Machine learning is becoming increasingly common in both research and industry, but scientists need to understand how these systems work to trust them. Dean Pomerleau, a robotics graduate student, faced this challenge in 1991 when he tried to teach a computer to drive a vehicle. He found that the system was a "black box," meaning it was difficult to understand how it made decisions. This problem has become more complex and urgent as technology has advanced. Deep learning, a type of machine learning, is now used in various applications, including self-driving cars and product recommendations. However, it is also difficult to interpret, making it hard to trust. Researchers are trying to understand how artificial neural networks work. One technique, called Deep Dream, allows researchers to see how a network processes images by modifying them to enhance responses from specific neurons. This has led to images that resemble hallucinations. Another technique, Eureqa, has been used to rediscover scientific laws by analyzing simple mechanical systems. Despite these advances, there are concerns about the reliability of deep learning. For example, a neural network might misinterpret images, leading to incorrect decisions. This could be dangerous in real-world applications, such as self-driving cars. Researchers are working to make machine learning more robust and transparent, but it is not a simple task. Some scientists believe that deep learning should be used alongside more transparent methods, not as a replacement. Ultimately, machine learning is a valuable tool for understanding complex phenomena, even if it is not always easy to understand how it works.Machine learning is becoming increasingly common in both research and industry, but scientists need to understand how these systems work to trust them. Dean Pomerleau, a robotics graduate student, faced this challenge in 1991 when he tried to teach a computer to drive a vehicle. He found that the system was a "black box," meaning it was difficult to understand how it made decisions. This problem has become more complex and urgent as technology has advanced. Deep learning, a type of machine learning, is now used in various applications, including self-driving cars and product recommendations. However, it is also difficult to interpret, making it hard to trust. Researchers are trying to understand how artificial neural networks work. One technique, called Deep Dream, allows researchers to see how a network processes images by modifying them to enhance responses from specific neurons. This has led to images that resemble hallucinations. Another technique, Eureqa, has been used to rediscover scientific laws by analyzing simple mechanical systems. Despite these advances, there are concerns about the reliability of deep learning. For example, a neural network might misinterpret images, leading to incorrect decisions. This could be dangerous in real-world applications, such as self-driving cars. Researchers are working to make machine learning more robust and transparent, but it is not a simple task. Some scientists believe that deep learning should be used alongside more transparent methods, not as a replacement. Ultimately, machine learning is a valuable tool for understanding complex phenomena, even if it is not always easy to understand how it works.