The technology of today is changing at a rapid pace. Businesses and client demands are also updating according to the technology changes as it facilitates better control and monitoring over the systems. While the future trends promise to enhance the user experience, the need for solutions that serve every demand and also promise to predict the possible user behaviors is increasing and Artificial Intelligence and Big Data enter the picture.
Artificial Intelligence: Expect Support From A Machine
The hunger for more has caused an evolution in technology and it has led us to the world where most of the systems are automatic or are on the way of becoming automatic. Artificial Intelligence performs human tasks with more efficiency in a way that gives human-like experience only. As it can run without stopping until commanded, businesses are loving the changes AI is influencing and they are shifting towards automating many business operations.
To order and automate the things, it is required to collect and order the data that can be processed and further it can guide the program to analyze the conditions and derive the outputs accordingly. The data sets are created from observing and collecting the surrounding data and fed to the system to enhance the intelligence and achieve faster processing and accurate outputs.
Big Data: Provides Humongous Data Storage Facility
Big data is all about making the data storage efficient. It aims to provide a distributed file system that can maximize the file storage and let data stored and retrieved easily when required. The real-world data which is later converted into data sets containing attributes take up larger space and are not easy to be processed.
The processing power of a program must be higher to process this data. However, the retrieval efficiency must be kept in mind to avoid any issues when a program is set to run. Big Data includes many aspects that significantly help in lowering the processing speed and they must be used if necessary to automate a system efficiently. However, are our systems really capable of performing such high energy-requiring tasks efficiently? Does big data always succeed in making automation more efficient than before?
Potential Risks During Processing Data Sets
Some of the successfully working AI systems of today are results of the processing of millions of data sets and the accurate outputs it has achieved from it. The machine intelligence of today is not so intelligent. It can efficiently find patterns in the data but sometimes, the difference between correlation and causation is missed here. The machine surely finds the patterns and gives outputs accordingly but if they are not the ones to be found, it can result in the degraded efficiency of the machine.
Correlation And Causation For AI
During the data set processing, there certainly are patterns that totally seem to correlate. The machine finds them and then processes the data being feed accordingly. However, what if the pattern we have identified is not the right one? For example, here is an interesting example of correlation found in data sets that implies that the number of films Nicolas Cage appears in influences the number of people who drown by falling into a swimming pool. Now, we can’t blame Nicolas Cage for that, he is a great artist!
Such deceiving results often lead AI systems to failure. There always will be such patterns that do not have any kind of connection but are still taken as one. Moreover, there is a great difference between correlation and causation. The AI system is required to find the cause and understand the relation and then process further. The improvement is yet to be done to increase the efficiency of AI systems as we can not stop eating cheese after knowing the causation (!) of people dying by getting tangled in the bedsheets are related to the per capita cheese consumption. (Obviously a big blunder it would make!!)
What Should Be Done To Stop This?
Gain an understanding of the problem you are aiming to solve. The use of appropriate algorithms and excellent statistics analysis can support you by eliminating unnecessary patterns and focus on searching on the ones that really matter. However, just numbers can not help you to derive a conclusion and will require you to understand the problem thoroughly to assess the results and find out the efficiency of the algorithm.
The More Data, The More Noise
The large data set becomes, the efficiency of the program increases. In some cases, yes but otherwise, no. Often more information and data turns out to be a little information as there always will be noise in the data sets. And as the data sets become larger, the noise increases in it which directly influences the efficiency and outputs achieved.
Therefore, using an appropriate algorithm and conducting a pattern analysis of the statistics received, the chances of noise affecting the output decreases to a great extent. However, there still will be some minor changes and less accuracy due to the large volume of data which must be reduced by using algorithms and filtering tools and methods available.
Also, the problem of P-Hacking can occur in larger data sets. As the data being fed into the system increases, the more patterns are found that majorly are related. However, it increases the work on the development front as they need to be filtered to find the ones that actually provide results.
Adding More Data: Should Add More Or Not?
As stated earlier, adding more data can increase the noise in the data existing. However, the larger the dataset is, the more accuracy can be obtained for the outputs. But the data to be added must not be just data, it must be the information. The ultimate aim is to process the data, search for the information, process it and provide relevant outputs. Therefore, it is advisable to add information (facts, data that can relate and help to find a meaningful pattern) and not just raw data. Again, once added, the entire data set must pass through filters to check for any unnecessary data remaining.
Automating most of the business tasks today has become necessary. Artificial Intelligence has even been introduced in mobile app development also. For example, the developers create a delivery tracking system that gets checked and tested through automated testing to ensure every module works perfectly. To let your developed app checked efficiently, the AI systems must be accurate at finding faults and errors in the developed solution. There are many industries where AI has been working perfectly and providing excellent outputs.
Artificial intelligence is the future technology. Soon everything will be automated and provide excellent outputs. However, the mentioned risks will still be there as the data to be processed will always be in large numbers and hence will ask for extra care when being processed for outputs.
Keeping the necessary constraints in mind and the requirements of data, AI systems can be created to work efficiently and provide accurate results for the entered data. The journey from data to information is not easy, but with an appropriately created training dataset and excellent support of big data, the outputs of the fed data can be improved to suit the requirements.