It may not come as a surprise that the amount of data on the internet has been growing to such an extent that it is now challenging to keep track of. We were barely dealing with 0.1 zettabytes of data a few years ago; today, that number is slightly beyond 20 zettabytes, and it is even predicted to reach a startling 47 zettabytes by next year. Apart from the fact that there is a vast amount of it, the issue is that it is largely unorganized. Giving inadequate or erroneous data to AI is the worst thing we can do for humanity.
It appears that we are only dealing with roughly 10% of structured data, with the remaining 90% being merely a huge jumble of untagged data that cannot be utilized by machines. Knowing that email does not qualify as structured data can help you comprehend the topic better. However, anything like a spreadsheet is regarded to be labeled and can be successfully scanned by computers.
This might not seem like a big deal, but if we want AI to improve our lives in areas like healthcare, autonomous cars, linked homes, and so on, we need to have clean and organized data. Ironically, we’ve gotten pretty excellent at producing material and data, but we still don’t understand how to use it effectively to meet our needs.
Data Scientists Face Similar Challenges
With more and more data scientists devoting their life to cleaning up the mess, it makes sense that data science is one of the fields that has advanced significantly over the past few years. Contrary to popular opinion, a recent survey shows that data scientists spend significantly less time executing this so-called “digital housekeeping labor” than they do design algorithms and examining data for patterns.—cleaning and organizing data. As you can see, the odds are definitely against a promising future for AI.
Those who have predicted that AI will inevitably wipe out humanity have obviously not considered the possibility that, even though machines can successfully replace the few data scientists who are actually mining data for patterns, they may not be able to replace the vast majority of scientists who spend the majority of their time gathering, cleaning, and organizing this data. Of course, rather than spending so much time and money fixing data after the fact, it’s better to just collect it in a more comprehensive way from the start. Fortunately, key figures in AI have gradually come to grasp this as well, and they have used their knowledge and power to change the direction that data science is going—and implicitly, AI with it.
AI is useful but not yet human-useful
We’ve all heard stories about machines that vanquished real people when they were up against them, like the time Google’s AlphaGo AI defeated the top Go player in the entire world. This merely demonstrates that AI is capable of astounding achievements in specialized jobs but that, overall, it still falls short of human capabilities. AI is just unable to handle many nuances and logical steps.
The limits of AI become even more apparent when dealing with legalese and financial documents. The problem is the same everywhere and here. AI computers will become gravely perplexed if they are not supplied with organized data, such as conventional contracts. As a result, it’s still up to skilled data scientists to clean up the mess for the time being.
AI can only be effective when everyone collaborates.
This area is difficult to advance in since it is expensive to acquire highly qualified data analysts. The secret is to approach the phase of data collection and modeling with technologies that can speed up the process.
The coordinated efforts of numerous agencies to address and resolve the problem that big data presents are another crucial factor. In order to appropriately identify any potential problems in the data they acquired from the start, financial and technological specialists must work together. For an issue to be successfully recreated by machines, the approach used by these specialists must also be recorded. The objective is to develop quality control algorithms that can identify model results that were related to past errors. The more of these models we can develop, the less room there will be for errors and abnormalities in the data.
Frequently asked questions:
Why big data matters?
Organizations may harness their data and use big data analytics to find new opportunities. This results in wiser company decisions, more effective operations, greater profitability, and happier clients. Businesses that use big data and sophisticated analytics gain advantages in many different ways, including cost savings.
Does machine learning leverage large data?
Big data is used by machine learning algorithms to forecast future trends for businesses. A network of interconnected computers enables a machine learning network to continuously learn new information on its own and improve its analytical abilities.
Which five applications of big data are there?
Big Data offers a wide range of applications in the public sector, including those for analyzing financial markets, detecting fraud, conducting health-related research, and preserving the environment.
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