What is Reinforcement Learning? An Easy Overview

What is Reinforcement Learning?
Reinforcement Learning (commonly abbreviated as RL) is an area and application of Machine Learning. Reinforcement, as described from its meaning, is about taking suitable actions to maximize reward in a particular situation. It is implemented after rigorous testing by various machines and complex software to find the best possible behavior or path that it should take in a specific condition.

Now, the question is how does reinforcement learning work? The primary specifics in the environment of reinforcement learning are summarized as follows:

  • Input: The input is defined to be an initial state from which the model will start.
  • Output: There are many possible outputs as there are a variety of solutions for a particular task.
  • Training: The training is wholly based upon the input provided, in return, the model returns a state, and then it is the user’s decision to decide whether to reward or punish the model based on its output.
  • The model keeps learning.
  • The maximum award determines the best solution.

How is it different from Supervised Learning?
Is reinforcement learning supervised or unsupervised learning? Supervised Learning is implemented based on a training set that acts as an answer key, so the model is trained according to the correct answer itself. In Reinforcement Learning, there is no answer, but the work is done by the Reinforcement Agent who decides what to do to perform the given task. In the absence of a dataset, it is bound to learn from experience. In Reinforcement Learning, each right step gives a reward while each wrong step subtracts the award.

 

REINFORCEMENT LEARNING

SUPERVISED LEARNING
Reinforcement learning is about making decisions sequentially. In more simpler words, we can say that the output depends on the state of the current input, and the next input depends on the output of the previous information. In Supervised learning, the decision is made on the initial data or the feedback given.
Reinforcement learning is decision dependent. So, labels are given to sequences of dependent decisions. Supervised learning the choices are independent of each other, so labels are assigned to each decision.
Example: Chess game Example: Object recognition

 

Applications and Use Cases of Reinforcement Learning
In the era of Convolutional Neural Network (CNN), Reinforcement Learning as a framework seems to be undervalued (Read Neural Networks Made Easy – Tech Crunch). Reinforcement Learning in Machine Learning is unique and has its own importance. The uses and examples of Reinforcement Learning are as follows:

  • Resource Management in Computer Clusters:

Reinforcement Learning can be used to automatically learn to allocate and schedule the computer resources for waiting jobs, with the primary objective to minimize the average job slowdown.

  • Traffic Light Control:

Researchers found a way to solve the traffic congestion problem using Reinforcement Learning. Though tested only on a simulated environment, a significant improvement is seen over conventional traffic methods.

Example: The below figure depicts five agents. These were put in a five-transaction traffic network, with a Reinforcement Learning agent at the central intersection to control traffic signaling. The state was defined as an eight-dimensional vector with each element representing the relative traffic flow of each lane. Eight choices were available to the agent, each representing a phase combination, and the reward function was defined as a reduction in delay compared with the previous time step.

  • Robotics:

Use of Reinforcement Learning techniques in AI and robotics has increased tremendously. It can be used to help the robot to learn policies to map raw video images to the robot’s action.

  • Web System Configuration:

The Reinforcement Learning helps in achieving the targeted response time and measured response time.

  • Chemistry:

Reinforcement Learning can also be applied in optimizing chemical reactions. Combined with LSTM to model the policy function, the Reinforcement Learning agent can optimize the chemical reaction with the Markov decision process (MDP) characterized by {S, A, P, R}, where S was the set of experimental conditions (like temperature, pH, etc.), A was the set all possible actions that can change the experimental conditions, P was the transition probability from current experiment condition to the next term, and R was the reward which is a function of the state.

As a conclusion, Reinforcement Learning is highly helpful in industry and daily life, though it is criticized for its industrial use. Although it has its weaknesses, Reinforcement Learning is useful in the space of corporate research given its vast potential in decision making.

In the future, Reinforcement Learning is assumed to be assisting human and evolve into Artificial General Intelligence (AGI). Imagine about a robot assisting you in your work. Amazing. Isn’t it?

AR in the Education Industry

What is Augmented Reality?
Augmented reality abbreviated as AR is an interactive experience of a real-world environment where the objects that reside in the real-world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory and olfactory. The information can be additive (adding more feel to the environment) or destructive (masking off the unnecessary natural environment).

Some common examples of the vast use of AR are mobile games like Pokemon Go and the popular social media photo app, Snapchat. These apps use AR for analyzing real-time user surroundings and further enhance user experience.

AR exhibits certain similarities with VR but has quite a few differences as well. Virtual Reality (VR) is entirely based on the virtual reception of the information, while in Augmented Reality (AR), the user is provided with more computer-generated information that enhances the perception of reality.

Taking a real-life example, VR can be used to create a walk-through simulation of a building under construction while AR can be used to show the building’s structures on a live view.

Uses of AR in Education
The field of AR recently showed some massive development after the immense popularity of apps like Pokemon Go. Recent upgrades have started to find its uses in the vast education industry. The traditional method of education is slowly becoming obsolete, and with the increasing technological growth, the education system is being digitized. The education technology industry giant, namely EdTech, is slowly adopting the use of AR and is predicted to reach around $252 billion by 2020, growing at a 17% annual rate.

Augmented Reality serves several purposes. It helps the students acquire, remember, and process the information. Additionally, AR makes learning easy and fun. Its use is not limited to the pre-school level but can be used equally till college and even at work.

Benefits of Augmented Reality
Due to a large number of benefits of Augmented Reality, its usage has become very frequent in learning. Its main advantages are –

  • Accessibility of Material – Augmented reality has the unique potential to replace the traditional paper textbooks, physical models, or printed manuals. It offers portable and less expensive learning materials. As a result of this, education becomes more accessible and mobile.
  • No special equipment requirements – Apart from a typical smartphone, Augmented Reality doesn’t need any more sophisticated equipment as in the case of VR.
  • Higher Engagement and Interest – Interactive AR based learning has a significant impact on students helping them in understanding and remembering the concepts for a more extended period.
  • Faster and effective Learning Process – Through visualization and immersion in the subject, AR ensures that the concept is deeply instilled in mind. A picture is worth a thousand words, Isn’t it? So instead of thousands of words of theory, the user can visualize the matter with their own eyes.
  • Practical Learning – The use of AR in professional learning gives an accurate reproduction of in-field conditions that can help in mastering the practical skills required for a specific job.
  • Improved Collaboration Capabilities – AR offers vast opportunities to diversify and shake up boing classes. Interactive lessons involving the whole level at the same time help in building qualities of team-work.
  • Safe and Efficient Workplace – Consider the field of heart surgery or a Space Shuttle. Without the introduction of actual dangerous equipment, the students can be taught in real life, how to solve problems.
  • Universality – Augmented Reality applies to any form of education and can significantly enhance the learning experience.

Challenges faced by Augmented Reality

There are certain challenges that you should take into account while using Augmented Reality:

  • Necessary Training Required – Conventional Teachers can find it difficult using new technologies into practice. Only the innovative and open-minded teachers will be ready to apply Augmented Reality in education.
  • Hardware Dependency – Requirement of AR equipment is necessary to make full use of this technology. All the students might not have a smartphone capable of supporting AR applications.
  • Platform-Based Issues – The AR app built must run equally well on the various available platforms.

 

Examples and Use Cases

The most popular application of Augmented Reality is unarguably in the field of education.

  1. It can help a teacher explain using a visual representation of the subject, which would help the students understand a subject better.
  2. Another case of Augmented Reality is distance learning. Students can learn even outside the classroom anytime, anywhere.

 

On a final note, Augmented Reality is a blessing in the education industry. It is not only beneficial to the students but also makes the work of teachers more comfortable and convenient.

Virtual Reality Explained – A deep insight

Virtual Reality (VR) is coined from the combination of two words – ‘virtual’ and ‘reality’. Virtual as from definition means near, and Reality is what we experience in our daily life. You probably won’t do things like diving deep in the oceans, standing beside a volcano, or going on a voyage to Antarctica, but with Virtual Reality, you might be able to do it all without even leaving your cozy sofa. All of this sounds tempting and proves that the future of virtual reality and artificial intelligence is bright and their scope is immense. Virtual reality is created in the real world using high-performance computers and some sensory equipment, like a headset and gloves. The idea of VR originated in the minds of the great Thomas Edison, who pioneered it with the name “Kinetograph.”

Here are a few examples of Virtual reality (VR) usage-

  • Virtual reality in education (e.g., military training, or pilots)
  • Virtual reality in games. VR systems use either the Virtual reality headsets for a portable VR experience or multi-projected environments for generating realistic images, sounds and other sensations that ensure a user’s physical presence in a virtual environment.

 

A person using VR can look around 360 degrees and can move around. This virtual effect is mainly created by the VR headsets that consist of a head-mounted display, with a small screen in the front of the eyes. Virtual Reality usually packages auditory as well as video feedback. Following are very crisp descriptions of everything you need to know about virtual reality.

Experiencing Virtual Reality can be categorized into various types:

  • Fully Immersive – Three things help in fulfilling a complete VR experience, a computer model, a powerful computer that can adjust to the actions made by the user, and some surround-sound loudspeakers.
  • Non–Immersive – An alternative way is using a widescreen and using headphones. It doesn’t fully immerse a user, though it is a kind of Virtual reality.
  • Collaborative – The virtual experience is the same as in the fully immersive state, but it offers the idea of sharing the virtual world with other people.
  • Web-based – This is a web-based virtual reality analogous to HTML namely VRML (Virtual reality markup language)
  • Augmented Reality – Mobile devices nowadays are as capable as computers used to be. It spawned the idea of Augmented Reality (AR). There are close links between virtual reality and augmented reality. (Augmented Reality also has numerous applications in the education industry).

With the introduction of power-packed features in personal computers and smartphones, Virtual Reality devices saw significant development and grew rapidly. On a large scale, Virtual Reality is used in the entertainment industry, particularly in the gaming industry for the enhanced gaming experience.

Which devices are used for VR on a Commercial Scale?

Datagloves

Giving people the ability to touch objects and feel things in the virtual world is one of the most significant achievements of the VR industry. One technical method of implementing this is using fiber-optic cables that records the data about how much a finger is stretched. Other technologies include strain gauges, electromechanical devices, or the piezoelectrical sensors to measure the finger movements.

Head-Mounted Display(HMDs)

It is the most critical component for a VR experience. The difference between a computer and a VR is the presence of a 3D screen on a VR screen which moves according to the user movements. The HMD looks like a giant motorbike helmet, which consists of two screens, a blackout blindfold that blocks outwards light and stereo headphones (not necessarily). They usually have built-in accelerometers that keep a check on the user’s movement and the direction.

Wands

Even more straightforward than a dataglove, a wand is like a stick that can be used to touch, to point to, or to otherwise interact with a virtual world. It has the position sensors or the motion sensors (such as accelerometers) built-in, along with some mouse-like buttons or scroll wheels. The advantage that the wands take over the conventional VR equipment is that they are wireless.

Concluding, Virtual Reality is instrumental in the gaming industry and the commercial use of Virtual Reality for the education industry for pilots and military training is a very creative use of VR. It is also extensively used for enhanced entertainment purposes for short VR shows for a deep insight into Virtual Reality.

Now the question arises – how much does virtual reality cost? A long time ago, the VR equipment was very costly for its personal use. However, the recent VR equipment by Google, namely Google Cards is a cheap and efficient solution for experiencing Virtual Reality in your own home. Virtual reality seems to be the future of extensive development.

Clustering: An Unsupervised Machine Learning Algorithm

Machine Learning in general is generally divided into two types, Supervised and Unsupervised Learning. Unsupervised Learning is also further divided into two main types. These are:

  • Clustering: A clustering problem is where there is a need to bring out the inherent groupings in data. Eg. – Grouping customers by their purchasing behavior.
  • Association:  An association rule learning problem is implemented when we want to discover those rules that describe large portions of our data. Eg. – The recommended content on most of the Online Shopping Websites, Social Networking Sites, etc. of the type “People that buy X also tend to buy Y.”

In this article we will learn more about clustering and how it is used!

Clustering, which is one of the forms of Unsupervised Learning, is where there is only the input data (X) and no corresponding variables like the dependent variable (y) or the variable which needs to be predicted. The goal of unsupervised learning is to model the underlying structure or distribution in the data to work and develop more facts about the data. This learning is called unsupervised learning because unlike supervised learning, there are no correct answers, and there is no teacher like the training set data that is used in the supervised learning in regression and classification. Algorithms are left to their devices to discover and present the impressive structure in the data.

Clustering Methods

 Clustering methods are broadly classified into the following categories −

  • Partitioning Method – Partitioning ‘n’ objects into ‘k’ partitions of data.
  • Hierarchical Method – It creates a hierarchical decomposition of the given set of data objects.
  • Density-based Method – The basic idea of this approach is to continue growing the given cluster for as long as the density in a particular neighborhood exceeds some fixed threshold.
  • Grid-Based Method – Here, the objects together form a grid.
  • Model-Based Method – Here, a model is hypothesized for each cluster to find the best fit of data for a given model.
  • Constraint-based Method – In this method, the clustering is performed by the incorporation of the user or application-oriented constraints.

It would not be inappropriate to say that life would be too difficult for us, if not for the assistance of clustering in our daily walks of life. Clustering finds its use in the industry in many ways. Some of them are:

  • It can also help the marketing managers to discover distinct groups and sub-groups in their customers based on their similarities, like the Age group, have a car or not, average expense, etc. which can undoubtedly help in using tactics for a better sale.
  • Clustering analysis is broadly used and finds its applications in market research, unique pattern recognition, image processing, and data analysis.
  • Identification of particular areas of similar land use in the Earth Observation Database, which also finds implementation in the identification of groups of houses in a city based on the house type, value, and geographic location.
  • In the Biological field, it can be used to derive animal and plant taxonomies, categorize their genes with similar and dissimilar functionalities and gain insight into structures inherent to populations.
  • The publicly available Taxi service provided by Uber, Ola, etc. process a large amount of valuable data using Clustering around traffic, transit-time, peak pickup localities, and more.
  • Classifying the documents on the web for Information discovery like a search Engine.
  • Outlier detection applications. Eg. : Detection of the Credit card fraud.
  • Clustering is also helpful in identifying Crime localities that require the special attention of the police.
  • The broadest and most extensive usage of Clustering is implemented in Data-mining. It is a technique by which different data elements are classified and put into related groups.
  • Call Record Detail Analysis (CDR) is the information captured by the worldwide telecom companies during the call, SMS, and the data usage activity of a customer.

Concluding, Unsupervised Learning portrays an extraordinary role in visualizing facts and figures, that can’t be seen and observed by human eyes. This processed information is not just useful for a company, but it has extensive application on a broad industry scale.

Python vs R – The Burning Question

R and Python are both open-source programming languages with a large community. They are very popular among data analysts. New libraries or tools are added continuously to their respective catalog. R is mainly used for statistical analysis while Python provides a more general approach to data science.

While Python is often praised for being a general-purpose language with an easy-to-understand syntax, R’s functionality is developed with statisticians in mind, thereby giving it field-specific advantages such as great features for data visualization. Both R and Python are state of the art in terms of programming language oriented towards data science and hence learning both of them is, of course, the ideal solution. But R and Python require a time-investment, and such luxury is not available for everyone.

Let us see how these two programming languages relate to each other, by exploring the strengths of R over Python and vice versa and indulging in basic comparison between these two.

Python can do almost all the tasks that R can, like data wrangling, engineering, feature selection, web scraping and so on. But Python is known as a tool to deploy and implement machine learning at a large-scale, as Python codes are easier to maintain and remains more robust than R. The programming language is up to date with many data learning and machine learning libraries. It provides APIs for machine learning or AI. Python is also usually the first choice when there is a need to use the results of any analysis in an application or a website.

R has been developed by academicians and statisticians in over 2 decades. It is now one of the richest ecosystems to perform data analysis.  Around 12000 packages are available in CRAN (open-source repository) now. A rich variety of libraries can be found for any analysis one needs to perform, making R the first choice for statistical analysis, especially for specialized analytical work.

One major difference between R and other statistical tools or languages is the output. Other than R, there are very good tools to communicate results and make presentation of findings easy. In R, Rstudio comes with the library knitr which helps with the same, but other than that it lacks the flexibility for presentation.

R and Python Comparison

Parameter R Python
Objective Data analysis and statistics Deployment and production
Primary Users Scholar and R&D Programmers and developers
Flexibility Easy to use available library Easy to construct new models from scratch. I.e., matrix computation and optimization
Learning curve Difficult at the beginning Linear and smooth
Popularity of Programming Language. Percentage change 4.23% in 2018 21.69% in 2018
Average Salary $99.000 $100.000
Integration Run locally Well-integrated with app
Task Easy to get primary results Good to deploy algorithm
Database size Handle huge size Handle huge size
IDE Rstudio Spyder, Ipthon Notebook
Important Packages and library tydiverse, ggplot2, caret, zoo pandas, scipy, scikit-learn, TensorFlow, caret
Disadvantages Slow High Learning curve Dependencies between library Not as many libraries as R
Advantages
  • Graphs are made to talk. R makes it beautiful
  • Large catalog for data analysis
  • GitHub interface
  • RMarkdown
  • Shiny
  • Jupyter notebook: Notebooks help to share data with colleagues
  • Mathematical computation
  • Deployment
  • Code Readability
  • Speed
  • Function in Python

Source: https://www.guru99.com/r-vs-python.html

The Usage

As mentioned before, Python has influential libraries for math, statistics and Artificial Intelligence. While Python is the best tool for Machine Learning integration and deployment, the same cannot be said for business analytics.

R, on the other hand, is designed by experts to answer statistical problems. It can also solve problems on machine learning and data science. R is preferred for data science due to its powerful communication libraries. It is also equipped with numerous packages to perform time series analysis, panel data and data mining. But R is known to have a steep learning curve and therefore is not recommended for beginners.

As a beginner in data science with necessary statistical knowledge, it might be easier to use Python and to learn how to build a model from scratch and then switch to the functions from the machine learning libraries. R can be the first choice if the focus is going to be on statistics.

In conclusion, one needs to pick the programming language based on the requirements and available resources. The decision should be made based on what kind of problem is to be solved, or the kind of tools that are available in the field.

Transforming Warehouse Operations using AI

Artificial Intelligence is creating waves of disruption across many industries, be it manufacturing or human resources (HR). One of the major industries which AI has penetrated today is supply chain and logistics. Experts say that by 2020, AI could be completely transforming warehouse operations, with improvements in efficiency, profits and targets. The warehouse powered by AI would become more responsive and dynamic.

How can AI help in Warehouse Optimization?

One way through which AI can optimize the warehouse is by increasing the productivity of their workforce, especially warehouses that deals with regular pick and pack operations. Another way would be to use AI to enhance the communication between different operational departments, which would in turn ensure a smooth running of day-to-day tasks (Read – How Artificial Intelligence Will Transform the Way We Communicate – Quantified Communications). For example, online supermarket Ocado uses robots that can converse back-and-forth at a very short span of time, thus eliminating various human inaccuracies.

This would help in achieving overall targets and ensuring that the tasks are completed, while using time efficiently.

Multiple operations in the supply chain industry are expected to become fully automated by 2030. Predictable physical activities can easily be replaced by smart machines, saving time and money usually spent on wages, human mistakes, lunch breaks among various others. Robots, such as Amazon’s Kiva robots, can pick up goods and distribute them to different stations within a warehouse in mere minutes, and only needs five minutes to charge every hour.

Although 30% of jobs have the potential to become automated, employees are not expected to be fully replaced by robots. Automation will be integrated into current operations to be used as an aid; something to work alongside workers and help with routine tasks.

How is AI useful in data processing and mining?

Another area that AI can efficiently take over is the task of processing data and collecting data obtained from different warehouse operations. Complex operations can be captured and used to recognize patterns, regularities, and interdependencies from unstructured data. A smart warehouse will then be able to adapt, dynamically and independently, to new situations within the entire logistics system. Data thus collected can be analyzed to arrive at better and improved business strategies that use AI to their advantage.

To conclude

Machine learning algorithms and AI can be implemented in warehouse operations and supply chain so that they are able to anticipate situations, and solve problems efficiently. Thus, decisions are made in a short time.

AI can use the real-time insights gathered at every touch point in the warehouse’s workflow, to improve inventory accuracy and increase turns. Warehouse activities can therefore be actively monitored, while anticipating the workflow and proactively recommending optimizations.

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.