The video discusses the application of artificial neural networks (ANNs) in machine learning, focusing on their use in image recognition tasks. It explains that ANNs are made up of an input layer, one or more hidden layers, and an output layer. Each layer consists of neurons that process and pass on data. The process begins with the input layer receiving data represented as numbers, each input neuron representing a single feature of the data. This data is then sent to every neuron in the next layer, the hidden layer. Each hidden layer neuron combines all the numbers it gets, and the goal is to measure whether the input data has certain components.
The video then provides an example of how a trained neural network, let's say John Green bot, recognizes a dog in a grayscale photo. Each feature will contain a number between 0 and 1 corresponding to the brightness of one pixel and passes this information to the hidden layer. Let's focus on one hidden layer neuron since the neural network is already trained. This neuron has a mathematical formula to look at a particular component in the image like a specific curve in the center of the dog's nose. It may not really care what's happening anywhere else so it would multiply or weigh the pixel values from most of these features by zero or close to zero because it's looking for bright pixels. It wouldn't multiply these pixel values by a positive weight this curve is also defined by a darker part below so the neuron will multiply these pixel values by a negative weight. This hidden neuron will add all the weighted pixel values from the input neurons and squish the results so that it's between 0 and 1. The final number basically represents the guess of this neuron thinking that a specific curve aka a dog knows appeared in the image.
After a few more hidden layers, we reach the output layer with one neuron. After one more weighted addition of the previous layers' data which happens in the output neuron, the network shall have a good estimate if this image is a dog. In this case, John Green bot should have a decision of 93% that this image is a dog. The video then explains that neural networks don't actually understand ideas like nose or eyelid. Each neuron is doing a calculation on the data it's given and just flagging specific patterns of light and dark.
The video also discusses the computational power needed to process images for neural networks. For example, Alex net needed more than 60 million neurons to achieve this which is a ton of math and could take a lot of time to compute. This is something we should keep in mind when designing neural networks to solve problems people are really excited about using deeper neural networks which are networks with more hidden layers to do deep learning. Deep networks can combine input data in more complex ways to look for more complex components and solve trickier problems. But we can't make all networks like a billion layers deep because more hidden layers means more math which again would mean that we need faster computers plus as a network gets deeper it gets harder for us to make sense of why it's giving us the answers that it does.
The video concludes by mentioning that neural networks are being used to make more in more decisions about our lives most banks for example use no networks to detect and prevent fraud many cancer tests like the Pap tests for cervical cancer use a neural network to look at an image of cells under a microscope and decide whether there's a risk of cancer and neural networks or how alexa understands what song you're asking to play and our facebook suggests tags for our photos understanding how all this happens is really important to being a human in the world right now.
1. The text is discussing the topic of artificial intelligence (AI) and its use in supervised learning.
2. A perceptron, a program that imitates one neuron, is used to teach AI how to learn.
3. The human brain makes decisions using a hundred billion neurons with trillions of connections between them.
4. AI can be improved by connecting multiple perceptrons to create an artificial neural network.
5. Artificial neural networks are more effective than other methods for certain tasks, such as image recognition.
6. The success of artificial neural networks lies in their hidden layers and their mathematical elegance.
7. Artificial neural networks are one of the most dominant machine learning technologies used today.
8. Recognizing real-world images, such as distinguishing a dog from a cat, was a challenging task for AI in the past.
9. The first step in developing AI that could recognize images was to create a huge public dataset of labeled real-world photos.
10. This dataset, called ImageNet, contains 3.2 million labeled images sorted into five thousand two hundred and forty seven nested categories of nouns.
11. The annual ImageNet competition was started in 2010 to encourage people to contribute their best solutions to image recognition.
12. Alex Khrushchev Sookie, a graduate student at the University of Toronto, applied a neural network to ImageNet in 2012, despite previous solutions not being successful.
13. His neural network, called AlexNet, had a couple of innovations that set it apart. It used a lot of hidden layers and faster computation hardware.
14. AlexNet outperformed the next best approaches by over ten percent, only getting three out of twenty images wrong.
15. Since 2012, no networks have taken over the annual competition, and the results keep getting better.
16. AlexNet sparked an explosion of research into neural networks, which are now being applied to lots of things beyond image recognition.
17. Understanding the architecture of neural networks is crucial. They consist of an input layer, output layer, and any number of hidden layers in between.
18. Each input neuron represents a single feature, which is some characteristic of the data. Features can be straightforward, like grams of sugar in a doughnut, or they can be more abstract, like sounds represented as the amplitudes of the sound wave.
19. Each hidden layer neuron mathematically combines all the numbers it gets, with the goal of measuring whether the input data has certain components for an image recognition problem.
20. The final hidden layer outputs are mathematically combined in the output layer to answer the problem. For example, if we're trying to label an image as a dog, we might have a single output neuron representing a single answer that the image is of a dog or not.
21. The key to neural networks and AI is math. Each neuron in the hidden layer does some slightly more complicated math and outputs a number, which is then sent to every neuron in the next layer.
22. AlexNet needed more than 60 million neurons to process an 8,000 by 1,000 pixel image, which required a ton of math and could take a lot of time to compute.
23. Deeper neural networks can combine input data in more complex ways to look for more complex components and solve trickier problems, but they require more math and faster computers.
24. There is a legal right in many countries to understand why decisions made by neural networks were made.
25. Neural networks are being used to make decisions about our lives in many areas, such as detecting and preventing fraud in banking, looking at an image of cells under a microscope to decide whether there's a risk of cancer, and understanding what song you're asking to play on Alexa.
26. The text concludes with a note that the program used in the video had already been trained to recognize dogs, and the next time will discuss the learning process used by neural networks.