Top 10 AI and Machine Learning Trends for 2022 for your business

Top 10 AI and Machine Learning Trends for 2022

Top 10 AI and Machine Learning Trends for 2022 for your business

Artificial intelligence has already been transforming business and society. Successful cases tend to zero in on the achievements and evolution of the relevant algorithms. For instance, Google’s BERT transformer neural network is a new type of algorithm that will revolutionize natural language processing. What are the AI and Machine Learning Trends trends in 2022 that we should be on the lookout for to best capitalize on?

The Status Quo of AI and Machine Learning Trends

Worthy of our attention, the new tools are being invented to automate machine learning directions and expedite the development process. In addition, the field of Artificial Intelligence is moving into new domains such as conceptual design, smaller devices and multi-modal applications, all of which expand its influence in many industries. Businesses should also keep an eye on the bleeding edge AI technologies now available for experimentation via cloud. Quantum AI is case in point.

10 AI and Machine Learning Trends for 2022

Automated machine learning (AutoML)

“Two prospects of automated machine learning will be improved tools for labeling data and the automatic calibration of neural net architectures.” said Michael Mazur, CEO of AI Clearing, using AI to enhance construction reporting.

The need for labeled data created a labeling industry of human annotators in countries like India, Central Eastern Europe and South America. As a result, the risks linked with using offshore labor made the market look at different ways of avoiding or minimizing this part of the process. Better chances in semi- and self-supervised learning are improving, which helps companies limit the amount of manually labeled data to a minimum.

In addition, by automating choosing and calibrating a neural network model, AI will become more affordable. This means that new solutions will take less time to reach the market.

AI-enabled conceptual design

In the past, AI was applied to streamline data, image and linguistic analytics. This is ideal for usage in financial, retail or healthcare industries and for clearly defined repetitive tasks.

However, recently OpenAI has developed two new models called DALL·E and CLIP (Contrastive Language-Image Pre-training). They are capable of combining languages and images to create new visual designs from a text description. The new models will facilitate the implementation of AI into creative industries on a production-level scale.

AI-enabled conceptual design

AI-enabled conceptual design

Multi-Objective Models

AI models are typically given one goal that focuses a particular business target like maximizing revenue.

Therefore, it is natural to predict that more companies will invest in multi-task models to consider multiple objectives. Targeting a single business goal without consideration of other objectives can lead to undesirable results. For example, if the engine only looks at customer conversion rate, the company may miss out on revenue opportunities such as new products that a customer did not buy before. In addition, the ever-growing importance of environmental, social and governance (ESG) goals means CIOs will have to balance between sustainability goals such as carbon footprint reduction and traditional business targets like reducing inventory and costs.

Multimodal learning

Within one ML model, AI is improving its performance in contexts such as text, vision, speech and IoT sensor data. Moreover, new ways are being researched to combine modalities to improve common tasks. Take document standing as an example.

In other words, patient data collected by healthcare systems can now include visual lab results, genetic sequencing reports, clinical trial forms and other documents. The layout and presentation can help doctors better understand their patients.

AI algorithms designed to use multi-modal techniques such as optical character recognition can optimize the presentation of results, enhancing medical diagnosis. This would require hiring or training data

AI-enabled employee experience

There has been a driving interest in using Artificial Intelligence to boost employee experience. “Everyone talks about delivering great customer experience, but the best way to do that is to deliver a great employee experience first!”, said Howard Brown, founder and CEO of – a call center tools provider.

AI support could be phenomenal in overstressed departments struggling to hire people, such as sales and customer success teams. When combined with process automation, AI could help automate repetitive tasks to allow more meaningful conversations with customers. It could also be used to improve coaching and training in general.

Democratized AI

Improvements in AI tooling have simplified the level of expertise to build AI models. Democratized AI will speed up AI development and provide the level of accuracy from subject matter experts. Frontline experts can see where new models can provide the most value and where they can create problems or need to be worked around.

Doug Rank, senior data scientist at Saggezza, says that this trend will mirror the direction of technologies like computers and networks. It all started from the point of being usable by technology professionals to wide embrace across the world. However, the biggest challenge is cleaning up the data and providing access with appropriate limits.

“With careful planning, IT businesses can ensure their data accuracy and completion across cloud migrations to realize the value of accessible AI,” Rank said.

Quantum ML

This promises huge breakthroughs in the coming years as quantum computers can become more powerful and satisfy the interest of and experimentation by the ML community,” said Scott Laliberte, managing director and leader, Protiviti – a digital transformation consultancy.

Quantum ML

Quantum ML

Tiny ML

Tiny ML is a rising star in terms of developing AI and ML models on hardware-constrained devices like microcontrollers for cars, refrigerators and utility meters. Tiny ML algorithms are expected to be utilized for local analysis of simple voice and gesture commands, sounds like a baby crying, asset whereabouts and direction, and environmental conditions. However, teams will need to embrace new methods for the development, security and management of Tiny ML.

Responsible AI

Initially, any laws concerning AI included guidelines on its transparency. However, now the authority in Europe and the Biden Administration in the U.S. are turning the heat up on the AI algorithms. As a result, trustworthy AI is growing in importance, not only to appease the governments and consumers but also to help business users understand AI better.

Thanneer Malai, senior technical program manager at Saggezza, predicts that companies will have to invest in training programs for trustworthy AI. This improved training will help us locate and address any problems that AI may miss.

Digital twins grow up

“The use of digital twins, which are virtual models that simulate reality, has been on the rise over the technology sectors recently. But now their use is starting to greatly accelerate.”, said Anand Rao, global AI lead at PwC.

Digital twins are considered important for businesses’ 2022 strategies by 88% of CIOs, according to PwC data.

Their sophistication has grown from real data-based digital twins to asset-based digital twins powered by internet of things (IoT), and to customer-based and ecosystem-based digital twins. They are also now implemented to model and simulate human behaviors and to assess alternative scenarios.

“The next stage of this development is the conjunction of scientific computing, industrial simulation and artificial intelligence to create simulation intelligence. This is where foundational simulation elements are integrated into operating systems,” Rao said.

The possibilities for digital twins provide firms with new approaches to leverage and forecast data. With more versatile digital twins, we can use AI simulation to predict real-world scenarios like disease progression, customer behaviors and economic impact of certain precursors. Digital twins are vital for companies expanding into ESG modeling, smart cities, drug design and other relevant applications.

Digital twin pilots are scaled and operationalized today. Therefore, CIOs should consider how to incorporate them into their business’s analytics architecture and cloud/IT-stack. Companies need to facilitate both a development and a production environment for simulations. Another crucial task for CIOs is to upskill employees to keep them well-equipped. In addition, enterprises should define a well-thought process for planning, developing, calibrating, implementing, and monitoring digital twins. Digital twins can certainly help CIOs transform a company only if the business is ready and well-prepared.

Why HDWEBSOFT should be your choice of AI and Machine Learning Trends

Machine learning and AI are two most exciting technology trends in 2022 which will have a profound impact on our life. Whilst the first is used to provide correct predictions based on data, artificial intelligence is utilized to develop machines that can independently think like we do. Consequently, selecting the right ODC is pivotal in creating the drive for growth and profits. It also ensures that any potential security risks will be removed and at the same time, core data will be kept confidential at the highest level.

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