The growth of popularity in AI has been mirrored by a growing number of concerns surrounding privacy, security and ethical use of data.
TL;DR:
With the steady rise in both popularity and progress in Artificial Intelligence (AI) over recent years, many have been quick to address potential privacy and security concerns, with buzzwords like ‘ethics’ and ‘responsibility’ never too far from discussion. While the initial public perception of AI was “will automation steal my job”, steady progress has seen facets of AI and Machine Learning technology present in our living rooms, cars, phones, and more, mostly without people knowing. That said, an important question has emerged: What level of trust can—and should—we place in these AI systems?In an age of increasingly complex governmental data privacy requirements, it can be hard to understand not only the level of personal data that’s available, but also how this is protected, both in law (GDPR, Information Privacy law etc.), but also through the development of solution provider products.Why do we need such swathes of data? Well, a simple input/output equation suggests that more data equates to the ability for increased training and training environments. This, in turn, leads to models that are often increasingly accurate due to both the level of training and the various training scenarios it has been placed inside . At this stage, it is also important to recognize the differentiation between both structured & unstructured data as well as supervised & unsupervised learning. You can read more on this here.To surmise, the main challenges faced by those in need of large data training sets include, but are not limited to, the following:
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