Microsoft's Azure, Google Cloud Machine Learning, Amazon Machine Learning, IBM Watson, and free platforms like Scikit.
democratize AI by packaging it into discrete components that are easy for developers to use in their own apps. Web and Universal Windows Platform developers can consume these algorithms through standard REST calls over the Internet to the Cognitive Services APIs.
Cognitive Services APIs are grouped into five categories…
Vision—analyze images and videos for content and other useful information.
Speech—tools to improve speech recognition and identify the speaker.
Language—understanding sentences and intent rather than just words.
Knowledge—tracks down research from scientific journals for you.
Search—applies machine learning to web searches.
Cognitive computing APIs:
An application programming interface (API) makes it easy for developers to incorporate a technology or service into the application or products they are building. The leading cloud vendors all offer an assortment of APIs for that allow developers to add a particular type of AI to their applications. For example, a developer that wants to make a photo-sharing app might use a facial recognition API to give the app the ability to identify individuals in pictures. Thanks to the API, the developer doesn't have to write the facial recognition code from scratch or even thoroughly understand how it works. He or she uses the API to allow the app to access that functionality in the cloud. APIs are available for a wide variety of different purposes, including computer vision, computer speech, natural language processing, search, knowledge mapping, translation and emotion detection.
Machine learning frameworks: These tools allow developers to create applications that can improve over time. Generally, they require developers or data scientists to build a model and then train that model using existing data. Machine learning frameworks are particularly popular in applications related to big data analytics, but they can be used to create many other types of applications as well. Accessing these frameworks in the cloud can be easier and less expensive than setting up your own hardware and software for machine learning tasks.
Fully managed machine learning services: Sometimes organizations want to add machine learning capabilities to an application, but their developers or data scientists lack some of the skills or experience necessary. Fully managed machine learning services use templates, pre-built models and/or drag-and-drop development tools to simplify and expedite the process of using a machine learning framework.
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.
Advanced infrastructure: AI applications, particularly machine learning and deep learning applications, perform best on servers with multiple, very fast graphics processing units (GPUs) that run workloads in parallel. However, those systems are very expensive, putting them out of reach for many organizations and use cases. AI as a service gives organizations access to those superfast computers at a price they can afford.
Low costs: Not only does "AI as a service" eliminate the need to pay for expensive hardware upfront, it also allows organizations to pay only for the time that they need that hardware. In cloud computing jargon, most AI workloads are said to be "bursty," that is, they require a whole lot of computing power for a short period of time. AI as a service charges organizations only for what they use, lowering their costs significantly.
Scalability: Like other types of cloud services, AI as a service makes it very easy to scale. Often organizations start with a pilot project that allows them to see how AI could be useful. With AI as a service, they can quickly move that pilot project into full production and scale up as demand grows.
Usability: Some of the best artificial
intelligence tools are available with open source licenses, but while they are inexpensive, these open source AI tools aren't always very easy to use. The cloud AI services generally make it easier for developers to access artificial intelligence capabilities without requiring them to be experts in the technology.
Drawbacks of AI as a Service
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.
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