QUANTUM COMPUTING – THE UNEXPLORED MIRACLE

What is Quantum Computing?
Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is specifically used to perform such calculation, which can be implemented theoretically or physically. The field of quantum computing is a sub-field of quantum information science, which includes quantum cryptography and quantum communication. The idea of Quantum Computing took shape in the early 1980s when Richard Feynman and Yuri Manin expressed the idea that a quantum computer had the potential to simulate things that a classical computer could not.

The year 1994 saw further development of Quantum Computing when Peter Shor published an algorithm that was able to efficiently solve problems that were being used in asymmetric cryptography that were considered very hard for a classical computer. There are currently two main approaches to physically implementing a quantum computer: analog and digital. Analogue methods are further divided into the quantum simulation, quantum annealing, and adiabatic quantum-computation.

Basic Fundamentals of Quantum Computing
Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits. These qubits are fundamental to Quantum Computing and are somewhat analogous to bits in a classical computer. Like a regular bit, Qubit resides in either 0 or 1 state. The specialty is that they can also be in the superposition of 1 and 0 states. However, when qubits are measured, the result is always either a 0 or a 1; the probabilities of the two outcomes depends on the quantum state they were in.

Principle of Operation of Quantum Computing
A quantum computer with a given number of quantum bits is fundamentally very different from a classical computer composed of the same number of bits. For example, representing the state of an n-qubit system on a traditional computer requires the storage of 2n complex coefficients, while to characterize the state of a classical n-bit system it is sufficient to provide the values of the n bits, that is, only n numbers.

A classical computer has a memory made up of bits, where each bit is represented by either a one or a zero. A quantum computer, on the other hand, maintains a sequence of qubits, which can represent a one, a zero, or any quantum superposition of those two qubit states; a pair of qubits can be in any quantum superposition of 4 states, and three qubits in any superposition of 8 states. In general, a quantum computer with n qubits can be in any superposition of up to different states. Quantum algorithms are often probabilistic, as they provide the correct solution only with a certain known probability.

What is the Potential that Quantum Computing offers?
Quantum Computing is such a unique field that very few people show their interest in it. There is a lot of room for development. It has a lot of scope. Some of the areas in which this is penetrating today are:

  • Cryptography – A quantum computer could efficiently solve this problem using multiple algorithms. This ability would allow a quantum computer to break many of the cryptographic systems in use today
  • Quantum SearchQuantum computers offer polynomial speedup for some problems. The most well-known example of this is quantum database search, which can be solved by Grover’s algorithm using quadratically fewer queries to the database than that is required by classical algorithms.
  • Quantum Simulation – Since chemistry and nanotechnology rely on understanding quantum systems, and such systems are impossible to simulate efficiently classically, many believe quantum simulation will be one of the most important applications of quantum computing.
  • Quantum Annealing and Adiabatic Optimization
  • Solving Linear Equations – The Quantum algorithm for linear systems of equations or “HHL Algorithm,” named after its discoverers Harrow, Hassidim, and Lloyd, is expected to provide speedup over classical counterparts.
  • Quantum Supremacy

In conclusion, Quantum computers could spur the development of breakthroughs in science, medication to save lives, machine learning methods to diagnose illnesses sooner, materials to make more efficient devices and structures, financial strategies to live well in retirement, and algorithms to direct resources such as ambulances quickly.  The scope of Quantum Computing is beyond imagination. Further developments in this field will have a significant impact on the world.

NATURAL LANGUAGE PROCESSING – GIVING MACHINES A VOICE

What is Natural Language Processing (NLP)?

Natural Language Processing commonly abbreviated as NLP is a subfield of computer science and artificial intelligence. It is mainly concerned with the interaction between computers and the languages humans speak, like English, Italian, French, among various others. It is used in particular to program machines to process and analyze large amounts of natural language data.

The development of NLP applications is quite challenging because computers traditionally require human beings to communicate to them through a programming language or a high-level language. Human speech, however, is not always precise, is often ambiguous and is dependent on factors like the emphasis on a particular word or expression. These are the factors that the computer finds very difficult to understand.

How does Natural Language Processing work?

Syntax and Semantic analysis are two main techniques that are used with NLP. The  Syntax is the arrangement of words in a sentence to make some grammatical sense. Different Syntax methods used are:

  • Parsing
  • Word segmentation
  • Sentence breaking
  • Morphological segmentation and
  • Stemming

The Semantic involves the use and meaning behind the words. NLP applies the algorithms to understand the grammar and meaning of the sentences. The techniques used by NLP in semantic Analysis are:

  • Named Entity Recognition
  • Natural Language Generation

The current approaches to NLP are mainly based on Deep Learning, which is a type of AI that examines and uses the patterns in data to improve programs understanding. It is basically dependent on supervised learning, which consists of a training set and a test set.

Three tools very commonly used for NLP are NLTK, Gensim, and Intel NLP Architect. Natural Language Toolkit(NLTK), is an open-source python module with data sets and tutorials. Gensim is a Python library for topic modeling and document indexing. Intel NLP Architect is also another Python library for deep learning topologies and techniques.

What are the Uses of Natural Language Processing?

Although, NLP came into existence for the first time by Alan Turing when he published an article titled “Computer Machinery and Intelligence,”. The vast use came into effect only from the 80s, after the introduction of Machine Learning. Before 1980, the most natural language processing systems were based on complex sets of hand-written rules. Writing these rules included a lot of labor and was inaccurate due to diversity in the pronunciation of a language. Introduction of Machine Learning speeded up the development of Natural Language Processing.

Natural language Processing is very widely used today in our daily routine. It finds its application in:

  • Chatbots – Chatbots handle various clients and answer their query without considerable human effort. Chatbots are trained on a vast set of data and hence process only the essential part from a conversation. Companies like Uber, Zomato use the Chatbots to minimize human involvement.
  • Voice Assistants – The most significant use of NLP is implemented for this purpose. Technological giants like Google, Microsoft, Amazon, etc. use their own personal voice assistant to help in communicating with smart devices quickly. Amazon assigns over 1000 personnel globally for enhancing its voice assistant.
  • Very brilliant use of NLP came with the name Grammarly, which is a tool that keeps on a check on writers’ write-ups, and points out grammatical errors and suggests better phrases.
  • Google translate also uses NLP to translate a webpage from one language to another by understanding its content.

 

What are the Challenges faced by NLP?

NLP, though a new technology with a lot of advantages isn’t completely developed. For Example – Semantic and Grammar Analysis is still a challenge for NLP. Other difficulties includes, NLP not relating to sarcasm easily, since NLP cannot figure the changing meaning of words on the basis of speaker emphasis. NLP is also challenged by the fact that the dialect of people changes with regions.

On a final note, Natural Language Processing is a very handy tool, although it is in the developing state and faces some difficulties. The recent development in the NLP has made it a gem for the Technological Giants. The future of NLP through Machine and Deep Learning seems quite bright.

REINFORCEMENT LEARNING

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.

The primary specifics 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 users 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?
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. After all, this branch of Machine Learning is unique and has its own importance. The uses 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:

Robotics witnesses a tremendous work on applying Reinforcement Learning. 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.

We at #Xaltius are creating a Mobile Application using AR called FastLearnAR which will help students learn about various kinds of real world equipment (like microscope, machines) faster and in a real live setting. If you are keen on partnering and working together get in touch with us today!

A DEEP INSIGHT INTO VIRTUAL REALITY

­­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. Virtual reality is created in the real world using high-performance computers and some sensory equipment, like headset and gloves. The idea of VR was originated in the minds of the great Thomas Edison, who pioneered it with the name “Kinetograph.”

Virtual reality (VR) finds its application in the educational field (e.g., military training, or pilots) and is extensively used in the gaming industry. 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.

Experiencing Virtual Reality can be categorized into the following 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.
  • NonImmersiveAn 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 AR and VR.

With the introduction of power packed features in personal computers and smartphones, Virtual Reality devices saw a 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.

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. The future of VR industry seems very bright for extensive development.

Read some of our latest blogs below

THE RISE OF AUTOMATED MACHINE LEARNING (AML)

What is AutoML? Automated machine learning (AutoML) is the term used for the technology automating the end-to-end process of applying machine learning to real-world problems. A typical machine learning problem requires a dataset that consists of some input...

QUANTUM COMPUTING – THE UNEXPLORED MIRACLE

What is Quantum Computing? Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is specifically used to perform such calculation, which can be implemented...

NATURAL LANGUAGE PROCESSING – GIVING MACHINES A VOICE

What is Natural Language Processing (NLP)? Natural Language Processing commonly abbreviated as NLP is a subfield of computer science and artificial intelligence. It is mainly concerned with the interaction between computers and the languages humans speak,...

REINFORCEMENT LEARNING

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....

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...
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