AI Introduction

Artificial Intelligence (AI) is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence in human behaviour – understanding language, learning, reasoning, solving problems, and so on. 

Some of the definitions from different authors are given below:

Artificial Intelligence is:

• “the science and engineering of making intelligent machines” where intelligence is the computational part of the ability to achieve goals in the world” (original definition by John McCarthy who coined the term ‘Artificial Intelligence’ in 1955)
• “ making a machine behave in ways that would be called intelligent if a human were so behaving” (alternative definition by John McCarthy who coined the term ‘Artificial Intelligence’ in 1955)
• “the science of making machines do things that would require intelligence if done by men” (definition offered by A.I. pioneer Marvin Minsky in 1968)
• “ a field of computer science that focuses on creating machines that can learn, recognize, predict, plan, and recommend,  plus understand and respond to images and language (Salesforce‘s definition).

Areas of AI

1. Machine Learning

Machine Learning is a subset of artificial intelligence focusing on a specific goal: setting computers up to be able to perform tasks without the need for explicit programming.Computers are fed structured data (in most cases) and ‘learn’ to become better at evaluating and acting on that data over time. Think of ‘structured data’ as data inputs you can put in columns and rows. You might create a category column in Excel called ‘food’, and have row entries such as ‘fruit’ or ‘meat’. This form of ‘structured’ data is very easy for computers to work with. Once programmed, a computer can take in new data indefinitely, sorting and acting on it without the need for further human intervention. 

Over time, the computer may be able to recognize that ‘fruit’ is a type of food even if you stop labelling your data. 

Types of Machine Learning:

• Supervised Learning: In this type of learning, data experts feed labelled training data to algorithms and define variables to algorithms for accessing and finding correlations. Both the input and output of the algorithm are particularised/defined.
• Unsupervised Learning: This type of learning include algorithms that train on unlabelled data, an algorithm analyses datasets to draw meaningful correlations or inferences
• Reinforcement Learning: For teaching a computer machine to fulfil a multi-step process for which there are clearly defined rules, reinforcement learning is practised. Here, programmers design an algorithm to perform a task and give it positive and negative signal to act as algorithm execute to complete the task

2. Neural Network

 The neural network is a branch of artificial intelligence that makes use of neurology ( a part of biology that concerns the nerve and nervous system of the human brain). Neural network replicates the human brain where the human brain comprises an infinite number of neurons and to code brain-neurons into a system or a machine is what the neural network functions.  

In simple terms, a neural network is a set of algorithms that are used to find the elemental relationships across the bunches of data via the process that imitates the human brain operating process. 

From forecasting to market research, they are extensively used for fraud detection, risk analysis, stock-exchange prediction, sales prediction and many more. 

3. Deep Learning

Deep learning is a subset of machine learning that uses artificial neural networks to mimic the learning process of the human brain.Machine learning is about computers being able to perform tasks without being explicitly programmed,but the computers still think and act like machines. Their ability to perform some complex tasks,gathering data from an image or video, for example, still falls far short of what humans are capable of. Deep learning models introduce an extremely sophisticated approach to machine learning and are set to tackle these challenges because they’ve been specifically modelled after the human brain. Complex, multi-layered “deep neural networks” are built to allow data to be passed between nodes (like neurons) in highly connected ways.

Deep learning will witness all possible human characteristics and behavioural databases and will perform supervised learning. This process includes:

• Detection of different kinds of human emotions and signs.
• Identify the human and animals by the images like by particular signs, marks, or features.
• Voice recognition of different speakers and memorising them.
• Conversion of video and voice into text data.
• Identification of right or wrong gestures, classify spam things, and fraud cases (like fraud claims).

4. Expert Systems

Under the umbrella of AI technology, an expert system refers to a computer system that mimics the decision-making intelligence of a human expert. It conducts this by deriving knowledge from its knowledge base by implementing reasoning and insights rules in terms with the user queries.The effectiveness of the expert system completely relies on the expert’s knowledge accumulated in a knowledge base.  The more the information collected in it, the more the system enhances its efficiency. For example, the expert system provides suggestions for spelling and errors in Google Search Engine.

Applications of Expert Systems

Some popular Applications of Expert System:

• Information management
• Hospitals and medical facilities
• Help desks management
• Employee performance evaluation
• Loan analysis
• Virus detection
• Useful for repair and maintenance projects
• Warehouse optimization
• Planning and scheduling
• The configuration of manufactured objects
• Financial decision making Knowledge publishing
• Process monitoring and control
• Supervise the operation of the plant and controller
• Stock market trading
• Airline scheduling & cargo schedules

5. Natural Language Processing

In layman’s terms, NLP is the part of computer science and AI that can help in communicating between computer and human by natural language. It is a technique of computational processing of human languages. It enables a computer to read and understand data by mimicking human natural language.

NLP is a method that deals in searching, analysing, understanding and deriving information from the text form of data. In order to teach computers how to extract meaningful information from the text data, NLP libraries are used by programmers. A common example of NLP is spam detection; computer algorithms can check whether an email is junk or not by looking at the subject of a line, or text of an email.

Some of the NLP applications are text translation, sentiment analysis, and speech recognition. For example, Twitter uses NLP technique to filter terrorist language from various tweets, Amazon implements NLP for interpreting customer reviews and enhancing their experience.The various types of translators that convert one language into another are another examples of the natural language processing system. The Google feature of voice assistant and voice search engine is also an example of this.

6. Fuzzy logic

In the real world, sometimes we face a condition where it is difficult to recognize whether the condition is true or not, their fuzzy logic gives relevant flexibility for reasoning that leads to inaccuracies and uncertainties of any condition. 

In simpler terms, Fuzzy logic is a technique that represents and modifies uncertain information by measuring the degree to which the hypothesis is correct. Fuzzy logic is also used for reasoning about naturally uncertain concepts. Fuzzy logic is convenient and flexible to implement machine learning techniques and assist in imitating human thought logically.

It is simply the generalisation of the standard logic where a concept exhibits a degree of truth between 0.0 to 1.0.  If the concept is completely true, standard logic is 1.0 and 0.0 for the completely false concept. But in fuzzy logic, there is also an intermediate value too which is partially true and partially false.

7. Computer Vision

Computer vision is a very vital part of artificial intelligence as it facilitates the computer to automatically recognize, analyse, and interpret the visual data from the real world images and visuals by capturing and intercepting them.

It incorporates the skills of deep learning and pattern recognition to extract the content of images from any data given, including images or video files within PDF document, Word document, PPT document, XL file, graphs, and pictures, etc.

Suppose we have a complex image of a bundle of things then only seeing the image and memorising it is not easily possible for everyone. The computer vision can incorporate a series of transformations to the image to extract the bit and byte detail about it like the sharp edges of the objects, unusual design or colour used, etc.

This is done by using various algorithms by applying mathematical expressions and statistics. The robots make use of computer vision technology to see the world and act in real-time situations.

The application of this component is very vastly used in the healthcare industry to analyse the health condition of the patient by using an MRI scan, X-ray, etc. Also used in the automobile industry to deal with computer-controlled vehicles and drones.

Advantages of AI:

1) Reduction in Human Error:

The phrase “human error” was born because humans make mistakes from time to time. Computers, however, do not make these mistakes if they are programmed properly. With Artificial intelligence, the decisions are taken from the previously gathered information applying a certain set of algorithms. So errors are reduced and the chance of reaching accuracy with a greater degree of precision is a possibility.

Example: In Weather Forecasting using AI they have reduced the majority of human error.

2) Takes risks instead of Humans:

This is one of the biggest advantages of Artificial intelligence. We can overcome many risky limitations of humans by developing an AI Robot which in turn can do the risky things for us. Let it be going to mars, defuse a bomb, explore the deepest parts of oceans, mining for coal and oil, it can be used effectively in any kind of natural or man-made disasters.

Example: Have you heard about the Chernobyl nuclear power plant explosion in Ukraine? At that time there were no AI-powered robots that could help us to minimise the effect of radiation by controlling the fire in early stages, as any human that went close to the core was dead in a matter of minutes. They eventually poured sand and boron from helicopters from a mere distance.

AI Robots can be used in such situations where intervention can be hazardous.

3) Available 24×7:

An Average human will work for 4–6 hours a day excluding the breaks. Humans are built in such a way to get some time out for refreshing themselves and getting ready for a new day of work and they even have weekly offers to stay intact with their work-life and personal life. But using AI we can make machines work 24×7 without any breaks and they don’t even get bored, unlike humans.

Example: Educational Institutes and Helpline centres are getting many queries and issues which can be handled effectively using AI.

4) Helping in Repetitive Jobs:

In our day-to-day work, we will be performing many repetitive tasks like sending a thank you letter, verifying certain documents for errors and many more things. Using artificial intelligence we can productively automate these mundane tasks and can even remove “boring” tasks for humans and free them up to be increasingly creative.

Example: In banks, we often see many verifications of documents to get a loan which is a repetitive task for the owner of the bank. Using AI Cognitive Automation the owner can speed up the process of verifying the documents by which both the customers and the owner will be benefited.

5) Digital Assistance:

Some of the highly advanced organisations use digital assistants to interact with users which saves the need for human resources. The digital assistants are also used in many websites to provide things that users want. We can chat with them about what we are looking for. Some chatbots are designed in such a way that it’s become hard to determine that we’re chatting with a chatbot or a human being.

Example: We all know that organisations have a customer support team that needs to clarify the doubts and queries of the customers. Using AI the organisations can set up a Voice bot or Chatbot which can help customers with all their queries. We can see many organisations have already started using them on their websites and mobile applications.

6) Faster Decisions:

Using AI alongside other technologies we can make machines take decisions faster than a human and carry out actions quicker. While making a decision humans will analyse many factors both emotionally and practically but AI-powered machines work on what is programmed and deliver the results in a faster way.

Example: We all have played Chess games on Windows. It is nearly impossible to beat the CPU in hard mode because of the AI behind that game. It will take the best possible step in a very short time according to the algorithms used behind it.

7) Daily Applications:

Daily applications such as Apple’s Siri, Windows Cortana, Google’s OK Google are frequently used in our daily routine whether it is for searching a location, taking a selfie, making a phone call, replying to a mail and many more.

Example: Around 20 years ago, when we were planning to go somewhere we used to ask a person who already went there for directions. But now all we have to do is say “OK Google where is Damak”. It will show you Damak’s location on google map and the best path between you and Damak.

8) New Inventions:

AI is powering many inventions in almost every domain which will help humans solve the majority of complex problems.

Example: Recently doctors can predict different types of cancers using AI.

Disadvantages of AI:

1) High Costs of Creation:

As AI is updating every day the hardware and software need to get updated with time to meet the latest requirements. Machines need repairing and maintenance which need plenty of costs. It’ s creation requires huge costs as they are very complex machines.

2) Making Humans Lazy:

AI is making humans lazy with its applications automating the majority of the work. Humans tend to get addicted to these inventions which can cause problems for future generations.

3) Unemployment:

As AI is replacing the majority of repetitive tasks and other work with robots,human interference is becoming less, which will cause a major problem in the employment standards. Every organisation is looking to replace the minimum qualified individuals with AI robots which can do similar work with more efficiency.

4) No Emotions:

There is no doubt that machines are much better when it comes to working efficiently but they cannot replace the human connection that makes the team. Machines cannot develop a bond with humans which is an essential attribute when it comes to Team Management.

5) Lacking Out of Box Thinking:

Machines can perform only those tasks which they are designed or programmed to do, anything out of that they tend to crash or give irrelevant outputs which could be a major backdrop.

Applications of AI:

Artificial Intelligence has various applications in today’s society. It is becoming essential for today’s time because it can solve complex problems in an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc. AI is making our daily life more comfortable and fast.

Following are some sectors which have the application of Artificial Intelligence:

1. AI in Astronomy

• Artificial Intelligence can be very useful to solve complex universe problems. AI technology can be helpful for understanding the universe such as how it works, origin, etc.

2. AI in Healthcare

• In the last five to ten years, AI has become more advantageous for the healthcare industry and is going to have a significant impact on this industry.
• Healthcare Industries are applying AI to make a better and faster diagnosis than humans. AI can help doctors with diagnoses and can inform when patients are worsening so that medical help can reach the patient before hospitalisation.

3. AI in Gaming

• AI can be used for gaming purposes. The AI machines can play strategy games like chess, where the machine needs to think of a large number of possible places.

4. AI in Finance

• AI and finance industries are the best matches for each other. The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and machine learning into financial processes.

5. AI in Data Security

• The security of data is crucial for every company and cyber-attacks are growing very rapidly in the digital world. AI can be used to make your data more safe and secure. Some examples such as AEG bot, AI2 Platform,are used to determine software bugs and cyber-attacks in a better way.

6. AI in Social Media

• Social Media sites such as Facebook, Twitter, and Snapchat contain billions of user profiles, which need to be stored and managed in a very efficient way. AI can organise and manage massive amounts of data. AI can analyse lots of data to identify the latest trends, hashtags, and requirements of different users.

7. AI in Travel & Transport

• AI is becoming highly demanding for travel industries. AI is capable of doing various travel related works such as from making travel arrangements to suggesting the hotels, flights, and best routes to the customers. Travel industries are using AI-powered chatbots which can make human-like interaction with customers for better and faster response.

8. AI in Automotive Industry

• Some Automotive industries are using AI to provide virtual assistants to their users for better performance. Such as Tesla has introduced TeslaBot, an intelligent virtual assistant.
• Various Industries are currently working on developing self-driven cars which can make your journey more safe and secure.

9. AI in Robotics:

• Artificial Intelligence has a remarkable role in Robotics. Usually, general robots are programmed such that they can perform some repetitive task, but with the help of AI, we can create intelligent robots which can perform tasks with their own experiences without being pre-programmed.
• Humanoid Robots are the best examples for AI in robotics, recently the intelligent Humanoid robot named Ameca and Sophia has been developed which can talk and behave like humans.

10. AI in Entertainment

• We are currently using some AI based applications in our daily life with some entertainment services such as Netflix or Amazon. With the help of ML/AI algorithms, these services show the recommendations for programs or shows.

11. AI in Agriculture

• Agriculture is an area which requires various resources, labour, money, and time for best results. Nowadays agriculture is becoming digital, and AI is emerging in this field. Agriculture is applying AI as agriculture robotics, solid and crop monitoring, predictive analysis. AI in agriculture can be very helpful for farmers.

12. AI in E-commerce

• AI is providing a competitive edge to the e-commerce industry, and it is becoming more demanding in the e-commerce business. AI is helping shoppers to discover associated products with recommended size, colour, or even brand.

13. AI in education:

• AI can automate grading so that the tutor can have more time to teach. AI chatbot can communicate with students as a teaching assistant.
• AI in the future can work as a personal virtual tutor for students, which will be accessible easily at any time and any place.