Top 5 Data Science, AI, And ML Trends for 2022

In this article, let’s take a look at data science, AI, and ML trends that are likely to rule the technical landscape in 2022.

The global artificial intelligence market is expected to reach over $200 billion by 2027. The big data market segment is anticipated to grow up to US$103 billion by 2027 with a share of 45% from the software segment. Similarly, the projected size of the global deep learning market will reach over $40 billion by 2027 at a CAGR of 39.2%.

Indeed, the implementation of technologies like data science, artificial intelligence, and machine learning in organizations has increased exponentially. In the last two years, during the pandemic outbreak, the technologies played a crucial role in saving lives and fostering economic resilience, showing many surprising trends. 

Let’s take a look at data science, AI, and ML trends that are likely to rule the technical landscape in 2022.

1- Small Data and TinyML

The core idea behind small data is to enable users to get actionable results without acquiring the cloud-based systems used in big data analytics. 

Acquiring small data sets is highly useful in situations when the interaction with the cloud is limited, or time and bandwidth are major constraints in processing the data.

In simpler terms, it offers a quick, cognitive analysis of the most vital data in situations when the user needs fast data analysis. For example, auto-pilot cars where the machine cannot rely on sending raw data and receiving analyzed data from a centralized cloud server while preventing a traffic collision on a busy road.

TinyML model is a machine learning algorithm designed for microcontrollers or low-powered hardware to make them smarter and more useful. It takes up very little space, yet is capable of handling large-scale applications in embedded computing applications, like IoT. 

Mantas Lukauskas, a data scientist at Zyro, says, “80% of your work should be focused on the data you’re using – the model and model deployment are just a part of a good AI application process”. Besides, microcontrollers are very affordable compared to full-sized computers or servers, making their use far more accessible and practical for small businesses or even individuals.

In 2022, small data and TinyML will create a high possibility of appearing in an increasing number of embedded systems, including cars, wearables, home appliances, industrial equipment, agricultural machinery, etc.  

2- Automated Machine Learning

The biggest benefit with AutoML is that more and more companies can access ML, despite having less expertise in AI in general. Hence, AutoML solutions can be used by anyone to create their own ML apps. 

Take a situation when a subject matter expert is capable of developing a solution to the bigger issue in that subject. But he/she lacks coding knowledge and needs to apply AI to those problems. This is where they can utilize an AutoML solution through simple, user-friendly interfaces. 

The UI will keep the inner workings of ML out of sight, hence providing room to focus entirely on building their solutions. It is opening the way for industries of any sector to embed AI and ML in their businesses.

The most sighted applications of AutoML are in Data Science. Hence, data science graduates are in high demand. Data scientists with a bachelor’s degree earn over $1,00,000 annually. 

Developers are now able to build high-scaled and efficient ML models in the least time while sustaining model quality. It is possible by automating iterative processes like data cleansing and preparation, using AutoML.

Big market giants like Google are already utilizing AutoML techniques for automating the process of discovering optimization models. 

3- Generative AI for Deepfake and Synthetic Data

Audio, video, and image manipulation techniques are now more sophisticated with AI’s deep learning. You can produce real-looking photographs, convert black and white photographs to color, day photos to night photos, produce realistic photographs from textual descriptions, and more.

The advanced AI can also improve old images and old movies by upscaling them to 4K and beyond. 

While generative AI is becoming more popular in fun apps, it has clear benefits in certain areas, such as education, accessibility, film production, criminal forensics, and artistic expression.

Besides, it is beneficial in protecting people who do not wish to disclose their identities in interviews or at workplaces. Also, in the healthcare sector, it is going to be effective in the early identification of potential malice and creating effective treatments. 

No wonder why generative AI is expected to account for 10% of all data produced by 2025.

4- AI-on-5G

AI and 5G, together, are going to power the next wave of innovation in 2022 through the fastest cloud accessibility and data processing. 

5G delivers millisecond latencies, huge bandwidth, and reliable connections. 5G’s low latency when coupled with AI’s decision-making capabilities, optimizes the speed of computation between devices and the cloud.

In some of the industries, the combination is already producing high-quality output, with their ability to deploy fast, secure, and cost-effective IoT devices and smart networks.

For example, in-vehicle manufacturing units, visual inspection software with deep learning algorithms on 5G is utilized for quicker identification of defects in vehicles. Hence, car manufacturers are able to recognize and analyze quality issues on the assembly line and watch smart devices respond in real-time.

The efficiency of AI on speedy 5G is also going to shape smart traffic in 2022 through faster analysis in real-time applications. Consequently, it would improve urban city safety and space management. 

Next is the Conversational AI in customer support, which is gradually evolving with AI and 5G. It is creating a faster mode of communication with humans using facial expression and contextual awareness.

The advancement in AI is expected to transform every industry for sure, where 5G is playing a catalyst.

 5- AI Chips

General-purpose hardware is capable of supporting AI tasks but lacks sufficient performance for deep learning techniques. Therefore, AI chips are emerging as an advanced solution to companies looking forward to using processors that are capable of running artificial intelligence applications more efficiently.

AI-specific processors are modified with particular systems to optimize performance for tasks like deep learning and have parallel computational capabilities. Also, specialized AI hardware is estimated to allocate 4 to 5 times greater bandwidth than traditional chips. 

This capability is going to dramatically increase the performance of the companies operating a wide network of data centers for commercial cloud services. Plus, it will also facilitate the company’s internal AI operations, demanding faster computation.

In some areas, the application of AI processor units has begun and is likely to extend in the coming year. For example, monitoring software where a security system involves real-time facial recognition, such as IP cams, door cameras, etc.

AI processors are also proving effective in chatbots and voice assistants for customer care, powered with natural language processing.

Summary

Data science, AI, and ML will continue to be an integral part of technological advancement in the coming years. The market is set to see more such developments and innovations, which will provide a better scope of improvement in different sectors. 

In order to stay relevant in this competitive market, you need to stay updated with changing technology trends to make the right decisions and increase returns.

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