TOP 10 AI & ML TRENDS 2022

Artificial intelligence has come a long way since the term’s inception in 1956 at Dartmouth College, such that the global AI market is expected to reach $90 million by 2025. Furthermore, the annual growth of AI is forecasted to grow more than 30%.Since Alan Turing’s groundbreaking research, the “Turing task” to Mr Pichai declaring it as the “most important thing the humanity is working on”, AI and ML have changed the landscape of technology

With this advancement, there are numerous trends that we can expect to see in 2022.

Here are top 10 trends everyone should be prepared for:

1. AI and Cloud Collaboration:

AI Cloud comprises AI use cases and a massive AI workload stored on the cloud infrastructure. The collaboration between AI and Cloud delivers a concept known as AI software as a service. It refers to off-the-shelf AI products that allow businesses to deploy and expand AI approaches. In cloud computing, “anything as a service” refers to any programme you can access via a network.

2. Low/No Code:

The popularity of no-code/low code platforms, which need little or no coding experience – is growing. This tendency is a stride toward computer science’s long-term aim of automating manual coding; giving businesses the freedom and agility to react to rapidly changing industry realities while also expediting the time-to-market of new applications to keep up with or shift markets.

3. IoT :

The Internet of Things (IoT) is a system that assists us in reimagining our daily lives; however, AI is the fundamental driving factor behind the IoT’s best ability. We are already using some of its applications in our daily lives like healthcare wearables, smart homes, smart cities, smart industries and what not. It is not limited to this, but scientists are exploring ways to develop other technology like Voice and Vision AI.

4. Tiny ML :

It is a field of Machine Learning that is proliferating. It consists of hardware, algorithm and software, which performs functions and tasks at as low power as the mW range, enabling enterprises and data scientists to carry out numerous always-on-use cases on battery-powered devices! It is even becoming feasible to use it on a commercial level and even for architectural purposes. Overall, being able to perform most of the conventional machine learning functions on low power makes it a huge asset for the future.

5. Hyper Automation :

As the name suggests, hyper-automation is the process of automating as many business and IT-related tasks as possible with the help of various tools, algorithms and technologies. However, Artificial Intelligence and Machine Learning technologies are the primary driving forces behind this. The idea is to automate everything from the easiest of tasks to the high-end, complex and lengthy tasks to improve the workflow in the organisation, making it faster and easier.

6. Metaverse :

Facebook changing its name to Meta shows that this is one of the hottest trends we can expect this year. It is introduced not as an advancement in technology, but it will soon become the ‘mainstream technology’. Metaverse is a 3D environment that combines VR, AR, ER, MR and even blockchain to give the user an immersive experience. AI makes metaverse much more interactive, automated and intelligent by using its hardware, software and algorithms.

7. Voice & Language Driven Intelligence :

it is, at its core, a system which uses voice commands in order to carry out tasks. It is also known as conversational AI, which uses Natural Language Processing to take commands from humans in human language and interpret them. This scales operations, increase efficiency and saves time. It involves coding and decoding by the AI/ML software. Leveraging voice and language recognition is again something that humans cannot do without in the near future.

8. Cybersecurity:

As opposed to the popular belief of most people, AI can actually help us in maintaining security – cybersecurity, to be precise. With the increase in thenumber of cyberattacks, under-resourced security analysts use AI to stay ahead of this. It will aid the most in secure application development, which is what is believed by IBM security. It helps one to protect, detect and respond beforehand.

9. Multi-modal Learning:

It utilizes a number of senses to make learning more comprehensive and interactive. Covid made multi-modal learning the need of the hour. As a result, the education system saw a significant turning point, as educators agreed that students’ learning should not suffer in lieu of the epidemic. Therefore, in the call of the hour, educators worldwide used technologies like auditory and visual learning and reading/writing as part of the multi-modal learning innovation.

10. Automated Machine Learning :

The method of automating the task of machine learning is called automated machine learning, in simple terms. AutoML essentially encompasses everything from curating or developing a raw data set and building a machine-learning algorithm to deploying it on the same.

CONCLUSION

With the increase in the need for machine learning and AI automated software, data scientists and business analysts predict that these trends will rule the world of technology in 2022. Machine learning engineers and AI experts can build on these trends to develop even more advanced technology to help companies leverage a competitive advantage. And the users – innovators and early adopters can capitalise on these trends by being the first ones to hop on them.

FAQs

1. What is multi-modal learning?

Ans. Multi-modal learning is an embodied learning environment in which the learner engages several sensory and action systems. This learning style is typically stressed for children with learning disabilities; however, it has become famous in the online mode of learning where the student’s comprehension is limited due to the virtual setting. It can involve a range of visual stimuli in addition to text.

2. What are the different examples of multi-modal learning?

Ans. Multi-modal learning uses the help of Kinesthetics, Visual, Auditory and Tactile senses to engage the learners better. An example could be people learning from images, photos and graphs. Students can also learn from kinesthetics by responding to physical signals such as gestures and movement.

3. What are the advantages of multi-modal learning?

Ans –

• Students are more engaged and less monotonous.
• The session becomes more interactive.
• The course is easier to understand as it involves comprehension from multiple senses.
• It challenges both students and teachers to be creative.