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Generative AI is revolutionizing industries by enabling machines to create text, images, audio, and even code. Below are key concepts with practical applications and commands to experiment with these technologies.
Applications of Generative AI
- Text & Code: Used in chatbots (ChatGPT), content writing, and code automation (GitHub Copilot).
- Media: AI-generated visuals (DALLĀ·E), audio (ElevenLabs), and videos (Runway ML).
Try it yourself:
Generate text using OpenAI's GPT-3 API
curl -X POST "https://api.openai.com/v1/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "text-davinci-003", "prompt": "Explain quantum computing", "max_tokens": 100}'
Ethical & Responsible AI
- Ensures fairness, transparency, and bias mitigation.
- Tools like IBMās AI Fairness 360 help detect biases.
Check for bias in datasets:
from aif360.datasets import BinaryLabelDataset
from aif360.metrics import BinaryLabelDatasetMetric
dataset = BinaryLabelDataset(df=your_dataframe, label_names=['target'], protected_attribute_names=['gender'])
metric = BinaryLabelDatasetMetric(dataset, unprivileged_groups=[{'gender': 0}], privileged_groups=[{'gender': 1}])
print("Disparate Impact:", metric.disparate_impact())
Hallucination in AI
AI sometimes generates incorrect or misleading information. Mitigate this by:
– Using fact-checking APIs (Google Fact Check Tools).
– Fine-tuning models with verified datasets.
RLHF (Reinforcement Learning from Human Feedback)
Improves AI responses by aligning them with human preferences.
Example: Fine-tuning with RLHF
from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments
model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="steps",
per_device_train_batch_size=4,
num_train_epochs=3,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_data,
eval_dataset=eval_data,
)
trainer.train()
Multimodal Models (CLIP, DALLĀ·E)
Process text and images together.
Generate an image with DALLĀ·E:
curl -X POST "https://api.openai.com/v1/images/generations" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt": "a cyberpunk city at night", "n": 1, "size": "1024x1024"}'
Generative Adversarial Networks (GANs)
Two neural networks (generator & discriminator) compete to create realistic data.
Train a GAN with TensorFlow:
from tensorflow.keras.layers import Dense, LeakyReLU from tensorflow.keras.models import Sequential generator = Sequential([ Dense(128, input_dim=100), LeakyReLU(0.2), Dense(784, activation='tanh') ]) discriminator = Sequential([ Dense(128, input_dim=784), LeakyReLU(0.2), Dense(1, activation='sigmoid') ])
Prompt Engineering
Optimize AI responses with techniques like zero-shot and few-shot learning.
Example:
Zero-shot: "Translate this to French: Hello" Few-shot: "Q: What is AI? A: Artificial Intelligence. Q: What is ML? A: Machine Learning. Q: What is NLP?"
Fine-Tuning Pre-trained Models
Adapt models like GPT-3 for specific tasks.
Fine-tune BERT for sentiment analysis:
from transformers import BertForSequenceClassification, Trainer
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
trainer = Trainer(model=model, args=training_args, train_dataset=dataset)
trainer.train()
Diffusion Models (Stable Diffusion)
Generate high-quality images by denoising random data.
Run Stable Diffusion locally:
git clone https://github.com/CompVis/stable-diffusion cd stable-diffusion pip install -r requirements.txt python scripts/txt2img.py --prompt "a hacker in a dark room" --plms
What Undercode Say
Generative AI is reshaping automation, creativity, and problem-solving. Mastering these concepts allows developers to build smarter, ethical AI systems.
Expected Output:
- AI-generated text, code, and media.
- Fine-tuned models for specialized tasks.
- Ethical AI deployments with reduced bias.
Prediction
By 2026, 70% of enterprises will integrate Generative AI into workflows, accelerating content creation, cybersecurity, and decision-making.
Relevant URLs:
https://youtube.com/T-ovlAimlHA
– Python Beginner’s Guide
– AI WhatsApp Group
IT/Security Reporter URL:
Reported By: Habib Shaikh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ā
