If you’re keeping up with news and developments in the world of big data, you’re probably already familiar with many of the common problems or hurdles which we are facing. From computing limitations and complex, shifting data to exponential growth and a lack of “intelligent” systems capable of breaking down mammoth pools of information, there are plenty of pressing issues when it comes to big data. Well, now it seems that an entirely new approach to dealing with big data is slowly emerging (although it’s still in a very formative state), we’re of course talking about the use of A.I. or Artificial Intelligence.
Utilizing A.I. doesn’t mean to imply that the machines will simply take over and begin organizing and analyzing big data without human intervention though. Rest assured, we a long way away from autonomous artificial intelligence that has the capability to apply advanced reasoning. However (or, perhaps in the mean time), it appears that there might be another potential use for certain types of cognitive computing, which would be cooperative assistance.
Given that machines are generally better at processing large volumes of data utilizing strict algorithms, and humans are more suited toward logic, reasoning and problem solving, it only makes sense for these two components to merge. Just think of it this way, the A.I. does all of the parsing and segregation only stopping occasionally for human input and direction. In other words, it would allow big data technicians to accurately process and analyze much larger reams of data while at the same time only having to focus on the truly important aspects, or on specific problems.
Perhaps the only thing standing in the way of this type of development is the lack of architectures and systems designed for this very purpose. For example, IBM has already announced that we have entered what they have deemed the “cognitive systems era”; which is an era in which analytics and management are moving closer and closer toward storage and data itself. In other words, we are moving closer toward a time where the concept of big data storage and analysis are virtually inseparable.
Arguably, it would be extremely beneficial to involve artificial intelligence systems into the big data field in a major way, especially given the cold, hard reality of increasing data aggregation. Simply put, without the help of thinking machines, we might not be able to match pace with the extreme amount of data that’s continuously piling up. Also, given that big data is being heralded as the “new oil or plastic” by tech gurus and commentators, it only makes sense to pursue some advanced methods for extracting value from these potential assets.
The main roadblocks to implementing and devising A.I. capable of helping us to carry out such tasks are many. For instance, a machine wouldn’t just have to deal with the issues inherent to large data sets themselves (like a complete lack of organization), it would also have to learn to contend with other unexpected variables, some of them malicious in nature. Humans make mistakes, they also occasionally create malevolent programs and viruses designed to wreak random havoc, the machines would have to learn to identify these variables in addition to run-of-the-mill file corruption and glitches in order to become truly useful big data tech assistants.
While scientists are busy trying to teach machines how to process information like the human brain does, some researchers are opting to focus on complex algorithmic sequences which perform certain tasks extremely well. One of the more interesting goals is to produce software that might be capable of carrying on conversations with human beings, for use in either advanced research or creative problem solving. Once again however, there are problems and limitations which are preventing us from initializing such an endeavor, power consumption being one of them.
Nevertheless, it’s quite easy to see how A.I.-based assistants might be able to help big data technicians break down and analyze big data in the coming years; after all, virtually all the elements needed to make it a reality are here already or are under development. Moreover, if the concept ever truly takes hold, these artificial intelligence systems will in effect, learn from their mistakes (and their controller’s) which will only increase their total individual value.
What this might mean is, after a certain period of time, it’s within reason to assume that certain types (of A.I.) will develop advanced protocols for extracting value from big data. If, or once this happens, it would be incredibly easy to copy this A.I. for use across the entire big data industry and beyond. Furthermore, once a solid model that can be used for analyzing big data is widely available, there will be a greater number of participants utilizing the technology, which in turn means an even larger number of refined approaches may also emerge.