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The Role of AI in Cloud Computing

admin 19th, February, 2020
The role of AI in cloud computing


Cloud computing services have morphed from platforms like Google App Engine and Azure to Infrastructure which involves the supply of machines for computing and storage. Additionally to the present, cloud providers also offer data platform services which span the various available databases. This chain of development points within the direction of the expansion of AI and Cloud Computing.

Cloud Computing making AI accessible

Cloud computing lowers the cost of accessing AI

  • Implementation cost is such a large problem for many businesses contemplating AI, cloud computing’s low cost is a huge contributing factor
  • Services delivered via serverless architecture allow companies to pay for only the computing power they use.
  • Services like AWS Lambda ensure that IT budget get used in one of the most cost-efficient ways currently available.
  • This means businesses can make use of, Serverless architecture relatively need to pay low price tag on the large amounts of computing power required to run AI
  • Ultimately, this all means less time and money spent worrying about how to power AI


Cloud computing helps define AI and its capabilities

  • Working with AI is much more intimidating when resources and possibilities aren’t pre-defined. When user don’t know what a technology is even capable of, it is not likely to know how to put it to work
  • Several cloud companies offer cloud-delivered services which pre-package AI’s capabilities
  • Amazon, Microsoft, IBM, and Google are just the largest of the companies currently offering cloud-delivered AI services (more on their specific offerings later)
  • These services take the guess work out of how AI can help business. They offer inspiring case studies, grounding limitations, and welcoming documentation.


Cloud computing delivers open data to make AI smarter

  • In 2012, Google conducted a deep learning experiment in which they trained a neural network to recognize both human and cat faces. To train the AI, Google used 10 million images extracted from YouTube videos.
  • Four years after publishing the report on their data-devouring experiment, Google released a dataset of millions of tagged photos and videos for the specific purpose of providing the machine learning community with data they could use to build and experiment. This perfectly exemplifies the concept of open data.
  • Open data is the idea that a particular set of data belongs to no one and everyone—it is freely available without concern for copyright or any other legal restrictions. For AI and machine learning to continue growing, more entities are going to need access to large sets of data. Open data offers a solution.
  • Cloud-delivered services are perhaps the greatest hope for the accessibility and adoption of AI


Applying cloud-delivered AI to business

  • The combination of cloud computing and AI is shaping up to be a disruptive force across many industries
  • Transparency Market Research predicted that the “machine learning as a service” market will increase from $1.07 billion in 2016 to $19.86 billion in 2025. This relationship not only brings a new degree of accessibility to AI, but it creates a new way of thinking about other existing technologies and methodologies.
  • The human element of customer service makes it a constant concern for both businesses and customers.
  • AI’s ability to understand language presents new customer service solutions like automated personal assistants and chatbots. All of the technology needed to power these solutions are available through cloud services like Amazon Lex chatbots and Facebook Messenger’s chatbot APIs. Microsoft CEO Satya Nadella has named this particular implementation of AI “conversation as a platform”. He believes the transition to AI conversational interfaces could be more disruptive than the role of the touchscreen in the smartphone revolution.


Vision and image recognition

  • Deep learning gives AI the ability to recognize images in a way that is similar to how our eyes and brain allow us to see. This makes it possible for AI to have vision that is equivalent to or better than human vision.
  • There are AI services for identifying the contents of an image, like people, animals, and objects. Similarly, AI can classify images with certain labels that make it much easier to sort through a large number of images.
  • This basic concept has been expanded into other, more specific services. Multiple APIs offer facial recognition, allowing programs to identify whether a certain person is present in an image. Also, when user considers that visual AI is able to process many images, it makes sense that they would also be able to process the frames of a video. This makes way for services like AI video editing and indexing.


Conversation recognition and automation

  • For at least a few years, there has been AI capable of passing the Turing test, which tests whether an AI is able to converse in a way that is indistinguishable from a human. While this doesn’t guarantee that any AI will be able to take the place of a company rep, it does potentially offer opportunities for business.
  • Speech recognition services allow AI to identify a speaker by their voice and convert speech into text that is usable by an application. Natural language processing makes it possible for AI to understand regular, human speech rather than robotic commands. Translation services translate text and speech in real-time with AI that is optimized for conversation.


Decision making

  • One of the greatest promises of modern AI is its ability to predict outcomes and facilitate strategizing around data. While many applications of this kind of AI will be specific to the business they’re built for, there are at least a couple of more general example available.
  • AI services help users to predict which parts of store or interface are most useful to their customers and adjust their UI/UX accordingly.
  • These are just some general examples of some of AI’s capabilities that are easily accessible through cloud-delivered services. For a more specific view, we can take a look at some of the services presented by companies like Amazon, Microsoft and IBM.


Build AI applications using cloud infrastructure

  • Build AI applications using cloud infrastructure
  • Currently, the greatest value of cloud-delivered AI services lies in their ability to enhance existing products and processes. This means businesses should research, brainstorm, and experiment to find ways to use the available services to bring value to their customers.
  • Fortunately, the infrastructure and computing resources provided by cloud computing allows companies to build and use AI with minimal resistance
  • For instance, Amazon’s machine learning service makes it possible to apply AI to data. Essentially, this means you are able to use the same algorithms that Amazon uses in their products to enhance applications.
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