Natural Language Processing and Computer Vision in AI

Intermediate

Two of the most impactful subfields of AI are Natural Language Processing (NLP) and Computer Vision.

Natural Language Processing enables machines to understand, interpret, and generate human language. Techniques include tokenization, embedding models like Word2Vec and BERT, and generative models for chatbots. Applications range from language translation (Google Translate) to sentiment analysis and conversational agents.

Computer Vision allows AI systems to interpret visual data. Techniques involve image classification, object detection, segmentation, and facial recognition utilizing CNNs and transformer-based models like Vision Transformers (ViT). Example applications include autonomous vehicle perception systems and medical image diagnostics.

Here's a simple NLP example with spaCy for tokenization:

import spacy
nlp = spacy.load('en_core_web_sm')

text = "Artificial Intelligence is transforming the world."
doc = nlp(text)

for token in doc:
    print(token.text, token.pos_)

And a basic object detection concept:

Object Detection: Locate multiple object types within an image with bounding boxes, e.g., YOLO or SSD architectures.

Together, NLP and computer vision expand AI capabilities, making systems more intuitive and capable of understanding complex multimedia data.