An interesting side effect of the evolution of technology is its impact on our collective vocabulary. Thanks to the hype marketing often associated with new technologies, these technology trends find their way into dinner table conversations, water cooler chats, and many other social settings.

The recent years have seen a ton of such technology trends; some continue to fascinate us and are becoming more relevant, while others are fading away from our memories. But even if we prefer not to engage with the new technology trends, there is usually no escape from the fashionable buzzwords.

In this blog post, we have compiled a list of buzzwords and tech lingos to help you keep up with tech advances that will likely shape our world over the next few decades.

Artificial Intelligence (AI) burst into public view with the launch of ChatGPT in November 2022. Although ChatGPT piqued our interest, it wasn’t our first interaction with AI. In fact, we’ve been using AI for a long time in the form of voice recognition (like Siri, and Google Assistant), camera settings (like portrait mode, and face detection ), and content recommendations (like on Netflix).

So, what is AI? Fundamentally, AI is a field that enables problem-solving by using computer science combined with a robust collection of data. In everyday conversations, when we use AI, we are referring to the application of artificial intelligence.

AI works by combining iterative processes and intelligent algorithms, which are nothing but processes or sets of rules followed in problem-solving operations, with large amounts of data to learn automatically. Typically, they learn by processing massive amounts of data, looking for patterns and features in the data upon which they model their own decision-making.

So, artificial intelligence can be defined as a software that can learn to make decisions and solve problems, on its own, without needing to be specifically programmed for each task.

AI is the brain that enables applications like digital assistants, online recommendations, and chatbots to perform tasks that usually humans would. And just like humans, AI can learn and adapt to become more helpful and efficient over time.

Generative artificial intelligence or generative AI is artificial intelligence capable of generating text, images, or other media. It uses statistical models to determine the likelihood of a given data point. Generative AI learns the patterns and structure of their input training data and then uses these patterns to generate new data that shares similar characteristics with the original dataset.

OpenAI’s ChatGPT, which creates text, is the most well-known generative AI. In simple terms, these models are able to generate text by assigning a probability to a sequence of words and based on that predicting the next word in the sequence.

In addition to language, generative AI models can work with software codes, images, medical data, and a variety of other data types. For example, a generative AI trained on images is able to determine correlations like “lips appear below the nose” and “eyes appear below eyebrows” and based on these correlations generate new images that look like real faces.

Machine learning and deep learning come up a lot when talking about AI as all three terms are interconnected. In simple terms, machine learning is a type of AI that can learn and adapt automatically with minimal human intervention. On the other hand, deep learning is a type of machine learning that processes data using interconnected nodes or neurons in a structure that is inspired by the human brain. So, machine learning is a subset of AI, and deep learning is a subset of machine learning.

Typically, a machine learning model is “trained” by feeding it large amounts of data. The model follows an algorithm, i.e. a set of rules, to analyze and draw inferences from the data. Then it applies what it has learned from the data to make informed decisions. The greater the amount of data the model learns from, the better it becomes at making decisions.

Netflix’s recommendation system is an example of machine learning. The Netflix machine learning model learns your preferences, based on which it offers new suggestions. Every time you watch a new movie or series, it updates its algorithm to offer you more personalized and accurate recommendations.

Deep learning can be thought of as an evolution of machine learning. Deep learning models use artificial neural networks that are inspired by the network of neurons in the human brain and try to replicate how a human would draw conclusions by continually analyzing data with a logical structure. A deep learning model can learn through its own method of computing, making it appear like it has its own brain.

The application of deep learning can be found in self-driving cars. Deep learning models are trained on large datasets of images, traffic, and driving data. Based on the training data, these models make predictions about real-time traffic data, identifying objects or deciding what the car should do in a given situation.

Blockchain is a technology used for maintaining a secure and decentralized record of transactions. Although commonly known for its role in Bitcoin, its use is not limited to cryptocurrency.

In simple terms, a blockchain is a database where you enter, save, and access information. But instead of storing it on one device, multiple copies are saved on many devices. An entry in the database is valid only if all its copies match. This decentralized and distributed nature of blockchain ensures that the records cannot be altered retroactively and are thus secure.

Typically, a blockchain works by collecting transaction information and entering it into a block. When the block is full, the information is run through an encryption algorithm, to create a hexadecimal number called the hash. This hash is added to the following block and encrypted along with the other information in that block. This creates a series of blocks chained together, hence the name blockchain.

Blockchain became popular in the public domain in the late 2000s thanks to the popularity of the cryptocurrency, Bitcoin. However, since then blockchain technology has found applications in many other areas such as decentralized finance (DeFi) applications, non-fungible tokens (NFTs), and smart contracts.

Distributed cloud is a cloud computing architecture that uses multiple clouds managed from a single control plane. The multiple clouds may include on-premises private cloud, public cloud provider’s infrastructure, third-party data centers, or colocation centers.

The distributed cloud architecture enables organizations to meet specific business goals and objectives such as performance requirements, compliance needs, or edge computing demands. The increase in deployment of distributed cloud can be attributed to the growth of the Internet of Things (IoT), edge computing, and artificial intelligence applications. These technologies handle large amounts of data and having the cloud resource as close to the application as possible helps greatly improve the performance of these applications.

Compliance and privacy regulations have also played an important role in the adoption of distributed cloud computing. Organizations are often required to store data within specific geographical locations and distributed clouds make it possible to achieve this even if the organization or its cloud provider doesn’t have a physical presence in that region.

Extended reality (XR) is an umbrella term used to describe immersive technologies that merge the physical and virtual worlds. It encompasses all the immersive technologies including augmented reality (AR), virtual reality (VR), and mixed reality (MR) as well as those that are still to be created.

Here’s a quick guide to the technologies included in XR:

• AR puts a digital layer over physical elements. An example of AR is the Pokémon GO game, which overlays digital creatures onto the real world.

• MR allows the digital elements to interact with the physical elements. An example of MR is Microsoft's HoloLens, which allows you to place digital objects into the room you are standing in.

• VR creates a fully immersive digital environment. An example of VR is the Oculus Rift headset which allows you to experience an interactive computer-generated environment.

So, extended reality is not a specific technology, rather it is a collection of existing and future technologies designed to alter reality, either by creating an entirely virtual environment or blending the virtual and the real worlds.

The term “metaverse” burst into the public sphere in 2021, when Mark Zuckerberg, Facebook’s CEO, announced the name change of his company from Facebook to Meta. Soon after that, many businesses made it part of their business strategy, calling it “the future of the Internet”.

So, what is the metaverse? A metaverse is a 3-dimensional virtual space powered by technologies, including VR, AR, and AI among many others, that allows people to have lifelike experiences interacting with other users as well as non-human avatars online.

Unlike the Internet, there isn’t one single metaverse. There are many different metaverses created by individual companies to serve their particular business needs. It is still a very new technology and standards for sharing user identities and data across different metaverses haven’t been developed yet. So, there is little to no interoperability among the existing metaverses.

Although the metaverse is often compared with the Internet amid claims that it is the evolution of the Internet. In reality, their concepts are very different. The Internet, in general, is a repository of the content generated by users about what happened in the “real world.” The metaverse, on the other hand, is a virtual space, which exists in parallel with the real world, with no connection with the “real world.”

The term superconductor was recently a source of short-lived excitement. To begin, let’s first try to understand the term. Any material that allows electricity to flow through it is called a conductor. For example, copper is an example of a conductor. But, in addition to its ability to allow electricity to pass through, it has another property called “electrical resistance”. This “resistance’ causes conductors to heat up when electric current passes through them.

There are materials whose resistance drops to zero when their temperature is lowered to a critical point. This critical temperature is typically very low, around  -164 degrees Fahrenheit. So, in a loop of superconducting wire, an electric current can persist indefinitely with no power source.

Superconductors are used, among other things, in quantum computers. And because the superconductors need to be kept at very low temperatures, they are cooled with liquid helium or liquid nitrogen. This makes quantum computers very bulky and expensive. If only there were superconductors that could work at reasonable temperatures, it could make quantum computers more accessible, dramatically altering the future of computing.

And that’s the reason for the recent excitement around superconductors. It was fueled by the news of the possibility of synthesizing room-temperature superconductors. Alas! The news turned out to be inaccurate. If you are interested, you can read more about it here: The room-temperature superconductor that wasn’t.

Quantum computing is a technology that uses the properties of quantum physics to perform computations that are too complex for traditional computers. Traditional computers perform computations using classical bits, which can either be 0 or 1. Quantum computers, on the other hand, use the quantum states of subatomic particles to store information and perform computations.

The unit of memory, in quantum computing, is called a quantum bit or qubit, which can be both 0 and 1 at the same time. This property of qubits is what gives quantum computers an edge over traditional computers in certain tasks where they can vastly outperform even supercomputers.

Qubits are made using physical systems, such as the spin of an electron, that exist in many different arrangements all at once. This property is known as quantum superposition. Additionally, qubits can be linked together using a phenomenon known as quantum entanglement.

In simple terms, qubits can hold greater information than classical bits. The amount of equivalent classical bits represented by “n” qubits is equal to 2 raised to the power of “n”. So, 2 qubits contain 4 classical bits of information. To truly appreciate the impact of exponents, we need to consider a higher numerical value. For example, in traditional computing, you need 8 bits to represent any number between 0 and 255. But in quantum computing, 8 qubits can represent every number from 0 to 255 at the same time. And if we go even further, 300 qubits would be enough to represent more numbers than there are atoms in the universe.

For a more in-depth explanation of how quantum computers work, check out this video:

Conclusion

The technology trends and buzzwords we have shared above are probably some of the most important and impactful ones of recent decades. I hope this blog post will serve as a good primer, helping you engage in conversations related to these trends and learn more in the process.

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