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What Machine Learning Has for Humans in the Coming 10 Years?

What Machine Learning Has for Humans in the Coming 10 Years?

Machine learning (ML) is a technique that allows a computer to do something it hasn’t been expressly instructed to do. As a result, machine learning plays a crucial role in bringing sentient machines to life. We wonder how close we are to being outperformed by these brilliant men with the unveiling of Sophia, an AI robot made by Hanson robotics. You’ve come to the right place if you’re wondering about the future of machine learning in the next ten years. Let’s get this party started.

Status Quo:

By introducing a mechanism for future systems to enrich their knowledge base from massive data sets while avoiding programming errors and logic concerns, machine learning has made it less complicated for them. Smart algorithms can now crunch this gigantic reservoir of both static and dynamic data and continuously learn and improve its efficiency thanks to the implementation of Big Data framework in mainstream applications.

This year, machine learning professionals focused on business applications of AI powered by machine learning and the idea of Deep Learning, rather than abstractions and theorising. In the real world, machine learning has been widely used in areas such as preventative healthcare, medicine, banking, finance, marketing, and media. ML isn’t slowing down anytime soon, based on its unblemished performance over the last five years.

Upgrade! Upgrade! Upgrade!

AI applications will become more widespread than ever in the next ten years, and people will be more accepting of machines among them. As a result, all service providers will need to upgrade their hardware (storage, backup, processing power, and so on) as well as software (servers, networks, ad-hoc networks, and so on).

To accommodate what’s coming, the computation power would need to be significantly increased, much as the parallel processing capacity supplied by GNUs has made present AI possible and viable. The technological workforce as a whole will be under tremendous pressure to improve and develop.

Trends to Come:

Machine Learning is in the PIE section (Peak of inflated expectations) and the ‘AI Everywhere’ column of the 2017 Hype Cycle for upcoming technologies. This demonstrates how the system is gaining popularity, and we are prone to expecting more from it than we should. With time, the flaws and overestimation will become evident, and we will gain a greater understanding of our position and skill in this subject. However, we must not be dismayed by the impending disillusionment trough. This is when substantial advancements in an area are made.

As a result, we should expect to see a lot more failed attempts and broken goals shortly. However, once we recognize the limitations of existing machine learning and search for real-world applications, we will undoubtedly hit a new milestone. The more widely used a technology becomes, the more likely it is to improve.

As a result, the future may be remembered as the golden age of AI and machine learning.

AI applications will become more popular than ever in the next ten years, requiring all service providers to enhance their hardware (storage, backup, computing power, etc.) as well as software (servers, networks, ad-hoc networks, etc.) capabilities.

Upcoming Professional Opportunities

With the advent of AI and machine learning development, numerous new professions and possibilities will emerge. Data scientists, AI/Machine Learning Engineers, Data Labeling Specialists, AI hardware specialists, and Data Security Professionals will be needed in the future of machine learning and AI. These specialists will have to be provided by the current workforce, and only those who adapt fast will be retained. These sectors, like all new vocations, will be unpredictable and difficult.

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AI/Machine Learning Engineers

  • To assist machine learning, an expert in this sector would need a solid understanding of the most commonly used programming languages and techniques. They also work together with the data scientist to ensure that their work is in sync.
  • The use of AI in medicine, healthcare, cybersecurity, natural language processing, speech to text, and translation systems will be unrivaled in ten years, as will the demand for programmers.
  • According to Element AI, a Montreal-based AI research group, there are only about 10,000 people in the world who are capable of solving severe AI challenges.
  • When it comes to job possibilities, tech behemoths like Facebook, Google, and others fight with the car sector for the best talent. Some in Silicon Valley joke that a pay cap system would be beneficial to the sector.
  • According to Gartner, a CIO in New York wanting to hire AI programmers has a total pool of 32 people, 16 of whom are qualified and just 8 of whom are actively looking for work.
  • To break into this field, you’ll need a master’s degree in machine learning or AI, as well as some hands-on experience managing AI projects.

Human Judgement: Thing of Past? 

One can ask if systems that process such a diversified and massive amount of data will require little or no human intervention or supervision. Regardless of how advanced our systems get, someone will be required to build and maintain them. Given the repeated failures of self-driving cars, one thing is clear: the need for human judgement will always be critical for AI.

Data scientists may also wish to investigate the perceived harm that smart systems pose and design procedures to prevent any mishaps. AI and machine learning research is now attempting to push the boundaries of these technologies, with some success expected in the next ten years.


Overall, despite the obstacles, machine learning is the path that will lead us to the most useful and advanced AI applications. The mantra for the tech sector is going to be “upgrade and update.” Data scientists will have to tread carefully to avoid serious consequences from poor data models, as these are what the AI understands. To avoid a crisis, we mobile application development companies properly examine the functionality and include human checkpoints when appropriate.

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