In their never-ending quest to stay ahead of the competition, a growing number of enterprises are looking for ways to incorporate artificial intelligence into their applications, products, services and big data analytics. And one of the easiest and most popular ways to get started with the technology is by using cloud-based AI as a service (AIaaS) offerings.
According to IDC, worldwide spending on cognitive and AI systems is expected to increase at a compound annual growth rate (CAGR) of 50.1 percent through 2021. That means total spending on these technologies will increase from $12.0 billion in 2017 to a whopping $57.6 billion by 2021.
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See our list of top artificial intelligence companies
Much of that spending will likely go to cloud-based AI services. The RightScale 2018 State of the Cloud Survey revealed that organizations are particularly interested in using cloud services related to one particular type of AI: machine learning. When survey respondents were asked which types of public cloud services they planned to use in the future, machine learning services were the number one vote-getter, with 46 percent either experimenting with the technology or planning to deploy it, despite the fact that only 12 percent are currently using those services.
Clearly, organizations are interested in AI as a service, and the cloud vendors are responding with a growing number of products.
There are myriads types of AI as a Service because “artificial intelligence” is a broad term that covers a wide array of technologies. At its core, AI is about creating machines that can do the same sorts of things that human brains can do. For example, AI includes computer vision technologies that can see and identify the objects in pictures. It also includes natural language processing technologies that enable systems to carry on a normal conversation, as well as machine learning technologies that allow computers to learn without being explicitly programmed.
AI as a service offerings make one or more of these types of artificial intelligence technologies available as a cloud service. Currently, the AI as a service products on the market generally fall into the following categories:
The “holy grail” of AI as a service would be to create a general artificial intelligence that could be accessed as a cloud service. A general artificial intelligence is a computer system that can think and communicate in all the same ways that humans can. Most experts believe that researchers are still many years away from creating general AI, if they will ever be able to do so at all.
Some organizations, mostly very large enterprises, choose to invest in their own AI research and hardware. However, many prefer to use AI as a service because this approach offers a number of benefits, including the following:
The two biggest drawbacks of AI as a service are two issues that are common to all cloud computing services: security and compliance.
Many AI applications — especially applications that incorporate machine learning capabilities — rely on vast quantities of data. If that data is going to reside in the cloud or be transferred to the cloud, organizations need to make sure that they have in place adequate security measures, including encryption both at rest and in transit.
In some situations, regulations may prevent some types of sensitive data from certain industries from being stored in the cloud. Other laws require that some data remains within the borders of the country where it was originated. In these cases, it may not be possible to use an AI as a service offerings for those specific use cases.
Another potential drawback is that AI as a service can be very complex. Organizations will have to invest time and effort in training and/or hiring staff with artificial intelligence and cloud computing skills. However, many organizations believe that this hurdle can be easily overcome and that AI as a service will pay off in the long run.
All of the leading cloud computing vendors offer AI as a service, and some smaller vendors has cloud-based AI services as well. Here’s an overview:
Vendor |
Bots |
APIs |
ML Frameworks |
Fully Managed ML |
AWS |
· Lex
|
· Polly
|
||
Microsoft Azure |
· Face API
|
|||
Google Cloud |
|
· AutoML |
||
IBM Cloud |
|
·Natural Language Understanding
|
N/A |
The list of additional entrants into the AI as a Service sector will certainly grow rapidly. As a start:
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