Introduction to AI

Introduction to AI

Ever since Alan Turing came up with the concept of programming a computer to make it behave intelligently in 1950, the technological space has never been the same. It has moved way forward and has demonstrated different ways of coming up with technologies that change the way we look at things. This technology than Turning spoke about way back in 1950 is popularly known as Artificial Intelligence throughout the globe. Some of the areas where we get to see the immense capabilities of Artificial Intelligence (AI) are – robotics, expert systems, speech recognition, and natural language processing and computer vision.

The success that stems from using this technology is due to the designing of the new systems which are able to use knowledge to its full potential. Human expertise too plays a very crucial role here. This is because of the fact that if there is no human expertise, there will be a lack of knowledge in the given domain and the machine will not be able to perform to its full potential. In order to improve a machine’s performance, human experience is a mandate which needs to be taken care of.

Characteristics of Artificial Intelligence

The core principle of artificial intelligence lies in the implementation and designing computer systems. These computer systems or machines are designed in a manner that lets them solve problems which usually require the human brain. The problems being talked about here relates to natural tasks and high complexity. These cannot be solved when classic algorithm methods are taken into consideration.

In order to solve complex problems that cannot be solved using classic algorithm methodologies, Artificial Intelligence (AI) alters symbolic information and not just numerical data as seen with the case of computer science. AI is a technology that handles various kinds of knowledge about different domains of application. This is the reason why the difficulties pertaining to representation and knowledge acquisition is the core of Artificial Intelligence’s research and development. When knowledge-based systems are represented it is seen to be one of the most active branches that artificial intelligence deals with.

Another essential characteristic of artificial intelligence is multidisciplinarity. Even though Artificial Intelligence is seen to have strong ties with computer science, the major development of the technology stems from linguistics, logic and cognitive sciences. This technology finds a place in numerous application fields such as bank and finance, medicine and even automobiles.

Where is Artificial Intelligence Used?

The primary characteristics of Artificial Intelligence find a place for itself in multiple domains. These domains are not just restricted to the field of data science, but find a place for itself on many different spheres. Some of them have been talked about below:

  • Natural Language Processing: Even though this term may sound very generic to someone who reads it for the first time, it is actually not so. It covers a wide array of activities such as generation and interpretation of sentences for man-machine interface, retrieving documents that have been lost due to some reasons, automatic indexing, and giving access to database and services, computer-aided translations and many more. The end product may seem extremely promising to many users, but the problems pertaining to these are not so easy to solve. It comes with large chunks of knowledge and efficient reasoning processes in order to decode the text or the sentence which is being processed.
  • Speech Understanding and Recognition: The domain being talked about here poses certain similarities to the one mentioned above, but are different on many levels. Decoding speech sounds is lot more difficult than decoding something which has been documented and fed into the computer. As of late commercial products finds a place for itself almost everywhere. This is the same for isolated word systems as well as for speech recognition systems that are continuous in nature. When designing man-machine dialog systems that are based on voice, it is an imperative to have strong knowledge based reasoning systems in place. This is a mandate as it helps in understanding the meaning of sentences which are formed.
  • Theorem Proving:  This kind of activity is taken care of during the initial stages of artificial intelligence. Inspite of the fact that great results have been obtained with the help of AI, no theorems have been depicted by a machine. Research and development in the field of gaming such as chees is essential as it caters to the fundamental aspects of logic and reasoning.
  • Robotics: As of late, the robots existing in the industry are only repeating actions and movements which have been taught to them in the preliminary phase. The efforts which have been put to develop old robots had more to do with mechanical engineering rather than intelligent behavior. This no longer remains the case with the advent of artificial intelligence (AI). The robots coming up in industry lately incorporate the advantages of AI to come up with unique features. This includes vision systems, action planning for reaching a specific goal, reasoning on a certain situation so as to find the most suitable action possible. To cite an example, the Stanford University team once developed a race where five robots raced for 131 miles in a desert without any human assistance. Even though the field of robotics has advanced a lot lately, there is lot left to do with regards to the same. It is essential here to see the true capabilities of man-made machines.
  • Image vision and interpretation: Applications where advanced kinds of image processing techniques were used have been operating for many years in the industry. When it comes to speech processing, the systems which were developed earlier relied solely on pure pattern recognition techniques. This included identifying typed characters and also easy to recognize two dimensional objects. With regards to this domain as well it is an imperative to come up with knowledge based information systems to improve image processing techniques.
  • Expert Systems: This is an extremely good example of knowledge-based systems. It was developed as early as 1970s by Dendral and MYCIN. Numerous systems have demonstrated the abilities of small amounts of knowledge for coming up with efficient decision making systems. This can be carried about autonomously or with the help of interactions with humans. Ever since these expert systems came up in the industry a considerable amount of progress has been made in coming up with reasoning modes which do not rely solely on logical deduction. This includes logic and analogy.

Artificial Intelligence Tools

The basic tools that artificial intelligence deals with can be divided into categories:

  • Knowledge-based systems (KBSs)
  • Computational Intelligence

Knowledge based systems in the field of artificial intelligence depicts knowledge in the form of symbols and words. This enables knowledge to be more easily understood by a human mind rather than when it is numerically derived as in the case of computational intelligence. The technique used in KBSs includes case-based and model-based reasoning. These were the first ways of implementing artificial intelligence and still remain a very major theme. Early research in the field of medicine, chemistry and computer hardware was based on specialized applications. This has been a driving force for AIs success in different technological spheres.

The main difference between a knowledge-based system and a conventional system lies in the framework. With regards to the simplest case, there are two different modules. The knowledge module is known as knowledge base and control module is known as interface engine.

In a knowledge based system, the developer demonstrates knowledge about the problem at hand. Most of the times, this information is present in a declarative format. This includes rules, facts and relationship and there are no concerns about how information needs to be applied. The other details are taken care of by the interface engine. A knowledge based system is similar to a human brain in the sense that control processes are consistent in nature. This includes the interface engine inspite of the fact that individual behavior is never consistent. It has the tendency to be continuously modified when new knowledge is fed into the system. For example a knowledge base.

As knowledge is represented simply in the knowledge base instead of being embedded within the structure of a program, it can be entered and updated with ease through domain experts who do not possess any knowledge about programming. A knowledge engineer provides the gap required between domain knowledge and computer implementation. He can use meta-knowledge that is knowledge about knowledge to make sure that the implementation is carried out efficiently.

Implementation of Artificial Intelligence

There are different technological models where artificial intelligence can be implemented. Some of them are mentioned below:

  • Machine Learning (ML): It is concept which is similar to artificial intelligence on many levels. Artificial Intelligence (AI) can here be seen as a broader concept whereas Machine Learning (ML) is the idea that machines can work for themselves provided they are fed with enough data sufficient to complete their tasks. It is often used to provide the users with recommendations and predictions for the future. As of late this is being extensively used in the retail space. One great example of this is Amazon that uses Machine Learning to give accurate recommendations to customers. Such machines need huge amounts of training and lots of input from the developer in order to come up with accurate recommendations. This has enhanced our shopping experiences online and can impact the same to a greater extent in the near future.
  • Augmented Reality (AR) and Virtual Reality (VR): These two are very similar, but have basic differences that give them these names. When it comes to Virtual Reality, it is basically the implementation of headsets to shut off from rest of the world. Augmented Reality is more like a reality where you remain in the world but still remain aloof to the same. To take an example, Virtual Reality gives you the ability to swim with sharks whereas augmented reality makes a shark pop out of your letterhead.

Augmented reality is not something that has come up recently in the technological space. Companies have been utilizing it from as late as 5 years ago. It has made rapid progress ever since. It is now available even on mobile phones and a number of other devices as well. The immense popularity of augmented reality (AR) lies in planning home interiors and trying out different clothes in the virtual space.

Virtual reality is limited when compared to augmented reality. As of now, not many fields other than gaming have been using this technology. It is used to some extent in the field of retail as well.

  • Mixed Reality (MR): This type of technology is a combination of both Virtual Reality (VR) and Augmented Reality (AR). To give an example, it uses headsets that are similar as seen in the case of Virtual Reality but this is seen through a translucent glass or viewport. It also shows visuals on the top of the user. A striking feature of mixed reality is its extremely interactive aspect and the real projection of our environment. Instead of merely depending on immersive content we can make good use of our natural body to enter the virtual world.

How are companies using Artificial Intelligence (AI)?

Even though for large scale organizations, knowledge based systems remain the most important, small artificially intelligent systems are also seen at their workplace as of late. Some examples include washing machines that have knowledge-based controls. This is for their personal computer management. By being in their own environment, these systems do not require large chunks of data and have the ability of making decisions based on sensor data. Another stage of artificial intelligence is the movement towards embedded AI. Here are a few reasons as to why you need to have artificially intelligent systems at your workplace.

  • Virtual Assistance: Have you ever wondered how a major airline like Emirates operates? Each and every day they are required to interact with thousands of people. This needs to be done across social media and also in the real time market. For a business like this, customer relationship management is extremely critical as customers are found in abundance. With the help of artificially intelligent chatbots, they can always be connected to the market. On the off chance that a customer flight is delayed, they can utilize systems that lets the customer know about different schedules with the help of personalized notifications. Artificially intelligent chatbots can also be used to respond to queries which a customer might have.
  • Generating Business Insights: As of late data can be seen as something that drives the economy. It becomes null and void if there are no cloud databases to fuel them. Artificial Intelligence can be used to get the data required to run a business. Not only this, it can also be used to generate predictions and get better business insights. One unique thing about AI remains that it is not consistent in nature. It can vary from time to time. The insights generated for different businesses can be used to get better return on investments.
  • Systems Automation: With the advent of industrial revolution technology has automated work on many levels. Be it tractors or automated hotel bookings, artificial intelligence finds a place for itself in almost every sphere of life. Lately, due to the bandwidth of work companies are unable to perform tasks manually hence the need arises for artificially intelligent systems which automates tasks for them. Unlike humans, there is no need for machines to take breaks while performing tasks, thus enabling them to be more efficient and give better inputs.
N E D E R L A N D . A I