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The Top Tech Trends In 2022 That Everyone Should be Aware of


As a the technology upgrades every year, To look ahead and predict the key to be in the trends that will shape the next few months. Now a days There are so many innovations and breakthroughs happening right now, and I can't wait to see how they help to transform business and society in upgrading the future tech.

Let’s take a look at my list of key tech trends that everyone should be ready for, starting today.
  • Computing Power.

  • Smarter Devices.

  • Quantum Computing.

  • Datafication. ...

  • Artificial Intelligence and Machine Learning.

  • Extended Reality.

  • Digital Trust.

  • 3D Printing.

  • New Energy Solutions.

  • Genomics.

1) Computing Power

What makes a supercomputer so super? Can it leap tall buildings in a single bound or protect the rights of the innocent? The truth is a bit more mundane. Supercomputers canprocess complex calculations very quickly. As it turns out, that's the secret behind computing power. It all comes down to how fast a machine can perform an operation. Everything a computer does breaks down into math. Your computer's processor interprets any command you execute as a series of math problems. Faster processors can handle more calculations per second than slower ones, and they're also better at handling really tough calculations. Within your computer's CPU is an electronic clock. The clock's job is to create a series of electrical pulses at regular intervals. This allows the computer to synchronize all its components and it determines the speed at which the computer can pull data from its memory and perform calculations. When you talk about how many gigahertz your processor has, you're really talking about clock speed. The number refers to how many electrical pulses your CPU sends out each second. A 3.2 gigahertz processor sends out around 3.2 billion pulses each second. While it's possible to push some processors to speeds faster than their advertised limits -- a process called overclocking -- eventually a clock will hit its limit and will go no faster. As of March 2010, the record for processing power goes to a Cray XT5 computer called Jaguar. The Jaguar supercomputer can process up to 2.3 quadrillion calculations per second [source: National Center for Computational Sciences].

Computer performance can also be measured in floating-point operations per second, or flops. Current desktop computers have processors that can handle billions of floating-point operations per second, or gigaflops. Computers with multiple processors have an advantage over single- processor machines, because each processor core can handle a certain number of calculations per second. Multiple-core processors increase computing power while using less electricity [source: Intel] Even fast computers can take years to complete certain tasks. Finding two prime factors of a very large number is a difficult task for most computers. First, the computer must determine the factors of the large number. Then, the computer must determine if the factors are prime numbers. For incredibly large numbers, this is a laborious task. The calculations can take a computer many years to complete. Future computers may find such a task relatively simple. A working quantum computer of sufficient power could calculate factors in parallel and then provide the most likely answer in just a few moments. However, quantum computers have their own challenges and wouldn't be suitable for all computing tasks, but they could reshape the way we think of computing power.

2) Smart Devices

Smart devices are all of the everyday objects made intelligent with advanced compute, including AI and machine learning, and networked to form the internet of things (IoT). Smart devices can operate at the edge of the network or on very small endpoints, and while they may be small, they are powerful enough to process data without having to report back into the cloud. They range from sensors to refrigerators and wearables to container transportation, capable of running autonomous workloads. Smart devices can be combined to bring intelligence to both objects and spaces, such as smart homes and buildings, and can help automate processes and controls. They can be used in almost any industry, from smart manufacturing to healthcare, helping to improve efficiency and optimize operations.

3) Quantum Computing


Quantum computing is a rapidly-emerging technology that harnesses the laws of quantum mechanics to solve problems too complex for classical computers.
These machines are very different from the classical computers that have been around for more than half a century. Here's a primer on this transformative technology. For some problems, supercomputers aren’t that super.
When scientists and engineers encounter difficult problems, they turn to supercomputers. These are very large classical computers, often with thousands of classical CPU and GPU cores. However, even supercomputers struggle to solve certain kinds of problems. If a supercomputer gets stumped, that's probably because the big classical machine was asked to solve a problem with a high degree of complexity. When classical computers fail, it's often due to complexity
Complex problems are problems with lots of variables interacting in complicated ways. Modeling the behavior of individual atoms in a molecule is a complex problem, because of all the different electrons interacting with one another. Sorting out the ideal routes for a few hundred tankers in a global shipping network is complex too.
A supercomputer might be great at difficult tasks like sorting through a big database of protein sequences. But it will struggle to see the subtle patterns in that data that determine how those proteins behave.
Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.
A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get

longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.
Quantum algorithms take a new approach to these sorts of complex problems -- creating multidimensional spaces where the patterns linking individual data points emerge. In the case of a protein folding problem, that pattern might be the combination of folds requiring the least energy to produce. That combination of folds is the solution to the problem.

4) Datafication

Datafication is a buzzword of the last several years, that is used actively along Big Data industry. Honestly, if you would search the term ‘datafication’ on the internet you probably won’t find that much relative information about it, yet it is a word we are hearing a lot these days. However, after analyzing the topic itself, I could say that many of us understand the meaning of the term, but probably named it another way. What is Datafication? Datafication, according to MayerSchoenberger and Cukier is the transformation of social action into online quantified data, thus allowing for real-time tracking and predictive analysis. Simply said, it is about taking previously invisible process/activity and turning it into data, that can be monitored, tracked, analysed and optimised. Latest technologies we use have enabled lots of new ways of ‘datify’ our daily and basic activities. Summarizing, datafication is a technological trend turning many aspects of our lives into computerized data using processes to transform organizations into data-driven enterprises by converting this information into new forms of value. Datafication refers to the fact that daily interactions of living things can be rendered inta format and put to social use.

5) AI & ML



In simple terms, Artificial Intelligence (AI) can be defined as intelligence demonstrated by machines, adjacent to the natural intelligence exhibited by humans. In other words, it is the ability of a device that recognizes its environment and instructions to take action for the accomplishment of aim. "Artificial Intelligence" is frequently used to describe machines that imitate reasoning functions linked with the human mind like "algorithm solving "and "memorizing."
Founded by Dartmouth professor John McCarthy in 1955, AI is described under two main categories of intelligence that are the Narrow and General Intelligence. Performing tasks such as analyzing images from X-rays and MRI scans in radiology comes under the first category, and having the human-like potential to memorize anything and narrating about it comes in the second category, respectively. Artificial intelligence has two critical parts; one is the engineering part, i.e., constructing tools that utilize intelligence. The other is the science of enabling a machine to introduce a result competing for a human brain, provided that the machine achieves it in a whole new way.
Talking about Machine Learning, ML is a class of AI. It enables a system to take decisions using historical data (structured and semi-structured data) without being explicitly programmed to generate detailed results based on the data. It termed as an application of Artificial Intelligence as machines have the ability to learn on their own without being unmistakably programmed. It allows applications to adjust themselves on the basis of data available and enables the programmers to formulate the programs in a more uncomplicated way.
Precisely, it means understanding and following the methods using specific algorithms for the performance of tasks without any human aid. The application of Machine Learning has shown progress lately as machines improve from their own faults. They are much faster and accurate than people, preserving time. This is the reason that they are extensively used in capturing photos from crime scenes and recognizing a face to capture thieves or anyone. However, one must not get confused between AI and ML. To explain further, let's know the difference between the two.

Artificial Intelligence

Machine Learning

AI is a technology that enables a machine to reproduce human action.

​It is a subset of AI enabling a machine to learn from history data without programming explicitly.

​Aims to make smart system to solve complex.

Aims to enable machines to learn from the past data to give accurate results.

Subsets are: Machine Learning, Deep Learning.

​Subsets of machine learning is Deep Learning.

Concerned about boost the success chance.

​Concerned mainly about pattern and accuracy.

​Main Application are: Siri, Catboats, online games, Humanoid Robot, etc...

​Main Applications are: Online Recommendation System, Google Search Algorithm, Auto Friend tagging on FACEBOOK, etc...

Types are: Weak AI, General AI, Strong AI.

​Types are: Supervised Learning, Unsupervised Learning, Reinforcement Learning.

​AI includes Reasoning, Learning, and Self-correction .

​ML includes self-correction and learning with new data.


6) Extended Reality


Extended Reality (XR) is the combination of human & computer-generated graphics interaction, which is in reality as well as the virtual environment. In basic terms, Extended Reality is a superset of Augmented Reality (AR), Virtual Reality (VR) & Mixed Reality (MR).
The concept of Extended Reality (XR) came into the picture when technologies like Augmented & Virtual reality, were being used by developers and tech- companies all across the globe. Many Sci-fiction movies have used the concept of Extended Reality (XR), but operating it in the real world is very different than in the reel world.

To understand the technical aspect of Extended Reality (XR), we need to understand the technologies which are used to create Extended Reality (XR) :
1. Augmented Reality (AR): The concept of augmented reality is that virtual objects and imaginations are put up in the real world. Augmented reality does not put us into any virtual or computer-generated graphics, rather it just creates a sense of illusion in digital gadgets. The users still have access to the real world & they can fully interact in both dimensions. The most common example is Pokémon-GO which used augmented reality so that the users can interact with the real as well as a virtual world with the help of digital gadgets. Other examples of Augmented reality are the filters that we see in many apps, these just create an illusion of being there, but they are not.
2. Virtual Reality (VR): In virtual reality, the users are put into a fully virtual environment, where they can interact only in the virtual world. The graphics generated are mostly computer and artificial objects are designed to give a feel of being real. The users can feel every bit of virtual reality. Special VR devices are needed to put users into this environment which gives them a 360-degree view of the virtual world. These devices are designed to give a much real illusion tousers.
3. Mixed Reality (MR): Mixed reality is a combination of both AR & VR, where one can interact with the digital as well as the real world simultaneously. Users can visualize their surroundings in special MR devices. These MR devices are much more powerful than VR, and costly too! But these devices give you the power to interact with the surroundings digitally. For example, putting on an MR device will give you a view of your entire surroundings. You can do whatever you want, throw a ball, close the windows, etc which will be digitally in your MR headset, but in actual reality, things will remain as they are. Many companies are investing a huge amount of money for deeper research in this field of reality.

In a nut-shell, using Extended Reality(XR), people can visit places virtually, feel the same as they are present at that place, interact with other individuals on XR. Thus, it is a combination of all three AR, VR & MR. 3 Major Challenges Faced by Companies Developing Extended Reality (XR) 1. Cost: Cost is the most prominent challenge, that is faced by companies developing XR. The XR devices are very costly. Since many technologies are working together & a lot of hardware goes into the making of these devices, the cost is very high. If the cost is higher, common masses may not be able to use this product and companies developing would not able to increase their sales, this would not motivate the investors to invest their money into XR.
2. Hardware: Developing the hardware of XR devices is also a challenge for companies in this field. Since a lot of technologies, software & components are being used, making hardware is a difficult task. The hardware should just not be robust but also be compact and able to process a lot of information very quickly and swiftly, and on top of that, the hardware should be cheaper.
3. Privacy: Privacy is a challenge will be faced both by the users as well as the companies. Since XR devices are required to create an environment based on the user requirement, a lot of private details might be needed to create a user- rich environment. Storage of such data can be costly on the company’s side, & privacy of the information can be a worry on the user’s side. Applications of Extended Reality (XR) 1. Entertainment Industry: The entertainment industry can hugely benefit from XR, just the same way they are benefitting from AR & VR. The entertainment industry can find new and amazing ways to utilize this technology and earn profits.
2. Sales & Marketing: Companies can advertise their product via XR, & can give their users a hands-on experience about their product or service. This can be beneficial, as companies will have to spend less on their advertisement, rather they can directly give their customers the experience of using the product.
3. Housing & Real Estate: One can easily find the suitable housing via a brief walkthrough using XR, & owners can also find potential buyers from various other locations, as there will be no need to go through physically. The role of brokers would be eradicated in such ascenario.
4. Education & Training: The use of XR can be a boon for this industry. Students all across the globe can find and choose the right colleges & study there is at their location. Anyone could use this technology to study in any institution around the globe. Also, the training of employees and workers can be done remotely using XR.
5. Work From Home for Remote Areas: The employees & staff can visualize a live environment of their office or workplace & can attend meetings from their homes, and also instruct others on how to work, from their homes. Especially, when the area is remote & difficult to work, XR can be used so that the work can be done from home.

7) Digital Trust:



In our daily lives we, all are hearing about Bitcoin, share markets or blockchain technologies. The reason behind this is very broad, but today we are here to learn about the trust throigh which one can get into online investments through Blockchain Technology. So, let’s start... The Blockchain word itself means ' A chain of blocks'. Blockchain technology is the technology which stores your data in a chain of blocks which are safe and secure and their data is not saved at one place, anyone can view them, they are available in easy language, and there is no owner of Blockchain Technology unlike Cryptocurrencies and share markets. It is a decentralized technology of investment. By every minute it is developing and new advances are coming in the blockchain process.
The main property of Blockchain is Decentralization, and the NFT’s are those units who store their data in blockchain through the principle of decentralization. We will discuss it in brief today and will leave its broader discussion for anther day.
First of all, who are beginner, let me give you some brief explanation of NFT’s. The NFT’S {Non-Fungible Tokens} are non-interchangeable units of data stored on a blockchain, a form of digital ledger, that can be sold and traded. The NFT data units may be associated with digital files such as photos, Videos, and audio. Now, we can understand how much this technology is developed. Now, let’s come onto the term Digital Ledger{DL}. It is a database shared by multiple participants in which each participant maintains and updates a synchronized copy of the data. DL's allow members to securely verify, execute, and record their own transactions without relying on an intermediary, such as a bank, or auditor. BLOCKCHAIN & DISTRIBUTED LEDGER ORIGINS Distributed ledger technologies, like blockchain, are peer to peer networks that enable multiple members to maintain their own identical copy of a shared ledger. Rather than requiring a central authority to update and communicate records to all participants, DLTs allow their members to securely verify, execute, and record their own transactions without relying on a middleman. Varieties: While there are a wide variety of DLTs on the market, they are all comprised of the same building blocks a public or private / permissioned / permission less distributed ledger, a consensus algorithm (to ensure all copies of the ledger are identical), and a framework for incentivizing and rewarding network participation.


8) 3D Painting


3D printing, also called additive manufacturing, is a family of processes that produces objects by adding material in layers that correspond to successive cross-sections of a 3D model. Plastics and metal alloys are the most commonly used materials for 3D printing, but it can work on nearly anything—from concrete to living tissue.
three easy ways to categorize the various additive manufacturing technologies:

1. Melted Solids: There’s a whole band of additive manufacturing technologies that rely on melting a material down and extrude it out of a nozzle or end effector of some kind. These additive technologies essentially reconstitute a “complete” material (like from a spool) into a new shape by melting and layering into a new form.
2. Solidifying Liquids: You probably didn’t see this coming, but yes, there is a process of additive manufacturing technology that is the total inverse of melting solids. Relying typically on photosensitive resins or polymers, these 3D printers will usually work by applying a laser or a projection to solidify a thin film of the resin into a solid object.
3. Fusing Powders: Possibly the most widely known technology format, powder fusion works exactly as the name suggests. The material you’re working with is a powder in its “raw” format and fuses together either through a binding agent or by melting the material with a heat source. Having dealt with a handful of the different ways you can additively manufacture things, let’s dive into the specific additive manufacturing processes. Additive Manufacturing Processes

FFF: Fused Filament Fabrication Chances are, when someone says 3D printing, you think of this additive technology. Easily the most prolific additive technology from the boom in desktop machines that started around 2010, FFF machines manufacture products with a spool of plastic that is driven through a hot end extruder that melts the plastic to liquid form, which is then laid out in a pattern that is one slice of the object. You may be aware of FFF thanks to additive manufacturing hardware companies like Ultimaker.
FFF Applications FFF is a fantastic workhorse additive manufacturing technology for prototyping, making basic products, testing ideas rapidly, and general ideation workflows. Of course, FFF can also be used with more “permanence” in mind to manufacture products too. FFF is a reliable technology for additive manufacturing, with few things that can go wrong, minimal downtime, and generally well-produced objects. It’s limited mostly by the resolution of printing, which will create a trade-off on accuracy for speed. FFF parts also require some post-processing for finishing, and the ridgelines usually need to be removed for painting.
SLA & DLP– Selective Laser Additive & Digital Light Processing Formlabs Form 3 printer. Arguably the second most popular/famous 3D printing process after FFF, this additive technology also benefitted from a boom in companies starting around 2010. These 3D printers use a photosensitive tank of resin, with the object being made by passing a laser over the layer to solidify the resin in place. DLP differs from SLA by projecting the entire image layer using a projector instead of a laser. Arguably DLP is faster, as the entire layer is projected at once instead of using a laser to trace, but there are again trade-

offs, typically around the surface finish. You are most likely aware of SLA printing through companies like FormLabs. There are a lot of resin options available, most of which simulate a plastic’s material properties. SLA benefits over FFF are typically accuracy and surface finish, so if you’re printing objects with lots of fine small details, SLA will serve you better. However, the SLA process demands more of you as an end-user, requiring extra steps after the printing is done for the part to be ready. SLA can also print big parts and is used at scale. You may recall seeing the Adidas Futurecraft 4D shoes with a 3D printed sole, which were achieved with SLA-based tech from Carbon.
MJF – Multi Jet Fusion
Whoa, jet fusion? And there are multiples of them? Yes. This additive technology is as amazing as its name suggests. Multi Jet Fusion produces nylon parts using an inkjet system not too dissimilar to what you would have in a regular paper printer. The head of a multi-jet fusion machine is considerably more complex than a regular printer head, sending material and binding agents. MJF tends to give a much more consistent finish and material property than its Selective Laser Sintering counterparts. MJF Applications For professionals, this process adds color and materiality together so that prototyping can get a lot closer to the final object than with other prototyping processes.

This additive manufacturing application is particularly convenient when color matters, not just from a finishing perspective but also for visual representations such as printing a heat map of stresses directly onto the part, making it easier to understand what’s going on when reviewing your object.
DMLS – Direct Metal Laser Sintering Generatively Designed Skate Trucks made using DMLS printing. Before we dive into this one, it’s worth noting that DMLS is a relatively new additive manufacturing process relative to other laser sintering processes. Most likely, you will know what SLS (Selective Laser Sintering) is and the nylon parts it makes. DMLS works by using the same process, using a laser to fuse metal powder. Typically used to prototype complex parts and manufacturing mass customized products, DMLS enables you to manufacture parts that will be much stronger (because, well… metal is stronger than plastic for the most part) and test.
Relative to other processes, DMLS is expensive, as it is a metal additive manufacturing process. This is expected given the materials, the technology, and the required safety protocols to house a DMLS machine are costly. But the cost is, of course, worth it to be able to test and validate processes. If you work in aerospace or automotive, a DMLS printer will be one of the most effective ways to prototype complex, unique parts and be as close to the finished part as possible. You might be thinking, “what about machining?” Of course, you can still use machining as part of any prototyping process, but we’re here to discuss objects that would necessitate the use of additive manufacturing.
DED – Direct Energy Deposition A DED print nozzle layering metal. DED printing is best thought of as the metal counterpart to FFF for plastics. DED machines will use either a powder or a wire (not too dissimilar to a plastic spool) to heat the metal at the extrusion point and depositing it with a nozzle.
DED Applications:

From the description of DED, you may think it would be used in similar applications to FFF, but with metal parts. In reality, DED’s most common use today is building off existing parts and being included in a hybrid manufacturing process for high-end additive manufacturing applications. One of the most famous examples would be the use of hybrid manufacturing in the Port of Rotterdam. They will 3D print parts onto damaged rudders to make a replacement part and then use a machining process to bring the part to a completed state, ready for use on a new ship.

9) New Energy Solutions



To avoid the most devastating impacts of climate change, we must limit global warming to 1.5°C. We urgently need to reduce energy-related CO2 emissions in the short-term, which means businesses need to use the low-carbon energy sources available today - from the way we heat and light buildings, to the way we transport goods, people and services. With proven technologies and low-

carbon fuels, we can already make significant headway in decarbonizing the energy system.
Humans are addicted to fossil fuels. This project identifies cutting-edge energy solutions that allow energy users to go low-carbon. By analyzing and pursuing the required business conditions for enabling these solutions, WBCSD member companies are reducing CO2 emissions in line with the Paris Agreement.

Many of these low-carbon energy solutions are cross-sectoral and require collaboration across the value chain to develop sound business cases. Solutions exist that replace CO2 intensive energy consumption with a low- carbon approach. But companies may not be aware of these new energy solutions and they may face higher up-front investment costs to implement them. New financing mechanisms and business models alongside cross- sectoral collaboration will help to make the business case resulting in long- term profitability, increased performance and climate change mitigation

10) Genome:


An organism's complete set of DNA is called its genome. Virtually every single cell in the body contains a complete copy of the approximately 3 billion DNA base pairs, or letters, that make up the human genome.

With its four-letter language, DNA contains the information needed to build the entire human body. A gene traditionally refers to the unit of DNA that carries the instructions for making a specific protein or set of proteins. Each of the estimated 20,000 to 25,000 genes in the human genome codes for an average of three proteins.

Located on 23 pairs of chromosomes packed into the nucleus of a human cell, genes direct the production of proteins with the assistance of enzymes and messenger molecules. Specifically, an enzyme copies the information in a gene's DNA into a molecule called messenger ribonucleic acid (mRNA). The mRNA travels out of the nucleus and into the cell's cytoplasm, where the mRNA is read by a tiny molecular machine called a ribosome, and the information is used to link together small molecules called amino acids in the right order to form a specific protein.

Proteins make up body structures like organs and tissue, as well as control chemical reactions and carry signals between cells. If a cell's DNA is mutated, an abnormal protein may be produced, which can disrupt the body's usual processes and lead to a disease such as cancer.

Nanotechnology is science, engineering, and technology conducted at the nanoscale, which is about 1 to 100 nanometers.

Fundamental Concepts in Nanoscience and Nanotechnology

Medieval stained glass windows are an example of how nanotechnology was used in the pre-modern era.

It’s hard to imagine just how small nanotechnology is.
One nanometer is a billionth of a meter, or 10-9 of a meter. Here are a few illustrative examples:
  • There are 25,400,000 nanometers in an inch

  • A sheet of newspaper is about 100,000 nanometers thick

  • On a comparative scale, if a marble were a nanometer, then one meter would be the size of the Earth

Nanoscience and nanotechnology involve the ability to see and to control individual atoms and molecules. Everything on Earth is made up of atoms—the food we eat, the clothes we wear, the buildings and houses we live in, and our own bodies.

But something as small as an atom is impossible to see with the naked eye. In fact, it’s impossible to see with the microscopes typically used in a high school science classes. The microscopes needed to see things at the nanoscale were invented in the early 1980s.
Once scientists had the right tools, such as the scanning tunneling microscope (STM) and the atomic force microscope (AFM), the age of nanotechnology was born.

Although modern nanoscience and nanotechnology are quite new, nanoscale materials were used for centuries. Alternate-sized gold and silver particles created colors in the stained glass windows of medieval churches hundreds of years ago. The artists back then just didn’t know that the process they used to create these beautiful works of art actually led to changes in the composition of the materials they were working with.

Today's scientists and engineers are finding a wide variety of ways to deliberately make materials at the nanoscale to take advantage of their enhanced properties such as higher strength, lighter weight, increased control of light spectrum, and greater chemical reactivity than their larger-scale counterparts.




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