All You Need To Know About Artificial Intelligence - In A Nutshell Part 1
Artificial Intelligence is on the rise - we cannot deny that. A huge amount of money is invested in the industry, especially in the US and China, and as we know, these two countries don't like burning money for no reason.
Some people call it "electricity of the XXI century" and some are afraid of the rise of Skynet from the movie Terminator. However, there are many exciting projects going on right at this moment: from autonomous cars to psychological analysis, image and voice recognition to even AI judges or policemen:
Let's look at what it actually means and how important AI can be.
So, what is AI? How powerful is it? Should we be afraid of creating a real-life thread like Skynet?
In general, AI is a concept where it is possible for a machine to "think" or react like humans. But what does that really mean?
First, we should dive a little bit deeper into how we, humans, make decisions. There are many branches of philosophy trying to answer this question (decadentism, modernism), but since we are scientists (or at least, interested in science) we can agree that empiricism is something that suits our approach very well. We base our work on observation, experience and knowledge. The more experience you have, in science, sports or playing guitar, the better you are. Knowledge comes from experience. If we repeat a certain task over and over again, even with a different parameter, but with the same model, we can verify if our initial thesis is correct or not, based on observations and assumptions.
Can we apply such an approach? Actually we can.
Technology that might enable us to create real AI in the (near) future is called Machine Learning. Would you believe it if I told you that the basic concepts were developed more than 50 years ago? I was surprised myself.
Machine learning was a forgotten branch of science for such a long time mostly due to technological limitations. It requires a substantial amount of computation power. Nowadays, we can play with machine learning with the same laptops that we carry in our backpacks. This is an outstanding technological leap that enables us to build upon ideas that were not possible 10 or 20 years before.
Before we dive into details, we should ask ourselves: what do we actually want to achieve when using Artificial Intelligence?
There are three main branches of machine learning:
- Supervised machine learning
- Unsupervised machine learning
- Reinforced machine learning
Supervised machine learning enables us to find patterns in data by comparing inputs and outputs.
Please stay with me for a little experiment. I will give you a few inputs and outputs. Your task is to try to find a correlation.
| Input | Output |
|---|---|
| 1 | 2 |
| 2 | 4 |
| 3 | 6 |
| 4 | 8 |
It was easy, huh? Yes, the output is obtained by multiplying the input by 2. Piece of cake. Do we really need a computer for that? Obviously not, but let's make the problem more complicated.
Let's challenge ourselves with the very famous Iris data. Our task is to define the species of a flower, based on its petal width and petal length. Suddenly the problem gets too complicated for a human mind. What if I told you that there are many, many algorithms that will categorize the same data in various ways? On top of that, various parameters will impact the fitting of the data, as presented below.
Figure 2. Iris data categorisation obtained by using the SVC algorithm with gamma = 0.2.
Figure 3. Iris data categorisation obtained by using the SVC algorithm with gamma = 100.
But why is that? When we gather data, we deal not only with noises and measurement errors but also with uncertainty.
What if I made the first thought challenge a little bit more complicated?
| Input | Output |
|---|---|
| 1 | 2 |
| 2 | 4 |
| 3 | 6 |
| 4 | 8 |
| 5 | 11 |
You can easily misinterpret the data if there is limited data. In reality we cannot have the whole picture. We need to agree on having misclassifications and errors, but by carefully choosing and using a lot of data, we can approximate the optimal solution. Rising computer calculation power and big data analysis enable us to solve many problems with supervised learning – voice and image recognition as basic examples.
I am planning to make a series about the basics of machine learning, so any feedback is welcome.
Are you interested in the topic? Don't hesitate to contact me.
Wojciech Orzechowski
Contact me:
E-mail: [email protected]