AI is having a substantial impact on cloud computing, from generating more demand for unique cloud-based compute, rigorous workloads for deploying Deep Learning and Machine learning applications; allowing developers to produce “Intelligent” apps leveraging cloud-based AI API’s; and leveraging AI to manage and monitor large data centers.
The 3 Cloud-enabled breakthroughs that are accelerating AI solutions
So why is it that Artificial Intelligence has suddenly become so important across so many different domains?
The present “revolution” in AI is the straightforward result of three important breakthroughs that have advanced the development of Practical AI solutions, they are: the emergence of affordable parallel processing; the availability of Big Data; and access to improved (Machine Learning) algorithms.
Let’s take a look at each of these three (Cloud-Computing) enablers in more detail:
Origination of high level parallel processing
Traditional processor architectures and platforms often need many days to estimate the option range of a Neural Network (Deep Learning algorithm). These days clusters of modern (GPUs) and/or specialized high end Processors such as the Intel Xeon Phi can achieve this much faster. A modern generation of Artificial Intelligence “optimized” processor architectures such as the Google’s Tensor Processing Unit (TPU) and Intel Nervana platform is now being developed to support the emerging Cloud Based AI-as-a-Service features and capabilities.
Not every organization has the skills and capital required to manage the industrial computing platforms that needed to run advanced Machine Learning. Public Cloud Service Providers such as Google Cloud Platform ML Engine, Amazon Web Services (AWS Machine Learning), Microsoft Azure ML and the IBM Watson Developer Cloud, offer Developers and Data scientists a scalable infrastructure optimized for Machine Learning at a minimum cost than setting up and configuring their own on-premise environment.
Artificial Intelligence has become a critical driver for best-in-class predictive analytics and decision making. According to Gartner Inc, Artificial Intelligence, Data Science and advanced Machine Learning will be among the top new technologies to model business trends in the future.
Availability of Big data
In 2024, the digital universe is expected to reach 44 Zettabytes (1 Zettabyte is equal to 1 Billion Terabytes). The data which is highly important for enterprises, Particularly unorganized data from non traditional sources and IoT devices, is estimated to stretch both in relative and absolute sizes.
AI needs “Big Data” and “Big Data” Analytics needs AI In order to develop “intelligence”, Deep Learning algorithms require access to huge amounts of data. This can happen only computers are trained with this kind of inputs, they can expand their capabilities. Once again, Cloud Computing is enabling access to “Big Data”.
Centralized processing and storage of “Big Data” in Data Centers is enhancing the present generation of AI applications. Conversely, AI is essential to the next wave of “Big Data” analytics. It is an important tool for achieving a higher scale and maturity in data analytics; and for allowing broader deployment of advanced analytics.
- The impending “flood” of Data
Looking ahead to 2024 in the new world of connected people and things, there will be a literal flood of data being generated on a daily basis. For instance, every self-driving car may produce up to 4,000 GB of data each day, and a connected (smart) factory could generate over 1 million GB in a single day!
- The role of Edge Computing Devices
Not all of this data will be stored and/or pushed up to the cloud. Most of it will be processed locally by specialized “Edge” computing devices.
These “Edge” devices will also be able to leverage analytics in Artificial Intelligence as a result of specialized chipsets such as the Intel Arria 10 FPGA (Field Programmable Gate Array) for real-time Deep Learning Inferencing, the Intel Movidius Myriad Vision Engine, and the Intel GNA Speech Recognition Engine.
Access to advanced (AI) Algorithms.
Leading Cloud Service Providers make way to advanced AI abilities such as Image Recognition, general purpose Machine Learning algorithms and Natural Language Processing. There is also an emerging market for big data sets and algorithms that can be used for AI applications.
For example, Algorithmia is focused on building a marketplace for algorithms that are accessible through a simple, scalable API. The aim of Algorithmia is to make apps smarter by building a developer community around smarter applications. More than 30,000 developers already have accessed their growing library of 3,000 algorithmic microservices.
Kaggle is a base for analytics competitions and predictive modelling in which researchers and companies post data, data miners and statisticians compete to produce the best models for describing and predicting the data.
This collaborative approach depends on the truth that there are endless strategies that can be applied to various predictive modelling tasks and it is difficult to know at the outset which analyst or technique will be most effective.
