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Programming A Driverless Car Chapter 3: INTRODUCTION TO Artificial Intelligence

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September 16, 2017

In this Chapter, we’ll be talking about

What is artificial Intelligence?
The capability of a machine to imitate intelligent human behavior, for example; recognizing speech, recognizing characters, measurement. Any task that requires human intelligence and machine is performing that task, the machine is said to have capability of artificial intelligence.
It is also a way of making a computer, a computer-controlled robot or a software to think intelligently, in the similar manner an intelligent human thinks.
Turing Test
The turing test is a test, developed by alan turing in 1950, of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
Turing test says that if a computer is performing a certain task and a human interpreter cannot distinguish whether it’s been done by a human or a machine is said to pass the turing test.
Passing the Turing Test: Capabilities
(Classic Turing Test)

Applications of Artificial Intelligence

The goals and rules are properly defined, so it’s easy for the vehicle to follow suite. In the case of a human driven car it isn’t governed by a rule, it can be unpredictable based on the driver.
Self Driving Cars Tasks

The Data for the AI in a Driverless Car is provided through these components.
The types of data creates the environment which the agent has to work. Let’s see what type of program we have to create in this course.
Some of the companies that have worked on a driverless car of their own are Waymo / Google Self-Driving Car, Tesla’s Autopilot, Uber, nuTonomy we’ll talk about how we can create the mind of the driverless.
First, let’s understand that like a human, an AI would also need to be trained continuously to improve. When a machine learns from it’s experiences, we call it Machine Learning.
In the words of Herbert Alexander Simon” Learning is any process by which a system improves performance from experience”
Machine learning is concerned with computer programs that automatically improve their performance through experience.

What is Machine Learning?

According to Arthur Samuel “The field of study that gives computers the ability to learn without being explicitly programmed.”
The field of study that gives computers the ability to learn without being explicitly programmed. On the above table, we have an explicit procedure programmed into the machine and requires prior knowledge.
This method of explicitly programming the output is known as explicit programming for decision making. Some of the machine learning algorithm that involve linear regression or neural network with one layer, there’s no direct relation between input and output. This is called implicit programming.
We define learning is a method of improving with experience at some task, Let’s say that there’s a task T (driving a car) and by doing this task, we are gaining experience (denoted by E), and the performance measure could be measured by P, based on the past experience. This is called Learning. We implement this methodology with Machine learning.
Some examples of Machine learning around us

We’re going to focus on the vehicle dynamics and self driving cars.
The Objective of a machine learning algorithm, when we’re designing one.

Supervised Vs Unsupervised
Supervised learning, in the context of artificial intelligence (AI) and machine learning, is a type of system in which both input and desired output data are provided. Input and output data are labelled for classification to provide a learning basis for future data processing.
If it is otherwise, without any data it is known as unsupervised.
Let’s take this example of a SPAM filter in your email
The systems that is in place to check for spam messages in your email uses algorithm that requires you to mark as spam to distinguish between spam content and real email.
This is categorized as a Supervised data
Some other Supervised data scenarios include but not limited to

The outputs are discrete.
Regression

All of this data is labeled, however the output is not discrete because there are a number of possible outcomes.

Anomaly Detection

Anomaly detection algorithm uses pattern data sets to understand the common patterns and compares it with the abnormal patterns to predict an outcome.
Let’s take this example of a Credit card fraud

In case of Network Intrusion,

In case of recommendation Engines

Based on the common properties, it suggest you a book. This is an unsupervised algorithm.
Clustering
When the algorithm combines different datas with the same property under a single cluster, based on the relevance is known as clustering and it’s unsupervised as no labeling is required.
Clustering examples

Some ML methodologies
Supervised Learning

Unsupervised Learning

Note: CHOOSING AN ALGORITHM
We can choose an algorithm based on evaluation of different models of data by looking at the relative success rates. Also, it’s wise to remember that some algorithms work better with some kinds of data.
 
Recommended Reading: Chapter 2: Linear Algebra For Machine Learning
Next: Chapter 4: Introduction to Supervised learning
To start reading from a topic of your choice, you can go back to the Table of Contents here
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