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Here's a short article for fullstack web developers on AI:

AI, or artificial intelligence, is a rapidly growing field that involves creating intelligent machines that can perform tasks that usually require human intervention.

There are three main components of AI:

1. Machine Learning, where machines learn from data and improve their accuracy over time.
2. Natural Language Processing, where machines can understand and interpret human language.
3. Robotics, where machines can perform physical tasks.

AI has many applications and is used in various industries, including healthcare, finance, and retail. In healthcare, AI is used to develop personalized treatment plans and to assist with medical diagnoses. In finance, AI is used to detect fraud, identify investment opportunities, and analyze large amounts of data. In retail, AI is used to personalize shopping experiences and to predict consumer behavior.

As with any emerging technology, there are pros and cons to AI. Some of the benefits of AI include increased efficiency, improved accuracy, and the ability to process large amounts of data quickly. However, there are also concerns about job displacement and the potential for AI to be used for malicious purposes.

Here's an example of how you can use Python to create an AI program that can recognize handwritten digits:

# Import libraries
import tensorflow as tf
from tensorflow import keras

# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Preprocess the data
x_train = x_train.reshape(60000, 28 * 28).astype('float32') / 255
x_test = x_test.reshape(10000, 28 * 28).astype('float32') / 255

# Build the model
model = keras.Sequential([
  keras.layers.Dense(512, activation='relu', input_shape=(784,)),
  keras.layers.Dropout(0.2),
  keras.layers.Dense(10)
])

# Compile the model
model.compile(loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
              optimizer=tf.optimizers.Adam(),
              metrics=['accuracy'])

# Train the model
model.fit(x_train, y_train, epochs=5)

# Evaluate the model
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print('\nTest accuracy:', test_acc)

In this example, we use TensorFlow and Keras to create a neural network that can recognize handwritten digits.

Here's an example of how you can use JavaScript to create a chatbot:

// Import libraries
const { Botkit } = require('botkit');

// Create a new bot
const controller = new Botkit({
  webhook_uri: '/api/messages',
});

// Handle incoming messages
controller.on('message', async(bot, message) => {
  // Respond to user
  await bot.reply(message, `Hello, you said: ${message.text}`);
});

// Start the bot
controller.webserver.get('/', (req, res) => {
  res.send(`This is a chatbot running on port ${process.env.PORT}`);
});
controller.ready(() => {
  console.log(`Bot is listening on port ${process.env.PORT}`);
});

In this example, we use Botkit to create a chatbot that responds to incoming messages. The chatbot is created using JavaScript and can be integrated into various platforms, such as Slack or Microsoft Teams.