If you can borrow some money from your friend, say $200, at 10% interest for one year, you will have to pay $220 to your friend at the end of the year.

If you invest these borrowed $200, along with your own $100, at a return of 15% in an year, you would have $345 at the end of the term. After returning $220 to your friend, you will be left with $125.

Effectively, you have made a profit of $25 on your initial capital of $100.

What you did was to ‘lever’ your own $100 with a loan of $200 and you made extra returns in one year.

This concept is widely used in the industry and is called leveraging. The ratio of loan (or debt) to own money (or equity) is called the lever ratio. In this case your leverage was 2:1 of Debt: Equity.

Astronomy is one of the oldest natural sciences. The early civilizations in recorded history made methodical observations of the night sky. These include the Babylonians, Greeks, Indians, Egyptians, Chinese, Maya, and many ancient indigenous peoples of the Americas. In the past, astronomy included disciplines as diverse as astrometry, celestial navigation, observational astronomy, and the making of calendars. Nowadays, professional astronomy is often said to be the same as astrophysics.

Objects of interest include planets, moons, stars, nebulae, galaxies, and comets. Relevant phenomena include supernova explosions, gamma ray bursts, quasars, blazars, pulsars, and cosmic microwave background radiation. More generally, astronomy studies everything that originates beyond Earth’s atmosphere. Cosmology is a branch of astronomy that studies the universe as a whole.

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https://youtu.be/U3PV8xydvsU

A glider is a fixed-wing aircraft that is supported in flight by the dynamic reaction of the air against its lifting surfaces, and whose free flight does not depend on an engine. Most gliders do not have an engine, although motor-gliders have small engines for extending their flight when necessary by sustaining the altitude (normally a sailplane relies on rising air to maintain altitude) with some being powerful enough to take off by self-launch.

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https://youtu.be/fMPc7DoF8O4

Without a doubt, this type of modeling is gaining immense popularity and has many prospects for the future. For this purpose, we want you to fully understand what the term means, how it is beneficial to society and what its real-life applications are.

3D modeling, in the simplest of terms, is a technique in computer graphics that is used for producing a 3D representation for any object. As the name suggests, the representation is called a 3D model and can be done with any object, living or inanimate. According to your own whims and desires, these 3D objects can either be generated automatically or even created manually. You can also go ahead and create a full physical representation of your personalized model by using the technology of a 3D printer!

To understand better, let us dive a little into the mechanisms of creating a 3D model. Someone who works with these models can be called a 3D artist or a modeler. The artist uses specific software to manipulate the points in virtual space. These points are termed mesh, also known as a collection of points that form an object. All these points are then mapped into a 3D grid and joined together in various shapes. Each point has its own individual position on the grid. By combining all these positions into shapes, the surface of the object is finally created.

While most models are exported to other software for games or movies, some create hyper-realistic scenes by using slightly complicated algorithms. The application of 3D models is also used in various mediums like architecture, engineering, illustration, commercial advertising, etc. Apart from these, let us discuss a list of 5 main fields where 3D modeling these days is considered the priority –

Creative Careers

The value of 3D modeling has been significantly increasing for a few years now. There is an extensive market for 3D models that exists online, and now that the world is making digital use the new normal, its value is as high as ever. Engineers and Architects use this type of modeling to plan and design their work. Most Hollywood movies use 3D modeling for special dramatic effects, speed up the production process, and cut costs. Finally, Animators are dependent on 3D modeling to give their ideas life and make them seem realistic.

3D Printing

The applications for 3D printing are vast since 3D modeling is needed to create designs before they reach the printer. This printer initially started with businesses and is now moving on to a legit customer base. We can easily build prototypes, manufacture replacement parts and create specialist tools. As the prices for 3D printers come down, more and more regular people are beginning to use them. 

Media

3D modeling is the best way to entertain people in today’s times. Even though creating special effects and on-screen characters have been a part of the media for years now, 3D modeling can also help create worlds that do not exist in real life. Professionals can easily remove or add items, and the models will be so realistic that viewers will not even realize that it is nothing but just a model. 3D modeling is the reason video games these days have become so addictive and believable. Be it advertising, publishing, or marketing – all media sectors use 3D Modeling in one way or another. 

Interior Visualization

This application refers to not only residential interiors but also retail spaces. 3D modeling helps to see buildings’ interior before the spaces are filled with furniture, color, and lighting. It helps the customers get a real-life idea of how the finished room will look and easily add and change items. 

Site Layout

3D modeling can be used to communicate the location of various equipment and landscape elements. Other utilities like gutters, piping, natural gas lines, etc. can also be easily mapped. Accessories such as benches, swings, and other playground equipment are also made possible through 3D modeling. Getting an idea about the rough layout helps the team get clarity about their placements and gain more information they need to place equipment at any given period of time. 

In simpler words, 3D modeling has made life easier for most of us, and it continues to do so. As time changes and technologies become more advanced, the phenomenon of this type of modeling will grow to be more popular and helpful. Knowing how 3D modeling works and putting it to use is a great skill to have in today’s time! 

So, would you like to take up 3D modeling as a hobby or a profession? No matter what you decide, we suggest you give it a shot for sure!

At ExperiHub, we teach Science and Mathematics by bringing concepts from the real world into the classroom. We conduct science experiments and have myriad science projects for better learning and understanding of concepts. If you want to learn more about Experiential Learning and cool home experiments, browse through our Online Sessions and Youtube Channel!

References – 

“What is 3D Modeling & What’s It Used For?” , Josh Petty. https://conceptartempire.com/what-is-3d-modeling/ 

“Guide to 3D Modeling.” , TOPS Marketing, Data Modeling. https://www.takeoffpros.com/2020/04/27/guide-to-3d-modeling/

So, we now know something about AI and Machine Learning and we have also got an idea of how it works.

In our discussion today, let us do a simple Machine Learning problem ourselves.

To begin with, you will need an environment (to write the code, access the libraries that have been designed by researchers, compile the code for the computer to understand it) to write the ML program. While there are ways to install various libraries in your computer, we will take a simpler route. There are certain platforms such as www.kaggle.com that offer web-based, ready-to-code environments for AI enthusiasts.

Step 1: Register/ Sign in to Kaggle (a great platform to work on AI problems and to participate in competitions)

Next we need to define the problem we want to solve and we will need data. This is an important point – for machine learning problems, we will need data to ‘train’ the machines.

Step 2: Go to https://www.kaggle.com/uciml/iris#

The problem that we are trying to solve using Machine Learning is as follows –

Assume that you own a garden that has 3 different types of flowers. While you can identify those flowers quickly, machines cannot. They need to learn before they can identify. So, we will have to train our machine to understand the characteristics of the 3 flowers. Once the machine has learnt the different characteristics, when it sees a new flower, it will be able to tell which type the flower belongs to.

So, you collect data to teach the machine. For this, you have taken 50 flowers of each type and have put down 4 properties of each flower in a table i.e. Petal Length, Petal Width, Sepal Length and Sepal Width. Of course, you have also mentioned the type (or species) for each of the flower. The 3 species in your garden are – Setosa, Versicolor and Virginica.

Luckily, this data is available here on Kaggle and you don’t need to go to the garden to collect it.

Step 3: Create a ‘New Notebook’ and select ‘Python’ language and ‘Notebook options’

The notebook is the place where we will write our code. So, with the above steps, now you have –

• An environment to run Python code i.e. a Jupyter notebook
  1. here you can code in Python
  2. you have access to all the Machine Learning libraries that you will need for this assignment
  3. you can compile and run the code i.e. make the computer understand what needs to be done
• Data on 150 flowers (50 flowers each of 3 species). We will use a part of this data to ‘train’ our machine and rest of the data to ‘test’ the machine if it has learnt the lesson well 😊

Step 4: Let’s write the code now. Note that we are keeping a very simple, minimal code at this stage just to demonstrate how the machines learn and are able to take decisions. There can be lot more steps that will improve the results of our machine but, for now, we will keep it as simple as possible.

The notebook that is now in front of you comprises of ‘cells’ (as shown in the below image) where you can write your code. After writing your code, from within the cell, press ‘Shift + Enter’ which will run the code in the cell and give the results below the cell. You can move to the next cell below to writer further code and run it.

On running the code in the cell, we get the output. So, your Python code is running. All that has been done is that we have imported libraries called numpy and pandas (that consist of pre-written code that is useful for machine learning problems) and have asked for the data files that are available to us.

Note that we have a file called Iris.csv which is the data on 150 flowers that we will use for our problem.

In the next cell, let us write –

iris = pd.read_csv(“../input/iris/Iris.csv”) #load the dataset

iris.head(2) #show the first 2 rows from the dataset

Here we are reading the file that has the data on the 150 flowers and then looking at what data is available for the first 2 flowers.

Next we will drop the ID column from this data table as it is not representing the flower in any way. We can check the data on first 2 flowers again to see that the Id column has been dropped.

iris.drop(‘Id’,axis=1,inplace=True) #dropping the Id column as it is unnecessary

iris.head(2) #show the first 2 rows from the dataset

Note that in the data that we have on the flowers, as we had discussed earlier, there are 4 features that are being used to identify the flower type – Sepal Length, Sepal Width, Petal Length and Petal Width.  The species could be one of the three – Setosa, Versicolor and Virginica. The features are the ‘Predictor Variables’ and species in this case is the ‘Predicted Variable’.

Let us check this in the structure of the table as follows.

iris.info()

At this point of time, we have data on 150 flowers (50 flowers of each of the 3 types). Before we train our machine to start understanding the difference of the flower types, we split the entire data into a training set and a test set. We would train the machine on one part of the data and then test the machine on the other data to see how accurately can the machine identify the flowers during the test.

To split the data, we want that the training data and test data should be randomly selected but both should have reasonable representation of all the 3 types of flowers. For this we would use a utility that is pre-coded in a library publicly available to us. The library is call sklearn and the utility is called train_test_split. We will split the data such that 70% of it is available for training and we test the machine on remaining 30%.

Let’s also check how the new datasets, train and test, look like i.e. how many flowers are available in the two datasets after the split. We see that 105 flowers’ data is in train and 45 in test.

from sklearn.model_selection import train_test_split

train, test = train_test_split(iris, test_size = 0.3)# in this our main data is split into train and test

# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%

print(train.shape)

print(test.shape)

Now we need to tell our Machine Learning models, which columns in the data are the Predictor Variables and which is the Predicted Variable. We need to provide this information for both training data and for the testing data.

train_X = train[[‘SepalLengthCm’,’SepalWidthCm’,’PetalLengthCm’,’PetalWidthCm’]]# taking the training data features

train_y=train.Species# output of our training data

test_X= test[[‘SepalLengthCm’,’SepalWidthCm’,’PetalLengthCm’,’PetalWidthCm’]] # taking test data features

test_y =test.Species   #output value of test data

Now we are ready to use some algorithms (machine learning programs that researchers have written that can look at patterns in the data and generate the best fit function between predictor and predicted variables) to train the machine and see how good the machine is learning. We will use 2 different algorithms here (Logistic Regression and Support Vector Machine or SVM) thought there are many more that could be tried if these do not give good results.

The steps that we are following here are –

• Import the algorithms that we need to use in our program
• Use Logistic Regression and SVM algorithms to train the model on train data
• Run the models on Test data and get the prediction from the models
• Print the accuracy that the machine is able to get on the Test data – accuracy being defined as how many of the flower species are accurately identified by the machine

# importing alll the necessary packages to use the various classification algorithms

from sklearn.linear_model import LogisticRegression  # for Logistic Regression algorithm

from sklearn import svm  #for Support Vector Machine (SVM) Algorithm

from sklearn import metrics #for checking the model accuracy

model = LogisticRegression()

model.fit(train_X,train_y)

prediction=model.predict(test_X)

print(‘The accuracy of the Logistic Regression is’,metrics.accuracy_score(prediction,test_y))

model = svm.SVC() #select the algorithm

model.fit(train_X,train_y) # we train the algorithm with the training data and the training output

prediction=model.predict(test_X) #now we pass the testing data to the trained algorithm

print(‘The accuracy of the SVM is:’,metrics.accuracy_score(prediction,test_y))#now we check the accuracy of the algorithm.

What we see is that in both the algorithms above, the accuracy is 1.0 or 100%. So, the models are good enough that they can predict the flowers with almost no error. If now we use one of the models and give the features (Sepal and Petal length and width) of a new flower, the model will be able to identify the specie of the flower with good accuracy.

Congratulations -you have made a real Machine Learning program and now your machine can make intelligent decision without any manual intervention!

Before we conclude, I would like to share that there are various types of problems that can be managed with Machine Learning and Deep Learning techniques today. There are a lot of ways that the accuracy of predictions can be improved (we saw a 100% accuracy above but that is normally not the case) and we could see some of them in our coming sessions.

However, the good thing is that the key steps of all the programs (even if you are building a self-driving car) will remain the same as what you have already done.

So, happy learning dear, budding Data Scientist!

In the last article, we had seen the reasons for the sudden growth of AI and also looked at certain real life scenarios where AI is being used very effectively and how it is impacting our daily lives, directly or indirectly.

Now is the time when we can peek under the hood to see how Artificial Intelligence works. How Data Scientists and Data Engineers are working to extract intelligent insights out of data.

We will need to get a little technical here, but I will try to keep it simple.

So, as you will recollect from the last article, machine learning and deep learning algorithms are the new kids on the block and the advancement in these has really fuelled the growth of AI. Deep Learning is a subset of Machine Learning, so let’s begin with Machine Learning first.

Machine Learning actually represents multiple techniques that researchers have put together to analyze data and to extract insights.

We will first look at an example and later look at the big picture of what all types of techniques comprise of Machine Learning.

Say, I want to buy a house in Pune and I ask you how much you think I should pay for it. Now you don’t know the prices in Pune market. So, what will you do?

First you will ask me the location of the house, the size of the house that I am interested in and the name and reputation of the builder who has built the house. Then you will try to find the price of the houses in the neighbourhood. You can safely assume that the bigger the house the more expensive it would be.

So, you will try to see how the price of houses in this locality is changing with the size of the houses. You might also look for some other factors such as quality of construction or the facilities available in the complex offered by the builder and make adjustment to price of the house accordingly. There could be other factors to consider but you are a busy person and do not want to do more research at this stage. So, the calculation that you would be doing in your mind is –

Price of House = a x (Size of the House) +

               Adjustment factor (say, b) for quality & amenities provided by the builder

Once you get information on 15-20 houses in the neighbourhood, you will start making a mental assessment of the values for ‘a’ and ‘b’ and then you will give me what you think is a reasonable value for the house that I want to buy.

As can be expected, more data you have on various houses in the neighbourhood, better your estimate for my house would be.

Now, if we look at Machine Learning, it follows a similar approach to solve such a problem. A Machine Learning problem sees ‘data’ and starts inferring the pattern (or the equation) that will give value of the unknown. In this problem, data is the prices of houses in the neighbourhood and how the prices are impacted by the size of the house and by the quality and amenities offered by the builder.

Note that ‘Size of the House’ and ‘Quality & Amenities provided by the builder’ are independent variables (or predictor variables) here and ‘Price of the House’ is a dependent variable (predicted variable).

A typical machine learning problem would first try to understand the pattern from the data that is available. Once the pattern can be framed in some sort of equation (could be a linear function or a polynomial, could be in based on one or two predictor variables or could be even in 100s of predictor variables), we can use the function to determine the value of the predicted variable in new scenarios!

The advantage of machine learning is that unlike human mind which has a a limitation to process large quantities of data and determine patterns in large number of variables, computers just need the processing power to manage complexities of any level.

This problem solving approach is called ‘Supervised Learning”. This is one type of approach taking in Machine Learning. There are other approaches such as ‘Unsupervised Learning’, ‘Deep Learning’ and ‘Reinforcement Learning’. We will touch upon them later.

So, you might be thinking, what is the big deal about Machine Learning and Artificial Intelligence. Computers have been doing such calculations since the time they came into existence. What’s different?

The interesting part here is that normal computer programs expect that the programmer would give values for ‘a’, and ‘b’in the above equation and for any combination of predictor variables, the computer would quickly give the value of the predicted variable.

However, in Machine Learning, it is the other way round. The values of ‘a’ and ‘b’ are not known beforehand. Machine tries to find these values by looking at data and extracting patterns out of data and guessing the equation that can be used for future calculations.

Is this not how most of the real-world problems are? Whatever decision you have to take (e.g. on seeing a photo if you have to decide if it is a dog or a cat, you base your judgement on your previous experience of how dogs and cats look like and take a decision), you need some previous experience and based on the patterns that you have framed in your mind, you decide.

So, get ready to outsource your brain to the machines!

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In our story, we have reached a point where computers and internet are making changes to our lives in all possible way and we are waiting for Artificial Intelligence to make a grand entry. So, continuing from there.

Now, computers though ubiquitous and seemingly omnipotent, are dumb machines (no hard feelings, buddy).  You tell them something to do and they do it.

Scientists always wanted these machines to act intelligent. Unfortunately, computers could not even differentiate between the image of a dog and of a cat (something, we, as humans, start doing as toddlers). Nor could the machines understand the context of what we said to them e.g. whether we are talking about Apple as a company or apple as a fruit, machines had no idea of what we wanted. Just imagine that you gave a lecture to a room full of machines on Apple being one of the most valuable corporations in the world and the machines murmuring among themselves that “do they need to find the stock price of Apple or provide the benefits of eating it”.

So, this has been the quest for a long time – how to make machines intelligent – how to make the computers mimic human behaviour and act sensible. While the research has been on since the days that the computers were born, it is a combination of three factors over the last two decades that started making this idea a reality –

• availability of huge amount of data
• tremendous increase in computing power of the machines and
• some very powerful algorithms coming out of research

Well, just in case you do not know, an ‘algorithm’ is a set of rules to be followed for solving a particular type of problem. Computers programs are implementation of various algorithms to solve different types of problems.

Let us look at each of these points a little closely. Data availability has been growing exponentially over the years. When you take a selfie, when you send an email, when you post a comment on facebook, you are creating new data.

These days machines also are ‘talking’ to each other (no, don’t think of Toy Story type of communication between machines) – machines are generating data through sensors that are monitoring performance of the machine on various parameters; or a modern day electric vehicle like Tesla and the self-driving cars that are being tested by various companies are generating a lot of data as they speed on the highways.

This information is being shared with other machines (mostly on ‘Cloud’ – these are computers stored in some remote locations very much on earth and not in the sky – and some of the largest Cloud service providers are none other than Amazon, Microsoft and Google) and this communication between machines is often referred to as IoT or Internet of Things (I know, there are so many terms floating around and it could get confusing).

Well, the point here is that there is a lot of data being generated today and this has been of tremendous help in making machines become intelligent.

The second factor is the way computing power has increased in our machines over the years. Our mobiles and smart watches are way smarter compared to the super computers that were crunching numbers for Apollo 11 in late 1960s (so, you can send a person to moon using your smartphone!). When you play a game on Xbox, have you wondered how well it renders images and how quickly to give a near real experience?

The third factor is the rise of the new algorithms. The Boss Babies in the world of algorithms can be categorized under ‘Machine Learning’ (a subset of Artificial Intelligence) and Deep Learning (which is a subset of Machine Learning). These algorithms started giving the machines the long-sought intelligence. We will discuss more of these in the next part of this article series.

Let us take a peek into how these intelligent machines are helping us today? We will take a few examples and see the impact that they are having on our daily lives.

• Watching movies on Netflix: Have you noticed the recommendations that Netflix makes for the series or movies that might be of interest to you? Are they the same recommendations that it is making to your aunt who lives in a different city? The recommendation systems for Netflix (and for a lot of digital content companies) are quite intelligent today. Before making a recommendation, it puts people of similar preferences, profiles, demographics into groups and assesses what content could a person in a group be interested in. This is different from the traditional recommendation systems that would suggest content based on the ‘most popular’ series without any personalization.

• Tagging friends on photos in Facebook and in Google Photo Albums: As I had mentioned, before the advent of AI, computers were not able to differentiate between the images of dogs and cats. But, today when you upload photos of your family after coming back from a recent vacation in your digital albums or on social media, the applications automatically tag your sister, your friends and your parents in the photos where they are present. You can later search through your albums to find only those where your best friend was present. How cool is that? This is all made possible because of AI.

• Typing a message or mail on your smartphone: Most of you have been using smart phones for at least 8-10 years. Just think that how has the experience of typing a text message on a phone changed. Earlier there was a QWERTY keyboard and Blackberry ruled the space. Now all phones offer a virtual keyboard on the screen itself.

It started when Apple started changing the size of on-screen keys (not visually but behind the scenes), based on the probability of what key a user will type after the previous key (e.g. after you typed ‘s’, keys ‘d’ and ‘f’ are less likely compared to ‘e’ and ‘a’). This unknowingly made life easy for users as the thumbs would normally span more than a single key when typing on the small screen.

The next change came in form of autocomplete – phones started offering the most likely words to the user in the middle of typing a word.

Taking it even further, using AI, phones are now offering predictive text. The phone has started showing three words just above your virtual keyboard—words that you are most likely to type next. For example, when you type “Best,” the phone offers “wishes”, “regards” and “of” as the likely options to choose from.

• Driving down to your cousin’s home who lives at the other end of the town: We are all using Google maps these days for navigation on the roads. While google tracks us on the road (and other vehicles too), in order to predict the time it would take us to reach our destination depending on the traffic conditions and road diversions, a lot of AI is running in the background.

There are a lot of other applications of AI these days – for example, the dynamic pricing of Uber cabs depending on the supply and demand and the time of the day, and, detection of fraud transactions in banks and identifying cancer cells in CT Scan reports and so on.

We will look at some of these industry use cases of AI in more details in the coming sessions and we will also try to peek into the approach that is taken to make the machines behave intelligently.

Well, it is possible that before you started going through this series of articles, you were of the impression that AI is all about amazing robots and machines with superhuman capabilities. The use of AI in the above real-world scenarios, might have been a dampener. For you, there is some good news – there are companies that are working on self-driving cars, humanoid robots and trying to make very interesting ‘Hollywood style’ machines.

Irrespective of the types of these applications, I think you must have realized that AI is already all around us and it is going to have a significant impact on our lives in the coming years.

Now that we have got an idea of what AI is and how it is being used in the industry today, let us look under the hood of how AI works. However, for that you will have to wait for the next article in this series. Till then, goodbye!

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However, Artificial Intelligence is real and is not a futuristic idea. There are lot of things that are happening in the AI space and they are all impacting us already. When you shop on an e-commerce platform like Amazon or watch a movie on Netflix or even search for anything on Google, AI is helping you in the background.

You want to know what is AI and how it is impacting our lives?

Let us see how AI has evolved. We will go back into history a little to put it all into perspective.

Let’s begin with machines. What are machines? Humans have always tried to make their lives easy. The early man made a wheel and it helped by reducing the effort against friction. Simple machines that we see around us are knives, screw drivers, thermometers and there are complex machines such as cars, aeroplanes, MRI machines and televisions. Machines help humans in so many ways to make life easy.

Then came computers. Computers, as the name suggest, were computing machines initially. They could make calculations faster, make repetitive calculations quicker and without mistakes.

Well, I have shown your smartphones and smartwatches in the above image along side the big, bulky desktops and laptops. This is not by mistake – your handheld and wearable devices are all mini-computers and most of them have lot more computing power than what was available to NASA scientists for sending the first person into space in 1969.

You must be wondering that how could faster calculations make computers do all that they do today. So, let’s take a peep inside these machines to get an idea.

What happens is that, unlike humans, who think of numbers in the base 10 (i.e. all the numbers that we can think of can be represented using 10 digits, from 0-9), computers process information in base 2 or in binary form i.e. 0 and 1. This could look like a limitation but it actually helped structure information using simple electric switches that could be either ‘on’ or ‘off’ i.e. behave like binary logic.

This representation of information in binary form began the phase of ‘digitalization’. Computer Scientists and Mathematicians started converting whatever they could convert to binary digits. For example, the photos that were earlier taken on camera reels and printed on photo paper were sliced into small squares (called pixels) and each square was shown as either 0 or 1 depending on the light intensity in that square. Gradually, the number of squares in which an image could be cut increased (even simple smartphones today cut each picture in 10-12 Megapixels or 10 – 12,000,000 small squares). With such fine breaking of image, human eye does not find the image “pixelated” and it is an experience nearly as good as the old, paper process. Just to add here, that this explanation makes sense to create a black-and-white photograph but for a color photo, each pixel was further divided into 3 binary numbers – for Red, Green and Blue hues – and the combination of information could help create a color photograph.

As the number of applications using computers exploded, the next big thing in this journey was already at the doorstep – the Internet.

The movement of information between computers was easy as only binary data was to be transported from one place to other – which would be equivalent of sending out signals that were on or off.

Internet started changing everything. Information was easy to get. We could communicate with anyone around the globe quickly and easily. We started shopping, making video calls and watching TV over DTH (Direct-to-Home) services or Cable services.  Imagine all this through 0’s and 1’s moving at ever increasing speeds around the world!

So, where is Artificial Intelligence in this story? Did I lose the trail and drifted into another direction? Well, no. I just wanted you to get a feel of how AI came into play.

However, while you are excited and want me to start talking about AI, like in a TV series, let’s take a pause at a crucial part of our story and we will continue from here in our next article.

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We talk about Aeroplanes, Ships, Bullet Trains making our life so fast and easily functioning. They are all inspired by our mother nature. Let’s take examples from our day to day life, designs of velcros, the shape of bullet trains, the smooth blades of wind turbines and what not, all these life changing ideas are inspired by nature. Believe it or not, engineers turn to nature to tackle some of the world’s biggest challenges. It wouldn’t be wrong to say that Biomimicry is the future.

By now you might be thinking what is Biomimicry? Biomimicry literally means the imitation of life. It is emulating nature’s time-tested patterns and strategies to seek sustainable solutions and solve human problems. In short, biomimicry is the process of taking the innovations that exist in nature and applying them to technology.

Connecting “Senses” and “Sensing”: The gemstone for the next-gen smart sensors

Nature is full of mysteries and these mysteries have many incredible examples of functional materials. One of them is Structural colour -the production of colour by tiny structured surfaces which interfere with visible light, as found in butterfly wings or opal gemstones. Alike, peacock feathers have brown pigment, which reflect blue and green light. Influenced by them researchers have developed a colour-changing material which could be the ‘keystone’ of smart sensors in the near future.

In a study published by the journal “Advanced Functional Materials”, researchers have shown how to produce photonic crystals containing a minuscule amount of graphene resulting in many desirable qualities with outputs even observable by the naked eye. They are intensely green under natural light, but change to blue when stretched and become transparent when heated. This makes them an attractive source for visual sensing applications. This method represents a simple and inexpensive approach to produce multifunctional graphene infused synthetic opals. These crystals are excellent indicators for visual strain sensors or integrated time‐temperature. These flexible photonic crystals can even predict when an earthquake might strike next.

 Few of its many potential applications are:

1. Time-temperature indicators (TTI) : The sensors could be able to give visual indication of food or pharmaceuticals experiencing undesirable results. 
2. Bio-sensing Systems: The photonic crystals can be used as tissue scaffolds for understanding human biology and disease. If functionalized with biomolecules could act as highly sensitive point-of-care testing devices for respiratory viruses offering inexpensive, reliable, user-friendly biosensing systems.
3. Safer Healthcare: A wrist band which changes colour, indicating patients if their health worker has washed hands before the examination. This property of sensors could help improve technique in sports players.
4. Fingerprint Scanning: Results with high precision, well-defined ridges and shape-memory characteristics are   attractive for biometric and anti-counterfeiting applications.

References:

1. https://www.engineersireland.ie/Engineers-Journal/Technology/pretty-as-a-peacock-the-gemstone-for-the-next-generation-of-smart-sensor
2. https://interestingengineering.com/biomimicry-9-ways-engineers-have-been-inspired-by-nature
3. https://www.researchgate.net/publication/254906399_Nature_inspired_design_Strategies_towards_sustainability