Impact of Artificial Intelligence

Future is now and the impact of AI is virtually everywhere. No major industry can survive without employing modern AI, or more specifically in technical terms without employing “Narrow AI”.

The application of machine learning models and deep learning algorithm using the data trained models with the objective of performance specified function falls under the purview of Narrow AI.

With the advent of robust IoT connectivity along with the proliferation of connected devices and ever-increasing computer processing power, data collection and analysis has become easier for machine-learning algorithms to operate and evolve.

Some of the organizations and business processes have already incorporated AI into their DNA, for example, TESLA, whereas others are gradually moving into the sphere of AI acceptance. However, it is undeniable that the impact of artificial intelligence is going to be very big in the coming days and cannot be ignored.

The major areas where AI has made an impact

Transportation: it will take another 10 years or more to affect the autonomous transportation process, however, it will become a reality that the autonomous cars will be seen on roads more frequently than the manually driven one.

In the manufacturing sector, AI-powered robots are seen to be working along with the humans in a limited range and capacity in the assembly and stalking lines. Predictive analytics and sensors are frequently used in the manufacturing units to keep the equipment running smoothly.

In the healthcare sector, AI has found its application in disease recognition and diagnosis. Speed up in the drug discovery and trial process in creating virtual nursing assistants, monitoring patient is possible due to the application of AI.

The application of Big Data Analytics is to create more personalization inpatient experience and service delivery.

The education sector is also impacted by the progress of AI with the digitization of textbooks. Early-stage virtual tutors and analysis of the emotions of students with the help of facial recognition and analysis software to tailor the delivery of classes according to the individual needs.

These are only the starting points of AI. 

With the ever-increasing advancement in the algorithm capacity, machine learning can handle intelligent software and the capability of AI is bound to increase leap and bound over the period of time and can deliver unimaginable results which we as humans are not even capable of comprehending as of now.

Major Tech Giants like Apple, Google, Amazon and Microsoft is spending billions of dollars in developing product and services in the Artificial Intelligence domain along with universities making specific related courses, Research and Development to explore the capability of artificial intelligence to deliver big results in the near future.

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Sensors

Let us try to understand what are the essential building blocks of the internet of things. One of the very essential components of the internet of things is sensors and the other one is actuators. The sensors basically sense the physical phenomena that are occurring around them.

Actuators actuate (take some actions) that means they perform some actions on the physical environment based on the sensed information by the sensors.

Sensors are of different types depending on the parameters they are going to sense. The sensor performs based on the temperature, pressure, humidity conditions and lighting conditions.

The sensed information is sent over a connected system network. The sensed information is transmitted based on what has been sensed and some physical activity is performed by an actuator based on the information sent by the sensors.

When we talk about sensing, we need to understand what is sensing. A sensor detects or senses the changes in the ambient conditions or it can also sense the state of another device, so maybe one sensor can sense the state of change in another device.

Sensing is done with the help of identifying some physical property of the ambient conditions of the environment in which the sensor is in or of another machine or a system that can be sensed with the help of sensors.

If you are interested to know more about Sensors, You can mail to smartsubu2020@gmail.com.

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K Nearest Neighbours

It is important to understand the different machine learning algorithm. We have already discussed Logistic regression (if you want to know about logistic regression, please

We will discuss K Nearest Neighbours

In the arena of classification algorithm, K Nearest Neighbours or KNN as known popularly is one of the most used algorithm.  The algorithm uses classification function to classify objects at the local level deferring all the calculations until complete classification is achieved, hence, this algorithm is also known as the lazy learning algorithm.

The algorithm works by selecting the k nearest neighbours from the training samples based on proximity probability and the distance measure for the test sample and then the algorithm predicts the test sample with the major class amongst k ( as specified by the user) nearest training samples.

If you are interested in how to apply K Nearest Neighbours classification algorithm and the mathematical intuition behind it, you can express your interest in joining the data science boot camp for free by email to

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What is Robotics?

The term: robotics was coined by Isaac Asimov in the year 1942. Isaac Asimov wrote one story, the name of the story was Runaround. And in that particular story, he used the term robotics during 1942, there was not even a single robot in this world.

In robotics, we use the fundamentals of different subjects, for example, physics, mathematics, mechanical engineering, electrical and electronics engineering, computer science. And, that is why it is a bit difficult to become true robotics.

We will have to know the fundamentals of all these basic subjects and robotics is actually a multi-disciplinary subject.

If you are interested more about Robotics, You can mail to smartsubu2020@gmail.com.

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What is GIL in python?

There are two concepts in python, one is threading and the other one is the processor. In order to speed up the execution of the code in python, multi-threading package is used in python. Global Interpreter Lock (GIL) is a construct in python which ensures that only one thread is executed at a time while running the code.

The mode of operation of GIL is that a particular thread acquires the GIL, execution is done and then the GIL is passed into the next thread.

It may seem that the threads are executed in parallel, but, actually, each of the threads are executed singly and thus GIL adds to the overhead while execution of a particular programme.

It is a process of multi-threading using the CPU core by taking individual threads at a time. GIL should be used based on the context of the programming and the number of core processor present on the computer.

If You are interested in Computer Programming, You can mail to smartsubu2020@gmail.com.

 

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What is deep learning?

In its very simplistic term, deep learning is the subset of artificial Intelligence design paradigm which is based on the philosophy to mimic the human way of learning. 

Why it is called Deep Learning is mainly contributed to the usage of the term by Geoffrey Hinton (Deep). But, another philosophical interpretation may be that it helps the machine to go deep into the learning dimensions and not stay at a superficial level.

The neurons based on the extent of trigger fires and send signals for appropriate decisions. 

In the same way machines generate experiential learning with the help of neural networks designed to facilitate the process of learning. 

This leads to the question, what is the neural network and how does it work. We will discuss that in Neural Networks.

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What is the difference between machine learning and deep learning?

Machine learning and deep learning are more or less on the same genre of machine learning algorithms but with a slight difference in the way they are being executed.

In the case of machine learning, what we do is that first of all we try to identify the defining features from the data set which correctly defines or classifies the data and this process are known as feature extraction. 

What we do next is that we feed these features into our classification algorithms to train the machines in order to identify and predict the correct output.

Deep learning is a subset of machine learning where the feature extraction and classification is done simultaneously by the algorithm itself with the help of neural networks set for the particular purpose in the algorithm. 

Deep learning is more influenced by the working of the human brains where the inputs are fed and the processing is done in the hidden neural layers and finally, the outputs are produced. 

Deep learning algorithms have become more popular in case of image recognition, computer vision and natural language processing.

Artificial Neural Networks, Recurrent Neural Network and Convolution Neural Network have played a significant role in deep learning. Don’t worry if you don't know anything about these neural networks we will discuss this particular ANN, CNN and RNN separately.

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ACTUATORS

Actuators basically perform certain physical actions based on the readings of the different types of sensors. 

Actuators are based on the working principles of electronics, electrical or mechanical systems with some kind of control signals.

These control systems act on the environment so an actuator requires some kind of a control signal and a source of energy for their functioning.

There are different actuators that are available for use in IoT (Internet of Things).

Control signals are required for the actuators, when these actuators receive a control signal they respond by converting that energy into mechanical motion. There are different kinds of actuators for e.g. hydraulic actuator, pneumatic actuator etc.

The actuators give an output based on which there can be some linear motion or some oscillatory motion or rotatory motion.

There are different kind of soft actuators which are actually polymer-based and are designed to handle fragile objects like fruit harvesting in agriculture or in biomedicine handling the internal organs of human beings.

Soft actuators are typically used in robotics and are quite popular. They use SMPS, which are Shape Memory Polymers and the behaviour of the polymers depend on the change of the stimuli of the environment.

 

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How AI is going to impact work force and job and its solution

Like all other technology, AI is also expected to have a greater impact on the workforce and the job scenario in the future. It is apprehended that most of the jobs are going to be replaced with machines. However, this apprehension needs to be justified with the fact that the jobs which are routine in nature are likely to be replaced with AI-first.

The bottom 50% of the world in terms of income or education is going to be badly hurt with job displacement. More repetitive the task and more quantitative the nature of this repetition, AI is expected to replace this kind of scripted task very quickly in the coming 5 to 10 years.

The task requiring creative skills and out of the box thinking requiring innovative interventions is likely to remain and it is expected to fetch a higher dividend in the future. Mechanization in terms of artificial intelligence has already begun and it cannot be stopped as of now, just like the wave of the Internet and other technological advancements which have become a part of modern life.

But what is the way out of this displacement in the workforce? The machines will lack in creativity and have no compassion for love. So it is important that human qualities can be harnessed and made available in the coming future rather than specializing in a repetitive task for routine works.

It is pertinent that new skills like the language of coding are incorporated in all spheres of life and people are made literate in the language of coding. Huge investment in the areas of education to teach the prerequisites of AI to people and retrain them for the new jobs will be beneficial.

People need to have a basic understanding of programming and they should be capable enough to learn the new language and it is important to learn the new language as quickly as possible because in future if somebody is coding illiterate and does not know to the program they are going to face great difficulty in finding any petty jobs.

The fast people are able to understand what the technology is capable of doing and they understand their domain very well they can start to make connections and identify what is the problem paradigm and find suitable solutions to it with the help of AI Technology.

If you are interested in Coding and Computer Programming, You can mail at smartsubu@gmail.com.

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