Fields and Applications of AI

Now that we have been introduced to AI, let's move on and see its application in real life.

Simulation of Human Behavior

Humans have five basic senses that can be divided into visual (seeing), auditory (listening), kinesthetic (moving), olfactory (smelling), and gustatory (tasting). However, for the purposes of understanding how to create intelligent machines, we can separate these disciplines as follows:

• Listening and speaking
• Understanding language
• Remembering things
• Thinking
• Seeing
• Moving

A few of these are out of scope for us because the purpose of this chapter is to understand the fundamentals. In order to move a robot arm, for instance, we would have to study complex university-level math to understand what's going on, but we will only be sticking to the practical aspects in this book:

• Listening and speaking: Using a speech recognition system, AI can collect information from a user. Using speech synthesis, it can turn internal data into understandable sounds. Speech recognition and speech synthesis techniques deal with the recognition and construction of human sounds that are emitted or that humans can understand.

  For instance, imagine you are on a trip to a country where you don't speak the local language. You can speak into the microphone of your phone, expect it to understand what you say, and then translate it into the other language. The same can happen in reverse with the locals speaking and AI translating the sounds into a language you understand. Speech recognition and speech synthesis make this possible.

  Note

  An example of speech synthesis is Google Translate. You can navigate to https://translate.google.com/ and make the translator speak words in a non-English language by clicking the loudspeaker button below the translated word.

• Understanding language: We can understand natural language by processing it. This field is called natural language processing, or NLP.

  When it comes to NLP, we tend to learn languages based on statistical learning by learning the statistical relationship between syllables.

• Remembering things: We need to represent things we know about the world. This is where creating knowledge bases and hierarchical representations called ontologies comes into play. Ontologies categorize things and ideas in our world and contain relations between these categories.
• Thinking: Our AI system has to be an expert in a certain domain by using an expert system. An expert system can be based on mathematical logic in a deterministic way, as well as in a fuzzy, non-deterministic way.

  The knowledge base of an expert system is represented using different techniques. As the problem domain grows, we create hierarchical ontologies.

  We can replicate this structure by modeling the network on the building blocks of the brain. These building blocks are called neurons, and the network itself is called a neural network.

• Seeing: We have to interact with the real world through our senses. We have only touched upon auditory senses so far, in regard to speech recognition and synthesis. What if we had to see things? If that was the case, we would have to create computer vision techniques to learn about our environment. After all, recognizing faces is useful, and most humans are experts at that.

  Computer vision depends on image processing. Although image processing is not directly an AI discipline, it is a required discipline for AI.

• Moving: Moving and touching are natural to us humans, but they are very complex tasks for computers. Moving is handled by robotics. This is a very math-heavy topic.

  Robotics is based on control theory, where you create a feedback loop and control the movement of your object based on the feedback gathered. Control theory has applications in other fields that have absolutely nothing to do with moving objects in space. This is because the feedback loops that are required are similar to those modeled in economics.

Simulating Intelligence – the Turing Test

Alan Turing, inventor of the Turing machine, an abstract concept that's used in algorithm theory, suggested a way to test intelligence. This test is referred to as the Turing test in AI literature.

Using a text interface, an interrogator chats to a human and a chatbot. The job of the chatbot is to mislead the interrogator to the extent that they cannot tell whether the computer is human.

What Disciplines Do We Need to Pass the Turing Test?

First, we need to understand a spoken language to know what the interrogator is saying. We do this by using Natural Language Processing (NLP). We also must respond to the interrogator in a credible way by learning from previous questions and answers using AI models.

We need to be an expert of things that the human mind tends to be interested in. We need to build an expert system of humanity, involving the taxonomy of objects and abstract thoughts in our world, as well as historical events and even emotions.

Passing the Turing test is very hard. Current predictions suggest we won't be able to create a system good enough to pass the Turing test until the late 2020s. Pushing this even further, if this is not enough, we can advance to the Total Turing Test, which also includes movement and vision.

Next, we will move on and look at the tools and learning models in AI.