AI Has A Big (Data) Problem
We are awash in an ocean of data that grows bigger by the second. And it’s a complete and utter mess.
It doesn’t matter how “smart” machines become if they’re fed data that is inaccurate or incomprehensible.
The total size of all global data hit 20 zettabytes in 2017. For 99% of people, that number probably means nothing, so picture this: if every 64-gigabyte iPhone were a brick, we could build 80 Great Walls of China with the iPhones needed to store all the world’s data.
We are awash in an ocean of data that grows bigger by the second. And it’s a complete and utter mess.
90% of web data is unstructured, meaning it’s in a format that cannot be easily searched and understood by machines. Poor data quality costs the US economy $3.1 trillion a year according to IBM (IBM). We have become a society that is excellent at producing, storing and sharing data, but we’re lousy at making it useful.
Poor data quality represents the single largest hurdle for developing useful artificial intelligence. It doesn’t matter how “smart” machines become if they’re fed data that is inaccurate or incomprehensible.
Poor data quality is a familiar problem for those who analyze data for a living. A recent survey found that 60% of data scientists devote the majority of their time to cleaning and organizing data, as shown in Figure 1.
Comparatively, just 9% of data scientists devote the bulk of their time to mining data for patterns. Cleaning and organizing data has become such a big task that it leaves precious little time for analysis.
When people predict that AI will make human workers obsolete anytime in the near future, they are ignoring the data quality problem. AI and machine learning may be able to replace those 9% of data scientists who are mining data for patterns, but it will still need the 80% working on collecting, cleaning and organizing data.
More importantly, data scientists need to re-orient their thinking around data quality. More time needs to be spent upfront and on collecting data in a high-integrity manner rather than retroactively “cleaning” it. We are not sure that it is possible to retroactively clean data enough to meet the needs of useful AI. If you cannot validate the data back to its source, then how do you know it is clean? And, if you are going back to the source to validate, then you might as well collect it from the source.[1]
Gradually, leaders in AI seem to be understanding that their job is as much, if not more, about data management as it is about new technologies. Facebook (FB) just hired Jérôme Pesenti, the former head of IBM’s Big Data group, to run its AI efforts. Pesenti replaces Yann LeCun, who will now focus on his core expertise of research.
While research into deep learning and neural networks makes the most headlines, Facebook understands that data management is crucial to delivering bottom-line business improvements.
Once you understand the limitations imposed by poor data quality, the challenges facing AI become much clearer. Successes like the Go-playing computer that beat the world champion are misleading because they are enabled by completely structured and easily interpreted data points. Accordingly, the subset of tasks that AI can perform effectively remains small, according to LeCun:
“In particular areas machines have superhuman performance, but in terms of general intelligence we’re not even close to a rat.”
Other researchers have faced similar hurdles. Machines can handle finite and discrete data points well, but even a minor degree of ambiguity can trip them up.
“We are still a long way from computers being able to read and comprehend general text in the same way that humans can.”
The last quote comes from Microsoft (MSFT) CTO, Kevin Scott, in a LinkedIn post celebrating the development of an AI that could read and answer questions about Wikipedia pages at the level of an average human.
