The speaker starts by discussing the absence of a mathematical equation to identify cats, despite extensive research and study. This leads them to the concept of machine learning, a fundamental element of artificial intelligence. They explain that machine learning is the process by which machines learn from data and experiences, enabling them to make decisions and predictions.
The speaker uses the example of recognizing Bengal cats to illustrate this concept. They explain that classical programming involves giving a program a set of rules and data, and the program will provide answers based on these inputs. However, this approach is not feasible for real-world tasks like recognizing cats, as the number of patterns and variations is virtually infinite.
Machine learning, on the other hand, involves feeding the machine with data and allowing it to find its own rules. The machine will learn from the data, identifying patterns and details that make a Bengal cat a Bengal cat. This process is akin to the functioning of convolution neural networks, a type of artificial intelligence.
The speaker emphasizes that machine learning's strength lies in its ability to automatically find the right rules. This technique is not limited to recognizing cats; it can also be applied to other tasks, such as predicting house prices based on large amounts of data.
However, the speaker notes that while machines can mimic human intelligence, they have not yet developed true intelligence, which is characterized by autonomy, learning, and an understanding of the world. They conclude by stating that while machine learning is an integral part of our lives today, we are still far from creating machines that can evolve and make decisions autonomously.
1. There is no equation for a cat.
2. Machine learning is used to identify cats.
3. Machine learning is a basis of artificial intelligence.
4. A machine can make decisions with just conditions, which we call classical programming.
5. A program can recognize Bengal cats based on patterns of features like eyelashes and ears.
6. It's not possible to create a mathematical equation that can recognize all Bengal cats without spending a lifetime and referencing where cats are possible.
7. Artificial intelligence comes into play when a system of rules is not possible in real life.
8. Machine learning allows a machine to find its rules by giving it data and answers as input.
9. The machine will learn the different components that make a Bengal cat a Bengal cat through repetition and having given it several thousand or even millions of cat images.
10. The machine will identify the shape of the ears, the spots, the shape of the body, the nose, the mouth, etc.
11. The machine will store all these patterns in its memory.
12. The machine will pass all the layers onto the new photos and each time the layer finds one of the shapes that corresponds to it, it will count a point.
13. The machine will conclude that it is a Bengal cat or not when there is enough points.
14. This is a real artificial intelligence technique called convolution neural networks.
15. The strength of machine learning is the automatic finding of the right rules.
16. Machine learning also works with prices of houses. For example, a machine can learn what makes a house cost a particular price by having seen large amounts of data before.
17. The machine will imitate this data and solve tasks complex without having been specifically programmed to carry them out.
18. The machine will learn the best way to succeed in its tasks.
19. We are still in a logic of a bit of mimicry of machines.
20. Machines have not yet been able to develop what constitutes the true intelligence of all living beings.
21. The best artificial intelligences at least have two senses like a rat then the match in learning and everything around us sees.
22. Machine learning is what many call algae at it is an integral part of our lives today.
23. In general, we are still very far from killer machines and all the fantasies that are sometimes sold to us in films or newspaper articles.