AI Is Changing The Face Of Data Security: Study
The introduction of the deep Learning (DL) approach to machine learning has led to incredible developments in artificial intelligence (AI) over the past few years, attracting interests from cloud computing and semiconductor chip businesses. According to TrendForce’s latest research on AI, finance and data security together have become the largest application category for machine learning/AI solutions, currently accounting for about 20% of the total value of the machine learning market. The second largest application category is digital advertising technologies, which makes up roughly 18% of the total market value. Other applications for AI are also growing rapidly.
According to TrendForce’s research, the three forces that drive advances in machine learning are software, hardware and data. AI systems based on DL requires vast amounts of data to train their abilities such as identifying objects and processing requests. Therefore quantity and quality of data directly affects AI systems’ accuracy. As cloud and software platform companies compete to bring AI solutions for different applications, ownership and access to data will become crucial to their strategies.
Cloud and software platform companies actively seek data control and access
At present, cloud and software platform providers are major players in the development of AI solutions for different applications. These players include Google, Amazon Web Services (AWS), Facebook, IBM, Microsoft, Apple, Baidu, Tencent and Alibaba. Cloud and software platform companies have huge databases that encompass Internet users’ online activities and information extracted from the usage of their software tools. These databases benefit the development of APIs and SDKs for AI systems.
“Owning databases and having access to them sometimes are two separate things,” said TrendForce analyst Christy Lin. “Providers of public and private cloud services such as AWS, Microsoft and Google do not necessarily have ownership of data from users and businesses. However, these major technology brands can have access to the data when users and organizations use their cloud-based APIs. The data can be used for the development of AI-powered products and the optimization of the software tools.”
Lin also noted the promotion of open source frameworks is also an important part of the AI strategy. Cloud and software platform providers want more developers of AI solutions to use their preferred open source frameworks because this creates follow-up opportunities linked to offerings of software and hardware support.
Some examples of these open source frameworks include Google’s TensorFlow, Facebook’s Torch, Microsoft’s CNTK and Intel’s Neon (which the company obtained by acquiring Nervana Systems). The competition in the open source framework market is intensifying due to the arrival of new entrants.
Devising AI systems for specific professional fields will require domain knowledge, said the TrendForce report, which said that data relevant to certain professions – will be a necessary component in improving the accuracy and reliability of AI systems. For instance, an AI system that is be deployed in the healthcare field (i.e. digital diagnosis assistant) has to be trained on data such as medical images and patient health records.
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