AI发展三大范式解析：从判别式AI到智能体AI的技术演进与实战指南

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AI 技术的发展经历了从判别式模型到生成式模型，再到智能体系统的演进过程。这三种范式在技术实现和应用场景上有着本质的区别：

判别式 AI（Discriminative AI）：
核心思想：学习输入数据到输出标签的映射关系
典型代表：卷积神经网络（CNN）、支持向量机（SVM）
技术特点：关注决策边界（decision boundary）的优化
生成式 AI（Generative AI）：
核心思想：建模数据的概率分布
典型代表：生成对抗网络（GAN）、变分自编码器（VAE）
技术特点：通过概率建模（probabilistic modeling）生成新数据
智能体 AI（Agent AI）：
核心思想：构建具有环境交互能力的自主系统
典型代表：强化学习（RL）、多智能体系统（MAS）
技术特点：强调闭环反馈（closed-loop feedback）机制

图像分类（Image Classification）
目标检测（Object Detection）
文本分类（Text Classification）

文本生成（Text Generation）
图像合成（Image Synthesis）
语音合成（Speech Synthesis）

自动驾驶（Autonomous Driving）
游戏 AI（Game AI）
机器人控制（Robotic Control）

import torch
import torch.nn as nn

# 数据预处理
class Dataset(torch.utils.data.Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx], self.labels[idx]

# 模型定义
class DiscriminativeModel(nn.Module):
    def __init__(self, input_dim, num_classes):
        super().__init__()
        self.fc = nn.Linear(input_dim, num_classes)

    def forward(self, x):
        return self.fc(x)

# 训练循环
model = DiscriminativeModel(input_dim=784, num_classes=10)
criterion = nn.CrossEntropyLoss()  # 交叉熵损失
optimizer = torch.optim.Adam(model.parameters())

for epoch in range(10):
    for data, labels in train_loader:
        outputs = model(data)
        loss = criterion(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# 生成器网络
class Generator(nn.Module):
    def __init__(self, latent_dim, output_dim):
        super().__init__()
        self.model = nn.Sequential(nn.Linear(latent_dim, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, output_dim),
            nn.Tanh()  # 输出归一化)

    def forward(self, z):
        return self.model(z)

# 判别器网络
class Discriminator(nn.Module):
    def __init__(self, input_dim):
        super().__init__()
        self.model = nn.Sequential(nn.Linear(input_dim, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 1),
            nn.Sigmoid()  # 二分类输出)

    def forward(self, x):
        return self.model(x)

# 对抗训练
G = Generator(latent_dim=100, output_dim=784)
D = Discriminator(input_dim=784)

g_optim = torch.optim.Adam(G.parameters())
d_optim = torch.optim.Adam(D.parameters())

for epoch in range(100):
    for real_data, _ in train_loader:
        # 训练判别器
        z = torch.randn(batch_size, 100)
        fake_data = G(z)

        real_loss = -torch.mean(torch.log(D(real_data)))
        fake_loss = -torch.mean(torch.log(1-D(fake_data.detach())))
        d_loss = real_loss + fake_loss

        d_optim.zero_grad()
        d_loss.backward()
        d_optim.step()

        # 训练生成器
        g_loss = -torch.mean(torch.log(D(fake_data)))

        g_optim.zero_grad()
        g_loss.backward()
        g_optim.step()

class QNetwork(nn.Module):
    def __init__(self, state_dim, action_dim):
        super().__init__()
        self.fc1 = nn.Linear(state_dim, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, action_dim)

    def forward(self, state):
        x = F.relu(self.fc1(state))
        x = F.relu(self.fc2(x))
        return self.fc3(x)

# 经验回放缓存
class ReplayBuffer:
    def __init__(self, capacity):
        self.buffer = deque(maxlen=capacity)

    def push(self, state, action, reward, next_state, done):
        self.buffer.append((state, action, reward, next_state, done))

    def sample(self, batch_size):
        return random.sample(self.buffer, batch_size)

# DQN 训练过程
q_network = QNetwork(state_dim, action_dim)
target_network = QNetwork(state_dim, action_dim)
target_network.load_state_dict(q_network.state_dict())

optimizer = torch.optim.Adam(q_network.parameters())
replay_buffer = ReplayBuffer(10000)

for episode in range(1000):
    state = env.reset()
    done = False

    while not done:
        # ε- 贪婪策略
        if random.random() < epsilon:
            action = env.action_space.sample()
        else:
            with torch.no_grad():
                action = q_network(torch.FloatTensor(state)).argmax().item()

        next_state, reward, done, _ = env.step(action)
        replay_buffer.push(state, action, reward, next_state, done)

        # 从缓存采样训练
        if len(replay_buffer) > batch_size:
            batch = replay_buffer.sample(batch_size)

            states = torch.FloatTensor([t[0] for t in batch])
            actions = torch.LongTensor([t[1] for t in batch])
            rewards = torch.FloatTensor([t[2] for t in batch])
            next_states = torch.FloatTensor([t[3] for t in batch])
            dones = torch.FloatTensor([t[4] for t in batch])

            current_q = q_network(states).gather(1, actions.unsqueeze(1))
            next_q = target_network(next_states).max(1)[0].detach()
            target = rewards + (1-dones) * gamma * next_q

            loss = F.mse_loss(current_q.squeeze(), target)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        state = next_state

    # 定期更新目标网络
    if episode % target_update == 0:
        target_network.load_state_dict(q_network.state_dict())