Bongard Problems

November 19, 2024

Bongard Problems require a combination of visual processing, abstraction, and reasoning, traits closely associated with human intelligence. As a measure of artificial general intelligence (AGI), Bongard Problems assess the ability of AI systems to generalize knowledge across different domains and scenarios.

Python

import cv2
import numpy as np
from sklearn.ensemble import RandomForestClassifier

# and preprocess images
def preprocess_image(image_path):
    image = cv2.imread(image_path)
    image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return cv2.resize(image_gray, (64, 64)).flatten()

# of loading images and extracting features
images = ['image1.jpg', 'image2.jpg', 'image3.jpg']  # Add paths to Bongard problem images
features = np.array([preprocess_image(img) for img in images])

# labels for two sets
labels = [0, 0, 1]  # Example labels for images

# classifier
clf = RandomForestClassifier()
clf.fit(features, labels)

# example
prediction = clf.predict(features)

Conclusion

Bongard Problems serve as a rigorous test for AI systems, emphasizing the need for a blend of perception, reasoning, and understanding. Their resolution pushes forward the boundaries of research in artificial intelligence and reflects steps toward achieving human-like reasoning in machines.