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Why Artificial Intelligence Cannot Survive Without Big Data | 7wData

Last updated: 09-10-2019

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Why Artificial Intelligence Cannot Survive Without Big Data | 7wData

Why Artificial Intelligence Cannot Survive Without Big Data
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It may come as no surprise that the internet has been swelling up with an increasing amount of data, so much so that it’s become difficult to keep track of. If in 2005 we were barely dealing with 0.1 zettabytes of data, this number is now just above 20 zettabytes and it is even estimated to reach a staggering 47 zettabytes by 2020. Apart from the sheer enormous quantity of it, the problem resides in the fact that it’s mostly unstructured. And there’s nothing more harmful for mankind than providing AI with incomplete or inaccurate data.
It seems that we are dealing with about only 10% of structured data, while the rest is just a great jumble of information that isn’t tagged and cannot be used in a constructive way by machines. For a better understanding on this subject, it’s good to know that email does not qualify as structured data, while anything such as a spreadsheet is considered to be tagged and can successfully be scanned by machines.
This may not seem that problematic, but we need to have clean and organized data if we expect AI to improve our lives in sectors such as healthcare, driverless cars, connected homes and so on. The irony is that we’ve become really good at creating content and data, but we haven’t yet figured out a way to accurately leverage it to serve our needs.
Little data analytics
It’s only natural that data science is one of the fields that gained a lot of ground across these past years, with more and more data scientists dedicating their lives to sort out the mess. However, a recent survey shows that contrary to popular opinion, data scientists spend a lot less time on building algorithms and mining data for patterns, but rather on doing this so-called digital janitorial work — cleaning and organizing data. As you can see, the numbers are certainly not in favor of a bright AI future.
Predictors of the impeding humankind wipe-out by AI have clearly not taken into consideration the fact that although machines can successfully replace the few data scientists that are actually mining data for patterns, they may not be able to replace the vast majority of scientists who devote most of their time to collecting, cleaning and organizing this data. Of course, it’s better to simply collect data in a more integral way straight from the get-go, rather than to allocate so much time and resources to ‘fix’ it retroactively. Fortunately, leaders in AI have slowly reached this understanding as well, using their skills and influence to redirect the path on which data science is headed — and implicitly with it, AI.
Artificial intelligence gets into auditing, what's next?
We’ve all heard cases of machines which proved to be superhuman when faced with actual humans, such as the case when the best Go player in the world was defeated by Google ’s AlphaGo AI. However, this only shows that AI can be capable of staggering results in niche tasks, but its overall capacity is still no match to human capabilities.
Lessons from two leading ladies of data
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