AI Agent Terminology Cheat Sheet

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The world of AI is buzzing. New terms emerge daily. Understanding them is key. This post deciphers some crucial AI agent terminology.

You Should Know:

1. Large Language Models (LLMs)

Definition: Advanced text generators predicting the next word in a sequence.

Linux Command:

curl -X POST https://api.openai.com/v1/completions -H "Authorization: Bearer YOUR_API_KEY" -d '{"model": "text-davinci-003", "prompt": "Explain LLMs", "max_tokens": 100}'

Python Example:

from transformers import pipeline 
generator = pipeline('text-generation', model='gpt2') 
print(generator("Large Language Models are"))

2. Reinforcement Learning (RL)

Definition: AI learns via trial and error using rewards.

Python Example (Q-Learning):

import numpy as np 
Q = np.zeros((state_space, action_space)) 
alpha, gamma = 0.1, 0.9 
Q[state, action] += alpha  (reward + gamma  np.max(Q[bash]) - Q[state, action])

3. Autonomous Agents

Definition: AI systems operating independently (e.g., self-driving cars).

Linux Command (ROS for Robotics):

roslaunch turtlebot3_autonomous_navigation turtlebot3_navigation.launch

4. Federated Learning

Definition: Training AI across multiple devices without centralizing data.

TensorFlow Federated Example:

import tensorflow_federated as tff 
@tff.federated_computation 
def federated_avg(model_weights): 
return tff.federated_mean(model_weights)

5. Explainable AI (XAI)

Tool: SHAP (SHapley Additive exPlanations)

pip install shap

import shap 
explainer = shap.Explainer(model) 
shap_values = explainer(X_test) 
shap.plots.waterfall(shap_values[bash])

6. Multi-Agent Systems (MAS)

Simulation Tool (NetLogo):
```
sudo apt-get install netlogo 
```

7. Online Learning

Python (Scikit-learn):

from sklearn.linear_model import SGDClassifier 
clf = SGDClassifier(loss='log_loss') 
clf.partial_fit(X_train, y_train, classes=np.unique(y_train))

What Undercode Say:

AI agents are revolutionizing automation, from chatbots to self-driving systems. Mastering these concepts requires hands-on practice with frameworks like TensorFlow, PyTorch, and ROS. Federated Learning and XAI are critical for privacy and transparency.

Expected Output:

A functional AI model (LLM/RL)
Trained federated learning setup
SHAP explanations for model decisions

Prediction:

AI agents will dominate customer service, cybersecurity, and IoT by 2026, with federated learning becoming the standard for privacy-compliant AI training.

Relevant Links:

IT/Security Reporter URL:

Reported By: Thealphadev Ai – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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