Machine intelligence is the last invention that humanity will ever need to make
Like many other revolutionary technologies of the modern-day, machine learning was once science fiction. However, its applications in real-world industries are only limited by our imagination. In 2021, recent innovations in machine learning have made a great deal of tasks more feasible, efficient, and precise than ever before.
Powered by data science, machine learning makes our lives easier. When properly trained, they can complete tasks more efficiently than a human.
Understanding the possibilities and recent innovations of ML technology is important for businesses so that they can plot a course for the most efficient ways of conducting their business. It is also important to stay up to date to maintain competitiveness in the industry.
In this article, we will discuss the latest innovations in machine learning technology in 2021 from our perspective as a machine learning software development company. We’ll go over 4 trends and explain how the latest innovations in machine learning technologies can benefit you and your business in 2022.
- No-Code Machine Learning
- AutoML
- Generative Adversarial Networks
- Full-stack Deep Learning
Trend #1: No-Code Machine Learning
Although much of machine learning is handled and set up using computer code, this is no longer always the case. No-code machine learning is a way of programming ML applications without having to go through the long and arduous processes of pre-processing, modelling, designing algorithms, collecting new data, retraining, deployment, and more.
Some of the main advantages are:
Quick implementation : Without any code needed to be written or the need for debugging, most of the time spent will be on getting results instead of development.
Lower costs : Since automation eliminates the need for longer development time, large data science teams are no longer necessary.
Simplicity : No-code ML is easier to use due to its simplistic drag and drop format.
Trend #2: Auto ML
Similar in objective to no-code ML, AutoML aims to make building machine learning applications more accessible for developers. Since machine learning has become increasingly more useful in various industries, off-the-shelf solutions have been in high demand. Auto-ML aims to bridge the gap by providing an accessible and simple solution that does not rely on the ML-experts.
Data scientists working on machine learning projects have to focus on preprocessing the data, developing features, modeling, designing neural networks if deep learning is involved in the project, post processing, and result analysis. Since these tasks are very complex, AutoML provides simplification through use of templates.
Another example of AutoML in action is OpenAI’s DALL-E and CLIP (contrastive language image pre-training) models. These two models combine text and images to create new visual designs from a text-based description.
Trend #3: Generative Adversarial Networks (GAN)
GAN technology is a way of producing stronger solutions for implementations such as differentiating between different kinds of images. Generative neural networks produce samples that must be checked by discriminative networks which toss out unwanted generated content. Similar to branches of government, General Adversarial Networks offer checks and balances to the process and increase accuracy and reliability.
A useful application of this technology is for identifying groups of images. With this in mind, large scale tasks such as image removal, similar image search, and more are possible.
Trend #4: Full Stack Deep Learning
Let’s imagine you have highly qualified deep learning engineers that have already created some fancy deep learning model for you. But right after the creation of the deep learning model it is just a few files that are not connected to the outer world where your users live.
As the next step, engineers have to wrap the deep learning model into some infrastructure:
- Backend on a cloud
- Mobile application
- Some edge devices (Raspberry Pi, NVIDIA Jetson Nano, etc.)
The demand of full-stack deep learning results in the creation of libraries and frameworks that help engineers to automate some shipment tasks (like the chitra project does) and education courses that help engineers to quickly adapt to new business needs (like open source fullstackdeeplearning projects).
Machine Learning: Powering Into the Future
With data science and machine learning, industries are becoming more and more advanced by the day. In some cases, this has made the technology necessary to remain competitive. However, utilizing this technology on its own can only get us so far. We need to innovate to achieve goals in novel and unique ways to truly stake a corner in the market and break into new futures that previously were thought to be science fiction.
Every objective requires different methods to achieve. Talking to experts about what’s best for your company can help you understand what technologies, such as machine learning, can improve the efficiency of your business and help you achieve your vision of supporting your clients.