5 Deep Learning Trends in 2022

08/26/2022 | AI, Featured Content, Technology Education

Deep learning is a subset of machine learning based on artificial neural networks. These neural networks mimic how the human brain learns, enabling them to learn from data without being explicitly programmed.

As deep learning continues to evolve, we can expect even more impressive advancements in the field. Deep learning will play a key role in improving our understanding of natural language processing and image recognition. Additionally, it will help us create more accurate models for predicting outcomes and prescribing actions. To peer into what the future holds, we’ll look at five deep learning trends we can expect to continue in 2022.

1. Improvements for Specific Tasks Such As Natural Language Processing

Deep learning algorithms have significantly improved natural language processing (NLP). Specifically, it has helped us better understand the meaning of words and phrases, as well as the context in which they are used. As a result, we have better machine translation and information retrieval.

Deep learning will continue to play a key role in improving NLP. We’re particularly looking forward to seeing how machines progress in their understanding of text sentiment. This improvement will especially benefit business applications like customer service and marketing. For example, deep learning is refining chatbot and digital assistant technology — helping people resolve issues and better routing them before they need to talk to a rep.

2. New Deep Learning Architectures and Algorithms

Deep belief networks (DBNs) were created to solve some of the challenges of deep learning, like slow performance and difficulty with massive data sets. DBNs are composed of several layers of neural networks that do not communicate horizontally or within their own layer. This structure makes it more efficient in its usage of neural network layers, speeding up complex tasks and improving performance when working with large data sets. Furthermore, DBNs are flexible and able to employ both supervised and unsupervised learning.

3. Leveraging Deep Learning Model Scale Up to Improve Accuracy

Deep learning model scale up is a term used to describe the process of increasing the size and/or complexity of a deep learning model. Scaling up can improve the accuracy and speed at which the deep learning model can learn.

There are two main ways to scale up deep learning models:

  1. Add more layers. Adding more layers or nodes can improve accuracy by helping the model learn more complex patterns and process more data. However, users must be strategic when adding layers, as too little and too many can impact your algorithm’s performance.
  2. Increase its size. Increasing the size of the deep learning model can improve its speed since the model can parallelize the learning process more effectively than a smaller deep learning model.
Neural network illustration.

Deep learning models are using increasingly large datasets.

4. Evolution from Predictive Modeling to Prescriptive Action

Deep learning has also made predictive modeling more accurate. In the past, predictive models were often based on shallow machine learning algorithms that struggled to learn from high-dimensional data. Nowadays, deep learning algorithms can learn from data with many features, making them more accurate.

As deep learning progresses, it will help us create predictive models that are more accurate and easier to interpret. Moreover, deep learning will better understand the cause-and-effect relationships in data, making it possible to prescribe actions rather than simply predict outcomes.

5. Greater Accessibility for Non-Data Engineers

Deep learning algorithms are often seen as complex and difficult to understand. But this is starting to change as deep learning becomes more accessible. Many tools and services now make it easy to develop deep learning models, even for those with limited experience. Consider DALL·E 2 from OpenAI as an example. DALL·E 2 allows users to easily generate unique artwork using AI by inputting natural language descriptions.

Since algorithms are becoming more general, advancements in unsupervised learning are also making deep learning more accessible. Users can now reuse models for different use cases, limiting the need for specialized expertise.

Deep Learning Is Changing How We Use AI and Machine Learning

Deep learning is profoundly impacting how we use AI and machine learning in the real world. And in the future, it will continue to change how businesses use AI and its prevalence in their applications. Therefore, to prepare your business to handle more deep learning applications, it’s important to ensure that your company’s hardware provides adequate performance.

Equus Compute Solutions helps companies design, deploy, and maintain high-performance computing solutions. We can help you determine the right level of computing infrastructure for your business today and in the future. If you want to talk more about how you can build out your deep learning infrastructure, let’s talk.


Share This:

Related Posts

Infrastructure Featured Content Technology Education

How Lyve Cloud Improves Multi-Cloud Efficiency

Businesses are using more cloud tools than ever before and need a way to ensure more apps don’t lead to...
Read More
Data Management Featured Content Infrastructure

Exploring the Importance of Data Infrastructure in Your Business

Data permeates every layer of a business’s operations. Learn why data infrastructure is key to unlocking insights that propel your...
Read More
Infrastructure Featured Content

Slash Costs and Automate Admin Work with Hyperconverged Infrastructure

IT leadership cares about metrics like ROI, TCO, and resource utilization. Learn how disaggregated hyperconverged infrastructure improves every one of...
Read More
Servers Data Management Technology Education

What Is an Enterprise Data Center?

Data continues to grow at an unstoppable rate. Learn how businesses use the enterprise data center to manage their growing...
Read More
Data Management Infrastructure Servers Technology Education

How Can Composable Infrastructure Benefit Your Data Center?

Data centers often suffer from poor resource utilization, which hurts both performance and efficiency. Learn how composable infrastructure changes this.
Read More
Data Management Featured Content Servers

Improving Our Partner’s Computing Capabilities Through Our First Lab as a Service Lab

Testing is a crucial step before releasing any product or service. Learn how we’re providing Lab as a Service to...
Read More
Featured Content Technology Education

Why Rocky Linux Is a Great Choice for Your Enterprise Needs

The departure of CentOS left a hole in the market for enterprise Linux. Learn how Rocky Linux fills that void,...
Read More
Data Management Featured Content Servers

The Anatomy of a Quality Full-Stack Data Center Solution

Building a traditional data center is often too slow for modern businesses. Learn why many are turning to full-stack data...
Read More
Servers Featured Content

5 CXL Benefits That Improve Data Center Performance

Compute Express Link is revolutionizing how servers communicate with each other and elevating resource utilization to new levels. Learn how...
Read More