Promising AI trends for 2022

Surveys have shown that there has been significant adoption of AI solutions by many businesses, including online casino. However, not that many organizations are completely run by Artificial Intelligence but the amount and level of AI applications is increasing all the time. The fact that many are prepared to experiment with AI bodes well for the future of AI and what that will likely produce in the coming years.

There are a number of reasons for the increase in the uptake of AI applications and these include the following:

• They wish is to make product development more user-friendly; putting user needs at the center of the process instead of expecting them to adjust their ways of working around the product.
• The desire to improve data-backed decision-making.
• Enhancing customer and worker experience.
• Building and fortifying competitiveness.

The 10 AI trends for 2022 are listed here below:

For anyone interested in optimizing the resources available and thereby improving performance.

Automated machine learning or AutoML – Iterative tasks, the process whereby things are created, tested and revised are also automated. It encompasses the whole process from the very basic raw material right through to the development of the ML model that will be implemented.  There are quite a few trends that are emerging in this sphere for example, improved tools for data labelling and automatic tuning of neural net architectures. This is likely to encourage more adoption of AI as the cost is likely to decrease.   After this, it is likely that the next step would be XOps and the improvement of processes like   PlatformOPs. MLOps and DataOps.

Design with AI – Creating new images from text.   Creating innovative designs that can be produced on a large production scale.

Multi Modalities – As AI grows, and develops, ML models are in a position to support multiple modalities.  These include IoT sensor data, text, speech, and also vision. This is being used to do regular tasks like understanding documents.  This can be widely used.  It can be of great benefit in areas of medicine, particularly in medical diagnosis where multi modal techniques like optical character recognition and also machine vision.

Tiny ML – AI and ML are now found in many devices of all sizes.  And Tiny ML is now quite popular for instance in microcontrollers that power cars, refrigerators, and utility meters. Specific analysis can be done for sound, gestures, vital signs, and environmental factors.  Further development needs to be done for solutions for the security and management of Tiny ML in order to make it more effective.

Multi-Objective Models. – At the moment AI models are developed for a single purpose at any given time.    In the future, multi-task models that are able to perform multiple tasks will become possible.   At that time, the outcomes of the AI models will improve because of a more inclusive approach to tasks.

Better experience for employees – AI will lighten the load on employees by removing many of the more repetitive jobs that often need so much more human effort to be exerted on tasks. This will make for better use of resources, lower personnel costs and help to ensure that businesses are able to work more effectively.

A democratized AI – Technical skills are not necessarily needed to use AI tools today. Therefore, it means that anyone, including all those non-technical staff, can use AI tools and create AI models.  It means that subject matter specialists will be able to get more involved in the AI development process and thereby hasten even more the time to market.

Responsible AI – The development of AI is highly regulated.  The GDPR and CCPA regulations ensure AI transparency because of the use of personal and private data for essential decisions. Developing AI algorithms will also mean Responsible AI will be important.

Quantum ML -Due to the use of quantum computing, powerful AI and machine learning models are becoming a possibility. Now we find that Cloud providers like Microsoft, IBM and Amazon are offering quantum computing resources and simulators that enable businesses to find solutions to problems that have not yet been found.

Digital Twins Mature – Virtual models that simulate reality and are very popular for replicating human behavior. It is possible for them to predict the future and come up with different answers or solutions.   Mixing digital twins with more traditional industrial modes and AI-based agent-based simulation can be of use in other applications like ESG modeling, smart cities, and drug design.

Example of AI for medical use

A recent study took place in Canada where a group of researchers was able to show that by using AI deep learning they were able to identify birth defects. The study was published in a scientific journal, Plos One, and reported that "deep learning algorithms' potential to detect defects such as cystic hygroma as early as in the first-trimester ultrasounds".

This condition is life-threatening as it causes a build-up of fluid around the head of the embryo.  It is possible to diagnose this condition prior to birth without the use of AI but the study did show that through ultrasound scans the AI modal did identify this condition 93% of the time.

AI improves outcomes and more and more businesses and organizations are investing in it. AI is now being used across functions and is improving decision-making.  However, collaboration is needed between the technology team and the subject in question in order for goals to be achieved.