How will AI (Artificial Intelligence) shape the Metaverse?

 

What is a Metaverse?

A metaverse is a digital universe created by the convergence of enhanced physical reality and persistent virtual reality. It encompasses all virtual worlds, augmented reality, and the internet. The concept of a metaverse is often associated with science fiction and virtual reality, but it is also being developed as a potential future reality. To create and support a metaverse, various technologies such as virtual and augmented reality, 3D modelling and animation software, game engines, cloud, and edge computing, blockchain, 5G networks, and artificial intelligence and machine learning are utilized. These technologies will continue to develop and improve as the metaverse progresses.

Let us dive deep into one such technology that is important in shaping and developing the metaverse: Artificial Intelligence.

 

How does Artificial Intelligence contribute to the metaverse?

Artificial Intelligence is instrumental in the development and operation of the metaverse. It allows for the creation of highly realistic and engaging virtual worlds, ensuring optimal performance, enhancing security measures, and providing users with personalized experiences. As the metaverse continues to expand and progress, the importance of AI in its growth will become increasingly evident.

 

Why is Artificial Intelligence important to the metaverse?

Artificial Intelligence can enhance the metaverse by allowing for the creation of realistic and interactive virtual worlds. The technology can improve the immersion of the metaverse by providing users with the ability to interact in a more natural way with the virtual environment. AI can be used to create virtual characters that can interact with users in a lifelike manner, and to develop virtual economies, tracking user behavior and analyzing data to create virtual marketplaces. Additionally, AI can be used to tailor virtual worlds to each user’s interests and preferences, providing a more personalized and engaging experience.

 

How will Artificial Intelligence shape the metaverse?

Artificial Intelligence creates sophisticated, responsive virtual characters and environments which is used to provide personalized experiences to users, based on their interactions and preferences. This helps to create a more engaging and immersive experience within the metaverse.

AI can manage and optimize the performance of the metaverse. It can be used to monitor and analyze data to identify and resolve bottlenecks, ensuring smooth and seamless experiences for users. This helps to ensure that the metaverse is always running at peak performance.

AI enhance security within the metaverse. It can be used to identify and prevent potential security threats, such as hacking or fraud. This can help to ensure that the metaverse remains a safe and secure place for users.

In conclusion, AI is a key technology that can help shape and enhance the metaverse. It can be used to create realistic and immersive virtual worlds, provide personalized experiences to users, manage, and optimize performance, and enhance security. As the metaverse continues to evolve and grow, AI will play an increasingly key role in its development and operation.

 

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Best Practices in Python and why Python is so popular

Python is a versatile language that has attracted a broad base of people in recent times. Python has become one of the most popular programming languages.  The popularity of Python grew exponentially during the last decade. According to an estimate, the previous five years saw more Python developers than the conventional Java/C++ programmers. Now the question is why is Python so popular? The primary reasons for this are its simplicity, speed, and performance.

Why does Python have an edge over the other programming languages? Let’s find out!

  • Everything is an object in Python
  • Support for Object-Oriented Programming – including multiple inheritances, instance methods, and class methods
  • Attribute access customization
  • List, dictionary, and set comprehensions
  • Generators expressions and generator functions (lazy iteration)
  • Standard library support of queues, fixed precisions decimals, rational numbers.
  • Wide-ranging standard library including OS access, Internet access, cryptography, and much more.
  • Strict nested scoping rules
  • Support for modules and packages
  • Python is used in the data science field
  • Python is used in machine learning and deep learning
  • Parallel Programming

As a Python developer, you must know some basic techniques and practices which could help you by providing a free-flowing work environment. Some of the best practices in Python are listed below.

Create Readable Documentation

In python, the best practice is readable documentation. You may find it a little burdensome, but it creates a clean code. For this purpose, you can use Markdown, reStructuredText, Sphinx, or docstrings. reStructuredText and Markdown are markup languages with plain text formatting syntax to make it easy to markup text and convert it into a format like HTML or PDF. Sphinx is a tool to create intelligent and beautiful documentation easily, while reStructuredText lets you create in-line documentation. It also enables you to export documentation in formats like HTML.

Follow Style Guidelines

Python follows a system of community-generated proposals known as Python Enhancement Proposals(abbreviated as PEPs) which attempt to provide the basic set of guidelines and standards for a wide variety of topics for proper Python Development. One of the most widely referenced PEPs ever created is PEP8, which is also termed as the “Python community Bible” for properly styling your code.

Immediately Correct your Code

When creating a python application, it is almost always more beneficial in the long-term to acknowledge quickly and repair broken code. (Join the Xaltius Academy to learn how!)

Give Preferences to PyPI over manual Coding

The above will help in obtaining a clean and elegant code. However, one of the best tools to improve your use of Python is the huge module repository namely The Python Package Index (short for PyPI). Not considering the level and experience of the Python Developer, this repository will be very beneficial for you. Most projects will initially begin by utilizing existing projects on PyPI. The PyPI has over 10,000 projects at the time of writing. There’s undoubtedly some code that will fulfill your project needs.

Watch out for Exceptions

The developer should watch out for exceptions. They creep in from anywhere and are difficult to debug.

Example: One of the most annoying is the KeyError exception. To handle this, a programmer must first check whether or not a key exists in the dictionary.

Write Modular and non-repetitive Code

A class/function should be defined if some operation is required to be performed multiple times. This will shorten your code, also increasing code readability and reducing debugging time.

Use the right data structures

The benefits of different data structures are very well known. This will result in higher working speed, storage space reduction, and higher code efficiency.

These are the good practices in Python that every Python developer must follow for a smooth experience in Python. Python is a growing language and its increased use in the field of Data Analytics and Machine Learning has proved to be very useful for the developers. Python for AI has also gained popularity in recent years. In the upcoming years, Python shall have a very bright future, and the programmers who are proficient in Python will have an advantage.

Renowned Data Science Personalities

With the advancement of big data and artificial intelligence, the need for its efficient and ethical usage also grew. Prior to the AI boom, the main focus of companies was to find solutions for data storage and management. With the advancement of various frameworks, the focus has shifted to data processing and analytics which require knowledge of programming, mathematics, and statistics. In more popular terms, this process today is known as Data Science. Few names stand out and have a separate base of importance when the name data science comes into the picture, largely due to their contributions to this field and who have devoted their life and study to reinvent the wheel. Let’s talk about some of the best data scientists in the world.


Andrew Ng

Andrew Ng is one of the most prominent names among leaders in the fields of AI and Data Science. He is counted among the best machine learning and artificial intelligence experts in the world.  He is an adjunct professor at Stanford University and also the co-founder of Coursera. Formerly, he was the head of the AI unit in Baidu. He is also an enthusiast researcher, having authored and co-authored around 100 research papers on machine learning, AI, deep learning, robotics, and many more relevant fields. He is highly appreciated in the group of new practitioners and researchers in the field of data science. He has also worked in close collaboration with Google on their Google Brain project. He is the most popular data scientist with a vast number of followers on social media and other channels.

DJ Patil

The Data Science Man, DJ Patil, needs no introduction. He is one of the most famous data scientists in the world. He is one of the influencing personalities, not just in Data Science but around the world in general. He was the co-coiner of the term Data Science. He was the former Chief Data Scientist at the White House. He was also honored by being the former Head of Data Products, Chief Scientist, and Chief Security Officer at LinkedIn. He was the former Director of Strategy, Analytics, and Product / Distinguished Research Scientist at eBay Inc. The list just goes on.

DJ Patil is inarguably one of the top data scientists around the world. He received his PhD in Applied Mathematics from the ‘University of Maryland College Park’.

Kirk Borne

Kirk Borne has been the chief data scientist and the leading executive advisor at Booz Allen Hamilton since 2015. Working as a former NASA astrophysicist, he was part of many major projects. At the time of crisis, he was also called upon by the former President of the US to analyze data post the 9/11 attack on the WTC in an attempt to prevent further attacks. He is one of the top data scientists to follow with over 250K followers on Twitter.

Geoffrey Hinton

He is known for his astonishing work on Artificial Neural Networks. Geoffrey was the brain behind the ‘Backpropagation’ algorithm which is used to train deep neural networks. Currently, he leads the AI team at Google and simultaneously finds time for the ‘Computer Science’ department at the ‘University of Toronto’. His research group has done some overwhelming work for the resurgence of neural networks and deep learning.

Geoff coined the term ‘Dark Knowledge’.

Yoshua Bengio

Having worked with AT&T & MIT as a machine learning expert, Yoshua holds a Ph.D. in Computer Science from McGill University, Montreal. He is currently the Head of the Montreal Institute for Learning Algorithms (MILA) and also has been a professor at Université de Montréal for the past 24yrs.

Yann LeCun

Director of AI Research at Facebook, Yann has 14 registered US patents. He is also the founding director of NYU Center for Data Science. Yann has a PhD in Computer Science from Pierre and Marie Curie University. He’s also a professor of Computer Science, Neural Science and the Founding Director of the Data Science Center at New York University.

Peter Norvig

Peter Norvig is a co-Author of ‘Artificial Intelligence: A Modern Approach’ and ‘Paradigms of AI Programming: Case Studies in Common Lisp’, some insightful books for programming and artificial intelligence. Peter has close to 45 publications under his name. Currently the ‘Engineering Director’ at ‘Google’, he has worked on various roles in Computational Sciences at NASA for three years. Peter received his Ph.D. from the ‘University of California’ in ‘Computer Science.’

Alex “Sandy” Pentland

Named the ‘World’s Most Powerful Data Scientist’ by Forbes, Alex has been a professor at MIT for the past 31 years. He has also been a chief advisor at Nissan and Telefonica. Alex has co-founded many companies over the years some of which include Home, Sense Networks, Cogito Corp, and many more. Currently, he is on the board of Directors of the UN Global Partnership for Sustainable Data Development.

These are some of the few leaders from a vast community of leaders. There are many unnamed leaders whose work is the reason why you have recommender systems, advanced neural networks, fraud detection algorithms, and many other intelligent systems that we seek help to fulfill our daily needs.

Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence, Machine Learning, and Deep Learning are one of the most prominent topics in the domain of technology at the present. Although the three terminologies Artificial Intelligence, Machine Learning, and Deep Learning are used interchangeably, are they really the same? Every technophile is stuck at least once in the beginning whenever there is a mention of artificial learning vs machine learning vs deep learning. Let us try to find out how actually these three terms differ.

The easiest way to think of the relationship between the above terms is to visualize them as concentric circles using the concept of sets with AI — the idea that came first — the largest, then machine learning — which blossomed later, and the most recent being deep learning — which is driving today’s AI explosion — fitting inside both.

Graphically this relation can be explained as in the picture below.

As you can see in the above image consisting of three concentric circles, Deep Learning is a subset of ML, which is also a subset of AI. This gives an idea that AI is the all-encompassing concept that initially erupted, which was then followed by ML that thrived later, and lastly, Deep Learning that is promising to escalate the advances of AI to another level.

Starting with AI, let us have a more in-depth insight into the following terms.

Artificial Intelligence

Intelligence, as defined by Wikipedia, is “Perceiving the information through various sources, followed by retaining them as knowledge and applying them with real-life challenges.” Artificial intelligence is the science that deals with machines that are programmed to think and act like humans. By Wikipedia, it is defined as the simulation of human intelligence in machines using programs and algorithms.

Machines built on AI are of two types – General AI and Narrow AI

General AI refers to the machines capable of using all our senses. We’ve seen these General AI in Sci-Fi movies like The Terminator. In real life, a lot of work has been done on the development of these machines; however, more research is yet to be done to bring them into existence.

What we CAN do falls in the hands of “Narrow AI”. These refer to the technologies that can perform specific tasks as well as, or better than, we humans can. Some examples are – classifying emails as spam and not spam and facial recognition on Facebook. These technologies exhibit some facets of human intelligence.

Where does that intelligence come from? That brings us to our next term -> Machine Learning.

Machine Learning

Learning, as defined by Wikipedia, is referred to as “acquiring information and finding a pattern between the outcome and the inputs from the set of examples given.” ML intends to enable artificial machines to learn by themselves using the provided data and make accurate predictions. Machine Learning is a subset of AI. More importantly, it is a method of training algorithms such that they can learn to make decisions. (Read – AI and ML. Are they one and the same?)

Machine learning algorithms can be classified as supervised and unsupervised depending on the type of problem being solved. In Supervised learning the machine is trained using data which is well labelled that is, some data is already tagged with the correct answer while in unsupervised learning the machine is trained using the information that is neither classified nor labelled and the algorithm is supposed to find a solution to it without guidance. Also, a term called semi-supervised learning exists in which the algorithm learns from a dataset that includes both supervised and unsupervised data.

Training in machine learning requires a lot of data to be fed to the machine which then allows the machine (models) to learn more about the processed information.

Deep Learning 

Deep Learning is an algorithmic approach for the early machine-learning crowd. Neural Networks from the base for Deep Neural Learning and is inspired by our understanding of the biology of the human brain. However, unlike a biological brain where any neuron unit can connect to any other neuron unit within a certain physical distance, these artificial neural networks (ANN) have discrete layers, connections, and directions of data propagation.

For a system designed to recognize a STOP sign, a neural Network model can come up with a “probability score”, which is a highly educated guess, based on the algorithm. In this example, the system might be 86% confident the image is a stop sign, 7% convinced it’s a speed limit sign, and 5% it’s a kite stuck in a tree, and so on.

A trained Neural Networks is one that has been analyzed on millions of samples until it is sampled so that it gets the answer right practically every time.

Deep Learning can automatically discover new features to be used for classification. Machine Learning, on the other hand, requires to be provided these features manually. Also, in contrast to Machine Learning, Deep Learning requires high-end machines and considerably significant amounts of training data to deliver accurate results.

Wrapping up, AI has a bright future, considering the development of deep learning. At the current pace, we can expect driverless vehicles, better recommender systems, and more in the forthcoming time. AI, ML, and Deep Learning (DL) are not very different from each other; but are not the same.

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 Search – Quantum 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.