One of the least
understood aspects of using tech-based devices, mobile applications, and other
cloud-based services is how much of our private, personal data is being shared
in the process—often without our even knowing it. Over the past year, however,
we’ve all started to become painfully aware of how big (and far-reaching) the
problem of data privacy is. As a result, there’s been an enormous spotlight
placed on data handling practices employed by tech companies. Over the next
year, I expect to see many more hardware and component makers take this to the
next level by talking not just about their on-device data security features,
but also about how on-board AI can enhance privacy.
At the same time,
expectations about technology’s ability to personalize these apps and services
to meet our specific interests, location, and context have also continued to
grow. People want and expect technology to be “smarter” about them, because it
makes the process of using these devices and services faster, more efficient,
and more compelling. The dilemma, of course, is that to enable this
customization requires the use of and access to some level of personal data,
usage patterns, etc.
Starting in 2019, more
of the data analysis work could start being done directly on devices, without
the need to share all of it externally, thanks to the AI-based software and
hardware capabilities becoming available on our personal devices. Specifically,
the idea of doing on-device AI inference (and even some basic on-device
training) is now becoming a practical reality thanks to work by
semiconductor-related companies like Qualcomm, Arm, Intel, Apple, and many
others.
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