There is a "symbiotic" relationship between Machine Learning (ML), Artificial Intelligence (AI), and cloud computing—the cloud can provide AI with the information needed to perform a variety of Machine Learning (ML) tasks, and in return, AI can increase enhance the information present in the cloud by generating more data.
According to a 2018 survey report by IBM, 3 out of 4 companies believe that, along with general computational systems, their organizations need computation through "cloud also," which "best fit" their needs. They aren't against the use of public clouds.
According to 2016 global study conducted by IBM titled, The Cognitive Advantage, which is also the latest available study on the impact of AI and cloud computing on cognitive computation, 65% of the companies who are the early adopters of AI claim that the combination of AI with cloud computing is important for the success of their organizational strategy.
There is a "symbiotic" relationship between Machine Learning (ML), Artificial Intelligence (AI), and cloud computing—the cloud can provide AI with the information needed to perform a variety of Machine Learning (ML) tasks, and in return, AI can increase enhance the information present in the cloud by generating more data. To be more precise, Machine Learning is a subset of Artificial Intelligence that focuses upon "narrower range of activities" than those performed by the latter. Machine Learning is, in fact, "the only real artificial intelligence with some applications in real-world problems." One of the most important and practical applications of Machine Learning is Data Science—a combination of the aspects of ML with other disciplines like cloud computing and Big Data analytics—that has a huge importance in solving many complex real-world problems.
A deep dive into the importance of Machine Leaning / AI, in combination with cloud computing, along with the perception of people or companies towards its applications in providing IT solutions is presented below.
HOW AI IS TRANSFORMING CLOUD COMPUTING
MAKING 'INTELLIGENT CLOUD'
In today's data-driven world, millions of people need the cloud for computing, storage and networking of the preexisting as well as current data along with large amount of every day processes. This huge source processed as well as unprocessed information could be useful for AI for its learning. Thus, the combination of cloud computing with AI is even more beneficial, and this amalgamation of cloud with AI is termed as 'Intelligent Cloud.' One of the biggest applications of this 'Intelligent Cloud' is cognitive computing, just like it is done by a human brain, to perform even the most complicated computational tasks which weren't possible before. Thus, cognitive computing, which involves the combination of AI and cloud, is source of "innovation and a means to accelerate change" in today's world in which digital transformation is not a one-time task but a constant journey. And, our Intelligent Cloud promises this change.
ARTIFICIAL INTELLIGENCE AS A SERVICE (AIAAS) IN PUBLIC CLOUD
From the previous discussion, it is already clear that Artificial Intelligence is responsible for the future growth of public cloud, and because of this fact only, cloud service providers are preparing themselves to offer AI as a Service (AIaaS) to their respective clients. For Deep learning and neural networks— which are advanced features of machine learning— there is big need of the combination of CPUs and GPUs, which are needed to perform faster calculations. And, cloud providers are providing GPU-powered virtual machines and containers through a "pay-by-use model" on public, private, and hosted clouds.
Additionally, general purpose cognitive computing can be easily performed through AI using generic APIs, available in common, on the cloud. However, cloud vendors are also offering their clients to provide custom data sets for performing, even more precise, custom cognitive computing on their data sets. With custom cognitive computing, "customers bring their own data to train cognitive services to deliver niche, specialized services." With this approach, there is no need of choosing right algorithm and customized models for training AI.
It is also a known fact that cloud vendors are investing in public cloud to attract customers, however, AI on public cloud is still in its development stage.
In today's data-driven world, millions of people need the cloud for computing, storage and networking of the preexisting as well as current data along with large amount of every day processes. This huge source processed as well as unprocessed information could be useful for AI for its learning. Thus, the combination of cloud computing with AI is even more beneficial, and this amalgamation of cloud with AI is termed as 'Intelligent Cloud.' One of the biggest applications of this 'Intelligent Cloud' is cognitive computing, just like it is done by a human brain, to perform even the most complicated computational tasks which weren't possible before. Thus, cognitive computing, which involves the combination of AI and cloud, is source of "innovation and a means to accelerate change" in today's world in which digital transformation is not a one-time task but a constant journey. And, our Intelligent Cloud promises this change.
ARTIFICIAL INTELLIGENCE AS A SERVICE (AIAAS) IN PUBLIC CLOUD
From the previous discussion, it is already clear that Artificial Intelligence is responsible for the future growth of public cloud, and because of this fact only, cloud service providers are preparing themselves to offer AI as a Service (AIaaS) to their respective clients. For Deep learning and neural networks— which are advanced features of machine learning— there is big need of the combination of CPUs and GPUs, which are needed to perform faster calculations. And, cloud providers are providing GPU-powered virtual machines and containers through a "pay-by-use model" on public, private, and hosted clouds.
Additionally, general purpose cognitive computing can be easily performed through AI using generic APIs, available in common, on the cloud. However, cloud vendors are also offering their clients to provide custom data sets for performing, even more precise, custom cognitive computing on their data sets. With custom cognitive computing, "customers bring their own data to train cognitive services to deliver niche, specialized services." With this approach, there is no need of choosing right algorithm and customized models for training AI.
It is also a known fact that cloud vendors are investing in public cloud to attract customers, however, AI on public cloud is still in its development stage.
USER'S PERCEPTION IN THE ADOPTION OF CLOUD COMPUTING IN AI
1. According to a 2018 survey report by IBM, on the use of AI and cloud computing by finance and telecom companies in the Adriatic region, 3 out of 4 companies believe that, along with general computational systems, their organizations need computation through "cloud also," which "best fit" their needs. They aren't against the use of public clouds.
2. The same survey also suggests that about one-third of the companies are "cloud resistant," even when the cloud vendor provides them all the functionality needed for their organization.
3. About one fourth of the surveyed banks one quarter of the banks are already using AI powered solutions for "fraud and risk detection and management."
4. As per the survey, private cloud services are mostly preferred option among every two out of five finance and telecom companies surveyed.
5. The most important insight of the survey is that none of companies surveyed have a "public cloud first approach" when they are trying to enhance and upgrade their computational and IT requirements.
6. However, IT experts have a different perception regarding the adoption of public clouds in AI. According to Aust, an analyst from Gartner, for cognitive computing, the companies need "to use [public] cloud to access large pools of publicly accessible information." Moreover, as per the recent article published in Techgenix, this crowdsourced data on public cloud is of great utility for the companies in providing futuristic AI-powered services like facial recognition, conversation, among many other, to their clients.
7. According to 2016 global study conducted by IBM titled, The Cognitive Advantage, which is also the latest available study on the impact of AI and cloud computing on cognitive computation, 65% of the companies who are the early adopters of AI claim that the combination of AI with cloud computing is important for the success of their organizational strategy.
8. The report predicts that the cognitive computing market, which was of global value $2.5 billion in 2014, will achieve a growth of more than $12.5 billion in 2019.
9. According to the survey, 46% of early adopters, however, struggle with a definite strategy for adoption of cognitive computing. In fact, only 7% of them have a comprehensive and definite roadmap for adoption of the new technology.
10. It is noteworthy that 57% of the early adopters also reported to have valid security concerns regarding the adoption of new technology, which involves amalgamation of AI with public cloud.
6. However, IT experts have a different perception regarding the adoption of public clouds in AI. According to Aust, an analyst from Gartner, for cognitive computing, the companies need "to use [public] cloud to access large pools of publicly accessible information." Moreover, as per the recent article published in Techgenix, this crowdsourced data on public cloud is of great utility for the companies in providing futuristic AI-powered services like facial recognition, conversation, among many other, to their clients.
7. According to 2016 global study conducted by IBM titled, The Cognitive Advantage, which is also the latest available study on the impact of AI and cloud computing on cognitive computation, 65% of the companies who are the early adopters of AI claim that the combination of AI with cloud computing is important for the success of their organizational strategy.
8. The report predicts that the cognitive computing market, which was of global value $2.5 billion in 2014, will achieve a growth of more than $12.5 billion in 2019.
9. According to the survey, 46% of early adopters, however, struggle with a definite strategy for adoption of cognitive computing. In fact, only 7% of them have a comprehensive and definite roadmap for adoption of the new technology.
10. It is noteworthy that 57% of the early adopters also reported to have valid security concerns regarding the adoption of new technology, which involves amalgamation of AI with public cloud.