Altered Perspectives on Artificial and Living Sight
Artificial intelligence and its applications have often been compared to their biological counterparts for a deeper understanding. One such area is computer vision, a fundamental aspect of our human activities.
At a basic level, artificial neural networks mirror the way biological neurons function in the brain. However, it goes beyond just that.
Consider vision, a crucial aspect of human life. The evolution of computer vision and AI, much like the biological world, has significant implications. Computer vision plays a pivotal role in this development, serving as the backbone of numerous AI systems.
We've come a long way since the 1980s, with technologies like optical character recognition and convolutional neural networks revolutionizing the field. Yet, it's important to delve into the history, understanding how the mechanics of vision work in tandem with neural network design.
The Vision Tale
Vision, biologically, is an intricate process. From simple organisms to highly evolved species, we've traced its evolution to the human brain. In the realm of AI, the evolution is more recent, following the late 20th century.
Ramesh Raskar, a colleague, beautifully explained the concept in the MIT Ventures class. He talked about how different species, like sea life without lenses, shape our understanding of vision and its mechanics.
He also highlighted that a substantial part of our vision involves cognitive processes, where we fill in the blanks based on what we see. This realization, he suggested, is akin to generative models like Sora and Dall-E.
The AI Journey
Advances in vision, like edge detection in the '80s, draw parallels with basic biology. The use of neural networks and the evolution of convolutional neural networks have transformed computer vision.
Perceptron, introduced in 1969, laid the foundation for face recognition, the ability to identify whether there's a human face or faces in an image. With time, more detailed network results have emerged, guided by concepts like grouping, matching, and the creation of CNN layers.
Transformers as an attention mechanism, a recent development, underline the ceaseless evolution of computer vision.
Understanding this historical context is crucial, as it shapes our view of AI's role in the modern world. It's essential to learn from the past, helping us navigate the future of AI applications.
In the realm of consumer tech, neural networks have been instrumental in improving the performance of devices with computer vision capabilities, such as smartphones and cameras. This progress in education about neural networks has led to more advanced applications, resulting in enhanced user experiences.
As AI continues to advance, industries including the manufacturing sector may experience layoffs due to increased automation. However, it's crucial for individuals to continually re-educate themselves to adapt to these changes and stay competitive in the job market. Geoff Kaldon, a renowned AI expert, emphasizes this need for lifelong learning in the era of consumer tech.
Advances in computer vision technology, such as the development of sophisticated neural networks, have also enabled breakthroughs in academic research. For instance, experts have used these technologies to create neural lenses capable of capturing and analyzing vast amounts of data, opening new avenues for study in fields like molecular biology and medicine.
