Emerging Technologies of 2023: From VR to Edge Language Models

TL;DR: The number of emerging technologies is growing every year, and it can be challenging to keep up and understand which ones can truly help companies achieve their goals. This article selects three emerging techs with transformative potential for you to explore and keep an eye on them over 2023 and beyond: virtual reality for remote team support, blockchain-enhanced cloud computing for decentralized data management, and advanced language models for accelerating AI language tech. 

Development speed for new and potentially disruptive technologies has been increasing rapidly for significant technological advancements and digital changes. In this context, it’s crucial to stay informed of what technology holds for the coming years. To help you achieve that, we’ve selected three emerging technologies that are generating significant buzz in the corporate world. Let’s explore what they are, their common applications, and the transformative impact they can have on different industries. 

What Does “Emerging Technology” Mean? 

Emerging technologies encompass a wide range of transformative innovations that are at the forefront of scientific research and development. These technologies are characterized by their disruptive potential, pushing the boundaries of what was previously thought possible.  

From artificial intelligence (AR) and virtual reality (VR) to blockchain and edge computing, emerging technologies are reshaping industries such as education, healthcare, and finance.  

By carefully choosing and embracing new technologies, companies can gain a competitive advantage and stay ahead in their respective sectors, adapting to evolving market needs and customer expectations.  

Top 3 Must-Watch Emerging Technology Trends 

In today’s ever-evolving tech landscape, three new emerging techs are set to reshape the way businesses operate. They hold the potential to drive innovation, improve productivity, and tackle different challenges. Those are: 

1. Virtual Reality: Breaking Barriers for Tech Teams 

In 2022, 85% of tech workers were working fully remotely or were following a hybrid model. This shift towards remote work has prompted companies to place greater emphasis on employee training and the implementation of digital tools to support remote teams and enhance skills.  However, these efforts often lack the real-world experience needed to achieve effective results. This is where virtual reality offers a solution that mimics real experiences and accelerates outcomes. 

Taking the example of the metaverse, a convergence of digital realities into a vast virtual universe, the neuroscience behind VR offers compelling reasons to explore this technology as a means of remote team support. By providing users with life-like experiences that stimulate the brain, virtual reality combines physical control and functionality with an immersive environment.  

As a great tool for learning new abilities and soft skills, it eases navigating unfamiliar environments and overcoming obstacles. VR can furthermore be used to foster empathy and improve communication within remote teams from all industries. 

The cost-value of integrating VR into any process should be seen as a long-term investment rather than a mere expense. Tools that could help are those from Meta, which has heavily invested in VR, foreseeing its profound impact on future digital interactions and Apple’s upcoming innovative Vision Pro glasses. These industry giants’ investments underscore VR’s promising value proposition, far outweighing initial costs.  

2. Enhanced Cloud Computing through Blockchain Technology 

Cloud computing has become an essential part of our lives thanks to the internet and other advancements in technology. While it has transformed how we manage and store data, few areas for improvement exist due to its centralized nature. 

Data centralization in cloud computing makes it vulnerable to hacking. With a few dominant providers, user data is easily accessible and can be manipulated for business purposes. Moreover, as centralized data management approaches its limits, users may face expensive storage options in the future. 

The challenges above emphasize the importance of a decentralized approach to cloud computing. The convergence of blockchain and cloud infrastructure furthermore brings forth innovative solutions across industries. Blockchain’s decentralized nature and security make it particularly suitable for information exchange, such as financial transactions, medical histories, and supply chain logistics data. 

When combined with the scalability and elasticity of the cloud, this integration has the potential to revolutionize the entire tech industry. It facilitates direct exchanges of value in digital environments, eliminating the need for intermediaries.  

Cloud computing can be costly due to the expenses associated with storage and bandwidth, but blockchain-enhanced cloud computing is often regarded as a cost-effective alternative as it eliminates the need for third-party fees. By 2030, Gartner projects that blockchain technologies will generate up to $3.1 trillion in new business value.  

Related Read: What You Need to Know About Multi-Cloud Databases 

3. Increased Budgets to Use for Large, Fine-Tuned, and Edge Language Models 

While a programming language serves as a formal tool for writing computer programs, a language model refers to an artificial intelligence system that understands and generates text. These models have gained considerable traction. Top tech business leaders are allocating even more budget to AI language technologies. As of 2023, various types of language models are being used for different tasks. Here are three examples: 

Large Language Models 

Large language models boast immense size, are often trained on vast amounts of text data, and possess a high parameter count. These models enable multiple downstream tasks with minimal training data and have been used extensively across various applications, including question-answering, document summarization, and translation.  

GPT-3, developed by OpenAI and used for ChatGPT, is a well-known large language model consisting of 175 billion parameters. GPT-3 has been trained on a wide range of text-based tasks, making it a versatile model for various natural language processing tasks. It’s worth noting that there is also a successor to GPT-3, called GPT-4, which has been recently introduced. Additionally, OpenAI provides a platform called “the playground” where users can experiment and interact with language models like GPT-3 and GPT-4. 

Fine-Tuned Language Models 

Fine-Tuned Language Models represent smaller versions of language models that excel in specific domains or tasks. Derived from existing models, they require less time and computational resources for training. The fine-tuned models prove particularly useful for mature tasks with abundant training data, such as machine translation and named entity recognition. They can also deepen a model’s understanding of specific subjects, like biomedical research.  

BERT, developed by Google, is a language model fine-tuned for various downstream tasks such as sentiment analysis, named entity recognition, and text classification. 

Edge Language Models 

Edge models are intentionally designed to be compact, providing important benefits. They’re highly efficient in terms of cost, privacy, and speed, making them perfect for on-device applications. By eliminating cloud usage fees and avoiding data transmission and analysis in the cloud, edge language manages to be cheaper than other models.  

An excellent example of an edge language model is TinyBERT, which was developed by researchers from Tsinghua University in collaboration with Microsoft Research Asia. TinyBERT is a compact version of the BERT language model, created to maintain high performance on edge devices. It enables efficient and localized natural language processing on resource-constrained devices. 

Among the three types of language models presented, the edge language one provides the most affordable solution as it can run offline and on-device without requiring payment for cloud fees. Google Translate, for example, uses edge language models to deliver offline translations. 

Always Narrow It Down to What’s Best for Your Business  

We hope the above account of the latest emerging technology implementations has been insightful to stay on top of what’s trending this year and beyond. Moreover, we hope these advancements start to make potential implementations in your business and projects a lot clearer. 

Understanding the different types of language models, for instance, is crucial to harnessing their capabilities and driving innovation in the AI landscape.  Yet, bear in mind the greatest tools should only help serve your big picture, strategic business goals, and other significant milestones.  

In that sense, when vision or teams are needed to boost your current time to market, innovation capacity, and other tech implementations, rest assured strategic partnerships are nowadays a highly effective way to align with technological trends and possibilities, a movement companies are naturally seeking for constant growth. 

Focused on nearshore outsourcing solutions of value, Nearsure’s expanding network of over 20,000 of the savviest tech experts in Latin America are helping out with the latest tech skills. Are you interested in the latest tech implementations for quick growth?  

Contact us now and let us know how we can help!