Artificial Intelligence

IIT DELHI SCHOOL OF AI—COMMENTS AND LOGO
Comment-1 By Dr.Ramesh Pokhriyal Nishank
IIT Delhi has established an independent School of Artificial Intelligence (ScAI) in its campus. The school is prepared to offer a PhD course from Jan 2021.Congratulations to Prof V Ramgopal Rao, Director - IIT Delhi for this exemplary step.
Comment-2 By Jatinder Pal Singh Broca
A great step to enhance knowledge about AI in minds of Indians so that they can compete with others in the whole world.
SHIMPOO 13TH SEPTEMBER 2020

A Poem On Artificial Intelligence

By Susan T. Aparejo

Though artificial but geniune,
Though not a human but has brains,
Though no emotions but teaches how to emote,
Though not eating but teaches how to cook,
Though not writing but teaches how to write,
Though not studying yet it gives tips on studying,
Though no diploma but a master of all degrees,
The master of all, but sometimes meets trouble,
Like humans, it feels tiresome,
Just unplugged and opens once more,
Your artificial gadget,
Never complains even being called as a
'Computer'

Susan T. Aparejo

COMPILED BY J S BROCA 22ND NOVEMBER 2019
LINK:
https://www.poemhunter.com/poem/
artificial-intelligence-2/

Artificial intelligence is smart, but does it play well with others? When Lincoln Laboratory researchers paired humans up with an AI model trained to play a collaborative card game, they found that humans hated playing with the AI teammate when compared to a simpler rule-based agent.

Artificial Intelligence Tutorial

All you need to know about AI

By Kurt--May 28,2019

Artificial Intelligence is a Buzzword in the Industry today and for a good reason. AI or Artificial Intelligence has already made so much progress in the Technological field and according to a Gartner Report, Artificial Intelligence is going to create 2.3 million Jobs by 2020, replacing the 1.8 million it will eliminate.

So, let’s get started with this Artificial Intelligence Tutorial in the following order:

What is Artificial Intelligence?
Importance of Artificial Intelligence
Artificial Intelligence Applications
Domains of Artificial Intelligence
Different Job Profiles in AI
Companies Hiring

What is Artificial Intelligence?
AI is a technique that enables machines to mimic human behavior. Artificial Intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation between languages.
If you ask me, AI is the simulation of human intelligence done by machines programmed by us. The machines need to learn how to reason and do some self-correction as needed along the way.
Artificial Intelligence is accomplished by studying how human brain thinks, learns, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

Importance of Artificial Intelligence
Artificial Intelligence (AI) has made it possible for machines to learn from experience and grow to perform human-like tasks. A lot of flashy examples of Artificial Intelligence you hear about like Self Driving Cars, Chess Playing Computers rely heavily on Deep Learning and Natural Language Processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.There are a lot of Areas which contribute to Artificial Intelligence namely:
Mathematics/ Sociology / Philosophy / Computer Science / Psychology / NeuroScience / Biology
If we have a look at the Importance of Artificial Intelligence:
AI automates Repetitive Learning and discovery through data. Artificial Intelligence performs frequent, high-volume, computerized tasks reliably and without fatigue
AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Google Assistant was added as a feature to a new generation of Mobile Phones.
AI adapts through progressive learning algorithms to let the data do the programming. The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play any game, it can teach itself what product to recommend next online.
AI analyzes more and deeper data using neural networks that have many hidden layers. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
AI achieves incredible accuracy through deep neural networks, which was previously impossible. AI techniques from deep learning, image classification, and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.

Applications of Artificial Intelligence
There are various applications of Artificial Intelligence in the Industry, here are a few of the important ones that are present in our Day to Day tasks.
Speech Recognition / Machine Translation/ Facial Recognition and Automatic Tagging /Virtual Personal Assistants / Self Driving Car / Chatbots

Domains of Artificial Intelligence
Artificial Intelligence covers a lot of Domains nowadays. The major domains in which heavy research is going on are :
Neural Networks:
Neural Networks are a class of models within the general machine learning literature. Neural networks are a specific set of algorithms that have revolutionized machine learning and Artificial Intelligence.
Robotics:
Robotics is a branch of AI, which is composed of different branches and application of robots. AI Robots are artificial agents acting in a real-world environment. Artificial Intelligence Robot is aimed at manipulating the objects by perceiving, picking, moving, and destroying it.
Expert Systems:
In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. It is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.
Fuzzy Logic Systems:
Fuzzy logic is an approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic on which the modern computer is based. Fuzzy logic Systems can take imprecise, distorted, noisy input information.
Fuzzy logic is a solution to complex problems in all fields of life, including medicine, as it resembles human reasoning and decision making.
Natural Language Processing:
Natural Language Processing (NLP) refers to the Artificial Intelligence method of communicating with intelligent systems using a natural language.
By utilizing NLP and its components, one can organize the massive chunks of text data, perform numerous automated tasks and solve a wide range of problems such as – Machine translation, Named Entity Recognition, Sentiment Analysis, Speech Recognition, and Topic Segmentation etc.

Artificial Intelligence Job Profiles
According to the job site Indeed, the demand for AI skills has more than doubled over the past three years, and the number of job postings is up by 119 %. This tutorial will be incomplete without different Jobs Profiles. So, if Artificial Intelligence appeals to you and you want a Job in the AI field, then here are the different Job Profiles you can apply for if you have AI Skills.
1. Machine Learning Engineer
Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction. Artificial intelligence is the goal of a machine learning engineer. They are computer programmers, but their focus goes beyond specifically programming machines to perform specific tasks. They create programs that will enable machines to take actions without being specifically directed to perform those tasks and earn a whopping $111,490 per annum.
2. Data Scientist
Data scientists are those who crack complex data problems with their strong expertise in certain scientific disciplines. They work with several elements related to mathematics, statistics, computer science, etc. The data scientist role is a position for specialists. You can specialize in different types of skills like speech-analytics, text analytics (NLP), image processing, video processing, medicine simulations, material simulation, etc. Each of these specialist roles is very limited in number and hence the value of such a specialist is immense with an average Salary of $91,470.
3. Artificial Intelligence Engineer
An artificial intelligence engineer works with algorithms, neural networks, and other tools to advance the field of artificial intelligence in some way. Engineers may also choose between projects involving weak or strong artificial intelligence, where different setups focus on different capabilities. the salary for an AI Engineer is around $105,244.
4. Business Intelligence Developer
A Business Intelligence developer spends a lot of time researching and planning solutions for existing problems within the company. The Business Intelligence Developer is responsible for aggregating data from multiple sources in an efficient data warehouse and designing enterprise-level solutions for very large multidimensional databases. Business intelligence developers play a key role in improving the efficiency and profitability of a business. It’s a career that’s in high demand and commands an annual median salary of $92,278.
5. Research Scientist
Research scientists are responsible for designing, undertaking and analyzing information from controlled laboratory-based investigations, experiments, and trials. You could work for government laboratories, environmental organizations, specialist research organizations or universities and earn an average salary of $105,666.
6. Big Data Engineer/Architect
Big data engineers and architects have among the best paying jobs in artificial intelligence. In fact, they command an annual median salary of $151,307. The Big Data solutions architect is responsible for managing the full life-cycle of a Hadoop solution. This includes creating the requirements analysis, the platform selection, design of the technical architecture, the design of the application design and development, testing, and deployment of the proposed solution.

Companies Hiring
Companies that hire top AI talent range from startups like Argo AI to tech giants like IBM. According to Glassdoor, these are the leading employers who hired top AI talent over the past year.

Note:
This Artificial Intelligence Tutorial is based on the AI and Deep Learning The course is curated by industry professionals as per the industry requirements & demands. The course has been specially curated by industry experts with real-time case studies.

Got a question for us? Please mention it in the comments section of “Artificial Intelligence Tutorial” and we will get back to you.

SHIMPOO 23RD JUNE 2019
LINK:
https://www.edureka.co/blog/artificial-intelligence-tutorial/?utm_campaign=Monthly%20Newsletter&utm_source=hs_email&utm_medium=email&utm_content=73919009&_hsenc=p2ANqtz-91EvPY2wrYbF3OF4cHGXYAKO820saWbGG76mcJf_ZJJRp8zAvzZg6457k0ye9oGqjmJnd3p64Kv3NHBh4lp85tcGKArQ&_hsmi=73919009

28 Best Quotes About Artificial Intelligence

Bernard Marr Jul 25, 2017

When it comes to the possibilities and possible perils of artificial intelligence (AI), learning and reasoning by machines without the intervention of humans, there are lots of opinions out there. Only time will tell which one of these quotes will be the closest to our future reality. Until we get there, it’s interesting to contemplate who might be the one who predicts our reality the best.

“The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded.”— Stephen Hawking told the BBC

“I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” —Claude Shannon

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We're nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” —Larry Page

“The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year timeframe. 10 years at most.” —Elon Musk

“The upheavals of artificial intelligence can escalate quickly and become scarier and even cataclysmic. Imagine how a medical robot, originally programmed to rid cancer, could conclude that the best way to obliterate cancer is to exterminate humans who are genetically prone to the disease.” — Nick Bilton

“I don’t want to really scare you, but it was alarming how many people I talked to who are highly placed people in AI who have retreats that are sort of 'bug out' houses, to which they could flee if it all hits the fan.”—James Barrat, author of Our Final Invention: Artificial Intelligence and the End of the Human Era, told the Washington Post

“I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish. I mean with artificial intelligence we’re summoning the demon.” —Elon Musk

“The real question is, when will we draft an artificial intelligence bill of rights? What will that consist of? And who will get to decide that?” —Gray Scott

“We must address, individually and collectively, moral and ethical issues raised by cutting-edge research in artificial intelligence and biotechnology, which will enable significant life extension, designer babies, and memory extraction.” —Klaus Schwab

“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we'll augment our intelligence.” —Ginni Rometty

“I'm more frightened than interested by artificial intelligence - in fact, perhaps fright and interest are not far away from one another. Things can become real in your mind, you can be tricked, and you believe things you wouldn't ordinarily. A world run by automatons doesn't seem completely unrealistic anymore. It's a bit chilling.” —Gemma Whelan

“You have to talk about 'The Terminator' if you're talking about artificial intelligence. I actually think that that's way off. I don't think that an artificially intelligent system that has superhuman intelligence will be violent. I do think that it will disrupt our culture.” —Gray Scott

“If the government regulates against use of drones or stem cells or artificial intelligence, all that means is that the work and the research leave the borders of that country and go someplace else.” —Peter Diamandis

“The key to artificial intelligence has always been the representation.” —Jeff Hawkins

“It's going to be interesting to see how society deals with artificial intelligence, but it will definitely be cool.” —Colin Angle

“Anything that could give rise to smarter-than-human intelligence—in the form of Artificial Intelligence, brain-computer interfaces, or neuroscience-based human intelligence enhancement - wins hands down beyond contest as doing the most to change the world. Nothing else is even in the same league.” —Eliezer Yudkowsky

“Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver.” —Diane Ackerman

“Someone on TV has only to say, ‘Alexa,’ and she lights up. She’s always ready for action, the perfect woman, never says, ‘Not tonight, dear.’” —Sybil Sage, as quoted in a New York Times article

“Some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower.” —Alan Kay

“Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.” —Ray Kurzweil

“Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition.” —Sebastian Thrun

“A year spent in artificial intelligence is enough to make one believe in God.” —Alan Perlis

“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” —Gray Scott

“Is artificial intelligence less than our intelligence?” —Spike Jonze

“By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” —Eliezer Yudkowsky

“The sad thing about artificial intelligence is that it lacks artifice and therefore intelligence.” —Jean Baudrillard

“Forget artificial intelligence - in the brave new world of big data, it's artificial idiocy we should be looking out for.” —Tom Chatfield

“Before we work on artificial intelligence why don’t we do something about natural stupidity?” —Steve Polyak

SHIMPOO 10TH NOVEMBER 2019
LINK:
https://www.forbes.com/sites/bernardmarr/2017/07/25/28-best-quotes-about-artificial-intelligence/

Top 12 Books on Artificial Intelligence Here is the content that book lovers and tech enthusists would love. Check out this article to learn more about the top 12 books on artificial intelligence.

Use of Artificial Intelligence in Banks
I read this article sometime back. Sharing it.
By Tim Sloane November 6, 2018

This article identifies many different solutions where Artificial Intelligence can enhance banking, but makes it appear these solutions are already widely deployed. While each solution is currently in-market by at least one large bank this is a far cry from broadly deployed. Mercator surveyed large banks and found 93 different Artificial Intelligence solutions deployed in 13 different departments. This article discusses less than 10.

“Machines are getting smarter globally. Thanks to thriving Artificial Intelligence (AI) concept, companies can make their devices more powerful and ‘intelligent’ to serve their customers in a better way. Both B2B and B2C businesses have started adopting this revolutionary technology as per their scale and size.

However, the pe*******on of AI in the banking sector is somewhat limited to date. The distinct datasets and the risk of confidential data are primarily responsible for the sluggishness of AI integration in the banking system. But then, as the online banking and mobile banking become increasingly popular as a tool for 24/7 transaction, we can expect that AI will soon take over.

The rise of AI in Banking
Robust and rapid processing needs, advent of mobile technology, data availability, and proliferation of open-source software offer AI a huge scope in the banking sector. Though AI has been used in banking for decades, it remained unnoticed. In today’s app-driven world, the banking sector eyes on leveraging with the help of mobile app development companies.
Digital personal assistants and chatbots have revolutionized the customer services and business communication. From assisting people in performing daily tasks to giving them a personalized experience, virtual assistants and chatbots have many applications. Talking about the banking sector, mobile app development services can integrate the AI technology for enhancing services.

Integration of AI in Mobile Apps for Banks

Most of the banks have started embracing AI and related technologies worldwide. As per the survey by National Business Research Institute, over 32 percent financial institutions use AI by the means of voice recognition and predictive analysis. Banks are using AI technology for enhancing the customer experience by giving it a personalized touch.

Millennials rely heavily on mobile banking, which means that AI-powered banking mobile apps can attract them. Such apps can readily meet the user’s expectations with personal, contextual, and predictive services. These are intelligent apps that can track the user’s behaviors and give them personalized tips and insights on savings and expenses.

How AI Enhances Customer Services

The banking and finance sector grows by leaps and bounds. Millions of transactions are done online irrespective of time and place worldwide. We can mention that automated processes and other applications are largely attributed to the integration of AI in banking system and mobile banking apps. The main role of AI in mobile banking domain is to improve the customer service.

Let’s start with customer support. Automated AI-powered customer service representative can serve the purpose with ease. After gathering the data from the user’s mobile devices, the AI-based mobile banking app processes the data through machine learning to provide the relevant information or redirecting the users to the source of information.

Secondly, it is easy for a banking app integrated with AI-related features to show services, offers, and insights in line with the user’s behavior. What’s more, the app handles the advice and communication part by analyzing the user’s data. Banks can give online wealth management services and other services by integrating AI advancements into the app.

When it comes to personalized planning, AI banking apps can work wonders. It is easy to assist the users in financial planning with AI strategies. For example, if the user wants to buy a new house, the mobile banking app can guide the user with budget and other related details on the basis of current expenditure and income.

Benefits of AI for Banking Sector

AI has an immense potential for the banking sector. It brings an automation and simplifies the process.

Here are a few noteworthy benefits of AI for the banks:

Reduce workload

Here is an example of a chatbot. It can act as an answering machine and serve the customers continuously throughout a day. It can answer the simple questions of the users of customized banking app and redirect them to the bank’s website if necessary. Direct and basic operations including opening or closing the account, transfer of funds, etc. can be done with the help of chatbots.

As compared to the phone call, the chatbot offers more feasible option to the user as it can provide the useful links for finishing the process. The chatbot can also offer instant connectivity and reduce the workload of customer care executives significantly. Though customer care executives are serving the customers well, they have limitations of time and the number of persons they can attend in a day.

Accumulate and analyze useful data

The revolutionary AI technology works on the principle of data collection and analysis. Any AI system can work well with better data sets. A tailored mobile banking app enriched with AI-based features can collect all the relevant and useful data of the users to improvise the learning process and enhance the overall user experience. After accumulating and analyzing the data, the experience can be made more personalized.

Also, the data regarding financial transaction can help the bank understand the expenditure pattern of the customer. The bank can come up with a customized investment plan accordingly and also assist the customers for budgeting. What’s more, banks can send the notification about the advice for keeping a check on the expenses and investments based on the data.

Drive banking business

Wealth management and portfolio management can be done effectively and efficiently with AI. It can bring ‘banking at your fingertips’ for the users who just hate to visit the banks. It strengthens the mobile banking facility by managing basic banking services. Customers can get the benefits of automated and safe transactions. They get notification instantly for any suspicious transaction as per their usual patterns.

Another useful application of AI is a card management system. It not only automates the credit and debit card management system but also makes it safer. It helps the customer get rid of a long authentication process in the case of losing the card. The AI system saves time and efforts of the customers and in a way, improves the mobile banking services.

Handle risk management

Risk assessment process while giving loans is very complex and critical process. It requires both accuracy and confidentiality. AI can handle and simplify this process by analyzing relevant data of the prospective borrower. AI can combine analyze the data related to the latest transactions, market trends, and the most recent financial activities to identify the potential risks in giving the loan.

Banks can also get the idea of the prospect’s behavior with AI-based risk assessment process. AI can minimize the probability of error in identifying even the slightest probability of fraud. The predictive analytics can manage the entire process smoothly.

Prevent frauds

Banks should be bankable for providing secure and swift transactions. AI is designed to detect the fraud in the transactions on the basis of a pre-defined set of rules. Also, the mobile app can find out any suspicious activity in the customer’s account on the basis of behavior analysis. For example, any online transaction of a huge amount from the customer’s account that has a history of small transactions can be figured out instantly.

AI also plays a vital role in protecting personal data. As we witness a rapid rise in the instances of cybercrimes in the recent years, AI-based fraud detection can lend a helping hand in preventing such attempts. So, for banking and finance sector, AI has a tremendous scope in the domain of cybersecurity. The mobile app development services can address the issue of fraud and data breach while developing an AI-powered mobile app for the banks.

Hedge fund management

Globally, hedge funds prefer AI-based models. It is because AI-related tools can fetch real-time data from various financial markets across the world. Also, AI models can analyze the mood or sentiments of different financial markets and come up with an accurate prediction. These inputs and sophisticated algorithms make AI models capable of assisting the users to take decisions quickly.

Hedge fund trading and management can be done on the move with the help of AI-based mobile app solutions for the banking sector. These solutions help the banks to mitigate the risks associated with overexposure and user intervention in the market.

In brief, AI can provide the next-gen security to the banking sector. A mobile app development company can integrate the necessary functionality and technological advancements of AI to make the most from this emerging technology. AI-based mobile applications can make the transaction quicker and safer. Banks can handle the customer-oriented operations with ease while reducing the cost of hiring additional employees.

Concluding Lines

AI has many benefits to offer for the banking sector. Be it an Android app development or iOS app development, the AI can bring revolutionary changes in the banking industry. The bank and financial institutions can understand the user’s behavior and give the personalized experience through an app.

Solution Analysts is a prominent IT solutions provider that offers customized business solutions by integrating the futuristic technologies like AR, VR, AI, and Blockchain. Our professionals are expert in using technological advancements for developing premium mobile app solutions in a cost-effective way.”

Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group

COMPILED BY J S BROCA 19TH AUGUST 2019
LINK:
https://www.paymentsjournal.com/the-18-top-use-cases-of-artificial-intelligence-in-banks/

New Artificial Intelligence Tool Accelerates Discovery of Truly New Materials The new artificial intelligence tool has already led to the discovery of four new materials. Researchers at the University of Liverpool have created a collaborative artificial intelligence tool that reduces the time and effort required to discover truly new materials. Reported in the journal N

Most Frequently Asked Artificial Intelligence Interview Questions
Oct 08,2020—By Zulaikha Lateef
Zulaikha is a tech enthusiast working as a Research Analyst at Edureka.
PART—I 13.10.2020
Artificial Intelligence Interview Questions:
Ever since we realized how Artificial Intelligence is positively impacting the market, nearly every large business is on the lookout for AI professionals to help them make their vision a reality.
In this Artificial Intelligence Interview Questions blog, I have collected the most frequently asked questions by interviewers.
These questions are collected after consulting with Artificial Intelligence Certification Training Experts.
In this blog on Artificial Intelligence Interview Questions, I will be discussing the top Artificial Intelligence related questions asked in your interviews. So, for your better understanding I have divided this blog into the following 3 sections:
Artificial Intelligence Basic Level Interview Questions
Artificial Intelligence Intermediate Level Interview Questions
Artificial Intelligence Scenario Based Interview Question
Artificial Intelligence Basic Level Interview Questions
Q1. What is Artificial Intelligence? Give an example of where AI is used on a daily basis.
“Artificial Intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.” “The capability of a machine to imitate the intelligent human behavior.” One of the most popular AI Applications is the google search engine. If you open up your chrome browser and start typing something, Google immediately provides recommendations for you to choose from. The logic behind the search engine is Artificial Intelligence. AI uses predictive analytics, NLP and Machine Learning to recommend relevant searches to you. These recommendations are based on data that Google collects about you, such as your search history, location, age, etc. Thus, Google makes use of AI, to predict what you might be looking for.
Q2. What are the different types of AI?
Reactive Machines AI: Based on present actions, it cannot use previous experiences to form current decisions and simultaneously update their memory. Example: Deep Blue
Limited Memory AI: Used in self-driving cars. They detect the movement of vehicles around them constantly and add it to their memory.
Theory of Mind AI: Advanced AI that has the ability to understand emotions, people and other things in the real world.
Self Aware AI: AIs that posses human-like consciousness and reactions. Such machines have the ability to form self-driven actions.
Artificial Narrow Intelligence (ANI): General purpose AI, used in building virtual assistants like Siri.
Artificial General Intelligence (AGI): Also known as strong AI. An example is the Pillo robot that answers questions related to health.
Artificial Superhuman Intelligence (ASI): AI that possesses the ability to do everything that a human can do and more. An example is the Alpha 2 which is the first humanoid ASI robot.
Q4. Explain the different domains of Artificial Intelligence.
Machine Learning: It’s the science of getting computers to act by feeding them data so that they can learn a few tricks on their own, without being explicitly programmed to do so.
Neural Networks: They are a set of algorithms and techniques, modeled in accordance with the human brain. Neural Networks are designed to solve complex and advanced machine learning problems.
Robotics: Robotics is a subset of AI, which includes different branches and application of robots. These Robots are artificial agents acting in a real-world environment. An AI Robot works by manipulating the objects in it’s surrounding, by perceiving, moving and taking relevant actions.
Expert Systems: An expert system is a computer system that mimics the decision-making ability of a human. It is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.
Fuzzy Logic Systems: Fuzzy logic is an approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) boolean logic on which the modern computer is based. Fuzzy logic Systems can take imprecise, distorted, noisy input information.
Natural Language Processing: Natural Language Processing (NLP) refers to the Artificial Intelligence method that analyses natural human language to derive useful insights in order to solve problems.
Q5. How is Machine Learning related to Artificial Intelligence?
Artificial Intelligence is a technique that enables machines to mimic human behavior. Whereas, Machine Learning is a subset of Artificial Intelligence. It is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so. Therefore Machine Learning is a technique used to implement Artificial Intelligence.
Q6. What is Q-Learning?
The Q-learning is a Reinforcement Learning algorithm in which an agent tries to learn the optimal policy from its past experiences with the environment. The past experiences of an agent are a sequence of state-action-rewards: In the above state diagram, the Agent(a0) was in State (s0) and on performing an Action (a0), which resulted in receiving a Reward (r1) and thus being updated to State (s1).
Q8. What is Deep Learning?
Deep learning imitates the way our brain works i.e. it learns from experiences. It uses the concepts of neural networks to solve complex problems. Any Deep neural network will consist of three types of layers:
Input Layer: This layer receives all the inputs and forwards them to the hidden layer for analysis
Hidden Layer: In this layer, various computations are carried out and the result is transferred to the output layer. There can be n number of hidden layers, depending on the problem you’re trying to solve.
Output Layer: This layer is responsible for transferring information from the neural network to the outside world.
Q9. Explain how Deep Learning works.
Deep Learning is based on the basic unit of a brain called a brain cell or a neuron. Inspired from a neuron, an artificial neuron or a perceptron was developed.
A biological neuron has dendrites which are used to receive inputs.
Similarly, a perceptron receives multiple inputs, applies various transformations and functions and provides an output. Just like how our brain contains multiple connected neurons called neural network, we can also have a network of artificial neurons called perceptron’s to form a Deep neural network. An Artificial Neuron or a Perceptron models a neuron which has a set of inputs, each of which is assigned some specific weight. The neuron then computes some function on these weighted inputs and gives the output.
Q10. Explain the commonly used Artificial Neural Networks.
Feedforward Neural Network
The simplest form of ANN, where the data or the input travels in one direction.The data passes through the input nodes and exit on the output nodes. This neural network may or may not have the hidden layers.
Convolutional Neural Network
Here, input features are taken in batch wise like a filter. This will help the network to remember the images in parts and can compute the operations. Mainly used for signal and image processing
Recurrent Neural Network(RNN) – Long Short Term Memory--Works on the principle of saving the output of a layer and feeding this back to the input to help in predicting the outcome of the layer. Here, you let the neural network to work on the front propagation and remember what information it needs for later use.This way each neuron will remember some information it had in the previous time-step.
Autoencoders: These are unsupervised learning models with an input layer, an output layer and one or more hidden layers connecting them. The output layer has the same number of units as the input layer. Its purpose is to reconstruct its own inputs. Typically for the purpose of dimensionality reduction and for learning generative models of data.
Q11. What are Bayesian Networks?
A Bayesian network is a statistical model that represents a set of variables and their conditional dependencies in the form of a directed acyclic graph. On the occurrence of an event, Bayesian Networks can be used to predict the likelihood that any one of several possible known causes was the contributing factor.For example, a Bayesian network could be used to study the relationship between diseases and symptoms. Given various symptoms, the Bayesian network is ideal for computing the probabilities of the presence of various diseases.
Q12. Explain the assessment that is used to test the intelligence of a machine.
In artificial intelligence (AI), a Turing Test is a method of inquiry for determining whether or not a computer is capable of thinking like a human being.
Artificial Intelligence Intermediate Level Interview Questions
Q1. How does Reinforcement Learning work? Explain with an example.
Generally, a Reinforcement Learning (RL) system is comprised of two main components:
An agent, An environment
The environment is the setting that the agent is acting on and the agent represents the RL algorithm. The RL process starts when the environment sends a state to the agent, which then based on its observations, takes an action in response to that state. In turn, the environment sends the next state and the respective reward back to the agent. The agent will update its knowledge with the reward returned by the environment to evaluate its last action. The loop continues until the environment sends a terminal state, which means the agent has accomplished all his tasks. To understand this better, let’s suppose that our agent is learning to play counterstrike. The RL process can be broken down into the below steps:
The RL Agent (Player1) collects state S⁰ from the environment (Counterstrike game)
Based on the state S⁰, the RL agent takes an action A⁰, (Action can be anything that causes a result i.e. if the agent moves left or right in the game). Initially, the action is random
The environment is now in a new state S¹ (new stage in the game)
The RL agent now gets a reward R¹ from the environment. This reward can be additional points or coins
This RL loop goes on until the RL agent is dead or reaches the destination, and it continuously outputs a sequence of state, action, and reward.
Q2. Explain Markov’s decision process with an example.
The mathematical approach for mapping a solution in Reinforcement Learning is called Markov’s Decision Process (MDP). The following parameters are used to attain a solution using MDP: Set of actions, A, Set of states, S, Reward, R, Policy, π, Value, V. To briefly sum it up, the agent must take an action (A) to transition from the start state to the end state (S). While doing so, the agent receives rewards (R) for each action he takes. The series of actions taken by the agent, define the policy (π) and the rewards collected define the value (V). The main goal here is to maximize rewards by choosing the optimum policy. Given the above representation, our goal here is to find the shortest path between ‘A’ and ‘D’. Each edge has a number linked with it, this denotes the cost to traverse that edge. Now, the task at hand is to traverse from point ‘A’ to ‘D’, with minimum possible cost.
In this problem, The set of states are denoted by nodes i.e. {A, B, C, D}. The action is to traverse from one node to another {A -> B, C -> D}. The reward is the cost represented by each edge. The policy is the path taken to reach the destination. You start off at node A and take baby steps to your destination. Initially, only the next possible node is visible to you, thus you randomly start off and then learn as you traverse through the network. The main goal is to choose the path with the lowest cost.
Q3. Explain reward maximization in Reinforcement Learning.
The RL agent works based on the theory of reward maximization. This is exactly why the RL agent must be trained in such a way that, he takes the best action so that the reward is maximum. The collective rewards at a particular time with the respective action is written as an equation.The equation is an ideal representation of rewards. Generally, things don’t work out like this while summing up the cumulative rewards. Let me explain this with a small game. In the figure you can see a fox, some meat and a tiger. Our RL agent is the fox and his end goal is to eat the maximum amount of meat before being eaten by the tiger. Since this fox is a clever fellow, he eats the meat that is closer to him, rather than the meat which is close to the tiger, because the closer he is to the tiger, the higher are his chances of getting killed. As a result, the rewards near the tiger, even if they are bigger meat chunks, will be discounted. This is done because of the uncertainty factor, that the tiger might kill the fox. The next thing to understand is, how discounting of rewards work?
To do this, we define a discount rate called gamma. The value of gamma is between 0 and 1. The smaller the gamma, the larger the discount and vice versa.
Q4. What is exploitation and exploration trade-off?
An important concept in reinforcement learning is the exploration and exploitation trade-off. Exploration, like the name suggests, is about exploring and capturing more information about an environment. On the other hand, exploitation is about using the already known exploited information to heighten the rewards. Consider the fox and tiger example, where the fox eats only the meat (small) chunks close to him but he doesn’t eat the bigger meat chunks at the top, even though the bigger meat chunks would get him more rewards. If the fox only focuses on the closest reward, he will never reach the big chunks of meat, this is called exploitation. But if the fox decides to explore a bit, it can find the bigger reward i.e. the big chunk of meat. This is exploration.
TO BE CONTINUED ….