Artificial Intelligence

Artificial Intelligence and Cloud Computing

The Role of Artificial Intelligence and Cloud Computing

In simple words, Artificial Intelligence (AI) is the ability of a computer to do a task that requires human intelligence and discernment. Software that can execute a series of coded instructions isn’t AI. But a computer or device that can learn, recognize speech, or solve problems is.

Cloud computing, although several decades younger than AI, helps support AI in many ways. In this article, we’ll explore more closely the role of cloud computing in artificial intelligence to see how the two are connected.

Artificial Intelligence and Cloud Computing – The Rise

artificial-intelligence-and-cloud-computingThe first AI applications appeared in the early 1950s. They were simple programs that could play chess and checkers. Interest in AI ebbed and flowed during the second half of the 20th century. There was some progress, but no mainstream breakthroughs to capture the world’s imagination.

Fast forward to 2011, and IBM’s question-answering supercomputer Watson defeated two multiple-time winners of the quiz-based television show Jeopardy!

This event and the success of the neural network dubbed AlexNet in the ILSVRC challenge a year later renewed interest in the capabilities of AI and its subsets like Deep Learning.
Today, businesses and other organizations across industries are investing in AI, with AI-related jobs expected to be in high demand in the years to come.

For its part, cloud computing really took off in the mid-2000s when Amazon launched the Elastic Compute Cloud service providing computing resources over the internet in the form of Infrastructure-as-a-Service (IaaS) computing followed by Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). All of these models can be integrated with AI.

Artificial Intelligence and Cloud Computing – Accelerates Growth

artificial-intelligence-and-cloud-computing-panelIaaS provides AI scientists with the computing infrastructure they need to create, test, deploy, and use AI solutions. Through IaaS, developers can access CPU, GPU, memory, disk, networking, and other resources much quicker and at a better cost than if this infrastructure would have to be created in the form of a data center specially for their project.

Meanwhile, PaaS helps AI engineers to develop AI applications more easily by using AI services like data catalogs. SaaS, on the other hand, has helped to distribute AI services within applications like customer relationship management (CRM) tools and increase their adoption.

Several cloud computing technologies have also helped AI scientists to access computer resources efficiently and build AI apps faster and at a better cost. These technologies include containers, data sets, and Kubernetes, which can automate the deployment and management of applications within containers.

Cloud computing also supports interest in AI through cloud-hosted learning platforms and competition platforms. On Udemy or Coursera, people can complete AI and deep learning courses. Meanwhile, on platforms like Kaggle, data scientists compete to build efficient AI and related algorithms.

Today, all the major cloud services providers including Amazon, Microsoft, Google, and IBM provide data science and machine learning platforms. These include:

    • Natural Language Processing (NLP), which allows computers to interpret human language and is used in translation, spam filters, or search engines.
    • Automated Machine Learning, which automates repetitive, time-consuming tasks to increase productivity.
    • Analytics capabilities, which enable Big Data processing for making predictions and gaining insights from large sets of data.

Artificial Intelligence and Cloud Computing – Summary

According to the World Economic Forum, cloud computing is the top priority for business leaders, with adoption rates set to continue to increase by 2025.

With cloud computing expected to be used by most businesses and organizations in one form or another, it’s likely going to continue to act as a catalyst for AI.

In the end, AI and subset services and technologies would not be as widely used today nor as accessible without the cloud. The cloud’s computing resources and its integration into cloud services have proved invaluable to its reach.

As both AI and cloud computing continue to grow, their special bond is likely to take them into new territories. At this point in time, it would be almost impossible to speak of the future of AI without cloud computing.

Edward Kuhn

Edward Kuhn is a software architect who leads technical teams across a diverse range of projects using various platforms for Insurance, Medical and Manufacturing Industries.
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