Which situation is an enabler for the rise of artificial intelligence (AI) in recent years?

  1. Availability of cloud-based, hosted machine learning platforms
  2. Limited computing power
  3. Customized machine learning algorithms
  4. Decline in conventional programming

Answer – Option (A) Availability of cloud-based, hosted machine learning platforms

The “Availability of cloud-based, hosted machine learning platforms” is the correct answer to the question “Which situation is an enabler for the rise of artificial intelligence (AI) in recent years?” The only hindrance artificial intelligence was facing during its evolution period was the lack of computing power required by most of the developers to process huge amounts of data.

The main purpose of any AI is to think like humans and reproduce their intelligence which was nothing but fantasy for many decades. If you want to make this happen you would require a super fast computer and only ultra-big corporates have the resources to create such a computer system.

However, in order for any technology to evolve into the next big thing it must be accessible to common users and developers. Thanks to cloud-based platforms this was no longer the issue and now anyone with adequate resources can use supercomputers through internet networks.

In technical terms, you can say that AI systems especially deep learning models need systems with massive computational capabilities that can access abysmal amounts of information and data, process it, and train from it. Any small-sized organization or user cannot have the budget to build or to maintain a system like this and have access to the amount of data required locally.

Cloud-based machine learning platforms now has completely changed the way. Providers such as Amazon Web Service (AWS), Google Cloud, and Microsoft Azure have put in the resources to build such super-computer systems that can not only handle the requests for their side but also support additional parties with small budges which does not have the power and capacity to have such infrastructure.

These cloud-based systems have strong graphical processing units (GPUs) and Tensor Processing Units (TPUs) needed for rapid training of deep neural networks. Small development teams and you yourself can rent these systems on a pay-as-you-go basis and develop custom artificial intelligence systems and machine learning algorithms.

Other than that, you also have access to a multitude of additional resources and tools like huge amounts of datasets, pre-built frameworks of machine learning, as well as mechanisms for model development and data processing.

This not only makes the development process easier but also helps developers to build new models and fine-tune them. As a result, absolutely anyone with knowledge of AI development can now dabble in and have access to advanced AI capabilities. This lowers the overall entry barriers which was not possible in the past.

You should also note that customized machine learning algorithms have also a part in the evolution of artificial intelligence. Many of the conventional algorithms were made just to handle general workload. However, many real-world scenarios needed custom algorithms to work as intended. These custom algorithms tackle the nuance and specific needs of a given scenario or industry.

Now, with cloud-based machine learning platforms developers are able to make, train, and use these models with custom algorithms without having to reinvent the wheel. This flexibility boosts the overall development of AI and makes it possible for traditional industries such as healthcare and finance or automobiles to incorporate them.


“Which situation is an enabler for the rise of artificial intelligence (AI) in recent years?” Among the given options, the “Availability of cloud-based, hosted machine learning platforms” makes the most sense in both theoretical and practical terms. The biggest obstacle with AI was accessibility, especially to small developer teams and individuals and it was solved with cloud-based platforms.  

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