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Future Developments Anticipated in AI and ML

by Sonal Shukla

Artificial intelligence (AI) has re-taken centre stage in the current scenario after several decades of research surrounding the topic fondly called knowledge engineering; the pattern suggests it won’t cede this premium position anytime soon. Artificial intelligence was initially visualised to make computers more capable of independent reasoning. 

On the flip side, after passing through a multi-wave evolutionary phase, algorithm-based machine learning (ML) has been successfully modelled, which is focused on perception, reasoning and generalisation. 

 

What are Artificial Intelligence and Machine Learning?

Artificial intelligence is intelligence shown by machines. It is a method to train computers so these machines can ape the behaviour of humans.

Machine learning, a subset of artificial intelligence, is a process through which computer systems automatically learn by themselves, accessing data without human intervention or being explicitly programmed, adjusting actions accordingly and improving from experience. 

The scope of artificial intelligence and machine learning is broad. They are being used to solve various real-life problems and their usage may not even be evident to the average person. These developments have also led to a surge in demand for professionals in the AIML space. Therefore, it would be beneficial to join any of the AI and ML courses and equip yourself with skills for a promising career. 

 

Future Developments in AI and ML

The future of AI and machine learning has been challenging since the launching of pilots, though deceptively easy, deploying them into production is notoriously difficult. Here are a few anticipated developments in AI and ML space:

 

AI will spearhead infrastructure decisions through 2023: AI models will require periodical refinement through specific infrastructure resources employed by the IT team to ensure high success rates. This strategy might need to standardise data pipelines or integrate machine learning models with streaming data sources. 

 

Manage future complexities of AI methods through Joint Ventures (JVs): One of the top anticipated future challenges in leveraging AI methods like ML in edge and IoT environments is handling complex data and analytics. Success in such cases could come not only by marrying a business with IT but also by chalking out a proactive plan, which will provide ready solutions when new business needs arise.

 

ML techniques could become more simple: According to a media report from Gartner, by 2022, more than 75% of organisations could use classical ML techniques to leverage their business verticals. The early AI adopters have already delivered value to their organisations by leveraging advanced ML solutions, coupled with deep learning methodologies, in simple ways.

 

Effective use of cloud service providers to simplify the process of AI deployment: According to the aforementioned media report from Gartner, by 2023, cloud-based AI is likely to take a quantum jump to 5X from 2019 levels. This development calls for simplification of the whole process of deployment of AI.

Interestingly, business organisations can make strategic use of cloud technologies, viz, cognitive APIs, containers and serverless computing, enabling ML models to serve as independent functions, helping simplify the complex process of deploying AI and reducing the cost of overheads.

 

Identity ML projects that can benefit from serverless programming:

A serverless programming version is very appealing in a public cloud environment due to its quick scalability. The IT team leaders should therefore identify the ML projects that can benefit from these new computing technologies.

 

Conclusion: There is a lot happening in the AI and ML space.

As per Gartner’s report, currently, around 37% of businesses from various industries are leveraging AI and ML tools and techniques in some form or the other to scale up their businesses. However, IT experts predicted that by 2022, 80% of all modern use of technology in an organisation would be complemented by AI and ML. Gartner’s report also reinforces the belief that in the future, 80% of IoT projects will use AI in some form by 2022.

Therefore, AI and ML disruptions in future businesses are expected to get significantly more robust, leading to an increased AI/ML adoption budget. This adoption, in turn, is likely to fuel the data scientist’s employment rate by around 76% Y-o-Y basis. 

Thus, the combination of AI and ML intertwined with IoT is expected to give a forward kick to businesses’ top and bottom lines and assist them in dealing with situations arising out of economic uncertainty or in exceptional cases like the COVID-19 pandemic.

According to Gartner’s 2019 CIO Agenda study, the number of companies using AI shot up from 4% to 14% between 2018 and 2019.

The scope of this tech and the speed of the adoptions, therefore, indicate that this is a happening sector. If this seems interesting to you, then you must ensure you have the right skillset. Consequently, it is imperative that those aspiring to join the industry immediately dive into mastering a post-graduate program in AI and Machine Learning. This course can assist you on your way to a successful career path in artificial intelligence and machine learning. 

 

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