Everyone's Guide to the World of Technology

Updated: Nov 15

The quantum leaps in technology are revolutionizing our world, but it's difficult for us to keep our knowledge au courant with the current technological advancements. The following guide is for anyone who wants to explore the realm of technology, especially for teens pioneering to be the innovators of tomorrow.

I introduce you to the "Technological Equilibrium" framework based on four concepts:


Training:

You must train yourself by choosing a technological field that will mark the future, foregather motivation, and start learning it at your pace in a flow-state.


Brainstorming:

Discussion is imperative to broaden your mind, form new ideas, and benefit people through your knowledge. Discussing with your friends or a discussion forum is recommended.


General Knowledge:

Having a deep awareness of things by watching out for the events of the technological world and forming a nexus of world news with technology is vital. Technical reasoning is a desideratum in forecasting the hype cycle, predicting markets, and formulating solutions.


Effectuation and Monetization:

Implementing your new skills and possibly making money is adroit. Then it's up to your entrepreneurial mindset to scale up. You can either delve into a new technological field or become a master of the existing one you have learned.


The atoms of Democritus

And Newton’s particles of light

Are sands upon the Red Sea Shore

Where Israel’s tents do shine so bright.

William Blake, “Mock On”


Without going into the philosophy of technology, I'll introduce you to the instruments for solving practical and theoretical problems whose applications will only grow in the future. The following article is a clear step-by-step guide to get you started.


Quantum Computing


What is the definition and future of Quantum Computing?


The Definition:

Quantum computing is the discovery of the true nature of computation. The bit is the most fundamental unit of information of a traditional computer that can only hold two values, either 0 or 1. Quantum computers use a quantum bit (qubit) that does not store the binary value 0 or 1 but numerous possible combinations of 1 and 0 known as superposition. When measured, the conversion to one of the binary values destroys the superposition of states. The superposition of states makes quantum computing so powerful. It leads us to quantum supremacy: the demonstration that quantum computers can solve problems that classical computers cannot find an answer to in any sensible amount of time.


The Future:

With the second quantum revolution, quantum computers can have superiority over today's Von-Neumann computers and find their niche in the global computing landscape. Quantum Computers will most likely scale up as a cloud service for end-users since they operate at temperatures near absolute zero. The increase in focus towards the mass production of quantum computers will also lay the foundations of a quantum ecosystem, a standardized quantum programming language, and compilers and debuggers. It will significantly influence scientific computing from both a software and hardware standpoint, while the societal applications of quantum computers will only increase. From computational finance to computational social sciences to accelerating artificial intelligence and quantum machine learning to have an in-depth understanding of the existence of black holes and the nature of dark matter, quantum computers have the potential to revolutionize many technical disciplines. How is all of this relevant to you? Since you want to get into technology, you can become one of the earliest developers in this field; early adopters become the innovators of tomorrow. I'll show you how.

IBM Quantum System One
IBM Quantum System One represents the world’s first integrated quantum computer system | Image Credit: IBM

How can you learn this field?

The book "Quantum Computing for Everyone" by Chris Bernhardt is a coherent introduction for anyone. You only need to be familiar with high school mathematics.

Do you want to learn Quantum Computing for free with hands-on exercises that execute on quantum systems provided by IBM Quantum Experience? Here's the link to the interactive textbook that will teach you the mathematics behind quantum algorithms, details about today's non-fault-tolerant quantum devices, and write code in Qiskit to implement quantum algorithms on IBM's cloud quantum systems:

Here's the link to a relatively shorter introduction to Quantum Computing which will take six hours. Remember to start lento and pianissimo and stay motivated; thirty minutes per day is enough.

For teens looking to do something productive in the summer vacations, why not enroll in the summer program offered by "Qubit by Qubit?" The program is for middle and high school students with full and partial scholarships. Taught by MIT PhDs, it's ad rem for any student regardless of their experience in technology to learn a powerful skill, delve into the destiny of computers, and have a substantial extracurricular on their college application.


Artificial Intelligence


What is the definition and future of Artificial Intelligence?


The Definition:

Defining Artificial Intelligence (AI) can be tricky. John McCarthy stipulates the following definition in his 2004 paper: "It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to biologically observable methods." We can divide AI into two categories: Narrow AI and General AI. Narrow AI encompasses the complex tasks machines can do without human intervention, such as classifying images, playing chess, or defeating the world's best payer at the game of Go. General AI is expected to stay science fiction for at least some years and will environ the virtues that make us human. It would be an alive and inventive entity that could have inductive or highly generalized reasoning like a philosopher. AI is valuable since it enables us to detect patterns, derive insights, and drive actions from the astonishing amount of data created each day.


The Future:

Artificial Intelligence is progressing prodigiously. We expect it to develop exponentially because of the growing amounts of big data, more accurate models, increased research, and improving computing power. Many people fear the "technological singularity," which is the uncontrollable growth of technology resulting in irreversible changes to human civilization. There's a prediction that computers will achieve humanlike intelligence by 2029 and singularity in 2045. It's going to impact everyone. Knowing about the ethics of AI and being adept at it could be very profitable in the upcoming years. It will significantly influence our lives individually, from a virtual assistant to getting incorporated in the software we use to possibly making its way to being someone's best friend. AI will continue to evolve in helping organizations with critical business operation tasks such as strategy planning, product design, marketing, and customer support. Now is the best time to learn applied AI or its domains to become the neoteric reformist of tomorrow.

Microchip Brain
There is a tendency to associate the brain with the highest level of technology, thus a microchip here.

How can you learn this field?

For learning AI, there are three strategies. The metaphysical approach through a book. The applied AI approach via learning to use AI to perform a wide variety of natural language tasks and build a chatbot. The technical approach via machine learning. You can accomplish any or all.


"Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom lays a footing for comprehending the future of humanity and intelligent life compared to human intellect and learning AI through the metaphysical approach by having a deep awareness.


I'll briefly cover accessing the GPT-3 language model that uses deep learning to produce human-like text via the OpenAI API. You'll learn to make a sample application through a step-by-step guide of Open AI.

You can apply the OpenAI API to nearly any task that involves understanding or generating natural language or code. There are detailed sections for chatbots and Machine Learning in the technical approach below.

What are chatbots?

Chatbots are an uncomplicated and practical application of AI if approached the right way. Chatbots are automated agents that can converse with you via text or voice. Here’s a common scenario: “Your call is important to us. Due to unusually high call volume, you may experience a greater hold time than usual.” Isn’t it annoying to wait for a simple query to get answered? It is where chatbots can help address numerous simple inquiries from customers. They can scale indefinitely, unlike human teams, and are available 24/7 every day of the year. In other words, they can act as the first line of defense for the customer care team, allowing customer support operations to scale inexpensively and provide a better service by having rapid answers around the clock for ordinary inquiries.


How can you learn to make chatbots and implement them?

To build a chatbot platform quickly and easily, IBM has a complete guide that takes you step-by-step. Here’s the link to the website, and it’s straightforward to get started:


Test chatbots for yourself:

Test ACE, a chatbot that will act as a representative for a fictitious florist chain of stores:

For reference: you can ask ACE about shop locations, shop timings, general shop information, floristry questions, complaints, reviews, flowers for special occasions, types of flowers, and special offers. It can also tell you about general Covid-19 guidelines, cases, and Standard operating procedures.


Wags will answer your questions about acid mine drainage and watersheds:


What is next after learning to make a chatbot?

Once you complete the guide, you can make a chatbot for your website, develop chatbots to make money, think of your use cases to deploy chatbots, and explore the profound world of cloud computing and applied AI through IBM Watson. You can also get started with Machine Learning and demonstrate how AI works. I'll show you how.


What is Machine Learning?

Machine Learning is a technical field of Artificial Intelligence that requires a knowledge of linear algebra and the Python programming language. An example to make you understand machine learning - humans can expertly recognize the face of someone they know within a big crowd, but how do we do it? We do it instinctively, and there is no logical reasoning or pattern behind it that we can identify. But don't machines require precise algorithms to perform a task? Here machine learning comes into play. It uses data and algorithms to copy how humans learn and improve the model's accuracy by finding patterns in data that humans can't see. Note that deep learning is a substructure of machine learning, and neural networks are a substructure of deep learning. They are all branches of AI.


How can you learn this field?

The concepts:

For a brief yet reasoned introduction to Machine Learning, I suggest reading "Machine Learning for Absolute Beginners" by Oliver Theobald. You can understand the book without any background in computer programming. The book lays a foundation for the fundamentals, mathematics, and statistics behind designing machine learning models. For those searching for a programming aspect of machine learning, Chapter 13 walks you through the entire process of setting up a supervised learning model using Python.


The Programming:

For Machine Learning and to be apt at technology in general, you need to know a programming language. I highly recommend Python since it is easy to learn, has extensive libraries, and all the technologies in this article use it. "Python Crash Course" by Eric Matthes is a comprehensive guide to Python. The hands-on and project-based approach makes it easy to follow and implement on any operating system. In the end, you make a game called Space Invaders that will pave the way for you to build your 2D games, a data visualization project, and a small web application called Learning Log.


The Mathematics:

The Mathematics for Machine Learning Specialization teaches you the prerequisite mathematics for machine learning and data science. You will earn a shareable certificate after completion, which will validate your new skill. Coursera offers financial aid, but I will show you the audit method to get individual courses for free within the specialization. Click here or scroll to the end of the article.


Decentralized Finance


What is the definition and future of Decentralized Finance?


The Definition:

Decentralized Finance (DeFi) is a financial technology term postulating the future evolution of finance and its regulation based on decentralization. Current developments in blockchain technology are empowering a new paradigm centered around decentralization and disintermediation. Blockchain technology has the potential to eliminate the need for intermediaries in financial transactions, as it can facilitate peer-to-peer transactions through distributed trust and decentralized platforms. As a result, blockchain technology can substantially expand the scope and efficiency of peer-to-peer transactions, turning previously infeasible business models into feasible ones. Financial services can become more decentralized, innovative, interoperable, inclusive, borderless, and transparent.


The Future:

Decentralized finance provides entrepreneurs and innovators with the opportunity of creating an open financial system with little to no involvement from financial institutions. It aims to reduce transaction costs, broaden financial inclusion, empower open access, encourage permissionless innovation, and create new business prospects. Blockchain technology is leading to new business models. Presently, decentralized currencies are the most common model, followed by contracting and payments. If victorious, decentralized business models can reshape existing industries and create a new landscape for entrepreneurship, innovation, and the possibility of decentralized autonomous finance.

An illustration of the unalterable secure chains of transactions distributed across the Blockchain network
An illustration of the unalterable secure chains of transactions distributed across the Blockchain network

How can you learn this field?

The Coursera course offered by Campbell R. Harvey goes over the origins of DeFi from the earliest barter economies to the present day, transaction mechanics, fungible and non-fungible tokens or NFTs, analyzes swaps or decentralized exchange, Bitcoin, Blockchain, smart contracts, and the risks and opportunities of this technology. The course explains the inclusive technology behind cryptocurrencies, Web3, and the metaverse. You can buy the specialization or audit individual courses.

The book "DeFi and the Future of Finance" by Campbell R. Harvey and Ashwin Ramachandran is apt for learning this field alongside the Coursera specialization mentioned above.

Appendix

A picture that shows how to audit courses on Coursera.
Auditing courses to access them for free | Screenshots used from Coursera

Thank you! I hope you learned a lot and are ready to get started.



“Your time is limited, so don't waste it living someone else's life."

― Steve Jobs



(Disclaimer: the links to Coursera, IBM Watson, OpenAI, Qiskit, and Qubit by Qubit are for your benefit only. They do not denote our affiliation with them.)


Written by M. Abdullah Khan


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