Rashmi Jain

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Rashmi Jain

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Rashmi began their journey in software development but found their voice in storytelling. Now, Rashmi simplifies complex tech concepts through engaging narratives that resonate with both engineers and hiring managers.
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Explore Rashmi Jain’s blogs for thoughtful breakdowns of tech hiring, development culture, and the softer skills that build stronger engineering teams.
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How can mathematics make you a better recruiter!

Hiring the right talent is crucial to any organization’s growth and success. “By implementing recruiting best practices and supporting technology, you can potentially reduce your time to hire by up to 50 percent, reduce cost per hire by up to 70 percent, and improve recruiter efficiency while finding the talent you need for driving business results.”1

Companies try everything from recruiting agencies to job boards to employee referrals to social media. But the efficacy of these approaches can be often debatable. But that’s a post for another day.

Here, we bring you some sure-fire concepts that will boost hiring efficiency.

Did you say mathematics?

Yes, you read it right. Mathematics! Perhaps the most hated subject ever, math can help recruiters solve one of the most prominent problems they face while trying to zero in on great talent.

Let’s see how exactly it helps.

How many candidates should you interview before making a decision to hire? Imagine a situation where you have a hundred applicants for a position. The problem is that neither will you interview just one candidate nor will you interview all hundred. The dilemma is not whom to pick but how many to even consider before you hire (or you give up).

The most intuitive answer would be that it requires a balance between looking and leaping - that you must look at enough candidates to build a standard and decide on whatever satisfies the established standard. This looks like the perfect answer but here is the catch. Most people can’t say what this standard or balance should be. Luckily, mathematics comes to your rescue and provides the answer. Optimal Stopping Theory...

It is an idea “that every decision is a decision to stop what you are going to make a decision.”2 The theory suggests that you should reject the initial 37% of all the applicants and hire only after that. After this point, you should select the next applicant who is better than all candidates you interviewed before the cutoff. This is not intuition or a compromise between looking and leaping. It is a probable result.



Here look at this example if you have five weeks to choose a primary contractor. You could expect to see possibly four a week; that is an anticipated total of 20 suppliers. If you selected normally and selected the first ‘good enough’ option, the probability of finding the optimum supplier is just 5%. However, if you rejected the first 37% suppliers, in this case, 18 suppliers, and then selected the next supplier that was better than all the previous suppliers, then your odds of selecting the optimum supplier would increase to 40% (For the more curious people, go here to read about the famous example, the Secretary problem.)

This is just one of the mathematical theories that can help recruiters. To list a few more, there is Negativity Threshold which can help you identify the candidates that are inconsistent in their interview answers or are withholding information. Negativity Threshold was presented by Hannah Fry in her TED talk “The Mathematics of Love.” It was coined by John Gottman by observing how couples interact with each other.

The equations look something like this:

Mathematics, Recruiter, Mathematics in hiring, mathematics example, Recruitment, Hiring, Mathematics in recruitment

The left-hand side of the equation tells how positive or negative a wife/husband will be in the next thing she/he says. Here, w is the mood of the wife in general, r_w.W_t is the mood of the wife when she’s with her husband and I_(HM) is the influence that her husband’s actions will have on her. Researchers have plotted the effects the two partners have on each other. The plot looks as follows:

Mathematics, Recruiter, Mathematics in hiring, mathematics example, Recruitment, Hiring, Mathematics in recruitment

Here, the term T_ is the negativity threshold. At this point, the husband’s negative impact becomes so high that the wife responds with more negativity. To know more about this theory you can watch [ted talk link] or read this.

The negativity threshold suggests you be upfront about any issues and get all sorts of concerns out in open to avoid issues further down the line.

Another interesting equation that is worth looking at is The Drake Equation. The equation was conceived in 1961 by Dr. Frank Drake in an attempt to find the number of potential extraterrestrial bodies with life in the universe. He took something extremely complex and daunting and broke it down into something easy to understand. The Drake equation looks something like this:
N = R*•fp• ne• fl• fi• fc• L

The equation involves various factors such as the average rate of star formation in our galaxy, the fraction of stars that have formed planets, and much more which we will not get into. But, what how does this apply to hiring practices? A very obvious similarity is that both use data to pinpoint something or “someone” out there.

An important step in hiring candidates is determining the business factors your company wants to improve, says Emilio J. Castilla, Nanyang Technological University professor of management at the MIT Sloan School of Management. Determining these factors brings clarity to the business and helps everyone understand their roles. For instance, there is something called Sales Velocity, defined as:
Sales Velocity = Work In Progress ? Win Rate ? Avg. Deal Size ÷ Time Taken To Close

This equation does not help in identifying the top performers but also helps in determining the areas where an individual needs to improve. Drake’s theory is extremely useful when it comes to bringing order to a chaotic world.

Although most of the recruiting process is often dominated by emotion, mathematics is the one subject which can be applied everywhere, even hiring, without this particular bias.

Can you math enthusiasts think of any more? Let us know in Comments.

& for some of you who are super busy or are less inclined to "appreciating" math concepts, let us do the work for you.

Take a free trial for our Online Assessment software to hire the best mathematician (or developers) in your talent pipeline

Minimax Algorithm with Alpha-beta pruning

It is the best choice so far for the player MAX. We want to get the highest possible value here.
Beta: It is the best choice so far for MIN, and it has to be the lowest possible value.

Note: Each node has to keep track of its alpha and beta values. Alpha can be updated only when it’s MAX’s turn and, similarly, beta can be updated only when it’s MIN’s chance.

How does alpha-beta pruning work?

  1. Initialize alpha = -infinity and beta = infinity as the worst possible cases. The condition to prune a node is when alpha becomes greater than or equal to beta.alpha beta pruning
  2. Start with assigning the initial values of alpha and beta to root and since alpha is less than beta we don’t prune it.
  3. Carry these values of alpha and beta to the child node on the left. And now from the utility value of the terminal state, we will update the values of alpha and be, so we don’t have to update the value of beta. Again, we don’t prune because the condition remains the same. Similarly, the third child node also. And then backtracking to the root we set alpha=3 because that is the minimum value that alpha can have.
  4. Now, alpha=3 and beta=infinity at the root. So, we don’t prune. Carrying this to the center node, and calculating MIN{2, infinity}, we get alpha=3 and beta=2.
  5. Prune the second and third child nodes because alpha is now greater than beta.
  6. Alpha at the root remains 3 because it is greater than 2. Carrying this to the rightmost child node, evaluate MIN{infinity,2}=2. Update beta to 2 and alpha remains 3.
  7. Prune the second and third child nodes because alpha is now greater than beta.
  8. Hence, we get 3, 2, 2 at the left, center, and right MIN nodes, respectively. And calculating MAX{3,2,2}, we get 3. Therefore, without even looking at four leaves we could correctly find the minimax decision.
Pseudocode (Source: NPTEL Course):
evaluate (node, alpha, beta)

if node is a leaf

return the utility value of node

if node is a minimizing node

for each child of node

beta = min (beta, evaluate (child, alpha, beta))

if beta <= alpha

return beta

return beta

if node is a maximizing node

for each child of node

alpha = max (alpha, evaluate (child, alpha, beta))

if beta <= alpha

return alpha

return alpha

Conclusion

Games are very appealing and writing game-playing programs is perhaps even more exciting. What Grand Prix racing is to the car industry, game playing is to AI.Just as we would not expect a racing car to run perfectly on a bumpy road, we should not expect game playing algorithms to be perfect for every situation.So is the minimax algorithm. It may not be the best solution to all kinds of computer games that need to have AI.But given a good implementation, it can create a tough competitor.

6 AI-based apps making smartphones smarter

Kids from the 80’s and 90’s would recall that there were

  1. Floppies for data storage with a capacity varying from 90 KB to 240 MB
  2. Walkman and cassettes for listening to music
  3. Camera films thatneeded several days to be developed just to discover that most of the pictures were not good

All of these have been replaced by a single device. Yes, you guessed it right. Smartphones! We now have phones witha memory of 128GB (=128000 MB). There are 41 MP cameras in smartphones (Nokia Lumia 1020) where we can instantly get to know the quality of the picture. Smartphones have been referred to as the most popular innovation in the field of technology over the last decade. With apps like Whatsapp, we have almost forgotten SMS text messages and MSN messenger.

Nowadays, it is difficult to imagine life without smartphones. But how can we make the smartphones smarter? Artificial Intelligence is the answer. There is nothing to fear because it is already there on phones.AI is no longer only apart of movies as fictional characters, it is ubiquitous now. We are leveraging it through smartphones and mobile apps.

The impact that artificial intelligence has in our everyday lives is similar to that of smartphones. The need to reduce human error has led to the rise of artificial intelligence. AI means to develop systems that exceed or at least equal human intelligence.To know more about AI, you can refer toArtificial Intelligence 101: How to Get Started.

Here we have listed 6 apps that make your life simpler and your phones smarter. This will surelygive you some ideas for building your own solution for IndiaHacks 2017.

India Hacks 2017-Artificial Intelligence

Allo

Allo is an AI-centric messaging app developed by Google. It was launched on September 21, 2016, and is available for both Android and iOS. At first glance,you’ll feel that it is very similar to many other messaging apps out there. Be it sign up with thephone number, one-on-one or group chats, voice messages, themed stickers, or images with adoodle, it is pretty much what any messaging app offers.

What is new in Allo? Allo hints atGoogle’s vision of an AI-filled future. Allo showcases its smartness through a feature called Smart Replies. The app suggests words or phrases throughout a conversation.

What’s even more exciting is its image recognition feature. It is able to suggest replies not only based on the text but also the images shared within the conversation. But this is just a beginning of what Allo has to offer.

Google Assistant is an AI-based chatbot. You can interact with itin two ways:

  1. Direct chat: Assistant appears along with other conversations in the app.
  2. Within a chat: It can be called while chatting with friends with a message that starts with @Google.

In both cases, it can be used for quick Google searches, translations, directions, conversions, and advanced features such asimage recognition.

Socratic

Every kid wishes that somebody could tell the answers to all the homework questions. There you go! Socratic is an app that says “Homework in a Snap.”

Socratic is a free app available on iOS for Apple devices. It is a tutor app that can give answers to math homework problems and also explain how to solve it just by taking a picture of the problem. Not only math, it currently provides help in 23 different subjects, including Chemistry, Anatomy and Physiology, Physics, English grammar, Calculus, History, and Psychology.

It uses AI to analyze the information required and then returns explanations along with videos to give step-by-step help.

The company explains that “Socratic’s AI combines cutting-edge computer vision technologies, which read questions from images, with machine learning classifiers built using millions of sample homework questions, to accurately predict which concepts will help you solve your question.”

Microsoft Pix

Microsoft Pix is a smartphone app that helps click better photographs automatically without extra effort. Its tagline is “Point. Shoot. Perfect.” It is only available for devices running on iOS 9 or newer such as iPhone 5s and newer, iPad Air & Air 2, and iPad Mini 2, 3, 4.

Some of its features include

  1. Smart Settings: It automatically checks the lighting and scene between each shutter tap and updates settings in between shots.
  2. Live Image: It creates a short looping video by joining together the frames from the burst into a live image. But it happens only when some motion is sensed in the shot so that it doesn’t waste the phone’s memory.
  3. Face recognition: It optimizes capture settings automatically when a face is detected in the shot to help people look their best.

Microsoft Pix captures 10 frames with each shutter click and uses AI to pick up to three best and unique shots.

The Roll

The Roll is an app released by EyeEm, a German startup. It is an intelligent camera roll that helps in finding the bestshots by assigning scores. It uses image recognition technology which helps groupthe photos based on their content and highlights the best ones.

Photos get automatically tagged and are displayed in groups according to these categories. It helps in searching and sharing without any confusion.

Thescoring technology uses the EyeEm Vision algorithm whichcombines the artistic principles in photography with cutting-edge deep learning technology. EyeEm Vision usesmillions of assisted photos to learn and replicate the choices of professionals and applies scores to photos from 0 to 100.

The Roll is current an iOS-only app but is anticipated to come to Android phones soon. It is an app that gives a critique in terms of the score and helps you become a better photographer.

EasilyDo

EasilyDo is an app whichhelps youmanage calendar, contacts, and to-do lists. The tagline is “Easily Do: Your Smart Assistant. Never Miss Anything.” It tries to do some jobs that a real-life assistant would do by offering an artificial intelligence-based organization.

It directly connects to email services such as Gmail and Exchange and to many other services and apps such as LinkedIn, Evernote, and Facebook.

Some of its features are

  1. It sends cards in the feed with any photos of you tagged on Instagram from the last 24 hours. Any of the photos can beliked or shared from the gallery view.
  2. All the email attachments received within the past 7 days arecollected and placed in a card in the feed which helps you searchfor an attachment required for quick reference.
  3. It automatically creates a digital boarding pass from an online check-in confirmation email in the EasilyDo feed.

Apart from these features, there are more such asFacebook birthday reminders and track packages. It helps complete the action for any actionable email.

EasilyDo is available on both Android and iOS. Most of the features are free, but the access to the full range starts at around $5 permonth.

ELSA

ELSA stands for English Language Speech Assistant, and it is a mobile app whichuses AI to help people improve their pronunciation. Itis the first and the world’s smartest AI pronunciation tutor. This app was co-founded byVu Van and Xavier Anguera. Vu Van herself suffered with pronunciation issues while she was studying at Stanford.

Different curriculum options can be accessed based on interests, such as professional or travel settings. Users arethen lead through a series of vocabulary phrases and words, and ELSA’salgorithms analyze their speech. In case, ELSAdetects a mispronunciation, it tells the user the correct pronunciation and also teaches the user how to move the tongue and lips to form the word correctly.

It is available on Android as well as iOS and is absolutely free. It won the award at SXSWEdu Launch 2016, the world’s largest startup competition.

This is not an exhaustive list of apps that have used Artificial Intelligence to make life simpler. There are many more AI-based apps out there.

Hope you could find yoursource of inspiration for thesolution you want to build for IndiaHacks 2017.

A beginner's tutorial on the apriori algorithm in data mining with R implementation

Now that we have looked at an example of the functionality of Apriori Algorithm, let us formulate the general process.

General Process of the Apriori algorithm

The entire algorithm can be divided into two steps:

Step 1: Apply minimum support to find all the frequent sets with k items in a database.

Step 2: Use the self-join rule to find the frequent sets with k+1 items with the help of frequent k-itemsets. Repeat this process from k=1 to the point when we are unable to apply the self-join rule.

This approach of extending a frequent itemset one at a time is called the “bottom up” approach.

Mining Association Rules

Till now, we have looked at the Apriori algorithm with respect to frequent itemset generation. There is another task for which we can use this algorithm, i.e., finding association rules efficiently.

For finding association rules, we need to find all rules having support greater than the threshold support and confidence greater than the threshold confidence.

But, how do we find these? One possible way is brute force, i.e., to list all the possible association rules and calculate the support and confidence for each rule. Then eliminate the rules that fail the threshold support and confidence. But it is computationally very heavy and prohibitive as the number of all the possible association rules increase exponentially with the number of items.

Given there are n items in the set [latex]I[/latex], the total number of possible association rules is [latex]3^n - 2^{n+1} + 1[/latex].

We can also use another way, which is called the two-step approach, to find the efficient association rules.

The two-step approach is:

Step 1: Frequent itemset generation: Find all itemsets for which the support is greater than the threshold support following the process we have already seen earlier in this article.

Step 2: Rule generation: Create rules from each frequent itemset using the binary partition of frequent itemsets and look for the ones with high confidence. These rules are called candidate rules.

Let us look at our previous example to get an efficient association rule. We found that OPB was the frequent itemset. So for this problem, step 1 is already done. So, let’ see step 2. All the possible rules using OPB are:

OP[latex]\longrightarrow[/latex]B, OB[latex]\longrightarrow[/latex]P, PB[latex]\longrightarrow[/latex]O, B[latex]\longrightarrow[/latex] OP, P[latex]\longrightarrow[/latex]OB, O[latex]\longrightarrow[/latex]PBIf [latex]X[/latex] is a frequent itemset with k elements, then there are [latex]2^k-2[/latex] candidate association rules.We will not go deeper into the theory of the Apriori algorithm for rule generation.

Pros of the Apriori algorithm

  1. It is an easy-to-implement and easy-to-understand algorithm.
  2. It can be used on large itemsets.

Cons of the Apriori Algorithm

  1. Sometimes, it may need to find a large number of candidate rules which can be computationally expensive.
  2. Calculating support is also expensive because it has to go through the entire database.

R implementation

The package which is used to implement the Apriori algorithm in R is called arules. The function that we will demonstrate here which can be used for mining association rules is

apriori(data, parameter = NULL)

The arguments of the function apriori are

data: The data structure which can be coerced into transactions (e.g., a binary matrix or data.frame).

parameter: It is a named list containing the threshold values for support and confidence. The default value of this argument is a list of minimum support of 0.1, minimum confidence of 0.8, maximum of 10 items (maxlen), and a maximal time for subset checking of 5 seconds (maxtime).

https://gist.github.com/HackerEarthBlog/98dca27a7e48694506db6ae413d7570e

Summary

Through this article, we have seen how data mining is helping us make decisions that are advantageous for both customers and industries. We have also seen a simple explanation of the Apriori algorithm, along with its implementation in R. It is not only used by the retail industry to provide us the discount on some bundles of products. The use cases of the Apriori algorithm stretch to Google's auto-completion features and Amazon's recommendation systems.This tutorial aims to make the reader familiar with the fundamentals of the Apriori algorithm and a general process followed to mine frequent itemsets. Hope you are familiar now!
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3 Types of Gradient Descent Algorithms for Small & Large Data Sets

  • Variation in gradient descent with learning rate-
  • Summary

    In this article, we learned about the basics of gradient descent algorithm and its types. These optimization algorithms are being widely used in neural networks these days. Hence, it's important to learn. The image below shows a quick comparison in all 3 types of gradient descent algorithms:Gradient_Descent_Types

    7 Artificial Intelligence-based movie characters that are now a reality

    “Artificial Intelligence (AI) is the science of how to get machines to do the things they do in the movies.”- Astro Teller

    Do you remember HAL 9000- the know-all machine, Baymax- the personal healthcare robot, Ava- the human looking robot, and WALL-E- the cleaning robot? I am sure you do. After all, they are famous fictional AI characters that made every sci-fi aficionado go nuts growing up.

    Apperceptive, self-aware robots are closer to becoming a reality than you think.

    Now, what exactly is AI?

    Artificial Intelligence (AI) is defined as the ability of a machine or a computer program to think, learn, and act like a human being.The bottom-line of AI is to develop systems that exceed or at least equal human intelligence.

    Sci-fi movies and TV shows have shown us multiple visions of how the future is going to be. The Jetsons, Ex Machina or Star Wars…they all had a unique take on what life would be like years later.

    So, how real are these fictional characters? (Ignore the oxymoron) Where are we with the technology?

    This article is sort of a brief history of AI with some fictional AI characters and their real counterparts to tell you how far we come on this amazing journey.

    History of AI

    We really can’t have history without some Greek bits thrown in. And unsurprisingly, the roots of AI can be traced back to Greek mythology. As the American author Pamela McCorduck writes, AI began with “an ancient wish to forge the gods.”

    Greek myths aboutHephaestus, the blacksmith who manufactured mechanical servants, and the bronze man Talos, and the construction of mechanical toys and models such as those made by Archytas of Tarentum, Daedalus, and Hero are proof.

    Alan Turing is widely credited for being one of the first people to come up with the idea of machines that think. He was a British mathematician and WWII code-breaker who created the Turing test to determine a machine’s ability to “think” like a human. Turing test is still used today.

    His ideas were mocked at the time but they triggered an interest in theconcept, and the term “artificial intelligence” entered public consciousness in the mid- 1950s, after Alan Turing died.

    The field of AI research was formally founded in a workshop conducted by IBM at Dartmouth College during 1956. AI has flourished a lot since then.

    Some fictional characters that are reality

    The following is a list of some fictional AI characters and their real counterparts with the features.

    HAL 9000 versus IBM Watson

    Remember the iconic scene of the movie, “2001: A Space Odyssey” when HAL refuses to open the pod bay doors saying, “I’m sorry, Dave. I’m afraid I can’t do that.” If you don’t remember, then take a lookthe clip below:

    The movie “2001: A Space Odyssey” gave one of the world’s best representations of AI in the form of HAL 9000.

    HAL stands for Heuristically Programmed Algorithmic Computer. It is a sentient computer (or artificial general intelligence) says Wikipedia. And it was the on-board computer on the spaceship called Discovery 1.

    It was designed to control the systems on the Discovery 1 spaceship and to interact with the astronaut crew of the spaceship. Along with maintaining all the systems on Discovery, it is capable of many functions such as speech recognition, lip reading, emotional interpretation, facial recognition, expressing emotions, and chess.

    HAL is a projection of what a future AI computer would be like from a mid-1960s perspective.

    The closest real counterpart to HAL 9000 that we can think of today isIBM Watson. It is a supercomputer that combines AI and analytical software. Watson was named after IBM’s first CEO, Thomas J. Watson. Watson secured the first position in Jeopardy in 2011, after beating former winners Brad Rutter and Ken Jennings.

    It is a “question answering” machine that is built on technologies such as advanced natural language processing, machine learning, automated reasoning, information retrieval, and much more.

    According to IBM, “The goal is to have computers start to interact in natural human terms across a range of applications and processes, understanding the questions that humans ask and providing answers that humans can understand and justify.”

    Its applications in cognitive computing technology are almost endless. It can perform text mining and complex analytics on large volumes of unstructured data.

    Unlike HAL, it is working peacefullywith humans in various fields such as R&D Departments of companies as Coca-Cola and Proctor and Gamble to come with new product ideas. Apart from this, it is being used in healthcare industries where it is helping oncologists find new treatment methods for cancer. Watson is also used as a chatbot to provide the conversation inchildren’s toys.

    Terminator versus Atlas robots

    One of the most recognizable movie entrances of all time is attributed to the appearance of ArnoldSchwarzenegger in the movieTerminator as the killer robot, T-800.

    T-800, the Terminator robot, has living tissue over a metal endoskeleton. It was programmed to kill on behalf of Skynet.

    Skynet, the creator of T-800, is another interesting character in the movie. It is a neural networks-based artificially intelligent system that has taken over the world’s’ all computers to destroy the human race.

    Skynet gained self-awareness and its creators tried to deactivate it after realizing the extent of its abilities. Skynet, for self-preservation, concluded that all of humanity would attempt to destroy it.

    There are no AIs being developed yet which have self-awareness and all that are there are programmed to help mankind. Although, an exception to this is amilitary robot.

    Atlas is a robot developed by the US military unit Darpa. It is a bipedal model developed by Boston Dynamics which is designed for various search and rescue activities.

    A video of a new version of Atlas was released in Feb 2016. The new version canoperate outdoors and indoors. It is capable of walking over a wide range of terrains, including snow.

    Currently, there are no killer robots but there is a campaign going on to stop them from ever being produced, and the United Nations has said that no weapon should be ever operated without human control.

    C-3PO versus Pepper

    Luke: “Do you understand anything they’re saying?”
    C-3PO: “Oh, yes, Master Luke! Remember that I am fluent in over six million forms of communication.”

    C-3PO or See-Threepio is a humanoid robot from the Star Wars series who appears in the original Star Wars films, the prequel, and sequel trilogy. It is played by Anthony Daniels in all the seven Star Wars movies. The intent of his design was to assist in etiquette, translations, and customs so that the meetings of different cultures can run smoothly. He keeps boasting about his fluency.

    In real life too, companion robots are starting to take off.

    Pepper is a humanoid robot designed by Aldebaran Robotics and SoftBank. It was introduced at a conference on June 5, 2014, and was first showcased in Softbank mobile phone stores in Japan.

    Pepper is not designed as a functional robot for domestic use. Instead, Pepper is made with the intent of “making people happy,” to enhance their lives, facilitate relationships, and have fun with people. The creators of Pepper are optimistic that independent developers will develop new uses and content for Pepper.

    Pepper is claimed to be the first humanoid robot which is “capable of recognizing the principal human emotions and adapting his behavior to the mood of his interlocutor.”

    WALL-E versus Roomba

    WALL-E is thetitle character of the animated science fiction movie of the same name. He is left to clean up after humanity leaves Planet Earth in a mess.

    In the movie, WALL-E is the only robot of his kind who is still functioning on Earth. WALL-E stands for Waste Allocation Loader Lift: Earth Class. He is a small mobile compactor box with all-terrain treads, three-fingered shovel hands, binocular eyes, and retractable solar cells for power.

    Arobot that is closely related to WALL-E is Roomba, the autonomous robotic vacuum cleaner though it is not half as cute as WALL-E.

    Roomba is a series of vacuum cleaner robots sold by iRobot. It was first introduced in September 2002. It sold over 10 million units worldwide as of February 2014. Roomba has a set of basic sensors that enable it to perform tasks.

    Some of its features include direction change upon encountering obstacles, detection of dirty spots on the floor, and sensing steep drops to keep it from falling down the stairs. It has two wheels that allow 360° movements.

    It takes itself back to its docking station to charge once the cleaning is done.

    Ava versus Geminoid

    Ava is a humanoid robot with artificial intelligence shown in the movie Ex Machina. Ava has a human-looking face but a robotic body. She is an android.

    Ava has the power to repair herself with parts from other androids. Atthe end of the movie, she uses their artificial skin to take on the full appearance of a human woman.

    Ava gains so much intelligence that she leaves her friend, Caleb trapped inside, ignoring his screams, and escapes to the outside world. This is the kind of AI that people fear the most, but we are far away from gaining the intelligence and cleverness that Ava had.

    People are experimenting with making robots that look like humans.

    A geminoid is a real person-based android. It behaves and appears just like its source human. Hiroshi Ishiguro, a robotic engineer made a robotic clone of himself.

    Hiroshi Ishiguro used silicon rubber to represent the skin. Recently, cosmetic company L’Oreal teamed up with a bio-engineering start-up called Organovo to 3D print human skin. This will potentially make even more lifelike androids possible.

    Prof. Chetan Dube who is the chief executive of the software firm IPsoft, has also developed a virtual assistant called Amelia. He believes “Amelia will be given human form indistinguishable from the real thing at some point this decade.”

    Johnny Cab versus Google self-driving car

    The movie Total Recall begins in the year 2084, where a construction worker Douglas Quaid (Arnold Schwarzenegger) is having troubling dreams about the planet Mars and a mysterious woman there. In a series of events, Quaid goes to Mars where he jumps into a taxi called“Johnny Cab.”

    The taxi is driver-less and to give it a feel like it has a driver, the taxi has a showy robot figure named Johnny which interacts with the commuters. Johnny ends up being reduced to a pile of wires.

    Google announced in August 2012 that itsself-driving car completed over 300,000 autonomous-driving accident-free miles. In May 2014, a new prototype of its driverless car was revealed. It was fully autonomous and had no steering wheel, gas pedal, or brake pedal.

    According to Google’s own accident reports, its test cars have been involved in 14 collisions, of which 13 were due to the fault of other drivers. But in 2016, the car’s software caused a crash for the first time. Alphabet announced in December 2016 that the self-driving car technology would be under a new company called Waymo.

    Baymax versus RIBA II

    Remember the oscar winning movie Big Hero 6? I’m sure you do.

    The story begins in the futuristic city of San Fransokyo, where Hiro Hamada, a 14-year-old robotic genius, lives with his elder brother Tadashi. Tadashi builds an inflatable robot medical assistant named Baymax.

    Don Hall, the co-director of the movie said, “Baymax views the world from one perspective — he just wants to help people; he sees Hiro as his patient.”

    In a series of events, Baymax sacrifices himself to save Hiro’s and Abigail’s (another character in the movie) lives. Later, Hiro finds his healthcare chip and creates a new Baymax.

    In Japan, the elderly population in need ofnursing care reached an astounding 5.69 million in2015. So, Japan needs new approaches to assist care-giving personnel. One of the most arduous tasks for such personnel is lifting a patient from the floor onto a wheelchair.

    In 2009, the RIKEN-TRI Collaboration Center for Human-Interactive Robot Research (RTC), a joint project established in 2007 and located at the Nagoya Science Park in central Japan, displayed a robot called RIBA designed to assist carers in the above-mentioned task.

    RIBA stands for Robot for Interactive Body Assistance. RIBA was capable of lifting a patient from a bed onto a wheelchair and back. Although it marked a new course in the development of such care-giving robots. Some functional limitations have prevented its direct commercialization.

    RTC’s new robot, RIBA-II has overcome these limitations with added functionalities and power.

    Summary

    Soon a time will come when we won’t need to read a novel or watch a movie to be teleported to a world of robots. Even then, let’s keep these fictional stories in mind as we stride into the future.

    AI is here already and it will only get smarter with time. The greatest myth about AI is that it will be same as our own intelligence with the same desires such as greed, hunger for power, jealousy, and much more.

    Read more on How Artificial Intelligence is rapidly changing everything around you!