AI comes as a forest fire in the changing climate of technology. To light this fire up, data acts as the fuel. Like quality fuel is important to keep the fire burning, AI performs better with quality data.
Artificial Intelligence is a section in computer science that is thought to revolutionize our idea of independent operations by computers. The main branch of artificial intelligence, which is the basis of all its models, is machine learning. Machine learning is the act of “learning” by a machine. Machines can learn like human beings do, with patterns. Therefore, machine learning can loosely be based on statistics. However, it is little different from general statistics as statistics dealt by humans are defined in two or three-dimensional planes. But unlike human beings, machines can form patterns from higher dimensions and make more predictions from the patterns.
AI development has been a popular topic among today’s developers as it has infinite potential. With artificial neural networks and reinforced learning mechanisms, computers can predict future outcomes, and make flawless decisions to reach a certain goal. AI is a reflection of the way human beings works, from speech recognition to image processing, from pattern recognition to computer vision. Artificial intelligence, like human intelligence, works on data. Like human beings gain knowledge from books, artificial intelligence forms patterns from data. Thus, data is the fuel that feeds the fire of artificial intelligence.
There has been a very interesting analogy between a pyramid of needs for humans and machines. The pyramid of needs for humans, made by human psychologist Maslow, put forth a general observation regarding human motivation. It states that human beings are motivated to fulfil their needs in a particular order. Before the need at the bottom of the pyramid is fulfilled, the need above it cannot be recognized, let alone be fulfilled. To draw parallels between humans and machines and the way they learn, AI scientist Monica Rogati attempted to make a pyramid of needs for machines, where AI comes on the top of the pyramid and data on the bottom-most block of the pyramid.
To elaborate further, Monica Rogati says that data collection is one of the first steps to add intelligence into any system. Data collection is not just about accumulating pieces of information. It is about collecting the right data, that is, data relevant to the ultimate goal a machine is trying to reach. This data, in the correct format and quantity, can be used as a guideline for artificial intelligence machinery. You must be careful about what data you feed into the machine because there exists something called dirty data. Dirty data is the data that is in an incorrect format or irrelevant to the context of learning. If such data is input into the machine, the results will not be as desired. This also becomes a burden for developers who need to sift through the data and find useful information to build a model.
Let’s take an example where you are trying to use AI to find out a fault in your machinery. The working of the machine is dependent on several variables, which include RPM, temperature, physical conditions, and volume of fluid. If you take into account one of the variables and provide all the data regarding the changing of that particular variable, your AI might not be able to give you even a probability of 50% accuracy in its predictions.
Thus, we see that the solution to a problem is not stuffing data into AI and waiting for results, but careful consideration of situational parameters that will aid the AI to find patterns and give a more probable outcome. For your machine, giving data about all the variables will get you a 90% probability of accurate failure detection.
Tech giants like Amazon and Google often have exceptionally well designed artificial intelligence services. Amazon’s suggestions based on previous buys or YouTube suggestions or Google’s photo recognition and detection are often flawless because of only one reason. Data. Google stores every single piece of information we ever enter into any of its applications. Due to the surplus of data, it has enough clean data to feed into its AI algorithms and present us with accurate predictions. Our data make their AI the most sophisticated. A developer, when beginning his journey towards AI, must begin with the map of data in his hand.
Now that you understand the value of clean data for a model based on AI, it becomes important to know how exactly this data must be utilized to get the best results out of it. If data is the fuel to AI, it is just as important to know when to add fuel to make the flame burn steadily.
In conclusion, we see that the entire process begins with data collection and ends in data testing. It roughly means that the AI is entirely dependent on data in its various stages of development.
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