How AI Works with Data
This is a guide on how AI works with Data.
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. An important thing to note here, AI will only learn from the data it has.
So, as we use algorithms to make decisions, make sure that the data is valid and that any biases are accounted for and corrected.
Today, you can collect data in many formats. We can classify data into four major groups. We have structured, semi-structured, quasi-structured, and unstructured.
Let us look at the characteristics of each type of data as well as some examples.
Structured data is a format that is probably familiar to you. This type of data is clearly labeled and organized in a neat table. Microsoft Excel is an example of structured data that you have probably seen and used before. In terms of advantages, it is easy to manipulate and display.
However, because it is so rigid, it is not suitable for many data sources that cannot be quickly categorized into rows and columns. Excel also has a limitation in terms of the amount of data that it can hold, and especially as your dataset grows, it can become slow and prevent you from doing calculations. So it is not always the best tool as your data continues to grow.
One step beyond structured data is semi-structured data. This format is labeled and can be found in a nested style. White it is organized, it is not in a table format.
So, it is a little more versatile and can incorporate different data sources, without needing to change the structure. It is important to remember that this versatility can become unwieldy, so you should be mindful about the number of attributes to include. Examples include email metadata and XML.
Next on the list is quasi-structured data. This has some patterns in the way it is presented, but it does not come with clear labels or structure.
It does not have metadata like semi-structured data, so it requires more work to format and sort through. Quasi-structured data includes clickstream data and Google search results.
Last but not least is unstructured data, which is considered to be the most abundant type of data that exists today. This is data that does not have any pre-defined format. When we think about the wealth of information on the internet today, such as videos, podcasts, pictures, all of these formats are considered unstructured.
While it allows us to look at more data, it does take a lot of time and effort to format the information for analysis. One piece that you should keep in mind is the amount of compute power that it can take to actually process this information.
So what exactly is big data? The definition has been described as the three V's. Characteristics of big data include High Volume. Typically, the size of big data is described in terabytes, petabytes, and even exabytes, much more than could be on a regular laptop.
It requires high velocity. Big data flows from sources at a rapid and continuous pace. And there should be a high level of variety. Big data comes into different formats from heterogeneous sources. If you are working with big data, see if those criteria fit with the information that you are working with.
Good quality of data leads to more accurate AI results, because it matches the problem that AI is addressing. Consistent data simplifies the data analysis process. When we are talking about quality data, there are a few components to keep in mind.
Incomplete data can lead to AI models missing important insights, so completeness in data is crucial for accurate AI training. This means that there are not many missing rows or columns.
Inaccurate data can cause AI models to generate unreliable insights and predictions. Accuracy in data is important for effective AI training, ensuring that the information used to teach the models reflects the real-world scenario as closely as possible.
Invalid data can undermine the integrity of AI models and jeopardize the reliability of their outcomes. Ensuring data is valid is crucial for building dependable AI models that follow specific rules.
This boosts the overall quality and trustworthiness of the insights these models provide. Inconsistent data can introduce errors and decrease the reliability and performance of AI models.
It is essential to have consistent data for reliable AI training and better predictive capabilities. When we are talking about consistent data, we are talking about uniform and standardized data across various sources.
So, for example, making sure that your variables are named consistently across different data sources. Relevant data is essential for AI to focus on what matters while irrelevant data can lead to confusion and inefficiency in models, and ultimately will not answer the question that you are asking.
It is also important to have fresh and current data, because old data can lead to predicting wrong outputs in terms of current patterns and trends. So while it is important to look at historical data in order to gather some of those trends, you want to make sure that you infuse it with current patterns to see how that might have shifted, and to make sure that the algorithm is taking that into account.
Using low quality data can negatively impact an AI application. Training a machine learning model with inaccurate or missing data leads to the wrong classification, unreliable recommendations, lower accuracy, and possible bias.
For example, a car's object detection system could not recognize a truck as an obstacle due to a flawed algorithm that was not designed to detect it from a particular angle. Because the training data lacked sufficient images of large trucks from that angle.
Outdated data collected significantly in the past, or obtained from different data sources, has the potential to negatively impact AI and ML models. This can result in reduced accuracy and introduce bias into the model.
For example, an algorithm learned from a decade of resumes submitted to Amazon that a successful application usually meant that that person identified as male, that led to gender bias in terms of the selection of resumes for interviews, and has been discarded by Amazon.
Having enough relevant and good quality data is important for AI systems to work effectively. It is crucial to balance the quantity and quality of the data for reliable outcomes, especially in AI applications.
Having more data results in improved statistical strength. It helps reduce the sampling bias. It empowers the use of more complex models. It captures a broader range of variations and patterns in data. And it catches more variability.
All of these pieces need to be considered before you use these datasets within your model.