Introduction to Artificial Intelligence

Talking of advancements, from Abacus to Super-Computers, the world has come a long way. The world, say a hundred years ago was highly dependent on manual execution. Simple tasks such as arithmetic operations too were long and tedious. Having realized this difficulty, various technologies capable of executing complex calculations were introduced.
These technologies grew rapidly and soon the world saw it’s potential. Calculations have now been quicker and more accurate. Such technologies saw a great implementation in various fields including research and development, defence, healthcare, business, etc.
But however efficient these machines were, there was always a lack of “Intelligence”. Computers may be reliable, accurate and a gazillion times faster than a human but they were all but “dumb machines”.
Artificial Intelligence is a concept far superior to any other concept and aims to make machines able to learn and respond on their own.
Though the term Artificial Intelligence has been around for more than 5 decades, it was not until 2 decades before that the world started realizing its huge potential. Artificial Intelligence has a plethora of applications in areas such as Natural Language Processing, Simulations, Robotics and Speech Recognition to name a few.
While the potential of Artificial Intelligence and its applications has been realized, but due to the complexities involved, the advancements in this field, as of now, is only restricted to the development of Weak Artificial Intelligence Systems also known as Narrow Artificial Intelligence Systems.
There has been a steady development in the field of Artificial Intelligence and the growth is exponential. Today, Artificial Intelligence is everywhere. From Google to Facebook and Shopping to Learning, Artificial Intelligence is at the forefront.

There are many technologies in existence today that have a direct or indirect application of Artificial Intelligence.

Top 10 Hot latest Artificial Intelligence Technologies

• Natural Language Generation

Popularly known as “Language Production” among Psycholinguists, Natural Language Generation is a procedure that aims to transform any structured data into a natural language. In layman terms, natural language generation can be thought of as a process that converts thoughts into words.
For example, when a child looks at a butterfly flying in a garden, he may think of it in various ways. Those thoughts may be called ideas. But when the child describes his thought process in his natural language (mother tongue), this process may be termed as Natural Language Generation.

• Natural Language Understanding

Natural Language Understanding is the opposite of Natural Language Generation. This procedure is more inclined towards the interpretation of Natural Language.
In the example above, if the child is told about the butterfly rather than shown, he may interpret the data given to him in a variety of ways. Based on that interpretation, the boy will make a picture of a butterfly flying in a garden. If the interpretation was correct, then one may infer that the procedure (Natural Language Understanding) was successful.

• Speech Recognition

As the name suggests, Speech Recognition is a technology that uses Artificial Intelligence to convert human speech into a computer-accessible format. The process is very helpful and acts as a bridge in human-computer interaction.
Using Speech Recognition technology, the computer can understand human speech in several natural languages. This further enables the computer to have a faster and smoother interaction with humans.
For example, let’s say that the child in the first example was asked, “How are you?” during a normal human to human interaction. When the child listens to the human speech sample, he processes the sample according to the data (knowledge) already present in his brain.
The child draws necessary inferences and finally comes up with an idea about what the sample is about. This way, the child can understand the meaning of the speech sample and respond accordingly.

• Machine Learning

Machine Learning is yet another useful technology in the Artificial Intelligence domain. This technology is focussed on training a machine (computer) to learn and think on its own. Machine Learning typically uses many complex algorithms for training the machine.
During the process, the machine is given a set of categorized or uncategorized training data pertaining to a specific or a general domain. The machine then analyses the data, draws inferences and stores them for future use.
When the machine encounters any other sample data of the domain it has already learned, it uses the stored inferences to draw necessary conclusions and give an appropriate response.
For example, let’s say that the child in the first example was shown a collection of toys. The child interacts (using his senses like touch, see, etc.) with the training data (toys) and learns about the toys’ properties. These properties can be anything from size, colour, shape, etc. of the toys.
Based on his observations the child stores the inferences and uses them to distinguish between any other toys that he may have any future encounters with. Thus, it can be concluded that the child has learned.

• Virtual Agents

Virtual Agents are a manifestation of a technology which aims to create an effective but digital impersonation of humans. Quite popular in the customer care domain, Virtual Agents use the combination of Artificial Intelligence programming, Machine Learning, Natural Language Processing, etc. to understand the customer and his grievances.
A clear understanding by the Virtual Agents is subject to the complexity and technologies used in the creation of the agent. These systems are nowadays highly used through a variety of applications such as chatbots, affiliate systems, etc. These systems are capable of interacting with humans in a humane way.
In the above-mentioned examples, if the child is considered a Virtual Agent and is made to interact with unknown participants, the child will use a combination of his already learned knowledge, language processing and other necessary “tools” to understand the participant.
Once the interaction is complete, the child will derive inferences based on the interaction and be able to address the queries posed by the participant effectively.

• Expert Systems

In the context of Artificial Intelligence, Expert Systems are computer systems that utilize a pre-stored knowledge base and mimic the decision-making ability of humans. These complex systems utilize reasoning ability and the predefined ‘if-then’ rules.
Contrary to conventional procedural code based machines, Expert Systems are highly efficient in solving complex problems. Extending the above examples a bit further, the child, based on his pre-existing knowledge base and inference deriving capability is capable of analyzing problems and suggest methods to solve them.
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• Decision Management

Modern Decision Management Systems highly rely on Artificial Intelligence abilities in interpreting and converting data into predictive models. These models, in the long run, help an organization to take necessary and effective decisions.
These systems are widely used in a vast number of enterprise-level applications. Such applications provide automated decision-making capabilities to any person or organization using it.
If the child in the above example is considered as a Decision Management System, based on the knowledge set and reasoning abilities, he shall be able to manage his decisions effectively. If the child is given access to a certain behavioural data of say 10 people, then the child will be able to make near-accurate predictions. Such predictions will govern the decisions the child will make to address the problem at hand.

• Deep Learning

Deep Learning is a special subset of Machine Learning based on Artificial Neural Networks. During the process, learning is carried out at different levels where each level is capable of transforming the input data set into composite and abstract representations.
The term “deep” in this context refers to the number of levels of data transformation carried out by the computer system. The technology finds its applications in a variety of domains such as Computer Vision, News Aggregation (sentiment-based), development of efficient chatbots, automated translations, rich customer experience, etc.
For the sake of a simpler example, if the child in the above examples carries out learning restricted to only a single level, then the output (response) may not be specific to the problem but general. Learning at a deeper level helps the child in understanding the problem better. Hence it can be inferred that deeper the learning is, more accurate is the response.

• Robotic Process Automation

Artificial Intelligence is also heavily used at industrial levels to automate various processes. While manual robotics is capable of completing the job, it lacks the necessary automation required to complete the task without human intervention.
Such automated systems help in larger domains where it is not feasible to employ humans. If the child, in the above examples, is considered a Robot without intelligence, he shall be dependent on others to carry out his chores.
While he may still be able to complete his work, he would not be able to do it all by himself. Intelligence enables him to work independently without having to rely on any external interventions.

• Text Analytics

Text Analytics can be defined as an analysis of text structure. Artificially Intelligent Systems use text analytics to interpret and learn the structure, meaning, and intentions of text they may come across.
Such systems find their applications in security and fraud detection systems. An Artificial Intelligence enabled system can distinguish between any two types of text samples without any human intervention. This independence makes such a system effective, efficient and faster than its human counterparts.
The child’s intelligence, in the above examples, will also be able to make him capable of distinguishing between the handwritings written by his family members.
To summarize, Artificial Intelligence finds a variety of applications in various fields. In all the examples mentioned above, the child was able to tackle all the problems independently because he was intelligent and was not dependent on external instructions but relied on his own inferences.

Conclusion

Being highly advanced and capable of solving very complex problems, Artificial Intelligence is the key to the future. Various industries and organizations today, are making extensive use of Artificial Intelligence to fulfil the requirements that were once considered very difficult to meet.
Modern research has suggested growth in the Artificial Intelligence domain at the rate of 36.6 % and shall be worth $190.60 billion by the year 2025.
While all the artificial intelligence technologies are expecting a massive growth, Deep Learning is expected to grow the highest in terms of the Compound Annual Growth Rate (CAGR).
In terms of market share, Artificial Intelligence based software has been forecasted to hold the largest market share. While in terms of geographical area, Asia Pacific is the top contender in terms of the highest Compound Annual Growth Rate (CAGR) and North America is to hold the largest market share.
In a span of just around two decades, Artificial Intelligence has made an exemplary mark on today’s Information Technology industry. It has further provided an impressive set of tools and applications having a wider range in various domains.
Artificial Intelligence has changed the understanding of the world regarding the power of reasoning and methods of problem-solving. Additionally, it has also enlightened us about the complexity of human intelligence.