B. Tech. in Artificial Intelligence | Engineering in B.Tech AI | B.Tech AI Engineering

 Engineering in B.Tech AI

Now these days the World scenario is changing rapidly due to information technology sector and tries to making minimum human interaction in the critical section of the organization to save human from damage. Currently the world scenario that world is growing in technology rapidly with artificial intelligence with new innovations. 

The artificial intelligence helps to make an automation of the industry due to that the accuracy of the product and quality maintain properly without any damage. In the field of computer science there are so many degree and diploma courses are running in the world. 

The field of computer science and engineering area increases day by day in the area of artificial intelligence field again very cover very vast area so that it very essential requirement of Bachelor of Technology (B.Tech.) program in the field of artificial intelligence.


Engineering in B.Tech AI

Now these days’ computer systems are designed to perform small object, for facial recognition, car driving etc. The main goal of artificial intelligence is to develop advanced systems and complex systems. The main goal of artificial intelligence is try to accurate correct all human activities and provide better solutions. 

For the long term, an automated system that does all the human functions from controlling maximum systems. The super artificial intelligence will be the greatest invention in world history. The invention of more advanced technologies has significantly helped in war eradication, fighting diseases and developing appropriate prevention parameters. Advanced artificial intelligence technology will be help in fighting against poverty in the world.

  Working of Artificial Intelligence


The following sections provide accessible introductions to some of the key technocrats that involve in artificial intelligence area. They are grouped into sections, which gives a sense of the chronology of the development of different approaches. The first phase describes beginning artificial intelligence methods and techniques, such as 'symbolic artificial intelligence. 

These methods are very relevant and successfully applied in several domain areas. The second phase describes more recent 'data-driven' approaches which have developed rapidly from the last 25 year and this responsible for the current development scenario of artificial intelligence in the world. The third phase explores the future phases of artificial intelligence, focusing on approaches that remain far from the organizations and market.

                   First phase:

Symbolic artificial intelligence:  Symbolic artificial intelligence refers to approaches to developing intelligent machines by encoding the knowledge and experience of experts into sets of rules that can be executed by the system. 

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This was the main approach to artificial intelligence applications from the 1950s to the 1990s but, other methods dominate the artificial intelligence areas. Now symbolic artificial intelligence is still used in many contexts, from thermostats to advanced robotics. There are two approaches, expert systems and fuzzy in symbolic artificial intelligence

B. Tech. in Artificial Intelligence

Expert systems:  

In these systems, a human expert in the domain of the application creates precise rules that a computer can follow, step by step, to decide how to respond intelligently to a given situation. The set of rules, known as algorithms, and expressed as a code in an if-then-else format. Symbolic artificial intelligence is best in constrained environments which do not change, where the rules are strict and the variables are unambiguous and quantifiable.

Fuzzy logic:

In the expert system describe each variable is either true or false. For it to work, the system needs to know an absolute answer to questions such as whether or not the patient has a fever. This could be reduced to a simple calculation of a temperature reading above 37 °C, but reality is not always so clear cut. 

Fuzzy logic is another approach to expert systems which allow variables to have a 'truth value' that is anywhere between 0 and 1, which captures the extent values that fits in suitable category.

The fuzzy logic is particularly useful for capturing intuitive knowledge, where experts make good decisions in the face of wide-ranging and uncertain variables that interact with each other within symbolic artificial intelligence systems require human experts to encode their knowledge in a way the computer can understand.

             Second phase:  

Machine learning and artificial intelligence, Machine learning refers to a wide range of techniques which automate the learning process of algorithms. This differs from the first phase approaches whereby improvements in performance are only achieved by humans adjusting or adding to the expertise which is coded directly into the algorithm. 

While the concepts behind these approaches are just as old as symbolic artificial intelligence, they were not applied extensively until after the turn of the century when they inspired the current resurgence of the field. Machine learning algorithm usually improves by training itself on data driven process.


The tremendous growth of data-driven artificial intelligence is itself, data-driven. Machine learning algorithms find their own ways of identifying patterns, and apply what they learn to make statements about data. Different approaches to machine learning are suited to different tasks and situations, and have different implications. 

B.Tech AI Engineering

The following sections present an accessible introduction to key machine learning techniques. The first provides an explanation of deep learning and how software can be trained before exploring several concepts related to data and the important role of human engineers in designing and fine-tuning machine learning systems. 

The final sections illustrate how machine learning algorithms are used to make sense of the world and even to produce language, images and sounds.


Artificial Neural Networks (ANN)

Artificial neural networks and deep learning as the name suggests, artificial neural networks are inspired by the functionality of the electro-chemical neural networks found in human (and other animal) brains. The working of the brain remains mysterious, although it has long been known that signals are transmitted through a complex network of neurons and, in doing so, both the signal and the structure of the network are transform . 

In artificial neural networks inputs are translated into signals that are passed through a network of artificial neurons to generate outputs that can be interpreted as responses to the original inputs. The signal generated at the output layer is the final output, which is interpreted as a decision about whether or not the image depicts a cat. 

The artificial neural networks is not really aware of what it is doing, or even what a loin is, but if we give it a picture, it will always tell us whether or not it ‘thinks’ it contains a loin.


            Deep learning:

Deep learning simply refers to artificial neural networks with at least two hidden layers, each containing many neurons. Having more layers allows artificial neural networks to develop more abstract conceptualizations of problems by breaking them into smaller problems tokens more responses. In theory three hidden layers may be enough to solve any kind of problem, in practice artificial neural networks tend to contain many more. 

Artificial Neural Networks

For example, Google's image classifiers use up to 30 hidden layers. The first layers search for lines they can identify as edges or corners, the middle layers try to identify shapes in these lines, and the final layers assemble these shapes to interpret the image. So, if the 'deep' part of deep learning is about the complexity of the artificial neural networks what about the 'learning' part? Once the right structure of the artificial neural networks is in place, it needs to be trained. 

While in theory this can be done by hand, it would require a human expert to painstakingly adjust neurons to reflect their own expertise of how to identify cats. Instead, a machine learning algorithm is applied to automate the process. In the following sections, two significant machine learning techniques are explained.

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          Training Neural Networks:

Machine Learning algorithms using in the back propagation and aim to gradually improve the artificial neural networks performance by minimizing this error. They do this by adjusting the artificial neural networks and checking whether the error has reduced before re-adjusting. This process is best explained through calculus; however the following paragraphs provide an accessible introduction. 

Back propagation deals with adjusting the neurons in the artificial neural networks. They excel in finding new clusters and associations within data which might not otherwise have been identified or labeled by humans. Since labels are often incomplete or inaccurate, many applications such as content recommendation systems combine both


Training approaches inspired by nature while gradient descent and back propagation are based upon mathematical concepts like calculus, another methods are inspired by evolutionary concepts such as survival of the fittest or daily human activities, recreation or reproduce and mutation. There are so many approaches in evolutionary training methods. Artificial neural networks create so may methods which are used in the industry. 

If an engineer is asked to explain why such an artificial neural networks made a action and show how its activities was determined by its structure using mathematical methods, but they cannot always explain why this structure generates good action. This leads to the problem of the transparency of algorithms. There are also many other interesting within symbolic artificial intelligence techniques inspired by biological and behavioral mechanisms observed in nature.

 Reinforcement learning is another branch of machine learning which focuses on developing a policy for making sequences of decisions under different conditions. The reinforcement learning algorithm identifies some features of the conditions and attempts some actions; it then receives feedback about the quality of the response, which is used to maintain a set of scores for different combinations of conditions and actions.

            Data Mining:

In the field of big data and data in the wild since data is so central to contemporary reinforcement learning development. Artificial intelligence engineers spend as much time thinking about data as they do about algorithms. 

The good quality data perform effective machine learning and test the results. In data mining basically we are focused on the automated identification of patterns and computation data sets.

 The mining process deploy in artificial neural networks statistics and modeling to identify useful features. Big data refers to data set that includes large and complex content from different sources, in different formats.


             The art of artificial intelligence:

The art of artificial intelligence

The artificial intelligence art is the creation on the basis of human activities and natural activities which observed and inbuilt in the form of artificial intelligence.

It might be tempting to think of machine learning as doing all the hard work, but the machine learning algorithm can only follow the precise instructions of its creator. This section highlights the art of the artificial intelligence engineer. They harness the power of concepts from a range of disciplines like computing, logic, statistics and artificial intelligence. 

An engineer needs to find out exact method that encoding the problem itself. The artificial neural networks engineer needs to express the positions as a signal to be sent to the input layer. They also need to find out a method of explaining the output as a valid action. As on the basis of research the artificial intelligence two types of learning

1.      Supervised Learning(Calculated learning)

2.      Unsupervised Learning(Imagination learning or predictable learning)  


           Making sense of the world:

Identifying language, images and sounds this section focuses on how second wave artificial intelligence algorithms are deployed to make sense of the world. That is, how they can respond in useful ways to language, images or sounds. A good example of this is spam detection. 

When users identify emails as 'spam', they provide labeled data that is used to train artificial neural networks to identify emails that look like spam, which are then automatically filtered into a junk folder. 

For an average user that's around two emails per month or more as granted, so it's far from perfect, but each time users label an email in their junk folder as 'not spam' they provide more labeled data to train and improve the artificial neural networks. Major platforms have access to more data, so they can develop more accurate tools.


            Imagination and creativity:

 The imagination and its creation based on the Natural language processing, images processing and sounds based observation studying in artificial intelligence can be deployed to create language, images or sounds. In one sense, all machine learning algorithms are creative inasmuch as they create their own ways of solving problems without expert advice. 

In another sense, algorithms can only follow precise sequences of instructions, so it is difficult to describe them as imaginative. Illustratively, computers cannot even spontaneously imagine the random numbers they need to simulate dice rolls or generate artificial neural networks. 

The best they can do is following precise instructions to produce numbers that are so unpredictable and well distributed that they appear to be random. For example, precise measurements of the milliseconds between two keystrokes can be taken, with the figures to the right of the decimal place used as though they were a sequence of random numbers. Basic language production is widely offered on smart phones.

              Quantum Artificial Intelligence:

Quantum computers harness the power of simultaneity to quickly find solutions to very complex problems, promising a revolutionary increase in computing power. If the problem is to find a one-in-a-trillion combination that works as a solution, a normal computer would have to check each possibility one by one, while a quantum computer can check them all at the same time, in a single operation. 

This means they are particularly well-suited to problems such as simulating environments, finding solutions, and optimizing them. Since these kinds of problems are central to artificial intelligence developments in quantum computing could enable significant advances in the field. While there have been some promising recent breakthroughs in quantum computing, their details often serve to illustrate how far the technology is from launching on the market.

           Computerized Approaches:


As per the approach of Vermesan and his colleagues in current scenario in the  world, automated means of reason, learning and the way people perceive have become part of people’s daily activities through the GPS or geo sensing  methods  anywhere in the world, to the use of Smartphone technology are good examples of the role artificial intelligence has played vital role in people’s  lives.  

With  artificial intelligence  there  has  been  the  minimal  occurrence  of  errors  especially  when  typing  since  the computers can predict what we are going to write and make corrections to wrongly typed words. That is a clear example of an artificial intelligence machine at work. Additionally, whenever people are uploading pictures on social sites, the artificial intelligence algorithm identifies the person and tags them (Smith & Eckroth 2017). 

The knowledge of artificial intelligence is well utilized in the banking and financial institutions to manage and organize statistical data accordingly. Utilization of artificial intelligence technology has reduced the number of errors and increasing the chances of achieving accuracy. 

            Reduced Human Effort:

Quantum Artificial Intelligence

Artificial intelligence has played an essential role in daily human life. Now days, many companies are using human technology in the production of machines that perform human activities (Frey and Osborne 2017). The many tools are used to create consistency in the rate of production with efficiency and effectiveness and assuring the quality work managed. Therefore, the introduction of artificial intelligence technology in every aspect of life, promises of an error-free world. 

It is clear that artificial intelligence has brought about increased production in production industries due to their ability to perform different roles. Additionally, artificial intelligence is used in companies in management system where they are used to keep employees’ records, extract data that helps in decision making. Majorly, the role of artificial intelligence has enabled processing and production industries to complete their tasks in good time and enhance business development.     

             Time Saving:

Time is of great essence in today’s world, and people are willing to develop machines that help in saving time. According to Gurkaynak and his colleagues (2016), artificial intelligence has proven to save time and adequately maximize on every minute. It can do several tasks at a go efficiently and at a higher speed compared to humans. 

Similarly they can collect data and offer solutions to the problems through the analysis of the same data much faster than humans. Artificial intelligence employees no longer work on repetitive tasks but instead concentrate on more complicated issues. Therefore, artificial intelligence has brought about changes that have significantly improved on our daily lives.  


The B. Tech. program in artificial intelligence has substantially improved on people’s lives in different ways, and people are not the same as before the introduction of artificial intelligence. Implementation of artificial intelligence has led to time-saving which  in turn  has led  to increased  output from  the businesses  and day to  day human  activities.  

Moreover, development of artificial intelligence has directed to the reduced human effort, computerized methods, automated transport system and involvement in dangerous jobs. Evidently, artificial intelligence has dramatically influenced the people’s lives and done wonders to help in the automation process of almost all their activities. 

Much of these methods take a lot of time and manual labor to complete. With artificial intelligence automation of these processes will contribute a lot to the actual activities of the people and industries and enable moving forward. 


1. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company. Computerization?. Technological Forecasting and Social Change, 114, 254-280.


2. Frey, C. B., & Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerization?. Technological Forecasting and Social Change, 114, 254-280.


3. Gurkaynak, G., Yilmaz, I., & Haksever, G. (2016). Stifling artificial intelligence: Human perils. Computer Law & Security Review, 32(5), 749-758.

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