Top 10 Hot Artificial Intelligence Technologies

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Introduction to Artificial Intelligence

Talking of advancements, from Abacus to Super-Computers, the world has come a long way. The world, say a hundred years ago was highly dependent on manual execution. Simple tasks such as arithmetic operations too were long and tedious. Having realized this difficulty, various technologies capable of executing complex calculations were introduced.

These technologies grew rapidly and soon the world saw it’s potential. Calculations now were quicker and accurate. Such technologies saw a great implementation in various fields including research and development, defence, healthcare, business, etc.

But however efficient these machines were, there was always a lack of “Intelligence”. Computers may be reliable, accurate and a gazillion times faster than a human but they were all but “dumb machines”.

Artificial Intelligence is a concept far superior to any other concept and aims to make machines able to learn and respond on their own.

Though the term Artificial Intelligence has been around for more than 5 decades, it was not until 2 decades before that the world started realizing its huge potential. Artificial Intelligence has a plethora of applications in areas such as Natural Language Processing, Simulations, Robotics and Speech Recognition to name a few.

While the potential of Artificial Intelligence and its applications has been realized, but due to the complexities involved, the advancements in this field, as of now, is only restricted to the development of Weak Artificial Intelligence Systems also known as Narrow Artificial Intelligence Systems.

There has been a steady development in the field of Artificial Intelligence and the growth is exponential. Today, Artificial Intelligence is everywhere. From Google to Facebook and Shopping to Learning, Artificial Intelligence is at the forefront.

There are many technologies in existence today that have a direct or indirect application of Artificial Intelligence. Let’s have a look at some of these latest Artificial Intelligence technologies to understand better:

Natural Language Generation

Popularly known as “Language Production” among Psycholinguists, Natural Language Generation is a procedure that aims to transform any structured data into a natural language. In layman terms, natural language generation can be thought of as a process that converts thoughts to words.

For example, when a child looks at a butterfly flying in a garden, he may think of it in various ways. Those thoughts may be called ideas. But when the child describes his thought process in his natural language (mother tongue), this process may be termed as Natural Language Generation.

Natural Language Understanding

Natural Language Understanding is the opposite of Natural Language Generation. This procedure is more inclined towards the interpretation of Natural Language.

In the example above, if the child is told about the butterfly rather than shown, he may interpret the data given to him in a variety of ways. Based on that interpretation, the boy will make a picture of a butterfly flying in a garden. If the interpretation was correct, then one may infer that the procedure (Natural Language Understanding) was successful.

Speech Recognition

As the name suggests, Speech Recognition is a technology that uses Artificial Intelligence to convert human speech into a computer-accessible format. The process is very helpful and acts as a bridge in human-computer interaction.

Using Speech Recognition technology, the computer can understand human speech in several natural languages. This further enables the computer to have a faster and smoother interaction with humans.

For example, let’s say that the child in the first example was asked, “How are you?” during a normal human to human interaction. When the child listens to the human speech sample, he processes the sample according to the data (knowledge) already present in his brain.

The child draws necessary inferences and finally comes up with an idea about what the sample is about. This way, the child can understand the meaning of the speech sample and respond accordingly.

Machine Learning

Machine Learning is yet another useful technology in the Artificial Intelligence domain. This technology is focussed on training a machine (computer) to learn and think on its own. Machine Learning typically uses many complex algorithms for training the machine.

During the process, the machine is given a set of categorized or uncategorized training data pertaining to a specific or a general domain. The machine then analyses the data, draws inferences and stores them for future use.

When the machine encounters any other sample data of the domain it has already learned, it uses the stored inferences to draw necessary conclusions and give an appropriate response.

For example, let’s say that the child in the first example was shown a collection of toys. The child interacts (using his senses like touch, see, etc.) with the training data (toys) and learns about the toys’ properties. These properties can be anything from size, colour, shape, etc. of the toys.

Based on his observations the child stores the inferences and uses them to distinguish between any other toys that he may have any future encounters with. Thus, it can be concluded that the child has learned.

Virtual Agents

Virtual Agents are a manifestation of a technology which aims to create an effective but digital impersonation of humans. Quite popular in the customer care domain, Virtual Agents use the combination of Artificial Intelligence programming, Machine Learning, Natural Language Processing, etc. to understand the customer and his grievances.

A clear understanding by the Virtual Agents is subject to the complexity and technologies used in the creation of the agent. These systems are nowadays highly used through a variety of applications such as chatbots, affiliate systems, etc. These systems are capable of interacting with humans in a humane way.

In the above-mentioned examples, if the child is considered a Virtual Agent and is made to interact with unknown participants, the child will use a combination of his already learned knowledge, language processing and other necessary “tools” to understand the participant.

Once the interaction is complete, the child will derive inferences based on the interaction and be able to address the queries posed by the participant effectively.

Expert Systems

In the context of Artificial Intelligence, Expert Systems are computer systems that utilize a pre-stored knowledge base and mimic the decision-making ability of humans. These complex systems utilize reasoning ability and the predefined ‘if-then’ rules.

Contrary to conventional procedural code based machines, Expert Systems are highly efficient in solving complex problems. Extending the above examples a bit further, the child, based on his pre-existing knowledge base and inference deriving capability is capable of analyzing problems and suggest methods to solve them.

Decision Management

Modern Decision Management Systems highly rely on Artificial Intelligence abilities in interpreting and converting data into predictive models. These models, in the long run, help an organization to take necessary and effective decisions.

These systems are widely used in a vast number of enterprise-level applications. Such applications provide automated decision-making capabilities to any person or organization using it.

If the child in the above example is considered as a Decision Management System, based on the knowledge set and reasoning abilities, he shall be able to manage his decisions effectively. If the child is given access to a certain behavioural data of say 10 people, then the child will be able to make near-accurate predictions. Such predictions will govern the decisions the child will make to address the problem at hand.

Deep Learning

Deep Learning is a special subset of Machine Learning based on Artificial Neural Networks. During the process, learning is carried out at different levels where each level is capable of transforming the input data set into composite and abstract representations.

The term “deep” in this context refers to the number of levels of data transformation carried out by the computer system. The technology finds its applications in a variety of domains such as Computer Vision, News Aggregation (sentiment-based), development of efficient chatbots, automated translations, rich customer experience, etc.

For the sake of a simpler example, if the child in the above examples carries out learning restricted to only a single level, then the output (response) may not be specific to the problem but general. Learning at a deeper level helps the child in understanding the problem better. Hence it can be inferred that deeper the learning is, more accurate is the response.

Robotic Process Automation

Artificial Intelligence is also heavily used at industrial levels to automate various processes. While manual robotics is capable of completing the job, it lacks the necessary automation required to complete the task without human intervention.

Such automated systems help in larger domains where it is not feasible to employ humans. If the child, in the above examples, is considered a Robot without intelligence, he shall be dependent on others to carry out his chores.

While he may still be able to complete his work, he would not be able to do it all by himself. Intelligence enables him to work independently without having to rely on any external interventions.

Text Analytics

Text Analytics can be defined as an analysis of text structure. Artificially Intelligent Systems use text analytics to interpret and learn the structure, meaning, and intentions of text they may come across.

Such systems find their applications in security and fraud detection systems. An Artificial Intelligence enabled system can distinguish between any two types of text samples without any human intervention. This independence makes such a system effective, efficient and faster than its human counterparts.

The child’s intelligence, in the above examples, will also be able to make him capable of distinguishing between the handwritings written by his family members.

To summarize, Artificial Intelligence finds a variety of applications in various fields. In all the examples mentioned above, the child was able to tackle all the problems independently because he was intelligent and was not dependent on external instructions but relied on his own inferences.

Artificial Intelligence Technologies


Being highly advanced and capable of solving very complex problems, Artificial Intelligence is the key to the future. Various industries and organizations today, are making extensive use of Artificial Intelligence to fulfil the requirements that were once considered very difficult to meet.

Modern research has suggested growth in Artificial Intelligence domain at the rate of 36.6 % and shall be worth $190.60 billion by the year 2025.

While all the artificial intelligence technologies are expecting a massive growth, Deep Learning is expected to grow the highest in terms of the Compound Annual Growth Rate (CAGR).

In terms of market share, Artificial Intelligence based software has been forecasted to hold the largest market share. While in terms of geographical area, Asia Pacific is the top contender in terms of the highest Compound Annual Growth Rate (CAGR) and North America is to hold the largest market share.

In a span of just around two decades, Artificial Intelligence has made an exemplary mark on today’s Information Technology industry. It has further provided an impressive set of tools and applications having a wider range in various domains.

Artificial Intelligence has changed the understanding of the world regarding the power of reasoning and methods of problem-solving. Additionally, it has also enlightened us about the complexity of human intelligence.

While some people may perceive Artificial Intelligence as a threat to human existence, responsible and limited use will help humans and technology to co-exist together. Such a co-existence, together, will help in reshaping the very reality we live in and change the face of this world entirely.

For those who are interested in pursuing a career in Artificial Intelligence, check out Great Learning’s PG program in Artificial Intelligence and Machine Learning.

What is Artificial Intelligence- A Complete Beginners’ Guide to AI- Great Learning

Reading Time: 20 minutes
  1. What is Artificial Intelligence?
  2. How do we measure if the AI is acting like a human?
  3. How Artificial Intelligence works?
  4. What are the three types of Artificial Intelligence?
  5. What is the purpose of AI?
  6. Where is AI used?
  7. What are the disadvantages of AI?
  8. Applications of Artificial Intelligence in business?
  9. What is ML?
  10. What are the different kinds of Machine Learning?
  11. What is Deep Learning?
  12. What is NLP?
  13. What is Python?
  14. What is Computer Vision?
  15. What are neural networks?
  16. Conclusion

What is Artificial Intelligence?

The short answer to What is Artificial Intelligence is that it depends on who you ask. A layman with a fleeting understanding of technology would link it to robots. They’d say AI is a terminator like-figure that can act and think on its own. An AI researcher would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. So to summarise, AI is:

– An intelligent entity created by humans.

– Capable of performing tasks intelligently without being explicitly instructed.

– Capable of thinking and acting rationally and humanely.

How do we measure if the AI is acting like a human?

Even if we reach that state where an AI can behave as a human does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity with the:

– Turing Test

– The Cognitive Modelling Approach

– The Law of Thought Approach

– The Rational Agent Approach

What is Artificial Intelligence

Let’s take a detailed look at how these approaches perform:

What is the Turing Test?

The basis of the Turing Test is that the AI entity should be able to hold a conversation with a human agent. The human agent ideally should not able to discern that they are talking to an AI. To achieve these ends, the AI needs to possess these qualities:

Natural Language Processing to communicate successfully.

– Knowledge Representation to act as its memory.

– Automated Reasoning to use the stored information to answer questions and draw new conclusions.

– Machine Learning to detect patterns and adapt to new circumstances.

Cognitive Modelling Approach

As the name suggests, this approach tries to build an AI model-based on Human Cognition. To distil the essence of the human mind, there are 3 approaches:

– Introspection: observing our thoughts, and building a model based on that

– Psychological Experiments: conducting experiments on humans and  observing their behaviour

– Brain Imaging – Using MRI to observe how the brain functions in different scenarios and replicating that through code.

The Laws of Thought Approach

The Laws of Thought are a large list of logical statements that govern the operation of our mind. The same laws can be codified and applied to artificial intelligence algorithms. The issues with this approach, because solving a problem in principle (strictly according to the laws of thought) and solving them in practice can be quite different, requiring contextual nuances to apply. Also, there are some actions that we take without being 100% certain of an outcome that an algorithm might not be able to replicate if there are too many parameters.

The Rational Agent Approach 

A rational agent acts to achieve the best possible outcome in its present circumstances.

According to the Laws of Thought approach, an entity must behave according to the logical statements. But there are some instances, where there is no logical right thing to do, with multiple outcomes involving different outcomes and corresponding compromises. The rational agent approach tries to make the best possible choice in the current circumstances. It means that it’s a much more dynamic and adaptable agent.

Now that we understand how AI can be designed to act like a human, let’s take a look at how these systems are built.

How Artificial Intelligence works?

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using it’s computational prowess to surpass what we are capable of.  AI can be built over a diverse set of components and will function as an amalgamation of:

– Philosophy

– Mathematics

– Economics

– Neuroscience

– Psychology

– Computer Engineering

– Control Theory and Cybernetics

– Linguistics

Let’s take a detailed look at each of these components.

What is Artificial Intelligence


The purpose of philosophy for humans is to help us understand our actions, their consequences, and how we can make better decisions. Modern intelligent systems can be built by following the different approaches of philosophy that will enable these systems to make the right decisions, mirroring the way that an ideal human being would think and behave. Philosophy would help these machines think and understand about the nature of knowledge itself. It would also help them make the connection between knowledge and action through goal-based analysis to achieve desirable outcomes.

Also Read: Artificial Intelligence and The Human Mind: When will they meet?


Mathematics is the language of the universe and system built to solve universal problems would need to be proficient in it. For machines to understand logic, computation, and probability are necessary.

The earliest algorithms were just mathematical pathways to make calculations easy, soon to be followed by theorems, hypotheses and more, which all followed a pre-defined logic to arrive at a computational output. The third mathematical application, probability, makes for accurate predictions of future outcomes on which AI algorithms would base their decision-making.


Economics is the study of how people make choices according to their preferred outcomes. It’s not just about money, although money the medium of people’s preferences being manifested into the real world. There are many important concepts in economics, such as Design Theory, operations research and Markov decision processes. They all have contributed to our understanding of ‘rational agents’ and laws of thought, by using mathematics to show how these decisions are being made at large scales along with their collective outcomes are. These types of decision-theoretic techniques help build these intelligent systems.


Since neuroscience studies how the brain functions and AI is trying to replicate the same, there’s an obvious overlap here. The biggest difference between human brains and machines is that computers are millions of times faster than the human brain, but the human brain still has the advantage in terms of storage capacity and interconnections. This advantage is slowly being closed with advances in computer hardware and more sophisticated software, but there’s still a big challenge to overcome as are still not aware of how to use computer resources to achieve the brain’s level of intelligence.


Psychology can be viewed as the middle point between neuroscience and philosophy. It tries to understand how our specially-configured and developed brain reacts to stimuli and responds to its environment, both of which are important to building an intelligent system. Cognitive psychology views the brain as an information processing device, operating based on beliefs and goals and beliefs, similar to how we would build an intelligence machine of our own.

Many cognitive theories have already been codified to build algorithms that power the chatbots of today.

Computer Engineering

The most obvious application here, but we’ve put this the end to help you understand what all this computer engineering is going to be based on. Computer engineering will translate all our theories and concepts into a machine-readable language so that it can make its computations to produce an output that we can understand. Each advance in computer engineering has opened up more possibilities to build even more powerful AI systems, that are based on advanced operating systems, programming languages, information management systems, tools, and state-of-the-art hardware.

Control Theory and Cybernetics

To be truly intelligent, a system needs to be able to control and modify its actions to produce the desired output. The desired output in question is defined as an objective function, towards which the system will try to move towards, by continually modifying its actions based on the changes in its environment using mathematical computations and logic to measure and optimise its behaviours.


All thought is based on some language and is the most understandable representation of thoughts. Linguistics has led to the formation of natural language processing, that help machines understand our syntactic language, and also to produce output in a manner that is understandable to almost anyone. Understanding a language is more than just learning how sentences are structured, it also requires a knowledge of the subject matter and context, which has given rise to the knowledge representation branch of linguistics.

Read Also: Top 10 Artificial Intelligence Technologies in 2019

What are the 3 types of Artificial Intelligence?

Not all types of AI uses all the above fields simultaneously. Different AI entities are built for different purposes, and that’s how they vary. The three broad types of AI are:

– Artificial Narrow Intelligence (ANI)

– Artificial General Intelligence (AGI)

– Artificial Super Intelligence (ASI)

What is Artificial Intelligence

Let’s take a detailed look.

What is Artificial Narrow Intelligence (ANI)?

This is the most common form of AI that you’d find in the market now. These AI systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of AI that exists today. They’re able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters.

What is Artificial General Intelligence (AGI)?

AGI is still a theoretical concept. It’s defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.

We’re still a long way away from building an AGI system. An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem, communicating with each other to mimic human reasoning. Even with the most advanced computing systems and infrastructures, such as Fujitsu’s K or IBM’s Watson, it has taken them 40 minutes to simulate a single second of neuronal activity. This speaks to both the immense complexity and interconnectedness of the human brain, and to the magnitude of the challenge of building an AGI with our current resources.

What is Artificial Super Intelligence (ASI)?

We’re almost entering into science-fiction territory here, but ASI is seen as the logical progression from AGI. An Artificial Super Intelligence (ASI) system would be able to surpass all human capabilities. This would include decision making, taking rational decisions, and even includes things like making better art and building emotional relationships.

Once we achieve Artificial General Intelligence, AI systems would rapidly be able to improve their capabilities and advance into realms that we might not even have dreamed of. While the gap between AGI and ASI would be relatively narrow (some say as little as a nanosecond, because that’s how fast AI would learn) the long journey ahead of us towards AGI itself makes this seem like a concept that lays far into the future.

What is the Purpose of AI?

The purpose of AI is to aid human capabilities and help us make advanced decisions with far-reaching consequences. That’s the answer from a technical standpoint. From a philosophical perspective, AI has the potential to help humans live more meaningful lives devoid of hard labour, and help manage the complex web of interconnected individuals, companies, states and nations to function in a manner that’s beneficial to all of humanity.

Currently, the purpose of AI is shared by all the different tools and techniques that we’ve invented over the past thousand years – to simplify human effort, and to help us make better decisions. AI has also been touted as our Final Invention, a creation that would invent ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality and human suffering.

That’s all in the far future though – we’re still a long way from those kinds of outcomes. Currently, AI is being used mostly by companies to improve their process efficiencies, automate resource-heavy tasks, and to make business predictions based on hard data rather than gut feelings. As all technology that has come before this, the research and development costs need to be subsidised by corporations and government agencies before it becomes accessible to everyday laymen.

Where is AI used?

AI is used in different domains to give insights into user behaviour and give recommendations based on the data. For example, Google’s predictive search algorithm used past user data to predict what a user would type next in the search bar. Netflix uses past user data to recommend what movie a user might want to see next, making the user hooked onto the platform and increase watch time. Facebook uses past data of the users to automatically give suggestions to tag your friends, based on their facial features in their images. AI is used everywhere by large organisations to make an end user’s life simpler. The uses of AI would broadly fall under the data processing category, which would include the following:

– Searching within data, and optimising the search to give the most relevant results

– Logic-chains for if-then reasoning, that can be applied to execute a string of commands based on parameters

– Pattern-detection to identify significant patterns in large data set for unique insights

– Applied probabilistic models for predicting future outcomes

What are the disadvantages of AI?

As is the case with any new and emerging technology, AI has its fair share of drawbacks too such as:

– Cost overruns

– Dearth of talent

– Lack of practical products

– Lack of standards in software development

– Potential for misuse

Let’s take a closer look.

Cost overruns

What separates AI from normal software development is the scale at which they operate. As a result of this scale, the computing resources required would exponentially increase, pushing up the cost of the operation, which brings us to the next point.

Dearth of talent 

Since it’s still a fairly nascent field, there’s a lack of experienced professionals, and the best ones are quickly snapped up by corporations and research institutes. This increases the talent cost, which further drives up AI implementation prices.

Lack of practical products

For all the hype that’s been surrounding AI, it doesn’t seem to have a lot to show for it. Granted that applications such as chatbots and recommendation engines do exist, but the applications don’t seem to extend beyond that. This makes it difficult to make a case for pouring in more money to improve AI capabilities.

Lack of standards in software development

The true value of AI lays in collaboration when different AI systems come together to form a bigger, more valuable application. But a lack of standards in AI software development means that it’s difficult for different systems to ‘talk’ to each other. AI software development itself is slow and expensive because of this, which further acts as an impediment to AI development.

Potential for Misuse

The power of AI is massive, and it has the potential to achieve great things. Unfortunately, it also has the potential to be misused. AI by itself is a neutral tool that can be used for anything, but if it falls into the wrong hands, it would have serious repercussions. In this nascent stage where the ramifications of AI developments are still not completely understood, the potential for misuse might be even higher.

Applications of Artificial Intelligence in business?

AI truly has the potential to transform many industries, with a wide range of possible use cases. What all these different industries and use cases have in common, is that they are all data-driven. Since AI is an efficient data processing system at its core, there’s a lot of potential for optimisation everywhere.

What is Artificial Intelligence

Let’s take a look at the industries where AI is currently shining.


– Administration: AI systems are helping with the routine, day-to-day administrative tasks to minimise human errors and maximise efficiency. Transcriptions of medical notes through NLP and helps structure patient information to make it easier for doctors to read it.

– Telemedicine: For non-emergency situations, patients can reach out to a hospital’s AI system to analyse their symptoms, input their vital signs and assess if there’s a need for medical attention. This reduces the workload of medical professionals by bringing only crucial cases to them.

– Assisted Diagnosis: Through computer vision and convolutional neural networks, AI is now capable of reading MRI scans to check for tumours and other malignant growths, at an exponentially faster pace than radiologists can, with a considerably lower margin of error.

– Robot-assisted surgery: Robotic surgeries have a very minuscule margin-of-error and can consistently perform surgeries round-the-clock without getting exhausted. Since they operate with such a high degree of accuracy, they are less invasive than traditional methods, which potentially reduces the time patients spend in the hospital recovering.

– Vital Stats Monitoring:  A person’s state of health is an ongoing process, depending on the varying levels of their respective vitals stats. With wearable devices achieving mass-market popularity now, this data is not available on tap, just waiting to be analysed to deliver actionable insights. Since vital signs have the potential to predict health fluctuations even before the patient is aware, there are a lot of live-saving applications here.


– Better recommendations: This is usually the first example that people give when asked about business applications of AI, and that’s because it’s an area where AI has delivered great results already. Most large e-commerce players have incorporated AI to make product recommendations that users might be interested in, which has led to considerable increases in their bottom-lines.

– Chatbots: Another famous example, based on the proliferation of AI chatbots across industries, and every other website we seem to visit. These chatbots are now serving customers in odd-hours and peak hours as well, removing the bottleneck of limited human resources.

– Filtering spam and fake reviews: Due to the high volume of reviews that sites like Amazon receive, it would be impossible for human eyes to scan through them to filter out malicious content. Through the power of NLP, AI can scan these reviews for suspicious activities and filter them out, making for a better buyer experience.

– Optimising search: All of the e-commerce depends upon users searching for what they want, and being able to find it. AI has been optimising search results based on thousands of parameters to ensure that users find the exact product that they are looking for.

– Supply-chain: AI is being used to predict demand for different products in different timeframes so that they can manage their stocks to meet the demand.

Human Resources 

– Building work culture: AI is being used to analyse employee data and place them in the right teams, assign projects based on their competencies, collect feedback about the workplace, and even try to predict if they’re on the verge of quitting their company.  

– Hiring: With NLP, AI can go through thousands of CV in a matter of seconds, and ascertain if there’s a good fit. This is beneficial because it would be devoid of any human errors or biases, and would considerably reduce the length of hiring cycles.

What is Artificial Intelligence

What is ML?

Machine learning is a subset of artificial intelligence (AI) which defines one of the core tenets of AI – the ability to learn from experience, rather than just instructions.

Machine Learning algorithms automatically learn and improve by learning from their output. They do not need explicit instructions to produce the desired output.  They learn by observing their accessible data sets and compares it with examples of the final output. The examine the final output for any recognisable patterns and would try to reverse-engineer the facets to produce an output.

What are the different kinds of Machine Learning?

The types of Machine learning are:

– Supervised Learning

– Unsupervised Learning

– Semi-supervised learning

– Reinforcement Learning

Read Also: Advantages of pursuing a career in Machine Learning

What is Supervised Learning?

Supervised Machine Learning applies what it has learnt based on past data, and applies it to produce the desired output. They are usually trained with a specific dataset based on which the algorithm would produce an inferred function. It uses this inferred function to predict the final output and delivers an approximation of it.

This is called supervised learning because the algorithm needs to be taught with a specific dataset to help it form the inferred function. The data set is clearly labelled to help the algorithm ‘understand’ the data better. The algorithm can compare its output with the labelled output to modify its model to be more accurate.

What is Unsupervised Learning?

With unsupervised learning, the training data is still provided but it would not be labelled. In this model, the algorithm uses the training data to make inferences based on the attributes of the training data by exploring the data to find any patterns or inferences. It forms its logic for describing these patterns and bases its output on this.

What is Semi-supervised Learning?

This is similar to the above two, with the only difference being that it uses a combination of both labelled and unlabelled data. This solves the problem of having to label large data sets – the programmer can just label and a small subset of the data and let the machine figure the rest out based on this. This method is usually used when labelling the data sets is not feasible, either due to large volumes of a lack of skilled resources to label it.

Read Also: Top 9 AI Startups in India

What is Reinforcement Learning?

Reinforcement learning is dependent on the algorithms environment. The algorithm learns by interacting with it the data sets it has access to, and through a trial and error process tries to discover ‘rewards’ and ‘penalties’ that are set by the programmer. The algorithm tends to move towards maximising these rewards, which in turn provide the desired output. It’s called reinforcement learning because the algorithm receives reinforcement that it is on the right path based on the rewards that it encounters. The reward feedback helps the system model its future behaviour.

What is Artificial Intelligence

What is Deep Learning?

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning concepts are used to teach machines what comes naturally to us humans. Using Deep Learning, a computer model can be taught to run classification acts taking image, text, or sound as an input.

Deep Learning is becoming popular as the models are capable of achieving state of the art accuracy. Large labelled data sets are used to train these models along with the neural network architectures.

Simply put, Deep Learning is using brain simulations hoping to make learning algorithms efficient and simpler to use. Let us now see what is the difference between Deep Learning and Machine Learning.

Deep Learning vs. Machine Learning

What is Artificial Intelligence

How is Deep Learning Used- Applications

Deep Learning applications have started to surface but have a much greater scope for the future. Listed here are some of the deep learning applications that will rule the future.

– Adding image and video elements – Deep learning algorithms are being developed to add colour to the black and white images. Also, automatically adding sounds to movies and video clips.

– Automatic Machine Translations – Automatically translating text into other languages or translating images to text. Though automatic machine translations have been around for some time, deep learning is achieving top results.

– Object Classification and Detection – This technology helps in applications like face detection for attendance systems in schools, or spotting criminals through surveillance cameras. Object classification and detection are achieved by using very large convolutional neural networks and have use-cases in many industries.

– Automatic Text Generation – A large corpus of text is learnt by the machine learning algorithm and this text is used to write new text. The model is highly productive in generating meaningful text and can even map the tonality of the corpus in the output text.

– Self-Driving cars – A lot has been said and heard about self-driving cars and is probably the most popular application of deep learning. Here the model needs to learn from a large set of data to understand all key parts of driving, hence deep learning algorithms are used to improve performance as more and more input data is fed.

– Applications in Healthcare – Deep Learning shows promising results in detecting chronic illnesses such as breast cancer and skin cancer. It also has a great scope in mobile and monitoring apps, and prediction and personalised medicine.

Why is Deep Learning important?

Today we can teach machines how to read, write, see, and hear by pushing enough data into learning models and make these machines respond the way humans do, or even better. Access to unlimited computational power backed by the availability of a large amount of data generated through smartphones and the internet has made it possible to employ deep learning applications into real-life problems.

This is the time for deep learning explosion and tech leaders like Google are already applying it anywhere and everywhere possible.

The performance of a deep learning model improves with an increase in the amount of input data as compared to Machine Learning models, where performance tends to decline with an increase in the amount of input data.

What is Artificial Intelligence

What is NLP?

A component of Artificial Intelligence, Natural Language Processing is the ability of a machine to understand the human language as it is spoken. The objective of NLP is to understand and decipher the human language to ultimately present with a result. Most of the NLP techniques use machine learning to draw insights from human language.

Read Also: Most Promising Roles for Artificial Intelligence in India

What are the different steps involved in NLP?

The steps involved in implementing NLP are:

– The computer program collects all the data required. This includes database files, spreadsheets, email communication chains, recorded phone conversations, notes, and all other relevant data.

– An algorithm is employed to remove all the stop words from this data and normalizes certain words which have the same meaning.

– The remaining text is divided into groups known as tokens.

– The NLP program analyzes results to spot deduce patterns, their frequency and other statistics to understand the usage of tokens and their applicability.

Where is NLP used?

Some of the common applications that are being driven by Natural Language Processing are:

– Language translation application

– Word processors to check grammatical accuracy of the text

– Call centres use Interactive Voice Response to respond to user requests; IVR is an application NLP

– Personal virtual assistants such as Siri and Cortana are a classic example of NLP

What is Python?

Python is a popular object-oriented programming language that was created by Guido Van Rossum and released in the year 1991. It is one of the most widely-used programming languages for web development, software development, system scripting, and many other applications.

Why is Python so popular?

What is Artificial Intelligence

There are many reasons behind the popularity of Python as the much-preferred programming language, i.e.,

– The easy to learn syntax helps with improved readability and hence the reduced cost of program maintenance

– It supports modules and packages to encourage code re-use

– It enables increased productivity as there is no compilation step making the edit-test-debug cycle incredibly faster

– Debugging in Python is much easier as compared to other programming languages

Read Also: Top Interview Questions for Python

Where is Python used?

Python is used in many real-world applications such as:

– Web and Internet Development

– Applications in Desktop GUI

– Science and Numeric Applications

– Software Development Applications

– Applications in Business

– Applications in Education

– Database Access

– Games and 3D Graphics

– Network Programming

How can I learn Python?

There is a lot of content online in the form of videos, blogs, and e-books to learn Python. You can extract as much information as you can through the online material as you can and want. But, if you want more practical learning in a guided format, you can sign up for Python courses provided by many ed-tech companies and learn Python along with hands-on learning through projects from an expert which would be your mentor. There are many offline classroom courses available too. Great Learning’s Artificial Intelligence and Machine Learning course have an elaborate module on Python which is delivered along with projects and lab sessions.

What is Computer Vision?

Computer Vision is a field of study where techniques are developed enabling computers to ‘see’ and understand the digital images and videos. The goal of computer vision is to draw inferences from visual sources and apply it towards solving a real-world problem.

What is Computer Vision used for?

There are many applications of Computer Vision today, and the future holds an immense scope.

– Facial Recognition for surveillance and security systems

– Retail stores also use computer vision for tracking inventory and customers

– Autonomous Vehicles

– Computer Vision in medicine is used for diagnosing diseases

– Financial Institutions use computer vision to prevent fraud, allow mobile deposits, and display information visually

What is Deep Learning Computer Vision

Following are the uses of deep learning for computer vision:

What is Artificial Intelligence

Object Classification and Localisation: It involves identifying the objects of specific classes of images or videos along with their location highlighted usually with a square box around them.

Semantic Segmentation: It involves neural networks to classify and locate all the pixels in an image or video.

Colourisation: Converting greyscale images to full-colour images.

Reconstructing Images: Reconstructing corrupted and tampered images.

What are Neural Networks?

Neural Network is a series of algorithms that mimic the functioning of the human brain to determine the underlying relationships and patterns in a set of data.

Read Also: A Peek into Global Artificial Intelligence Strategies

What are Neural Networks used for?

The concept of Neural Networks has found application in developing trading systems for the finance sector. They also assist in the development of processes such as time-series forecasting, security classification, and credit risk modelling.

What are the different Neural Networks?

What is Artificial Intelligence

The different types of neural networks are:

Feedforward Neural Network: Artificial Neuron: Here data inputs travel in just one direction, going in from the input node and exiting on the output node.

Radial basis function Neural Network: For their functioning, the radial basis function neural network consider the distance between a point from the centre.

Kohonen Self Organizing Neural Network: The objective here is to input vectors of arbitrary dimension to discrete map comprised of neurons.

– Recurrent Neural Network(RNN): The Recurrent Neural Network saves the output of a layer and feeds it back to the input for helping in predicting the output of the layer.

– Convolutional Neural Network: It is similar to the feed-forward neural networks with the neurons having learn-able biases and weights. It is applied in signal and image processing.

– Modular Neural Networks: It is a collection of many different neural networks, each processing a sub-task. Each of them has a unique set of inputs as compared to other neural networks contributing towards the output.

What are the benefits of Neural Networks?

The three key benefits of neural networks are:

– The ability to learn and model non-linear and complex relationships

– ANNs can generalize models infer unseen relationships on unseen data as well

– ANN does not impose any restrictions on the input variables


Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.

Is Deep Learning Better Than Machine Learning?

Reading Time: 3 minutes

Overview :

What is deep learning?

When, where and why is deep learning used.

Is deep learning better than conventional machine learning?

Which came first? The chicken or the egg?

Centuries have passed and we haven’t been able to answer this question. But soon, maybe a machine will! Can it? Will it? Let’s figure out!

Introduction :

What is deep learning? How is it related to machine learning? Is it better than conventional machine learning? A lot of questions at once, isn’t it? Deep learning is a part of the machine learning family which is based on the concept of evolutionary algorithms. It basically mimics biological processes like evolution.

So what exactly is machine learning?

Machine learning is a subset of AI that uses statistical strategies to make a machine learn without being programmed explicitly using the existing set of data. It evolved from the study of pattern recognition in AI. For a detailed understanding of machine learning, you can watch this video-

Deep Learning:

Conventional machine learning methods tend to succumb to environmental changes whereas deep learning adapts to these changes by constant feedback and improve the model. Deep learning is facilitated by neural networks which mimic the neurons in the human brain and embeds multiple-layer architecture (few visible and few hidden). It is an advanced form of machine learning which collects data, learns from it, and optimises the model. Often some problems are so complex, that it is practically impossible for the human brain to comprehend it, and hence programming it is a far fetched thought. Primitive forms of Siri and Google assistant are an appropriate example of programmed machine learning as they are found effective in their programmed spectrum. Whereas, Google’s deep mind is a great example of deep learning. Essentially, deep learning means a machine which learns by itself by multiple trial and error methods. Often a few hundred million times!

A simple understanding of basic deep learning concepts can be grasped from this video on neural networks:

Now that was pretty impressive, right?

Let us think of writing a program which differentiates between an apple and an orange. Although it may sound like a simple task to accomplish, it is indeed a complex one as we cannot program a machine to know the difference merely by observing it. We as humans can, machines can’t! So if we were to program, we would mention a few specifications of the apple and the orange but it would work for simple and clear images like these.

deep learning vs machine learning

But what if we place a banana?

The machine would probably be befuddled! This is where deep learning comes into the picture. A conventional machine learning method helps a machine to efficiently perform only a predetermined set of instructions and tends to become unworthy in case new variables are introduced in the system. It can be understood better with this video: 

Deep learning helps a machine to constantly cope with the surroundings and make adaptable changes. This ensures versatility of operation. To elaborate, deep learning enables a machine to efficiently analyse problems through its hidden layer architecture which are otherwise far more complex to be programmed manually. So, deep learning gets an upper hand when handling colossal volumes of unstructured data as it does not require any labels to handle the data.

So Let’s Summarise :

Deep learning is an advanced form of machine learning which comes in handy when the data to be dealt with is unstructured and colossal. Thus, deep learning can cater to a larger cap of problems with greater ease and efficiency. Technological breakthroughs like Google’s Deepmind is the epitome of the heights that current AI can reach, facilitated by deep learning and neurological networks.

So maybe we can’t predict which came first, the chicken or the egg but will AI be able to? Stick around to find out!

Machine learning and deep learning can be daunting and difficult to learn by yourself. A gamut of online free courses have come forward to make things simpler but if you want to take up a rigorous well-respected course that employers will respect then the Post Graduate Program in Machine Learning by Great Learning which offers 130 hours of content and personalised mentorship in an extremely easy to grasp manner is an excellent choice.

How will Cybernetics And Artificial Intelligence build our future?

Reading Time: 4 minutes

We live in a world where what was considered science fiction mere decades ago has become a reality. Global, wireless internet coverage, 3D printed technologies, the Internet of Things powered by AI-based assistants, and, of course, cyborgs, are all part of the reality we live in.

Cyborgs? Yes, those are real. Look at Dr Kevin Warwick. The man can operate lights, switches, and even computers with the power of his mind thanks to a handy chip implant. Neil Harbisson has overcome achromatopsia thanks to an implant that allows the artist to process colours in real-time on a level unachievable by anyone else on the planet. 

If you were to do some research, you’d find out that these pioneers are merely the tip of a cybernetically enhanced iceberg bringing up the real question: if we’ve already come so far, what awaits us in the future?


Some of the most prominent projects diving into the exploration of cybernetics feel like they were taken from a cyberpunk novel. And yet they are real. More on the matter, they mark what is potentially the future of humankind as a species. 

Full-spectrum vision. Typically, humans believe that the way we “see” the world is the only possible way. Cybernetics engineers would beg to disagree. A simple injection of nanoantenna has proven to give lab mice the superpower of night vision. The experiment taking place in the University of Massachusetts has only recently moved towards practical studies of the effects the antenna have on rodents, but it has already proven itself to be among the first stepping stones towards cybernetically enhanced eyesight. Additional breakthroughs in the field have shown promising results in turning eyes into video cameras, or even development of artificial retinas capable of returning sight to the blind. 

Artificial brain cells. Modern advancements in the niche of cybernetics have already grown neurons – the basic components of a human brain – in laboratory conditions. These cells, artificially raised on an array of electrodes are proving themselves as a superior replacement to the hardware and software controllers we have today. 

More on the matter, scientists are already using brain-computer interfaces in medicine. Most are designed for therapeutic purposes such as the Deep Brain Stimulation designed to aid patients with Parkinson’s disease.

We will be able to use said technology to create connections that operate via the remaining neural signals allowing amputee patients to feel and move their limbs with the power of their mind. In some cases, as it was with Nigel Ackland, some might even go as far as to use the word enhancement when talking about top tier prosthetics.

Enhanced mobility. Stronger, faster, more durable – those are the goals of military-grade exoskeletons for soldiers that are already branching out into the medical niche and serve as prosthetics for amputee victims.  The combination of hardware and AI-based software eliminate the boundaries of human capabilities while minoring the vitals of the wearer in real-time. 

Technopsychics. The University of Minnesota is working on a computer to brain interface capable of remotely piloting drones. The machines can detect the electrical signals transmitted by the brain to control functioning machines in real-time. If you can navigate a quadcopter through an obstacle course using only the power of your mind today, imagine what we’ll be piloting remotely tomorrow. 

Nanorobots. Self-repair, growth, and immunity to diseases will soon be true thanks to a simple infusion of nanobots into your bloodstream. Modern researches explore the idea of developing your blood cell’s little helpers that can be controlled in the cloud from your smartphone!

Artificial Intelligence

As you may have deduced from the examples above, the advancements in the cybernetics niche are directly related to the progress we make with Artificial Intelligence or Machine Learning technologies. 

We need the software capable of driving the hardware to its limits if we are to dive deeper into cyborg technology. Artificial Intelligence is supposed to become the bridge between the man and the machine according to prominent research such as Shimon Whiteson and Yaky Matsuka. These scientists are exploring new ways AI can help amputee patients to operate their robotic prosthetics. 

Furthermore, AI is expected to take control of machines doing sensitive work in hazardous areas. According to BBC, we already have smart bots capable of defusing bombs and mines yet they still require a human controlling them. In the future, these drones (and many more, responsible for such challenging tasks as toxic waste disposal, deep-sea exploration, and volcanic activity studies, etc.) will be powered purely by algorithms. 

Lastly, machines are expected to analyze and understand colossal volumes of data. According to Stuart Russell, The combination of AI-powered algorithms and free access to Big Data can identify new, unexpected patterns we’ll be able to use to mathematically predict future events or solve global challenges like climate change. 

What a time to be alive! 

If you wish to learn more about Artificial Intelligence technologies and applications, and want to pursue a career in the same, upskill with Great Learning’s PG course in Artificial Intelligence and Machine Learning.


Critical skill-sets to make or break a data scientist 

Reading Time: 4 minutes

Ever since data took over the corporate world, data scientists have been in demand. What further increases the attractiveness of this job is the shortage of skilled experts. Companies are willing to pour their revenue into the pockets of data scientists who have the right skills to put an organization’s data at work.

However, that does not mean it is easy for candidates to grab a job at renowned organizations. If you’ve been wanting to establish a career in data science, know that it takes the right set of skills to be considered worthy of the position.

What exactly then do you need to become an in-demand data scientist?

Here are a few valuable skills required for data scientist to inculcate before hitting the marketplace looking for your ideal job.

Programming or Software Development Skills

Data scientists need to toy with several programming languages and software packages. They need to use multiple software to extract, clean, analyze, and visualize data. Therefore, an aspiring data scientist needs to be well-versed with:

– Python – Python was not formally designed for data science. But, now that data analytics and processing libraries have been developed for Python, giants such as Facebook and Bank of America are using the language to further their data science journeys. This high-level programming language is powerful, friendly, open-source, easy to learn, and fast.

– R – R was once used exclusively for academic purposes, but a number of financial institutions, social networking services, and media outlets now use this language for statistical analysis, predictive modelling, and data visualization. This is a reason why R is important for aspiring data scientists to get their hands on.

– SQL – Structured Query Language is a special-purpose language that helps manage data in relational database systems. SQL helps you in inserting, querying, updating, deleting, and modifying data held in database systems. 

– Hadoop – This is an open-source framework that allows distributed processing of large sets of data across computer clusters using simple programming models. Hadoop offers fault tolerance, computing power, flexibility, and scalability in processing data.

Problem Solving and Risk Analysis Skills

Data scientists need to maintain exceptional problem-solving skills. Organizations hire data scientists to work on real challenges and attempt to solve them with data and analytics. This needs an appetite to solve real-world problems and cope with complex situations. 

Additionally, aspiring data scientists also need to be a master at the art of calculating the risks associated with specific business models. Since you will be responsible for designing and installing new business models, you will also be in charge of assessing the risks that entail them. 

skills required for data scientist
Summary of critical skills required for data scientists

Process Improvement Skills

Most of the data science jobs in this era of digital transformation have to deal with improving legacy processes. As organizations move closer to transformation, they need data scientists to help them replace traditional with modern.

As a data scientist, it falls upon you to find out the best solution to a business problem and improve relevant processes or optimize them. 

It makes a lot of sense for data scientists to develop a personalized approach to improving processes. If you can show your potential employer that you can enhance their current business processes, you will significantly increase your chances of landing the job.

Mathematical Skills

Unlike many high-paying jobs in computer science, data science jobs need both practical and theoretical understanding of complex mathematical subjects. Here are a few skills you need to master under this set:

– Statistics – No points for guessing this one, but statistics is and will be one of the top data science skills for you to master. This branch of mathematics deals with the collection, analysis, organization, and interpretation of data. Among the vast range of topics you might have to deal with, you’ll need a strong grasp over probability distributions, statistical features, over and undersampling, Bayesian statistics, and dimensionality reduction. 

– Multivariable calculus and linear algebra – Without these technologies, it is hard to curate the modern-day business solutions. Linear algebra happens to be the language of computer algorithms, while multivariable calculus is the same for optimization problems. As a data scientist, you will be tasked with optimizing large-scale data and defining solutions for them in terms of programming languages. Therefore, it is essential for you to have a stronghold over these concepts.

Deep Learning, Machine Learning, Artificial Intelligence Skills

Did you know, as per PayScale, the data scientists equipped with the knowledge of AI/ML get paid up to INR 20,00,000 with an average of INR 7,00,000? Modern-day businesses need their data scientists to have a basic understanding, if not expertise, over these technologies. Since these areas of technology have to do a lot with data, it makes sense for you to have a foundational understanding of these concepts.

Learning the ins and outs of these concepts will highly increase your data science skills and help you stand out from other prospective employees.

Collaborative Skills

It is highly unlikely for a data scientist to work in solitude. Most companies today house a team of data science experts who work on specific classes of problems together. Even if not in a team of data scientists, you will definitely need to collaborate with business leaders and executives, software developers, and sales strategists among others.

Therefore, when putting all of the necessary skills in perspective, do not forget to inculcate teamwork and collaborative skills. Define the right ways of bringing issues in front of people and explaining your POV without exerting dominance.

It might also help you to be able to explain data science concepts and terminologies in a simple language to non-experts.

For the year 2019, the total number of analytics and data science job positions available are 97,000, which is more than 45% as compared to the last year. Trends like this act as a magnet to attract fresh graduates towards a career in Data Science. As a data scientist, you need to wear multiple hats and ace them all. Since the field is currently expanding and evolving, it is hard to predict everything that a data scientist needs to know. However, start by working on these preliminary skills required for data scientist and then move your way up.

If you are interested in moving ahead with a career in Data Science, then you should start inculcating the above-mentioned skills to improve your employability. Upskilling with Great Learning’s PG program in Data Science Engineering will do the most of it for you!