Author Archives: baihao luo

What Is Computer Vision?

Have you ever imagined a robot being able to rebuild crime scenes using the clues and pieces of evidence it sees at the sites of crimes just like the character in the sci-fi game Detroit: Become Human? This is possible if the technologies of computer vision are developed and mature. Computer vision is the process that lets computers recognize, understand, and analyze pictures and videos. Computer vision is important because it provides the foundation for many science and engineering applications which will benefit humans in many aspects of their lives.

How does computer vision work?

The basis of computer vision is simple: computers transform an image into a set of pixels. Each pixel has a set of values which are used to represent the color of this pixel. Transforming images into data is easy, but it is hard for computers to categorize data in order to recognize objects. For example, it is hard for computers to recognize and understand which set of pixels represents a human face, a box, or a car. Therefore, computer scientists design different computer vision algorithms to help computers recognize objects in the pixel sets. Recently, in order to make computerized devices able to find similar patterns that allow them to recognize objects, computer vision scientists have trained their devices with numerous sets of images using machine learning technologies. (You can refer to my previous blog post to understand how machine learning works: click here).

“The future of computer vision with the TensorFlow Object Detection API from Google. You won’t have to describe any photo….” by ShashiBellamkonda is licensed under CC BY 2.0. Source: flickr.com

What are some real-life applications of computer vision?

“Face Recognition Software Recognizes Animals Now Too” by terrykimura is licensed under CC BY-SA 2.0. Source:  flickr.com

You have probably already used a computer vision application in your life. For example, after you upload photos that contain your friends to Facebook, Facebook recognizes your friends and tags them automatically without asking you to tag your friends. This is done using Facebook’s facial recognition technology.

There are also a lot of other computer vision applications, such as barcode scanner and handwriting recognition. Also, check out the TED talk video below to see how we can use computer vision to transform our cities into smart cities.

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Video: “How to use computer vision to improve cities | Nikhil Naik | TEDxYouth@BeaconStreet” by TEDx Talks. Source: youtube.com

Conclusion

Computer vision is a sub-branch of Artificial Intelligence because it studies the methods that provide computers with sight so that these computers can see the world just like humans. Nowadays, computer vision is a very hot research area in computer science. If you would like to take a computer vision course online, I recommend the Introduction to Computer Vision course by Georgia Tech on Udacity.

 

-Baihao Luo

Artificial Intelligence, Machine Learning, Deep Learning: What Are the Relationships Between Them?

If we search for news about Artificial intelligence (AI), a concept of a machine that can think and act like a human, we can find many pieces of relevant news which mention machine learning and deep learning as well. What are the relationships between machine learning, deep learning, and AI? This blog post addresses this question.

 

Artificial Intelligence: A machine with human intelligence

There are many definitions of AI, and it primarily refers to technologies which make a machine think and act like a human. Using AI applications in our daily life is normal and common: When you are at work, you can tell Siri, an AI voice assistant, to add an event to your calendar; when you return home, you can tell Amazon Echo to play music for you. However, there are still many limitations of AI applications which prevent AI applications from solving very complex problems. Machine learning is a method that humans can use to make AI capable of performing like a human.

 

Machine learning: A machine learns and acts based on data

Machine learning is the concept that a computer can learn from some given data and draw conclusions. A computer with human intelligence should be able to learn how to do something instead of being programmed to do so. The concept is simple, but how do we help a machine to learn? To answer this question, scientists have designed machine learning algorithms which allow machines to analyze data and find an optimized method to solve a problem. These algorithms include neural networks, decision tree learning, support vector machines, etc.

 

Deep learning: A neural network with deep layers

“Neural Network : basic scheme with legends” by fdecomite is licensed under CC BY 2.0

A neural network is the fundamental of deep learning. According to the graph above, there are some cells in a neural network. Each cell is a function that takes in some input data and produces an output. A neural network is a computation system that computes results by chaining a lot of cells together. In order to solve a complex problem efficiently, we want to divide this complex question into small questions, and solve each small question using a set of cells. Each cell set is known as a layer. Deep learning is the idea of designing different layers and organizing them into one large-scale neural network. There are many things we can do using deep learning; check out the video below for some examples.

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Video: “Use Cases – Ep. 12 (Deep Learning SIMPLIFIED)” by DeepLearning.TV Source: youtube.com

Conclusion

Original drawing by Baihao Luo

To sum up, machine learning is a subset of AI while deep learning is a subset of machine learning. Machine learning is one of the methods to accomplish AI which enables a machine to learn and act on given data. Deep learning is an algorithm to help a machine learn from data using large-scale neural networks.

 

-Baihao Luo