.criteria[criterion].append(value)\n",
        "\n",
        "    def summary_report(self):\n",
        "        return {k: sum(v) / len(v) for k, v in self.criteria.items() if v}\n",
        "\n",
        "# Example of audit logging\n",
        "audit = EthicalAudit()\n",
        "audit.log_audit(\"bias\", 0.1)  # Lower values indicate lower bias\n",
        "audit.log_audit(\"privacy\", 0.9)  # Higher values indicate stronger privacy protection\n",
        "audit.log_audit(\"transparency\", 0.75)\n",
        "report = audit.summary_report()\n",
        "print(f\"Ethical audit report: {report}\")"
      ],
      "metadata": {
        "id": "K-bVoG9xr6K6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "# Define a model for multi-GPU use\n",
        "class AGIModel(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(AGIModel, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return torch.relu(self.fc2(torch.relu(self.fc1(x))))\n",
        "\n",
        "# Distribute model across GPUs\n",
        "model = AGIModel(input_dim=10, hidden_dim=20, output_dim=5)\n",
        "if torch.cuda.device_count() > 1:\n",
        "    model = nn.DataParallel(model)\n",
        "\n",
        "# Dummy data and training loop\n",
        "data = torch.randn(64, 10)\n",
        "target = torch.randint(0, 5, (64,))\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "# Training on multiple GPUs\n",
        "model.train()\n",
        "for epoch in range(10):\n",
        "    optimizer.zero_grad()\n",
        "    output = model(data)\n",
        "    loss = nn.CrossEntropyLoss()(output, target)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    print(f\"Epoch {epoch}, Loss: {loss.item()}\")"
      ],
      "metadata": {
        "id": "hBNN6GG_sxYs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "\n",
        "# Generate adversarial examples\n",
        "def generate_adversarial_example(model, data, target, epsilon=0.1):\n",
        "    data.requires_grad = True\n",
        "    output = model(data)\n",
        "    loss = nn.CrossEntropyLoss()(output, target)\n",
        "    loss.backward()\n",
        "\n",
        "    # Modify data slightly in the direction of gradient\n",
        "    perturbed_data = data + epsilon * data.grad.sign()\n",
        "    return perturbed_data\n",
        "\n",
        "# Test with adversarial example\n",
        "data = Variable(torch.randn(1, 10), requires_grad=True)\n",
        "target = torch.tensor([3])\n",
        "model = nn.Linear(10, 5)  # Example model, replace with your actual model\n",
        "perturbed_data = generate_adversarial_example(model, data, target)\n",
        "print(f\"Original data: {data}\\nAdversarial data: {perturbed_data}\")"
      ],
      "metadata": {
        "id": "n_B5Sfoit5IT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UomTvqZMvINV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit import QuantumCircuit, Aer, execute\n",
        "\n",
        "def quantum_algorithm(data):\n",
        "    # Example quantum circuit for demonstration\n",
        "    qc = QuantumCircuit(2)\n",
        "    qc.h(0)\n",
        "    qc.cx(0, 1)\n",
        "    qc.measure_all()\n",
        "\n",
        "    simulator = Aer.get_backend('qasm_simulator')\n",
        "    job = execute(qc, simulator, shots=1000)\n",
        "    result = job.result()\n",
        "    counts = result.get_counts(qc)\n",
        "    return counts\n",
        "\n",
        "# Example usage\n",
        "data = [0, 1]  # Example data\n",
        "quantum_result = quantum_algorithm(data)\n",
        "print(f\"Quantum Algorithm Result: {quantum_result}\")"
      ],
      "metadata": {
        "id": "_8OMF3llvIlq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "class DummyLanguageModel:\n",
        "    def predict(self, bci_data):\n",
        "        # Simulate a prediction\n",
        "        return \"Predicted response based on BCI data\"\n",
        "\n",
        "class BCIAgent:\n",
        "    def __init__(self, model):\n",
        "        self.model = model\n",
        "\n",
        "    def process_bci_signal(self, bci_data):\n",
        "        # Process BCI data and generate response\n",
        "        response = self.model.predict(bci_data)\n",
        "        return response\n",
        "\n",
        "# Example usage\n",
        "bci_data = np.random.rand(128)  # Simulated BCI data\n",
        "model = DummyLanguageModel()  # Replace with actual model\n",
        "bci_agent = BCIAgent(model)\n",
        "response = bci_agent.process_bci_signal(bci_data)\n",
        "print(f\"BCI Agent Response: {response}\")"
      ],
      "metadata": {
        "id": "okQdn4JOvx_Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "class DummyLanguageModel:\n",
        "    def analyze(self, biological_data):\n",
        "        # Simulate analyzing biological data\n",
        "        return \"Analyzed insights based on biological data: \" + str(biological_data)\n",
        "\n",
        "class SyntheticBiologyAGI:\n",
        "    def __init__(self, biological_model):\n",
        "        self.biological_model = biological_model\n",
        "\n",
        "    def interact_with_biological_data(self, biological_data):\n",
        "        # Process biological data and generate insights\n",
        "        insights = self.biological_model.analyze(biological_data)\n",
        "        return insights\n",
        "\n",
        "# Example usage\n",
        "biological_data = {\"gene_sequence\": \"ATCG\"}  # Example biological data\n",
        "biological_model = DummyLanguageModel()  # Replace with actual model\n",
        "agi_system = SyntheticBiologyAGI(biological_model)\n",
        "insights = agi_system.interact_with_biological_data(biological_data)\n",
        "print(f\"Insights from Biological Data: {insights}\")"
      ],
      "metadata": {
        "id": "fpBJD-Yyw51w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class ScientificDiscoveryAGI:\n",
        "    def __init__(self, model):\n",
        "        self.model = model\n",
        "\n",
        "    def automate_experimentation(self, data):\n",
        "        # Simulate experimentation\n",
        "        result = self.model(data)\n",
        "        return result\n",
        "\n",
        "    def data_analysis(self, data):\n",
        "        # Analyze data\n",
        "        analyzed_data = np.mean(data, axis=0)\n",
        "        return analyzed_data\n",
        "\n",
        "    def generate_hypothesis(self, data):\n",
        "        # Generate hypothesis based on data\n",
        "        hypothesis = f\"Predicted outcome: {self.model(data)}\"\n",
        "        return hypothesis\n",
        "\n",
        "# Example usage\n",
        "data = torch.randn(100, 5)\n",
        "model = nn.Linear(5, 1)\n",
        "scientific_agi = ScientificDiscoveryAGI(model)\n",
        "experiment_result = scientific_agi.automate_experimentation(data)\n",
        "analyzed_data = scientific_agi.data_analysis(data.numpy())\n",
        "hypothesis = scientific_agi.generate_hypothesis(data)\n",
        "print(f\"Experiment Result: {experiment_result}\\nAnalyzed Data: {analyzed_data}\\nGenerated Hypothesis: {hypothesis}\")"
      ],
      "metadata": {
        "id": "86tFZQoS0rWA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class HealthcareAGI:\n",
        "    def __init__(self, model):\n",
        "        self.model = model\n",
        "\n",
        "    def personalized_treatment(self, patient_data):\n",
        "        # Generate personalized treatment plan\n",
        "        treatment_plan = self.model(patient_data)\n",
        "        return treatment_plan\n",
        "\n",
        "    def early_disease_detection(self, patient_data):\n",
        "        # Detect disease early using patient data\n",
        "        detection_score = torch.sigmoid(self.model(patient_data))\n",
        "        return detection_score\n",
        "\n",
        "# Example usage\n",
        "patient_data = torch.randn(1, 10)  # Simulated patient data\n",
        "model = nn.Linear(10, 1)  # Example model, replace with your actual model\n",
        "healthcare_agi = HealthcareAGI(model)\n",
        "treatment_plan = healthcare_agi.personalized_treatment(patient_data)\n",
        "disease_detection_score = healthcare_agi.early_disease_detection(patient_data)\n",
        "print(f\"Personalized Treatment Plan: {treatment_plan}\\nDisease Detection Score: {disease_detection_score}\")"
      ],
      "metadata": {
        "id": "oZm4plq2116_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "\n",
        "class EnvironmentalSustainabilityAGI:\n",
        "    def __init__(self, model):\n",
        "        self.model = model\n",
        "\n",
        "    def model_ecological_systems(self, ecological_data):\n",
        "        # Model ecological systems\n",
        "        modeled_system = self.model(ecological_data)\n",
        "        return modeled_system\n",
        "\n",
        "    def resource_management(self, resources):\n",
        "        # Simulate resource management\n",
        "        optimized_resources = np.max(resources, axis=0)\n",
        "        return optimized_resources\n",
        "\n",
        "    def develop_sustainable_technologies(self, innovation_data):\n",
        "        # Develop new technologies based on innovation data\n",
        "        technologies = self.model(innovation_data)\n",
        "        return technologies\n",
        "\n",
        "# Example usage\n",
        "ecological_data = torch.randn(50, 10)  # Simulated ecological data\n",
        "innovation_data = torch.randn(20, 10)  # Adjusted to match input dimensions of the model\n",
        "model = nn.Linear(10, 1)  # Simple linear model for demonstration\n",
        "\n",
        "environmental_agi = EnvironmentalSustainabilityAGI(model)\n",
        "modeled_system = environmental_agi.model_ecological_systems(ecological_data)\n",
        "optimized_resources = environmental_agi.resource_management(ecological_data.numpy())\n",
        "new_technologies = environmental_agi.develop_sustainable_technologies(innovation_data)\n",
        "\n",
        "print(f\"Modeled Ecological System: {modeled_system}\")\n",
        "print(f\"Optimized Resources: {optimized_resources}\")\n",
        "print(f\"New Technologies: {new_technologies}\")"
      ],
      "metadata": {
        "id": "zNAfsu_94IYy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s47GTlKI0saH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class KnowledgeAugmentedAGI:\n",
        "    def __init__(self, knowledge_graph, language_model):\n",
        "        self.knowledge_graph = knowledge_graph\n",
        "        self.language_model = language_model\n",
        "\n",
        "    def query_knowledge_graph(self, query):\n",
        "        # Simulate querying the knowledge graph\n",
        "        for item in self.knowledge_graph[\"data\"]:\n",
        "            if query.lower() in item.lower():\n",
        "                return item\n",
        "        return \"No relevant information found\"\n",
        "\n",
        "    def generate_insight(self, query):\n",
        "        # Query the knowledge graph for structured insights\n",
        "        structured_info = self.query_knowledge_graph(query)\n",
        "\n",
        "        # Use language model for unstructured insight generation\n",
        "        unstructured_info = self.language_model(query)\n",
        "\n",
        "        # Combine and return both sources of insight\n",
        "        return structured_info, unstructured_info\n",
        "\n",
        "# Sample usage\n",
        "knowledge_graph = {\"data\": [\"E=mc^2 relates energy to mass\", \"DNA is a double helix\"]}\n",
        "language_model = lambda query: f\"Insight based on unstructured data: {query}\"\n",
        "agi_system = KnowledgeAugmentedAGI(knowledge_graph, language_model)\n",
        "result = agi_system.generate_insight(\"Explain energy-mass relationship\")\n",
        "print(f\"Structured and Unstructured Insights: {result}\")"
      ],
      "metadata": {
        "id": "VkZuFaCS7GJE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "class SkillRetentionProgram:\n",
        "    def __init__(self):\n",
        "        self.training_sessions = []\n",
        "\n",
        "    def schedule_training(self, skill, date, duration):\n",
        "        session = {\"skill\": skill, \"date\": date, \"duration\": duration}\n",
        "        self.training_sessions.append(session)\n",
        "        return session\n",
        "\n",
        "    def get_training_schedule(self):\n",
        "        return self.training_sessions\n",
        "\n",
        "# Example usage\n",
        "skill_program = SkillRetentionProgram()\n",
        "skill_program.schedule_training(\"Data Analysis\", \"2024-12-01\", 2)  # 2-hour session\n",
        "skill_program.schedule_training(\"Machine Learning\", \"2024-12-05\", 3)  # 3-hour session\n",
        "print(f\"Training Schedule: {skill_program.get_training_schedule()}\")"
      ],
      "metadata": {
        "id": "spoLFEHz8h8K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class TransparentAGI:\n",
        "    def __init__(self, model):\n",
        "        self.model = model\n",
        "\n",
        "    def explain_decision(self, input_data):\n",
        "        # Generate a decision\n",
        "        decision = self.model(input_data)\n",
        "\n",
        "        # Provide an explanation\n",
        "        explanation = f\"Decision based on input features: {input_data}\"\n",
        "        return decision, explanation\n",
        "\n",
        "# Example usage\n",
        "input_data = torch.randn(1, 5)  # Example input data\n",
        "model = nn.Linear(5, 1)  # Example model\n",
        "transparent_agi = TransparentAGI(model)\n",
        "decision, explanation = transparent_agi.explain_decision(input_data)\n",
        "print(f\"Decision: {decision}\\nExplanation: {explanation}\")"
      ],
      "metadata": {
        "id": "EYvLV-XY-OEl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class EthicalConstraintAGI:\n",
        "    def __init__(self, model, restricted_areas):\n",
        "        self.model = model\n",
        "        self.restricted_areas = restricted_areas\n",
        "\n",
        "    def make_decision(self, input_data, area):\n",
        "        if area in self.restricted_areas:\n",
        "            return \"Decision not allowed in this area\"\n",
        "        else:\n",
        "            return self.model(input_data)\n",
        "\n",
        "# Example usage\n",
        "input_data = torch.randn(1, 5)  # Example input data\n",
        "model = nn.Linear(5, 1)  # Example model\n",
        "restricted_areas = [\"Healthcare\", \"Financial Decisions\"]\n",
        "ethical_agi = EthicalConstraintAGI(model, restricted_areas)\n",
        "decision = ethical_agi.make_decision(input_data, \"Healthcare\")\n",
        "print(f\"Decision: {decision}\")"
      ],
      "metadata": {
        "id": "SmhsO6TBATg-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class FederatedAGINetwork:\n",
        "    def __init__(self, agents):\n",
        "        self.agents = agents\n",
        "\n",
        "    def collaborate(self, query):\n",
        "        # Each agent provides its insight\n",
        "        insights = {agent.name: agent.analyze(query) for agent in self.agents}\n",
        "        # Combine insights for a holistic response\n",
        "        return insights\n",
        "\n",
        "# Define example agents with specialized insights\n",
        "class HealthcareAGI:\n",
        "    name = \"Healthcare AGI\"\n",
        "    def analyze(self, query): return f\"Medical perspective on {query}\"\n",
        "\n",
        "class EnvironmentalAGI:\n",
        "    name = \"Environmental AGI\"\n",
        "    def analyze(self, query): return f\"Environmental perspective on {query}\"\n",
        "\n",
        "# Initiate collaboration\n",
        "network = FederatedAGINetwork([HealthcareAGI(), EnvironmentalAGI()])\n",
        "collaborative_insights = network.collaborate(\"pandemic management\")\n",
        "print(f\"Collaborative insights: {collaborative_insights}\")"
      ],
      "metadata": {
        "id": "MxJBp1cxBdmk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ValueAlignment:\n",
        "    def __init__(self, human_values):\n",
        "        self.human_values = human_values\n",
        "\n",
        "    def align_values(self, agi_objectives):\n",
        "        # Ensure AGI objectives align with human values\n",
        "        aligned_objectives = []\n",
        "        for objective in agi_objectives:\n",
        "            if objective in self.human_values:\n",
        "                aligned_objectives.append(objective)\n",
        "        return aligned_objectives\n",
        "\n",
        "# Example usage\n",
        "human_values = [\"fairness\", \"transparency\", \"privacy\"]\n",
        "agi_objectives = [\"efficiency\", \"transparency\", \"profit\"]\n",
        "value_alignment = ValueAlignment(human_values)\n",
        "aligned_objectives = value_alignment.align_values(agi_objectives)\n",
        "print(f\"Aligned Objectives: {aligned_objectives}\")"
      ],
      "metadata": {
        "id": "_ZbFPEBtCuda"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ParticipatoryDesign:\n",
        "    def __init__(self):\n",
        "        self.stakeholders = []\n",
        "\n",
        "    def add_stakeholder(self, stakeholder):\n",
        "        self.stakeholders.append(stakeholder)\n",
        "        return self.stakeholders\n",
        "\n",
        "    def collect_feedback(self, agi_design):\n",
        "        # Simulate collecting feedback from stakeholders\n",
        "        feedback = {}\n",
        "        for stakeholder in self.stakeholders:\n",
        "            feedback[stakeholder] = f\"Feedback from {stakeholder} on {agi_design}\"\n",
        "        return feedback\n",
        "\n",
        "# Example usage\n",
        "participatory_design = ParticipatoryDesign()\n",
        "participatory_design.add_stakeholder(\"Ethicist\")\n",
        "participatory_design.add_stakeholder(\"Computer Scientist\")\n",
        "participatory_design.add_stakeholder(\"Sociologist\")\n",
        "feedback = participatory_design.collect_feedback(\"AGI System v1.0\")\n",
        "print(f\"Stakeholder Feedback: {feedback}\")"
      ],
      "metadata": {
        "id": "KZ3izTpcDfiq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ContinuousEthicalReview:\n",
        "    def __init__(self):\n",
        "        self.ethical_reviews = []\n",
        "\n",
        "    def conduct_review(self, agi_system, review_date, findings):\n",
        "        review = {\"agi_system\": agi_system, \"review_date\": review_date, \"findings\": findings}\n",
        "        self.ethical_reviews.append(review)\n",
        "        return review\n",
        "\n",
        "    def get_reviews(self):\n",
        "        return self.ethical_reviews\n",
        "\n",
        "# Example usage\n",
        "ethical_review = ContinuousEthicalReview()\n",
        "ethical_review.conduct_review(\"AGI System v1.0\", \"2024-12-01\", \"No ethical concerns found\")\n",
        "ethical_review.conduct_review(\"AGI System v1.1\", \"2025-01-15\", \"Privacy risks identified\")\n",
        "print(f\"Ethical Reviews: {ethical_review.get_reviews()}\")"
      ],
      "metadata": {
        "id": "Apy3S-jPEyYr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class PublicEducationCampaign:\n",
        "    def __init__(self):\n",
        "        self.campaigns = []\n",
        "\n",
        "    def launch_campaign(self, topic, audience, start_date):\n",
        "        campaign = {\"topic\": topic, \"audience\": audience, \"start_date\": start_date}\n",
        "        self.campaigns.append(campaign)\n",
        "        return campaign\n",
        "\n",
        "    def get_campaigns(self):\n",
        "        return self.campaigns\n",
        "\n",
        "# Example usage\n",
        "education_campaign = PublicEducationCampaign()\n",
        "education_campaign.launch_campaign(\"AGI Benefits\", \"General Public\", \"2024-12-01\")\n",
        "education_campaign.launch_campaign(\"Ethical Challenges\", \"Students\", \"2024-12-10\")\n",
        "print(f\"Education Campaigns: {education_campaign.get_campaigns()}\")"
      ],
      "metadata": {
        "id": "RPmX4eXRF89E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class RegulatorySandbox:\n",
        "    def __init__(self):\n",
        "        self.sandboxes = []\n",
        "\n",
        "    def create_sandbox(self, technology, purpose, start_date):\n",
        "        sandbox = {\"technology\": technology, \"purpose\": purpose, \"start_date\": start_date}\n",
        "        self.sandboxes.append(sandbox)\n",
        "        return sandbox\n",
        "\n",
        "    def get_sandboxes(self):\n",
        "        return self.sandboxes\n",
        "\n",
        "# Example usage\n",
        "regulatory_sandbox = RegulatorySandbox()\n",
        "regulatory_sandbox.create_sandbox(\"Autonomous Vehicles\", \"Safety Testing\", \"2025-01-01\")\n",
        "regulatory_sandbox.create_sandbox(\"Healthcare AGI\", \"Ethical Evaluation\", \"2025-02-01\")\n",
        "print(f\"Regulatory Sandboxes: {regulatory_sandbox.get_sandboxes()}\")"
      ],
      "metadata": {
        "id": "jShmRqJ7GpoX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class WorkforceReskilling:\n",
        "    def __init__(self):\n",
        "        self.reskilling_programs = []\n",
        "\n",
        "    def start_reskilling_program(self, skill, target_group, duration):\n",
        "        program = {\"skill\": skill, \"target_group\": target_group, \"duration\": duration}\n",
        "        self.reskilling_programs.append(program)\n",
        "        return program\n",
        "\n",
        "    def get_reskilling_programs(self):\n",
        "        return self.reskilling_programs\n",
        "\n",
        "# Example usage\n",
        "reskilling = WorkforceReskilling()\n",
        "reskilling.start_reskilling_program(\"Data Science\", \"IT Professionals\", 6)  # 6-month program\n",
        "reskilling.start_reskilling_program(\"AI Ethics\", \"Policymakers\", 3)  # 3-month program\n",
        "print(f\"Reskilling Programs: {reskilling.get_reskilling_programs()}\")"
      ],
      "metadata": {
        "id": "MLDzrjqgHcX5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class EthicalAlignment:\n",
        "    def __init__(self, human_values):\n",
        "        self.human_values = human_values\n",
        "\n",
        "    def align_values(self, agi_objectives):\n",
        "        # Ensure AGI objectives align with human values\n",
        "        aligned_objectives = []\n",
        "        for objective in agi_objectives:\n",
        "            if objective in self.human_values:\n",
        "                aligned_objectives.append(objective)\n",
        "        return aligned_objectives\n",
        "\n",
        "    def address_bias_and_fairness(self, data):\n",
        "        # Placeholder for bias detection and fairness correction\n",
        "        corrected_data = data  # Implement bias correction logic here\n",
        "        return corrected_data\n",
        "\n",
        "# Example usage\n",
        "human_values = [\"fairness\", \"transparency\", \"privacy\"]\n",
        "agi_objectives = [\"efficiency\", \"transparency\", \"profit\"]\n",
        "ethical_alignment = EthicalAlignment(human_values)\n",
        "aligned_objectives = ethical_alignment.align_values(agi_objectives)\n",
        "print(f\"Aligned Objectives: {aligned_objectives}\")"
      ],
      "metadata": {
        "id": "YPJnDtKOIonj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class TransparentDesign:\n",
        "    def __init__(self, model):\n",
        "        self.model = model\n",
        "\n",
        "    def explain_decision(self, input_data):\n",
        "        # Generate a decision\n",
        "        decision = self.model(input_data)\n",
        "\n",
        "        # Provide an explanation\n",
        "        explanation = f\"Decision based on input features: {input_data.numpy()}\"\n",
        "        return decision, explanation\n",
        "\n",
        "# Example usage\n",
        "input_data = torch.randn(1, 5)  # Example input data\n",
        "model = nn.Linear(5, 1)  # Example model\n",
        "transparent_design = TransparentDesign(model)\n",
        "decision, explanation = transparent_design.explain_decision(input_data)\n",
        "print(f\"Decision: {decision}\\nExplanation: {explanation}\")"
      ],
      "metadata": {
        "id": "QpnXFitPJtZD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ResilientCollaboration:\n",
        "    def __init__(self):\n",
        "        self.collaborators = []\n",
        "\n",
        "    def add_collaborator(self, field, name):\n",
        "        collaborator = {\"field\": field, \"name\": name}\n",
        "        self.collaborators.append(collaborator)\n",
        "        return collaborator\n",
        "\n",
        "    def get_collaborators(self):\n",
        "        return self.collaborators\n",
        "\n",
        "# Example usage\n",
        "collaboration = ResilientCollaboration()\n",
        "collaboration.add_collaborator(\"Ethics\", \"Dr. Smith\")\n",
        "collaboration.add_collaborator(\"Computer Science\", \"Dr. Lee\")\n",
        "collaboration.add_collaborator(\"Sociology\", \"Dr. Nguyen\")\n",
        "print(f\"Collaborators: {collaboration.get_collaborators()}\")"
      ],
      "metadata": {
        "id": "und61IRAKs_I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CVYsq2O8MZQL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class InternationalRegulations:\n",
        "    def __init__(self):\n",
        "        self.regulations = []\n",
        "\n",
        "    def add_regulation(self, regulation, description, enforcement_date):\n",
        "        reg = {\"regulation\": regulation, \"description\": description, \"enforcement_date\": enforcement_date}\n",
        "        self.regulations.append(reg)\n",
        "        return reg\n",
        "\n",
        "    def get_regulations(self):\n",
        "        return self.regulations\n",
        "\n",
        "# Example usage\n",
        "regulations = InternationalRegulations()\n",
        "regulations.add_regulation(\"Data Privacy\", \"Ensuring all AGI systems comply with data privacy standards\", \"2025-01-01\")\n",
        "regulations.add_regulation(\"Ethical AI\", \"Mandating ethical guidelines for AGI research and deployment\", \"2025-06-01\")\n",
        "print(f\"International Regulations: {regulations.get_regulations()}\")"
      ],
      "metadata": {
        "id": "MWUTc0GJMaQa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ResearchTransparency:\n",
        "    def __init__(self):\n",
        "        self.disclosures = []\n",
        "\n",
        "    def disclose_research(self, title, description, date):\n",
        "        disclosure = {\"title\": title, \"description\": description, \"date\": date}\n",
        "        self.disclosures.append(disclosure)\n",
        "        return disclosure\n",
        "\n",
        "    def get_disclosures(self):\n",
        "        return self.disclosures\n",
        "\n",
        "# Example usage\n",
        "transparency = ResearchTransparency()\n",
        "transparency.disclose_research(\"AGI Safety Protocols\", \"Details of safety measures for AGI systems\", \"2025-03-01\")\n",
        "transparency.disclose_research(\"AI Ethics Framework\", \"A comprehensive framework for ethical AI research\", \"2025-04-15\")\n",
        "print(f\"Research Disclosures: {transparency.get_disclosures()}\")"
      ],
      "metadata": {
        "id": "6P2z2p1GNnHY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CollaborativeInstitution:\n",
        "    def __init__(self, name):\n",
        "        self.name = name\n",
        "        self.members = []\n",
        "        self.activities = []\n",
        "\n",
        "    def add_member(self, member_name, role):\n",
        "        member = {\"name\": member_name, \"role\": role}\n",
        "        self.members.append(member)\n",
        "        return member\n",
        "\n",
        "    def schedule_activity(self, activity, date):\n",
        "        scheduled_activity = {\"activity\": activity, \"date\": date}\n",
        "        self.activities.append(scheduled_activity)\n",
        "        return scheduled_activity\n",
        "\n",
        "    def get_institution_info(self):\n",
        "        return {\"name\": self.name, \"members\": self.members, \"activities\": self.activities}\n",
        "\n",
        "# Example usage\n",
        "institution = CollaborativeInstitution(\"International AGI Regulatory Agency\")\n",
        "institution.add_member(\"Dr. Smith\", \"Ethics Officer\")\n",
        "institution.add_member(\"Dr. Lee\", \"Research Lead\")\n",
        "institution.schedule_activity(\"Annual AGI Safety Review\", \"2025-09-01\")\n",
        "print(f\"Institution Information: {institution.get_institution_info()}\")"
      ],
      "metadata": {
        "id": "Ru4VoBAkOoHs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GlobalAGIPolicyFramework:\n",
        "    def __init__(self):\n",
        "        self.policies = []\n",
        "\n",
        "    def add_policy(self, name, details, implementation_date):\n",
        "        policy = {\"name\": name, \"details\": details, \"implementation_date\": implementation_date}\n",
        "        self.policies.append(policy)\n",
        "        return policy\n",
        "\n",
        "    def get_policies(self):\n",
        "        return self.policies\n",
        "\n",
        "# Example usage\n",
        "global_policy = GlobalAGIPolicyFramework()\n",
        "global_policy.add_policy(\"Research Accountability\", \"Requirements for research disclosure\", \"2025-01-01\")\n",
        "global_policy.add_policy(\"Audit Mechanisms\", \"Regular audits to assess compliance\", \"2025-02-01\")\n",
        "global_policy.add_policy(\"Shared Safety Protocols\", \"Protocols for addressing emergent risks\", \"2025-03-01\")\n",
        "print(f\"Global AGI Policies: {global_policy.get_policies()}\")"
      ],
      "metadata": {
        "id": "H-i8MWxMP9Bz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shap\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# Sample model for interpretability\n",
        "model = nn.Sequential(nn.Linear(10, 50), nn.ReLU(), nn.Linear(50, 1))\n",
        "\n",
        "# Sample data\n",
        "data = torch.randn(100, 10)\n",
        "explainer = shap.DeepExplainer(model, data)\n",
        "shap_values = explainer.shap_values(data)\n",
        "\n",
        "# Plot SHAP values for explanation\n",
        "shap.summary_plot(shap_values, data.numpy())"
      ],
      "metadata": {
        "id": "KoxWUdB9RTaj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ImageModel:\n",
        "    def detect_fire(self, satellite_image):\n",
        "        # Simulate fire detection in satellite image\n",
        "        return {\"hotspot_1\": \"intense\", \"hotspot_2\": \"mild\"}\n",
        "\n",
        "class ResourceOptimizer:\n",
        "    def optimize(self, hotspots):\n",
        "        # Simulate resource allocation based on hotspots\n",
        "        return f\"Resource plan for {len(hotspots)} hotspots\"\n",
        "\n",
        "class CrisisResponseAGI:\n",
        "    def __init__(self, image_model, resource_optimizer):\n",
        "        self.image_model = image_model\n",
        "        self.resource_optimizer = resource_optimizer\n",
        "\n",
        "    def assess_damage(self, satellite_image):\n",
        "        # Analyze satellite image for wildfire hotspots\n",
        "        hotspots = self.image_model.detect_fire(satellite_image)\n",
        "        return hotspots\n",
        "\n",
        "    def allocate_resources(self, hotspots):\n",
        "        # Allocate resources based on intensity and location of hotspots\n",
        "        return self.resource_optimizer.optimize(hotspots)\n",
        "\n",
        "# Imaginary models for fire detection and resource allocation\n",
        "image_model = ImageModel()\n",
        "resource_optimizer = ResourceOptimizer()\n",
        "\n",
        "response_system = CrisisResponseAGI(image_model, resource_optimizer)\n",
        "satellite_image = \"dummy_image_data\"\n",
        "hotspots = response_system.assess_damage(satellite_image)\n",
        "resource_plan = response_system.allocate_resources(hotspots)\n",
        "print(f\"Hotspots detected: {hotspots}\\nResource Allocation: {resource_plan}\")"
      ],
      "metadata": {
        "id": "5U7eIQIDSown"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn, optim\n",
        "\n",
        "class OnlineLearningModel(nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(OnlineLearningModel, self).__init__()\n",
        "        self.fc = nn.Linear(input_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.fc(x)\n",
        "\n",
        "# Initialize model and optimizer\n",
        "model = OnlineLearningModel(10, 2)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "# Dummy continuous data stream\n",
        "for new_data, target in [(torch.randn(1, 10), torch.tensor([1])) for _ in range(10)]:\n",
        "    optimizer.zero_grad()\n",
        "    output = model(new_data)\n",
        "    loss = nn.CrossEntropyLoss()(output, target)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    print(f\"Updated model with loss: {loss.item()}\")"
      ],
      "metadata": {
        "id": "PpF84rbSTyaN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class AGISafetyProtocol:\n",
        "    def __init__(self, max_allowed_deviation):\n",
        "        self.max_allowed_deviation = max_allowed_deviation\n",
        "\n",
        "    def monitor(self, agi_output):\n",
        "        # Check if AGI's actions deviate beyond allowed limit\n",
        "        deviation = abs(agi_output - expected_output)\n",
        "        if deviation > self.max_allowed_deviation:\n",
        "            return \"Activate containment protocol\"\n",
        "        return \"Safe operation\"\n",
        "\n",
        "# Sample monitoring\n",
        "expected_output = 0.5  # Reference value\n",
        "protocol = AGISafetyProtocol(max_allowed_deviation=0.1)\n",
        "agi_output = 0.7  # Hypothetical AGI output\n",
        "status = protocol.monitor(agi_output)\n",
        "print(f\"Safety Check Status: {status}\")"
      ],
      "metadata": {
        "id": "WlKqVabuUxZf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class HumanCenteredAGIInterface:\n",
        "    def __init__(self, agi_system):\n",
        "        self.agi_system = agi_system\n",
        "\n",
        "    def analyze_data(self, data):\n",
        "        # Provide interpretable insights and control options\n",
        "        analysis = self.agi_system.analyze(data)\n",
        "        return f\"Analysis result: {analysis}. You can adjust parameters here.\"\n",
        "\n",
        "    def override_decision(self, user_input):\n",
        "        # Allow users to modify AGI recommendations\n",
        "        return f\"Decision overridden based on user input: {user_input}\"\n",
        "\n",
        "# Mock AGI system\n",
        "class AGISystem:\n",
        "    def analyze(self, data): return f\"Processed analysis on {data}\"\n",
        "\n",
        "agi_ui = HumanCenteredAGIInterface(AGISystem())\n",
        "print(agi_ui.analyze_data(\"user data\"))\n",
        "print(agi_ui.override_decision(\"new analysis direction\"))"
      ],
      "metadata": {
        "id": "zn0kBJRCVOnj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class TerrainModel:\n",
        "    def analyze(self, image_data):\n",
        "        # Simulate terrain analysis\n",
        "        return \"Rocky terrain with mineral deposits\"\n",
        "\n",
        "class AtmosphereModel:\n",
        "    def analyze(self, gas_composition_data):\n",
        "        # Simulate atmospheric gas analysis\n",
        "        return \"Potential traces of methane and oxygen\"\n",
        "\n",
        "class PlanetaryAnalysisAGI:\n",
        "    def __init__(self, terrain_model, atmosphere_model):\n",
        "        self.terrain_model = terrain_model\n",
        "        self.atmosphere_model = atmosphere_model\n",
        "\n",
        "    def assess_terrain(self, image_data):\n",
        "        # Analyze terrain features in the image data\n",
        "        return self.terrain_model.analyze(image_data)\n",
        "\n",
        "    def assess_atmosphere(self, gas_composition_data):\n",
        "        # Analyze atmospheric gases for habitability signs\n",
        "        return self.atmosphere_model.analyze(gas_composition_data)\n",
        "\n",
        "# Mock models for demonstration\n",
        "terrain_model = TerrainModel()\n",
        "atmosphere_model = AtmosphereModel()\n",
        "\n",
        "planet_analysis = PlanetaryAnalysisAGI(terrain_model, atmosphere_model)\n",
        "print(planet_analysis.assess_terrain(\"satellite_image_data\"))\n",
        "print(planet_analysis.assess_atmosphere(\"gas_composition_data\"))"
      ],
      "metadata": {
        "id": "vnXOJTYCXey-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class DigitalTwinAGI:\n",
        "    def __init__(self, user_profile):\n",
        "        self.user_profile = user_profile\n",
        "\n",
        "    def manage_schedule(self, tasks):\n",
        "        # Autonomously prioritize tasks based on user preferences\n",
        "        return sorted(tasks, key=lambda t: self.user_profile[\"priority\"][t])\n",
        "\n",
        "    def respond_to_email(self, email):\n",
        "        # Generate personalized response based on email content\n",
        "        response = f\"Reply to {email['sender']} with: '{self.user_profile['preferred_response']}'\"\n",
        "        return response\n",
        "\n",
        "user_profile = {\"priority\": {\"meeting\": 1, \"email\": 2}, \"preferred_response\": \"Thank you, I'll review this.\"}\n",
        "digital_twin = DigitalTwinAGI(user_profile)\n",
        "tasks = [\"email\", \"meeting\"]\n",
        "print(digital_twin.manage_schedule(tasks))\n",
        "print(digital_twin.respond_to_email({\"sender\": \"colleague\"}))"
      ],
      "metadata": {
        "id": "C1S-3npGYaCh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ReflectiveAGI:\n",
        "    def __init__(self, state):\n",
        "        self.state = state\n",
        "\n",
        "    def reflect(self):\n",
        "        # Perform introspective assessment of its current state\n",
        "        self.state[\"thoughts\"] = f\"I am processing {self.state['task']} and am aware of my role.\"\n",
        "        return self.state[\"thoughts\"]\n",
        "\n",
        "# Simulate an AGI with a self-reflective state\n",
        "agi_mind = ReflectiveAGI({\"task\": \"data analysis\"})\n",
        "print(agi_mind.reflect())"
      ],
      "metadata": {
        "id": "H_zHIrZYZd8z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class EthicalFramework:\n",
        "    def __init__(self):\n",
        "        self.guidelines = []\n",
        "\n",
        "    def add_guideline(self, guideline, description, field):\n",
        "        guideline_entry = {\"guideline\": guideline, \"description\": description, \"field\": field}\n",
        "        self.guidelines.append(guideline_entry)\n",
        "        return guideline_entry\n",
        "\n",
        "    def get_guidelines(self):\n",
        "        return self.guidelines\n",
        "\n",
        "# Example usage\n",
        "ethical_framework = EthicalFramework()\n",
        "ethical_framework.add_guideline(\"Bias Mitigation\", \"Ensure AGI does not favor any group\", \"Ethics\")\n",
        "ethical_framework.add_guideline(\"Privacy Protection\", \"Safeguard user data in AGI systems\", \"Law\")\n",
        "print(f\"Ethical Guidelines: {ethical_framework.get_guidelines()}\")"
      ],
      "metadata": {
        "id": "RvWLfMpMbEeG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class RiskAssessment:\n",
        "    def __init__(self):\n",
        "        self.risks = []\n",
        "\n",
        "    def assess_risk(self, risk, impact, field):\n",
        "        risk_entry = {\"risk\": risk, \"impact\": impact, \"field\": field}\n",
        "        self.risks.append(risk_entry)\n",
        "        return risk_entry\n",
        "\n",
        "    def get_risks(self):\n",
        "        return self.risks\n",
        "\n",
        "# Example usage\n",
        "risk_assessment = RiskAssessment()\n",
        "risk_assessment.assess_risk(\"Data Breach\", \"High\", \"Security\")\n",
        "risk_assessment.assess_risk(\"Psychological Impact\", \"Medium\", \"Psychology\")\n",
        "print(f\"Risk Assessments: {risk_assessment.get_risks()}\")"
      ],
      "metadata": {
        "id": "1cq_anBkcwhl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class InnovationLab:\n",
        "    def __init__(self):\n",
        "        self.projects = []\n",
        "\n",
        "    def start_project(self, project_name, fields):\n",
        "        project_entry = {\"project_name\": project_name, \"fields\": fields}\n",
        "        self.projects.append(project_entry)\n",
        "        return project_entry\n",
        "\n",
        "    def get_projects(self):\n",
        "        return self.projects\n",
        "\n",
        "# Example usage\n",
        "innovation_lab = InnovationLab()\n",
        "innovation_lab.start_project(\"Bias Mitigation in AGI\", [\"Computer Science\", \"Ethics\"])\n",
        "innovation_lab.start_project(\"AGI and Human Interaction\", [\"Cognitive Science\", \"Sociology\"])\n",
        "print(f\"Innovation Projects: {innovation_lab.get_projects()}\")"
      ],
      "metadata": {
        "id": "0iWbzKrWdyqj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class InterdisciplinaryResearchLab:\n",
        "    def __init__(self, name):\n",
        "        self.name = name\n",
        "        self.members = []\n",
        "        self.activities = []\n",
        "\n",
        "    def add_member(self, field, name):\n",
        "        member = {\"field\": field, \"name\": name}\n",
        "        self.members.append(member)\n",
        "        return member\n",
        "\n",
        "    def initiate_activity(self, activity_name, fields):\n",
        "        activity = {\"activity_name\": activity_name, \"fields\": fields}\n",
        "        self.activities.append(activity)\n",
        "        return activity\n",
        "\n",
        "    def get_lab_info(self):\n",
        "        return {\"name\": self.name, \"members\": self.members, \"activities\": self.activities}\n",
        "\n",
        "# Example usage\n",
        "research_lab = InterdisciplinaryResearchLab(\"Global AGI Research Lab\")\n",
        "research_lab.add_member(\"Machine Learning\", \"Dr. Alice\")\n",
        "research_lab.add_member(\"Cognitive Science\", \"Dr. Bob\")\n",
        "research_lab.add_member(\"Law\", \"Dr. Carol\")\n",
        "research_lab.initiate_activity(\"Assessing AGI Bias\", [\"Machine Learning\", \"Ethics\", \"Law\"])\n",
        "research_lab.initiate_activity(\"AGI-Human Interaction Studies\", [\"Cognitive Science\", \"Sociology\"])\n",
        "print(f\"Research Lab Information: {research_lab.get_lab_info()}\")"
      ],
      "metadata": {
        "id": "imN51aOHes5E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IahU57KthaRq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class DataModel:\n",
        "    def predict(self, historical_data):\n",
        "        # Simulate predicting future climate trends\n",
        "        return {\"temperature\": 2.5}\n",
        "\n",
        "class ClimateAGI:\n",
        "    def __init__(self, data_model):\n",
        "        self.data_model = data_model\n",
        "\n",
        "    def analyze_trend(self, historical_data):\n",
        "        # Analyze historical climate data to predict future trends\n",
        "        trend = self.data_model.predict(historical_data)\n",
        "        return f\"Predicted temperature rise: {trend['temperature']}°C in next 50 years\"\n",
        "\n",
        "    def suggest_action(self, trend):\n",
        "        # Suggest actions based on trend predictions\n",
        "        if trend['temperature'] > 2.0:\n",
        "            return \"Urgent action: Implement carbon capture and reforestation.\"\n",
        "        return \"Sustainable growth path.\"\n",
        "\n",
        "# Mock data model\n",
        "data_model = DataModel()\n",
        "climate_agi = ClimateAGI(data_model)\n",
        "historical_data = \"climate_data_set\"\n",
        "trend = climate_agi.analyze_trend(historical_data)\n",
        "action_plan = climate_agi.suggest_action({\"temperature\": 2.5})\n",
        "print(trend, action_plan)"
      ],
      "metadata": {
        "id": "vnyaRtrzhTb5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MockAGISystem:\n",
        "    def process_input(self, user_input):\n",
        "        # Simulate AGI's response to user input\n",
        "        return f\"Simulated response to {user_input}\"\n",
        "\n",
        "class PublicAGIPlatform:\n",
        "    def __init__(self, agi_system):\n",
        "        self.agi_system = agi_system\n",
        "\n",
        "    def interactive_demo(self, user_input):\n",
        "        # Provide a demo of AGI’s capabilities based on user input\n",
        "        response = self.agi_system.process_input(user_input)\n",
        "        return f\"AGI response to your question '{user_input}': {response}\"\n",
        "\n",
        "# Mock AGI system\n",
        "agi_system = MockAGISystem()\n",
        "platform = PublicAGIPlatform(agi_system)\n",
        "user_question = \"What are the AGI’s ethical considerations?\"\n",
        "print(platform.interactive_demo(user_question))"
      ],
      "metadata": {
        "id": "mfqiLuGNiiwA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MockDiagnosticModel:\n",
        "    def predict(self, patient_data):\n",
        "        # Simulate diagnosing based on patient data\n",
        "        return \"Possible condition: early-stage diabetes\"\n",
        "\n",
        "class MedicalAGIAssistant:\n",
        "    def __init__(self, diagnostic_model):\n",
        "        self.diagnostic_model = diagnostic_model\n",
        "\n",
        "    def suggest_diagnosis(self, patient_data):\n",
        "        # Analyze patient data and suggest possible diagnoses\n",
        "        diagnoses = self.diagnostic_model.predict(patient_data)\n",
        "        return f\"Suggested diagnosis: {diagnoses}\"\n",
        "\n",
        "# Mock diagnostic model\n",
        "diagnostic_model = MockDiagnosticModel()\n",
        "assistant = MedicalAGIAssistant(diagnostic_model)\n",
        "patient_data = \"symptom_data\"\n",
        "print(assistant.suggest_diagnosis(patient_data))"
      ],
      "metadata": {
        "id": "5cege3a4jtEh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}