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

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  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

Philosophy

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 

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

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.

Neuroscience

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

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.

Linguistics

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.

Healthcare:

– 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.

E-commerce

– 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

Conclusion

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.

The Must-Haves On Your Artificial Intelligence Resume

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Robots, self-driving cars, and chatbots have swapped man for a machine with artificial intelligence (AI). At the same time, they have created a huge marketplace for a diverse range of job roles with varying skill sets. While traditionally, analytics job roles required applicants with statistics or math background, the increased use of computation and sheer volumes of data now demand technical knowledge. So whether you are an engineer wanting to make your mark in an AI-driven environment, or a data analyst seeking to make a career in AI, you need to have certain core competencies mentioned on your Artificial Intelligence resume.

Before heading to the must-haves on your AI resume, let’s see what is AI?

These are the top must-haves on your AI resume

What are the skills required for Artificial Intelligence?

High-level object-oriented programming languages such as Java, Python, R, C, and C++ are navigated by most engineers during their education. However, if you have come to data science from statistics or a non-engineering background, you need to learn these languages that are fundamental to AI. At the same time, learning server-side scripting languages such as PHP, ASP, JSP, Ruby or Perl is of immense use.

Learning any or all of these is not very time-consuming. You can use their libraries to practice projects or take short certificate courses. At the end of the day, the more languages that feature on your resume, the higher the chances of your CV being considered.

Fuzzy Logic, Neural Networks, Cognitive Computing, Natural Language Processing (NLP), Data Analysis and Search technologies, APIs, enterprise architecture and security architecture; are some technological underpinnings of AI. Learning or mastering any of these will take you a step further towards landing a job role in AI. Each of these tools and technologies has applications in various scenarios or problem-solving. So wait no more. Go ahead and learn these AI skills-sets to score higher in the job application process. As your knowledge is expected to bring value to the company, you are more likely to be selected with these competencies on your resume.

AI jobs require a blend of expertise and skills in programming, data management, technology platforms and business development. However, these traits continue to be in short supply. So developing a balance between these skills in demand and work experience with computer science disciplines; enhances your prospects of getting the AI job you covet.

Also Read: Top 9 Artificial Intelligence Startups in India

How do you say Machine Learning on a Resume?

A part of AI, machine learning is increasingly considered indispensable for trends-spotting and iterative self-learning. With every industry applying AI, machine learning expertise is highly sought after. Its wide range of cutting-edge applications, emerging areas of machine learning-as-a-service, and applications overlapping with AI; have made it central to data-driven hiring. A study of machine learning can help you get a good job in companies leveraging machine learning and AI in their business intelligence process.

So look out for an institute in your city that trains you in machine learning, and provides expert help to train you on projects. Your machine learning certification will surely be a feather in your cap and lay the foundations of your AI career graph.

What is the role of programming languages on an Artificial Intelligence resume?

Engineers and specialists in AI require technical skills for designing, maintaining and repairing technology and software programs. Understanding your data, open-source frameworks, computing platforms, supporting programming languages and coding; ultimately help you prepare for the rapid pace of technological innovation in data-driven insights. For instance, AI platforms like Microsoft Azure require knowledge of cloud, machine learning, and custom R or Python coding, while TensorFlow open-source library requires deep knowledge of architecture.

As AI is a way of making the machine or software, intelligent, it is a foregone conclusion that degrees or certifications in computer science disciplines or tools are a plus factor for any AI resume.

Also Read: Top 10 Books on Artificial Intelligence for Beginners

Physics, Engineering, and Robotics

As a student with a science or engineering background, physics, and engineering applications will be the mainstays of your knowledge-base. However, learning AI-based add-on courses will strengthen your foundations in math, logic, and engineering. For instance, knowledge of sensors is a sought-after talent for its various applications of AI.

With AI robots being used across industries, like logistics and manufacturing, a short course on robotics enhances your Artificial Intelligence resume. Companies always look for multi-talented multi-functional professionals who can work across technologies and platforms, and grow in sync with the company visions. So the secret here is to look around and understand the industries and businesses implementing AI and the various use cases. Find which among these interests you and set about learning the tools that contribute to your resume, when you apply for the target job role.

 

While the technical skills on your resume will surely land a good job in Artificial Intelligence, you cannot ignore the supporting written and verbal communication skills that are needed to convey how the AI tools and services are deployed within the business or industrial processes.

Also, a mention of all your important projects and their outcomes is a highlight on your resume that attracts the recruiters like a magnet. Your achievements and initiatives showing your leadership quality, team management skills, and being a team player is also a plus point on the resume. A good mix of technical skills and certifications along with the soft skills and hands-on projects makes up a complete resume for your AI job.

So learn the technologies and programs required for an artificial intelligence job role, during your spare hours or over the weekend and build a great resume that takes you places!

Click here to explore a career in Artificial Intelligence.

Machine Learning and Its 5 New Applications

Reading Time: 4 minutes

In less than 4 years your own personal devices will be so sophisticated that they’ll know more information about you than the closest people around you. This is just one of the many scenarios that are in front of us thanks to the advancements made by artificial intelligence. Machine learning is moving towards these advancements and for better or worse it is going to play a huge role in the world’s most important industries. And more and more IT professionals are shifting their careers in this sector that is only destined to grow.

Can we give a definition to machine learning and why do we have to pay attention to it? On a large scale, we can say that machine learning models are an application of AI in which algorithms independently predict outcomes. To get an idea of what we are talking about, machine learning takes advantage of these models that are capable to process large datasets extract insights and make accurate predictions without the need for much human intervention.

Many of this models implement machine learning with r and it’s no wonder that a lot of programmers choose to take a machine learning course to advance in their career or change their professional ladder altogether.

Going back to what machine learning is, we can state that many value-generating implications result from the accelerated development of this technology, and many are poised to radically streamline the business world.

In this article, you will find 5 new applications that are certain to change our day to day life in the nearest future, at least our business life at the beginning.

Reviews that can make or break businesses

Now more than ever people go online and browse sites such as Yelp to look at the reviews other customers left for a certain restaurant and make up their mind and choose which venue they will spend the evening at. Yelp knows that and in the last couple of years, it has implemented machine learning to better its picture classification technology. Yelp’s machine learning algorithms help the company’s human staff to compile, categorize, and label images more efficiently. It will surely make a big difference when choosing a restaurant and when complaining about it!

The moment an algorithm understands your diagnosis.

Big Data and data science are already making a revolution by leaps and bounds in the healthcare sector. But what if incredibly accurate machine learning algorithms were capable to process large sets of healthcare data without breaching confidentiality contracts? what if a couple of lines of code could give you the results of your analysis or diagnosis, could tell you if you are at risk for particular diseases or genetic issues?

Now, this scenario may become a reality in the near future as Dr. Ed Corbett says in a recent scientific paper: “It’s clear that machine learning puts another arrow in the quiver of clinical decision making.

“Machine learning in medicine has recently made headlines,” said Corbett, the medical officer at Health Catalyst. “Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithm to identify skin cancer.”

The retail store that already knows what you’ll buy tomorrow

 Do you need another incentive to learn machine learning? Just think for a minute about this statistic: the international retail sector has consistently generated over $20 trillion in sales a year for the past few years. This not only testifies that well being of this industry but also tells you that retail and online retail stores are gathering millions of raw and consumer-behavior data that could be split into consumer shopping patterns and tendencies. And yet, many enterprises are not taking advantage of this incredible amount of information, that’s why your role as programmer or engineer with a machine learning course come to play. There is a massive opportunity to implement machine learning models that enable retailers to better understand their customers and provide a more personalized customer experience.

Thanks to this well of data, you could enable any retailer with the ability to provide recommendations for the end user or better impact the whole customer’s journey, from his first visit to purchase, to the subsequent follow-up.

Separating chaff from the wheat in content and news delivery.

The case of fake news invading our social media feed has escalated to a point where Facebook announced that they will hire 3000 employees with the only purpose to monitor the platform’s newsfeed. The curation of content is not only a problem for Facebook and Twitter though. Other Silicon Valley giants like Google are creating teams of experts with the only intent to stop fake news from spreading.

Emerging machine learning and AI platforms, such as Orions Systems, are providing proprietary systems to “grow and adapt the interactions between humans and artificial intelligence” for tasks like moderating content at scale.

Taking a new stand against cybercrime

Recent studies have found out that cybercrime will cost to enterprises and companies more than 6$ trillion per year by 2021. It says that companies will invest over 1$ trillion in cybersecurity within that same year to prevent cyber attacks and data breaches. Researchers are developing clever ways to implement machine learning models to detect fraud, prevent phishing and defend against cyber attacks.

It is clear that machine learning is opening the doors to an exciting and interesting field for programmers and IT professionals. Taking a machine learning online course will benefit your career and give you the possibility to work in an ever-changing field and in different business verticals.

Top 10 Emerging Data Analytics Startups in India

Reading Time: 4 minutes

Powered by specialized software and real-time analytics systems, big data & analytics caters numerous benefits for companies worldwide. Thus, the demand for big data Hadoop firms has been increasing exponentially. Moreover, it has led to opportunities for budding data scientists, data professionals, statisticians, and analysts. Keeping that in mind, we bring you the ten emerging data analytics startups in India to look out for applications.

10. TURING ANALYTICS

Turing Analytics resorted to machine learning as the main technology to provide business solutions across the globe. With a team of 7+ intellectuals, TA has successfully established big data analytics solutions for national clients, like Shopclues, and global clients such as Tata, Kimberley Clark, etc. The founders believe that visual search is the next generation of search and users will interact with retail platforms visually in the future.

Establishment year: 2015
Founder(s): Divyesh Patel and Aditya Patadia
Headquarter: Bangalore

USP: Implementation of neural networks and machine learning to provide visual recommendations and visual searches with quick and real-time data analytics solutions.

9. The Math Company

TMC emerged as a collaborative venture of big data analytics specialists, who recognized the demand for long-term Hadoop, big data, and analytics requirements among firms globally. With 50+ employees and growing, this data analytics company uses ML, visualizations, design thinking, and domain expertise to serve its clients, more than 15 of which are a part of the Fortune 500 list. The company is consistently disrupting the analytics services with their data engineering, data science and solution deployment services.

Establishment year: 2016
Founder(s): Anuj Krishna, Aditya Kumbakonam, and Sayandeb Banerjee
Headquarter: Bangalore

USP: The Math Company commits to end-to-end decision-making analytics and data-driven tactics for companies by collaborating with them at ground level.

8. Razorthink

Razorthink established as an emerging enterprise AI solutions provider. Funded by individual investors, the company targets adept computing through a distinct accumulation of AI optimization, automation, dynamic models, and data science. Their team of 100+ professionals has exhaustively supported big and small businesses to utilize AI-based customer services, Risk Management, fraud detection, intellectual process automation, and business predictions.

Establishment year: 2015
Founder(s): Gary Oliver, Dr. Nandu Nandakumar, Murali Mahalingam, Tom Drotleff, Harsha Nutalapati, Barbara Reichert, Rupesh Rao
Research Lab Headquarter: Bangalore

USP: Razorthink’s BigBrain is the main AI-based data analytics system that offers a simple drag-and-drop layout for dealing with model deployment, optimization, and data analytics.

Read Also: 15 Most Common Data Science Interview Questions

7. G Square

G-Square sprouted as a collaboration between Gopi Suvanam and Gurpreet Singh in 2014. G Square Solutions targets B2B aspects where they provide clients with Data Science, data marketing, sales, and financial technology. The firm was also awarded at CYPHER 2016 as the ‘Emerging Analytics Services Startup of the year.’

Establishment year: 2014
Founder(s): Gopi Suvanam, Gurpreet Singh
Headquarter: Mumbai

USP: G Square is proud to present its SaaS business model for data & analytics using unique plug and play solutions in financial services for family offices, NBFCs, banks and other wealth management firms.

6. Hiddime/Lead Semantics

Hiddime, or Lead Semantics, is a one-of-a-kind Cloud Business Intelligence company that focuses on data science solutions integrated with deep semantics via the internet. The firm created their tool for facilitating IT growth among entrepreneurs with little knowledge of the analytics world. Their internet-based data analytics model is a user-friendly interaction tool integrated with semantics, machine learning, graphs and NLP. Their achievements also awarded them with the “Analytics Product of the Year” award at CYPHER 2016.

Establishment year: 2013
Founder(s): Prasad Yalamanchi
Headquarter: Hyderabad

USP: Hiddime has successfully implemented point-and-click narrative analytics, which businesses can utilize for business predictions, cognitive analytics, and deeper insights.

5. Realbox

Realbox aims to deliver a distinct experience to its clients, mainly comprising of modern-day physical businesses.  The firm has been solving real-time issues related to big data analytics resulting from large quantities of customer data in India and abroad. The firm holds great pride in its flagship tool known as Pulse that works similar to Google Analytics and takes care of big data Hadoop analytics for physical enterprises.

Establishment year: 2015
Founder(s): Saurabh Moody, Arjun Sudhanshu, Preksha Kaparwan
Headquarter: Delhi

USP: Realbox uses Pulse as its primary predictive analysis and BI tool for utilizing risk management and boosting ROI.

Read Also: 5 Big Data Analytics Skills That Will Boost Your Salary

4. Recosense Labs

Reconsense Labs have enabled an independent interpretation tool for generating optimized meta-data for users. This automated feature personalizes the content for individual users, promoting revenue generation, content exploration, and increase engagement. Their data analytics solutions incorporate ML and NLP frameworks to derive data based on user IPs, which is a more efficient model, unlike conventional click stream data analytics.

Establishment year: 2014
Founder(s): Amith Srinivas, Shivkumar Janakiraman, Raghunandan Patthar
Headquarter: Bangalore

USP: Recosense have proven data and analytics solutions for travel, eCommerce, and financial services, which are generated based on real-time user interactions.

3. 3LOQ

3LOQ formulates NLP and ML frameworks to optimize the Fintech industry. The firm targets building AI-driven data analytics for permeating big data and consumer psychology so that they can extract revenue through BI. 3LOQ strengthens money and asset management systems in terms of digital banking services so that the end customers get streamlined and personalized communication. 3LOQ is growing stronger every day with 15+ employees.

Establishment year: 2014
Founder(s): Anirudh Shah, Sunil Motaparti
Headquarter: Mumbai, Hyderabad, Bangalore

USP: 3LOQ offers its Habitual AI platform that offers data-driven solutions for enhanced customer engagement in the financial sector.

2. Lymbyc

Lymbyc has a dedicated team of intellectual data scientists and analysts that have developed sturdy analytical tools accompanied with advanced technology. Their primary purpose is to enforce practical and optimized decision-making system for business leaders so that they can structure business foresight through natural language-based data inputs. Lymbyc works with 52+ employees to provide solutions in India and overseas.

Establishment year: 2012
Founder(s): Ashish Rishi, Satyakam Mohanty
Headquarter: Bangalore

USP: Lymbyc offers its unique expert analytics tool through its patented application MIRA (Machine Intelligence for Research & Analytics), which filters contextual data and formulates the necessary actions for converting them into data insights. MIRA works as a virtual data analytics scientist like none other in the commercial world.

1. CropIn

CropIn has proven its worth in the terms of interpolating SaaS model with the agricultural sector. Keeping the crisis in the agrarian business in mind, CropIn started providing solutions for farmers concerning financial analytics, weather foresight, data interpretation, satellite monitoring, geo-tagging, AI, big data analytics, etc.

Establishment year: 2010
Founder(s): Krishna Kumar, Kunal Prasad (Great Lakes alumnus), Chittaranjan Jena
Headquarter: Bangalore

USP: CropIn provides with ML- and AI-driven weather analytics, satellite monitoring, proprietary algorithms, for the agricultural technology. It provides with improved farming sales, risk management, data security, etc.

Data analytics is the future where every data company requires proficient scientists to handle the huge chunks of data that needs to be filtered for maximizing output. Therefore, you can learn big data online through reliable courses that commit to tutoring in various data and analytics courses at reasonable rates.

To explore a career in Data Analytics, click here.

Non IT? No Problem! You’re Still Welcome In Business Analytics

Reading Time: 3 minutes

Does it sound too good to be true?  Entering a competitive job market such as business analytics is not an easy path. You could feel even more discouraged because you don’t have job experiences in IT or because you don’t hold a degree or an executive MBA in this area. Fear not, you are still eligible for a great career in business analytics and companies are demanding a great number of business professionals that can blend their skills with analytics skills even though you don’t have prior experience!

Without going too further in the past, business analytics training was all about sifting through data of corporate databases, downloading the data into a desktop spreadsheet tool such as Excel, and creating pie charts, bar charts, columnar reports. “The tedium of the process generates challenges for our business partners, who might run many different types of promotions with both flat and tiered offer structures, but are still expected to rapidly turn around the results of their efforts,” said John Stanisic, manager of customer and marketing analytics for Points, a company that provides analytics tools for business loyalty programs and on the other side offers the end customer the ability to manage the loyalty points they earn.

Thanks to a business analytics certification even non-IT people can manage big data without losing weeks or even months of business hours. This is the answer to a big knowledge gap that people without an engineering background are filling up quickly, thanks also to the skills they developed in their past job experiences.

Let’s dive into some solid statistics now. With a simple research on business analyst course, you can find out by yourself that at the moment there is a huge demand for business analysts. There are more than 26000 open positions for this job highlighted by Google alone. What if you could enroll in a business analytics certification and find yourself within 6 months in a brand new position, with an even increased salary? And yes, this could become a reality within this year if you act quickly.

Business analytics is such a multidimensional role within the realm of big data analytics because you have to deal with the IT department as well as the customers. Having programming skills is not central in the business analyst role (as you can find out in a business analyst training), since providing soft skills and knowing how to analyze data is much more important than taking on the job that belongs to IT engineers and programmers. In a  business analyst course, you won’t be bothered with any programming at all. Your main requirement will be understanding, developing, managing, and functional testing. So as an entry level business analyst in a business analyst training you have to master 4 business analytics skills. Let’s see what they are.

The first skill you want to hone is requirement analysis and modeling. These techniques and processes are related to the core IT processes. This skill may need a learning curve for professional with no prior job experience in IT. Which is why you should take a business analyst course.

A break-up of these skills are as follows:

  • – SDLC & Requirements understanding (User Stories, Use cases etc)
  • – Process Modelling (Activity diagrams & flow chart)
  • – Data Modelling using E-R diagrams
  • – Developing requirements specifications (SRS, Backlogs etc)

Another crucial skill you want to get the grips of in a business analytics training is functional testing. Functional testing is the last step before a software is delivered to the customer for acceptance testing. This is the part where you have to interact with a customer to gather their needs and observations. So this stage of the process requires you to make sure the software can match the standards of your customers and can meet or go beyond their expectations.

When it comes to functional testing you have to foresee every possible scenario where your product could be employed. At every point of the customer’s journey, the interaction between them and your product has to run smoothly. There must be no mistakes on your part.

Talking about the customer’s journey, as a business analyst, it is of paramount importance that you understand how your customer behaves and interacts with your end product. Gathering all this knowledge will require some time while at the same time you keep making more and more tests. For non-IT professionals, this is an advantage because you have already gained this skill in previous jobs. The same can’t be said for programmers or IT engineers.

Finally, you have to get a deeper understanding of how to communicate with your customers and your development team as well. These two parties don’t talk the same language, so you play the part of an intermediary between these two worlds.

Going from one job environment to another which is completely different from the previous one is no easy task. And yet you have great chances to succeed. It takes courage, perseverance and the will to study a new skill set, and you will be on your way to becoming a well-rounded business analyst.

How To Solve The Biggest Industry Problems With Big Data Analytics

Reading Time: 4 minutes

Picture this scenario: you’re at the bank for a normal transaction. Even before it’s your turn an employee informs you that you are eligible for a special agreement on your loan. And that’s because you are one of the few bank clients that have an excellent credit score and your business venture has been categorized by your bank as reliable and successful.

Does it sound too good to be true? How is it possible that your bank has real-time data about your business? It all happens thanks to big data analytics and its smart employment. Some decades ago it would have taken the effort of hundreds of people to gather and measure data that is now available at your fingertips with a simple click.

Big data analysis doesn’t stop at bank transactions, it is revolutionalizing the healthcare industry, as well as IoT devices, and the study of customer behavior.

Here you will find the biggest industry problems data and analytics are solving right now.

More often than not, patients in hospitals and other healthcare facilities are the victims of identity theft, fraud, and abuse. Big data is playing a huge part in decreasing the number of fraud cases. Big data analysis helps financial service firms to recognize behavioral patterns and anomalies so that they can protect their customers’ security while reducing loss due to fraud.

Talking about the healthcare sector, sentiment analysis and big data are capable to collect data about the customer behavior and the customer journey. Big data tools such as Hadoop can sift through millions of tweets to determine customers’ opinions about a certain drug and enable a pharmaceutical company with better advertising decision about their product. Hadoop and big data make easier to gather information about a customer and create targeted ads accordingly.

Besides drugs and fraud, more and more hospitals are now embracing RFID chips and sensors to track the patient’s experience within their facilities. The patient is now seen as a customer, not just someone looking for a treatment. These chips can tell the hospital how the patient is behaving in the structure, how he or she is interacting with doctors and the hospital staff, what the pain points are during his visit and what can be improved during his stay. Within a relatively short period of time, the hospital can gather a well of information about the customer experience and can create an experience that is satisfying for the patient. The healthcare facility can shape a workflow for its visitors and solve business problems with real-time data.

Another interesting revolution big data analytics is leading is how databases are changing the way they used to work. Unverified, untagged data was all over the floor once. But now thanks to data and analytics more and more companies are building structured and functional databases. For example, the U.S. Patent Office is managing now five million patent applications by entering two million pages of data every month with OCR scanning and tagging. Their database now is user-friendly since users now can enter a specific query to find what they’re looking for in a matter of seconds.

Another sector closely related to big data analytics is SaaS solutions. SaaS clients can collect real-time data about their customers’ experience and performance. Collecting this data allows them to recognize in time unusual behaviors. This is used to recognize abnormal patterns of network activity or behavior, such as service degradations, bandwidth events, and security incidents such as DDoS attacks. Big data analysis for network routing and peering lets customers understand how their traffic is behaving as it transits neighboring networks, and plan network changes to optimize cost and service quality.

Going back to customer experience, online retailers and e-commerce are relying on data analytics more than ever. To personalize their customer’s journey on their site, they have to provide solutions in a timely fashion, they have to constantly improve their interaction with the consumer. Big data analytics offer an answer to all these questions. And thanks to the improvement brought by big data, a customer is more likely to become a returning client for the e-commerce site.

What about retailers that interact with customers every day? What’s in it for those vendors who have to deliver the physical products to their clients in time and in a perfect state? Big data plays a big role for them as well. By crossing traffic and weather data, a retailer can predict and overview how and when the package will be delivered. Retailers are tracking when the truck is due so they can have the necessary people to unload the truck.

Let’s go back to the bank example, shall we? Many banks hit a roadblock when legitimating a small business information. Many struggle to identify whether that business exists or not. They may hire hundreds of people to get that piece of information. Now, thanks to big data, banks can access a structured workflow platform where every subscribed business has a name, search on Google, maps, and website attached.

To wrap this up, we can fairly say that big data analytics goes well beyond some fleeting numbers flowing in the air. Data & analytics solve real-world problems, and in a big way!

Which Career Path Does a Supply-Chain MBA Hold for You?

Reading Time: 3 minutes

According to an ASSOCHAM report, India spends close to 14% of its GDP on transportation and logistics, which is almost double of what other countries spend – around 8%. Besides, a Business Standard report on the Indian logistics market shares that India is growing by leaps and bounds in this sector and is expected to become a US$ 307 billion industry by the year 2020.

The Growing Demand for Skilled Supply-Chain Specialists

Amy Cathy, Executive Director of the supply-chain MBA program at University of Tennessee, Knoxville, gives a very precise version of what supply-chain is all about. According to her, in a supply-chain, one has to source goods, make them, and get them to consumers.

Apart from the growing demand of consumers, other contributing factors for skilled supply-chain specialists are technology and the global market.

Michael Hugos, the author of the book Essentials of Supply Chain Management, states that projected job growth is one of the major reasons for the booming of the industry along with the impact of technology and dynamic changes in the global market. He adds that the life of electronic goods is not measured in years but months, pushing companies to evolve their supply-chain systems.

If you are a supply-chain MBA aspirant, this breakdown of in-demand job roles will give you an idea of the scope of your career path in this field.

Industry Analyst – Responsible for interviewing personnel from manufacturing, logistics, warehousing, and procurement divisions to set up business processes and optimize supply-chain workflows.

Project Manager – Liaise with a team of consultants to ensure everyday operations take place without hassles, supervise the work of analysts, and ensure projects are delivered under agreed cost and time metrics.

Global Logistics Manager – From warehousing and distribution operations to planning, forecasting, managing customer service personnel, and taking care of logistics information systems, the global logistics managers have a number of tasks to oversee. Besides, they also come up with supply-chain metrics, strategize, negotiate and initiate contracts with suppliers and vendors, and supervise everyday operations.

Transportation Director – They oversee outbound and inbound delivery of materials and products from distribution centers, budget transportation costs, and maintenance carriers, supervise third-party transport vendors, and manage invoicing. They are also responsible for the smooth moving of the carriers and freights across frontiers.

Supply-Chain Sales Manager – They outsource tasks and work to third-party vendors who offer logistics solutions, connect and sell supply services, and manage accounts.

Supply-Chain Consultant – A consultant works with several companies, comes up with strategies for coordinating supply-chain processes, provides tips and insights, and optimizes processes. Consultants are in demand owing to logistics companies liaising with decentralized distribution centers in different countries.

Procurement Analyst – They work closely with a company’s purchasing department, analyze historical data, assess purchasing cost of materials, estimate future costs, and research and find prospective vendors. They also negotiate costs, initiate contracts, and manage suppliers once they are on board.

The pay scale of supply-chain specialists is lucrative in India and other international markets. The annual Salary Survey conducted by Logistics Management revealed that the base salary for supply-chain managers on average is $111,994 in the United States. In India, according to a PayScale study, the median average salary for supply-chain managers is Rs. 8 lakhs and that of a supply-chain consultant is Rs. 9 lakhs, both of which increase with experience.

How Globalization has Opened Up New Avenues for Supply-Chain Management Graduates

John Flower, Carey’s supply-chain management division’s chairman, shares that MBA graduates in the supply-chain sector will need to keep in mind the global market when taking up the course. In his words,”They will be working a global network of suppliers and a global network of customers.” When it comes to the required skills, he adds, “It requires the mental agility of a good stockbroker. You’re always weighing things. You’re always watching world markets.”

The statements hold weight as India is expanding rigorously in terms of infrastructure, supply-chain network, and working on waterways, railways, and cargo to meet the increasing demands. According to Business Standard, the ‘Make in India’ campaign and the estimated growth of the Cargo and Logistics in India by CAGR of 16% in the coming years are bound to create more opportunities in this sector. This indicates a promising and rewarding career path for an MBA graduate in supply-chain.

Difference Between Data Science, Machine Learning, and AI

Reading Time: 6 minutes

Even though the terms data science, machine learning, and artificial intelligence (AI) fall in the same domain and are connected to each other, they have their specific applications and meaning. There may be overlaps in these domains every now and then, but essentially, each of these three terms has unique uses of their own. 

We will start with the term Data Science, as it assumes the top-most position in the hierarchy of data-related technologies.

What is Data Science?

Data science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. Data scientists use a combination of tools, applications, principles and algorithms to make sense of random data clusters. Since almost all kinds of organizations today are generating exponential amounts of data around the world, it becomes difficult to monitor and store this data. Data science focuses on data modelling and data warehousing to track the ever-growing dat set. 

The information extracted through data science applications can be used to guide business processes, which brings us to the next question- 

What are the Scopes of Data Science?

One of the domains that data science influences directly is business intelligence. Having said that, there are functions that are specific to each of these roles. Data scientists primarily deal with huge chunks of data to analyse the patterns, trends and more. These analysis applications formulate reports which are finally helpful in drawing inferences. A Business Intelligence expert picks up where a data scientist leaves – using data science reports to understand the data trends in any particular business field and presenting business forecasts and course of action based on these inferences. Interestingly, there’s also a related field which uses both data science and business intelligence applications- Business Analyst. A business analyst profile combines a little bit of both to help companies take data driven decisions.  

Data scientists analyse historical data according to various requirements, by applying different formats, namely:

    • Predictive causal analytics: Data scientists use this model to derive business forecasts. The predictive model showcases the outcomes of various business actions in measurable terms. This can be an effective model for businesses trying to understand the future of any new business move.  
    • Prescriptive Analysis: This kind of analysis helps businesses set their goals by prescribing the actions which are most likely to succeed. Prescriptive analysis uses the inferences from the predictive model and helps businesses by suggesting the best ways to achieve those goals.

Data science uses a wide array of data-oriented technologies including SQL, Python, R, and Hadoop, etc. However, it also makes extensive use of statistical analysis, data visualization, distributed architecture, and more to extract meaning out of sets of data.

Data scientists are skilled professionals whose expertise allows them to quickly switch roles at any point in the lifecycle of data science projects. They can work with AI and machine learning with equal ease. In fact data scientists need machine learning skills for specific requirements like:

  • Machine Learning for Predictive Reporting: Data scientists use machine learning algorithms to study transactional data to make valuable predictions. Also known as supervised learning, this model can be implemented to suggest the most effective courses of action for any company. 
  • Machine Learning for Pattern Discovery: Pattern discovery is important for businesses to set parameters in various data reports and the way to do that is through machine learning. This is basically unsupervised learning where there are no pre-decided parameters. The most popular algorithm used for pattern discovery is Clustering.

Sidetrade, a leading company in the domain of data-science, realized, early-on, the data exploitation challenges its clients faced and immediately set up a dedicated Data Scientist team to work with its Product Managers, aptly put it:

“Data Scientists, of course, have to work closely with IT development teams to guarantee the usability of any solution once it’s in production”

Jean-Cyril Schütterlé VP Product & Data Science, Sidetrade Group

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

What is Artificial Intelligence?

AI, a rather hackneyed tech term that is used frequently in our popular culture – has come to be associated only with futuristic-looking robots and a machine-dominated world. However, in reality, Artificial Intelligence is far from that. 

Simply put, artificial intelligence aims at enabling machines to execute reasoning by replicating human intelligence. Since the main objective of AI processes is to teach machines from experience, feeding the right information and self-correction is crucial. AI experts rely on deep learning and natural language processing to help machines identify patterns and inferences.

What are the Scopes of Artificial Intelligence?

    • Automation is easy with AI: AI allows you to automate repetitive, high volume tasks by setting up reliable systems that run frequent applications.
    • Intelligent Products: AI can turn conventional products into smart commodities. AI applications when paired with conversational platforms, bots and other smart machines can result in improved technologies.
    • Progressive Learning: AI algorithms can train machines to perform any desired functions. The algorithms work as predictors and classifiers.
    • Analysing Data: Since machines learn from the data we feed them, analysing and identifying the right set of data becomes very important. Neural networking makes it easier to train machines.

Are machine learning and data science related?

Artificial Intelligence, much like data science is a wide field of applications, systems and more that aim at replicating human intelligence through machines. Artificial Intelligence represents an action planned feedback of perception.

Perception > Planning > Action > Feedback of Perception

Data Science uses different parts of this pattern or loop to solve specific problems. For instance, in the first step, i.e. Perception, data scientists try to identify patterns with the help of the data. Similarly, in the next step, i.e. planning, there are two aspects:

  • Finding all possible solutions
  • Finding the best solution among all solutions

Data science creates a system which interrelates both the aforementioned points and helps businesses move forward.

What is Machine Learning?

Machine Learning is a subsection of Artificial intelligence that devices means by which systems can automatically learn and improve from experience. This particular wing of AI aims at equipping machines with independent learning techniques so that they don’t have to be programmed to do so. 

Machine learning involves observing and studying data or experiences to identify patterns and set up a reasoning system based on the findings. The various components of machine learning includes:

  • Supervised machine learning: This model uses historical data to understand behaviour and formulate future forecasts. This kind of learning algorithms analyse any given training data set to draw inferences which can be applied to output values. Supervised learning parameters are crucial in mapping the input-output pair. 
  • Unsupervised machine learning: This type of ML algorithm does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabeled data to help systems infer a function properly. Algorithms with unsupervised learning can use both generative learning models and a retrieval-based approach. 
  • Semi-supervised machine learning: This model combines elements of supervised and unsupervised learning yet isn’t either of them. It works by using both labelled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data turns out to be expensive. 
  • Reinforcement machine learning: This kind of learning doesn’t use any answer key to guide the execution of any function. The lack of training data results in learning from experience. The process of trial and error finally leads to long-term rewards.

Machine learning delivers accurate results derived through the analysis of massive data sets. Applying AI cognitive technologies to ML systems can result in the effective processing of data and information.

How are Data Science, Machine Learning, and AI Related?

Although it’s possible to explain machine learning by taking it as a standalone subject, it can best be understood in the context of its environment, i.e., the system it’s used within.

Simply put, machine learning is the link that connects Data Science and AI.

That is because it’s the process of learning from data over time. So, AI is the tool that helps data science get results and the solutions for specific problems. However, machine learning is what helps in achieving that goal.

Read Also: Difference Between Data Science & Business Analytics

A real-life example of this is Google’s Search Engine.

  • Google’s search engine is a product of data science
  • For instance, if a person types “best jackets in NY” on Google’s search engine, then the AI collects this information through machine learning
  • Now, as soon as the person writes these two words in the search tool “best place to buy,” the AI kicks in, and with predictive analysis completes the sentence as “best place to buy jackets in NY” which is the most probable suffix to the query that the user had in mind.

 

The diagram above is a helpful visual representation of the linkage between AI, Machine Learning and Data Science.

To be precise, Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology, which is deep learning.

Deep Learning is a form of machine learning but differs in the use of neural networks where we stimulate the function of a brain to a certain extent and use a 3D hierarchy in data to identify patterns that are much more useful.

Even though the areas of data science, machine learning and artificial intelligence overlap, their specific functionalities differ and have respective areas of application. The data science market has opened up several services and product industries, creating opportunities for experts in this domain. Upskilling in any of these areas will take your career a step ahead.

Click here to explore a career in Artificial Intelligence.

Top 5 E-Learning Trends in 2017 and Beyond

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The last few years have been exciting for the e-learning industry as it evolved dynamically to accommodate the latest advances in technology and changing user expectations. It experimented with different e-learning tools, delivery methods, and platforms. Looking ahead, this space is headed for further transformation as the conventional, one-size-fits-all approach of learning becomes passé; and personalized, interactive content becomes the order of the day.

Let’s look at the key trends that will drive the e-learning transformation in 2017 and beyond:

  1. Keeping it Short: In 2017, lengthy, content-heavy course modules will give way to short, bite-sized courses. Survey results indicate that human attention span has been shrinking and Millennials have an average attention span of mere 90 seconds. Thus, the mantra for e-learning institutes is to keep the content short and crisp. Given this fact, microlearning, a methodology of delivering content in very specific bursts, is projected to gain momentum. Expect short learning nuggets of 4-6 minutes, focusing on just one learning objective, which can be accessed on-the-go on smartphones and tablets.
  2. Thinking Mobile First: Mobile learning has been a top e-learning trend for almost 5-6 years now and this year is no different. In fact, it is here to stay and grow bigger. Major efforts in the last couple of years have been directed towards offering courses that have the ability to run seamlessly on multiple devices—from tablets, smartphones to laptops. As we look ahead, the emphasis would be on adopting a mobile first approach while designing an e-learning course.
  3. Personalized Content: Personalization has become a buzzword today in every industry and is also slated to receive its fair share of attention in the e-learning domain. As we look ahead, leading institutes will start analyzing data to target learners with specific content customized for them. Armed with data insights, institutes can provide a personalized learning experience for learners based on the skill level. For instance, different content can be served to learners based on their scores in the assessments.
  4. From Passive to Interactive Video: Recent years have seen a phenomenal rise in the usage of video content thanks to mobile devices with powerful display coupled with high-speed connectivity. According to the findings of a Cisco study, 60% of mobile data traffic today is video, which is expected to account for 78% of the world’s mobile data traffic by 2021. Nearly a million minutes of video content is expected to cross the network every second by 2020. The trend of increasing video usage can also be seen in the e-learning domain. Reputed institutes are increasingly integrating video lectures and tutorials in their courses to make learning fun and engaging. Going ahead, we will see the focus moving from passive videos towards making videos more interactive. Typical video courses today cover an entire topic content, which is then followed by an assessment. In order to add more value to the learning experience and measure student comprehension, the emphasis will now be on engaging students within a video.
  5. The Rise of Social and Collaborative Learning: Social learning is more than just a fad. Forward-thinking institutes are increasingly using platforms where learners can network, share ideas and learn others’ perspective on a particular topic. Going forward, social interaction and collaborative learning through live chats, message boards, or instant messaging to enrich the overall learning experience will gain further traction. The emphasis would be on moving away from one-way learning to collaborative learning, which will encourage teamwork and exchange of ideas for problem-solving.

With all these changes underway, interesting times lie ahead for the e-learning industry. One thing is quite evident, modernizing the learning experience to meet the needs of today’s learners will be the core factor in shaping the future evolution of the industry.

Also Read: 

How Gamification Can Transform Education

Adoption of technology in online education