ImageNet and The Challenge of Building Large Datasets

The video discusses the evolution of machine learning, emphasizing the role of data sets like Imagenet in advancing computer vision. It highlights the creation of Imagenet, its impact, and the challenges it faces, such as biases and ethical concerns. The video also explores future solutions for data set issues, including synthetic data and regulations.

What was one of the main reasons machine learning models struggled to gain traction before the 2000s?

• Insufficient data

• Complex algorithms

• High costs

• Lack of computing power

How was the Imagenet data set primarily created?

• By manually photographing objects

• By using existing labeled data sets

• By scraping images from the internet

• By generating synthetic images

What was a significant outcome of the Imagenet Challenge?

• It led to the development of new data labeling techniques

• It demonstrated the effectiveness of GPUs in training models

• It resulted in the creation of a new data set

• It highlighted the need for more diverse data

What is a common bias found in models trained on Imagenet?

• Lighting bias

• Color bias

• Shape and texture bias

• Size bias

What is one of the ethical concerns related to the Imagenet data set?

• The use of outdated technology

• The lack of diverse categories

• The potential violation of copyright

• The high cost of data collection

What is one approach to addressing data set challenges mentioned in the video?

• Increasing data set size

• Using synthetic data

• Improving data set labeling

• Reducing data set categories

What legal action was taken against Clearview AI?

• They were required to delete their data set

• They were banned from using social media data

• They were sued for violating biometric privacy laws

• They were fined for data breaches