Intelligent Systems - Activity 1


  • We can use Intelligent System in many fields including engineering, Space, Medical, military, entertainment, Games etc.
  • Intelligent systems covers many techniques that work Fantastic and provides, in different-different forms, information processing capabilities for handling real life situations.
  • An intelligent system is not only adaptive, fault-tolerant, self-learning & self-repairing at each level of hierarchy, but also deals with It touching most aspects of our life.

Example biometric systems in offices, metro entry gates etc.

What is AI?

  • In AI machines work similar as human even machines never make mistakes in some cases. AI is a science of making machines smart. The possibilities of AI are endless.
  • Now the question is what is the intelligence?


It is capacity to learn and solve problems, the problem can be of any type.

  • What is artificial intelligence?
  • AI is a technique of making devices/machines smart. The possibilities of AI are endless.
  • AI means when a machine act like human , like metro entry gates open when we swap metro card on its gate, it recognize person , person’s ID associated with metro card etc. this is example of AI.

Artificial Intelligence (AI) is amongthe emerging technologies which is simulating human reasoning in AI systems.

AI Was John invented by Sir McCarthy in theyear 1950.

Types of Artificial Intelligence

Artificial intelligence can be classified in two categories

1 strong Artificial intelligence

2 weak artificial intelligence

 * Weak AI is also known as Tapered AI.

It's a type of intelligent system which is designed and trained to perform a particular type of tasks.

*Strong Artificial intelligence:- it's a type of AI which have same intelligence as a humans.

It's also called as artificial general intelligence.

Artificial Intelligence Technologies and Tools:

  • Artificial Intelligence involves a variety of technologies, some of the recent technologies are as follows:
  • Speech Recognition:Speech recognition is a ability of a machine by which it can recognize the words or phrases spoken by a human and make them readable to the machine.
  • The solid example of this technology is everyone's house these day Alexa.
  • Virtual Agent (VA): Virtual agent is a new concept of AI and it is very popular nowadays.
  • Virtual agent is a computer generated character which represents the company or the website to the costumers. These virtual agent can do basic conversation with the costumers and give answers to the questions of the costumers.

Ex- Louise, the Virtual Agent (VA) of eBay Company.

  • Machine Learning (ML): Machine learning is a class of AI that gives the potential to learn by itself and improve the programs by the experience without reprogramming it's data. There are many algorithms which helps it to solve the problems.ex (neural networks).
  • Deep Learning Platforms: Deep learning is nothing but a type of machine learning which utilizes the neutral networks to determine the different factors. Nowadays this technology is used for the recognition of patterns.These neural networks of different factors are comes up with a structure which looks similar to human neural system.
  • Biometrics: It uses different-different techniques for unique recognition of human physical or behavioral traits.

It nowadays mostly useful in Offices, schools, hospitals, metro everywhere.

Intelligent Systems - Activity 2

Deep learning and image processing:

Deep learning:- Deep learning is a category of machine learning which comes under the AI. In other words we can say it’s a type of machine learning which is based on the machine leaning algorithms. These algorithms of machine learning are inspired by the human brain and somehow they form a structure similar to the human neural system. These algorithms are multi-layered and complex. The term deep defines the complexity of these layers of these networks.

The learning process:

  • In Deep learning neural network system the information transmitted from one layer to another layer as it's consists several layers.
  • in learning process the system are trained with a huge amount of data so the system can recognise the solution of the problems fast and accurate.

The learning process of deep neural networks:

A deep neural network combines many non-linear processing layers, using simple elements operating in parallel. This is inspired by the biological nervous system, it consists of an input layer and many hidden layers, and an output layer. The layers are interconnected using nodes, or neurons, with each hidden layer using the output of the previous layer will be its


Over fitting

  • Over fitting is a condition in which a model is trained with a huge amount ofdata that it negatively impacts the performance ofthat particular model.
  • The model learns the random information from the dataset and learns them as a concept which impacts the accuracy of the particular model.
  • The main cause of this condition is training a model with non-parametric and non direct strategies.
  • To reduce the over fitting in a model we have to set straight information to the model or setting the boundaries like the maximal profundity.


Techniques to reduce over fitting

  1. Reduce the complexity of hidden layers
  2. Ridge regularization and lasso regularization.
  3. During training phase stop the training process before the learner reach that particular point.
  4. The dropout neural networks can be used for overcome from over fitting

Intelligent Systems - Activity 3

Is it a dog? 

Task 1: Using Prolog type in the following facts and rules: 

gives milk-No. 

eats - Meat. 

spots -Yes. 

Color - tawny with black spot. 



mammal-Yes :- gives milk-Yes. 

carnivore-Yes :- mammal-Yes ,eats-Meat. 

animal(cheetah):- carnivore-Yes,




animal(lion):- carnivore-Yes,




animal(tiger):- carnivore-Yes,




animal(fox):- carnivore-Yes,




Task 2: Using this above to ask the question is the animal a dog? 

Task 3: Change the facts so the animal is a Fox. 

Task 4: Change the facts and rule for the animal being a dog? 

Task 5 : Create a rule for a type of Wolf. 

Task 6: Add a rule for amphibian and define Salamander.

Intelligent Systems - Activity 4

Patterns in IoT based answers for medicinal services: Moving AI to the edge

Artificial intelligence in healthcare sector


At present times the healthcare industry is fastly moving towards IT based fast and smart systems to give a fast treatment to the needful patients.

But sometimes the data is huge and the system takes time to process it properly .

In critical cases the latency in data processing can be converted into lethal consequences.

Answer for this kind of lethal consequences is the Edge computing.

Edge computing

Edge computing is a very new concept in Artificial intelligence technology. It’s all about to reduce the latency in the data processing .

It's about processing the data within the system or closer than where the data is stored.

This technology can prevent the latency of data processing in the system and the improvement in the performance of the system.

  • The advantages of edge computing include customers having the ability to run shortlatency applications better , alsoas cache or process data on the brink of the infosource to scale backbackhaul traffic volumes and costs .
  • Some Areas where Edge Computing is useful:
  • Manufacturing: Reduce Human work , because all work is done by machines in factories , like manufacturing memory cards , different-different types of chips etc.

Ex- HP company use edge computing in manufacturing devices.

  • Enterprise ICT: Applications like desktops could run from the sting to scale back the quantity of latency experience when running these from the cloud .
    Healthcare: Allow hospitals CIOs to innovate and use more cloud-like applications , it still ensuring data is handling in a secure way , instead of during a remote server .
    Oil and gas: Reducing the reliance on the cloud to enable IoT-based 

consumers benefit from edge computing:

  • Healthcare:analyzing data from IoT devices at the sting to scale back the quantity of knowledgetransmitted over the network and avoid overloading central servers with data.
  • Media/CDN: optimizingvideo streaming by caching media even closer to the end-user .
  • Gaming: : both hard-core multiplayer gaming and thereforethe enjoyedge by reducing lag for the end-gamer .
  • AR/VR experience: To be readyto provide rich experiences using augmented reality (on smart phones, headsets or digital signage), edge are going tobe critical to scale back latency and optimizethe end-user experience .

Strength and limitations of the idea of Edge computing

Edge Computing: -Using Edge Computing we can process a big amount of data to users. Stored Data is Analyzed locally . Edge computing allows data from Internet of things device to be analyzed fringe of network before being send to a knowledge center or cloud.

Companies use Edge Computing to increase potentials of there applications.

In Cloud computing data source can be thousands of km away from machines or devices.

Example of biggest companies are Dell Technologies, Cisco , Google and Zepedathese are exploring edge computing.


  • It offers high speed in work.
  • Reduce wastage of time / Save time.
  • Need less manpower.
  • More chances of accuracy in data.
  • It provide security by using storage and apps on several range of devices.
  • It allow companies to speed up their working capacity using IoT and edge computing.


  • It requires more storage capacity.
  • It is important to have so much storage capacity for this.
  • edge computing is very expensive.
  • It requires advanced Machines.
  • Security challenges are too much because of too much information.
  • It only analyzes the data.

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