A Look at the Most Used Terminology Around Artificial Intelligence

A Look at the Most Used Terminology Around Artificial Intelligence

Artificial Intelligence (AI), once only present in science fiction, is now a science reality manifesting itself in every industry. It raises questions that make us wonder about how we should explore the possibilities of AI for our organization, institution, home, or city. But what do we really mean when we speak about AI? 

In general, AI is a broad field of science encompassing much more than just computer science. AI includes also psychology, philosophy, linguistics, and other areas. How do these disciplines interconnect with each other? 

AI is a deep topic and demands an equally deep understanding of each of its aspects, as well as getting familiar with the terminology around it before we jump into the pool of getting deeper, supporting, or condemning it. 

So, let's begin with the basics and grow our knowledge with the Interesting Engineering series toward getting deep into the knowledge and understanding of Artificial Intelligence.

A  whitepaper by the Artificial Intelligence Center of Expertise Deloitte in the Netherlands explains the many different faces of Artificial Intelligence and how the different AI terms we use relate and differ from one another. 

Artificial Intelligence, Machine Learning, Robotics, and Smart Machines are part of the terminology that we see around frequently in headlines and that have become part of our daily conversations. Let's have a closer look at each of them.  

Artificial Intelligence is a wonderful mix of computer science, philosophy, psychology, linguistics, and other areas. When these disciplines are put together, and are embedded into software and hardware, they can be used to perform tasks that would normally require a certain degree of human intelligence. 

An AI system is able to combine and utilize Machine Learning and other big data analytic methods to resemble human reasoning and solve complex problems at a very high scale of intelligence and at a super high speed that goes beyond human capacity. 

AI can be divided into Narrow and General. Currently, all existing AI is Narrow AI, which means it can only do what it has been designed for doing.

Narrow AIs are better at the tasks they have been made for than humans are. This includes facial recognition, chess computers, calculus, and translation. Basically, Narrow AI means that a specific algorithm needs to be designed in order to solve each specific problem. 

On the other hand, and according to Deloitte, General AI is the holy grail of AI; a single system that can learn about any existing problem and then solve it.  

The concept of intelligence, therefore, refers to the ability to plan, reason, and learn, to later on sense and build a perception of knowledge capable of allowing the human or the machine to communicate in natural language.  

Machine Learning is the process used by which a computer analyzes and extracts meaning and value from big data sets. Algorithms learn to identify certain patterns, like the occurrence of certain words, or combination of words.

An algorithm can be trained to identify certain pictures in collections of pictures, transform speech into text, handwriting into structured data. and so on. These examples would require labeled training sets.

The difference between Machine Learning and Artificial Intelligence is that a Machine Learning algorithm is not able to understand what it was trained to do.

For instance, a Machine Learning algorithm can be trained to identify spam. However, it will not know or understand what spam is or why it is important to identify it. 

Machine Learning is at the basis of AI systems. However, Machine Learning algorithms are not as smart as per the AI definition. They just look smart. 

Cognitive analytics deals with cognitive behavior that is associated with thinking.

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