Fuel to AI magic is data

“FUEL To AI MAGIC is Data”, we will justify this statement in this blog. AI uses data at every step of the journey.

What is data?

Data is everything you could record on paper. For example:

  • continuous variables (things that can be measured as numbers) like times, sizes, weights, temperatures, and colors.
  • categorical variables are a bit like a header on a list, categories might be things like fixed/broken, acceptable/unacceptable, or country of birth.

Ordinal data & time series data

There is also something called ordinal data, which in some sense is a mix of numerical and categorical data. In ordinal data, the data still falls into categories, but those categories are ordered or ranked in some particular way. An example would be a class difficulty, such as beginner, intermediate, and advanced.

Time series data is a sequence of numbers collected at regular intervals over some period of time. It is very important, especially in particular fields like finance. Time series data has a temporal value attached to it, so this would be something like a date or a timestamp that you can look for trends in time. For example, we might measure the average number of home sales for many years. The difference between time-series data and numerical data is: rather than having a bunch of numerical values that don’t have any time ordering, time-series data does have some implied ordering e.g. monthly home sales.

Features & Labels

There are two key kinds of data we will use again and again and again: Features & Labels.

  • Features 
    • Aspects of an event/object/person that we are interested in. 
    • These can be continuous or categorical.
    • For example, a person’s features might include:
      • their height (continuous)
      • favorite sport (categorical)
  • Labels
    • Categories we put events/objects/people into. 
    • For example:
      • we might label a cake “good” or “bad”
      • label a person a “Republican”, “independent”, or “Democrat”
      • label a particular image as “containing fish” or “not containing fish”.

Frequently Asked Questions

Why is data important for AI?

Data is crucial to AI because it gives machine learning models the knowledge they need to be trained to make accurate predictions and judgments based on data patterns.

What are the three types of data in AI?

Structured, semi-structured, and unstructured data are the three forms of data used in AI. Unstructured data lacks any organization, whereas semi-structured data has some organization but doesn’t fit into a certain format. Structured data is organized into a specific format.

How is data collected in AI?

AI may gather data using a variety of techniques, including surveys, sensors, social media, and site scraping. Following cleaning and preprocessing, the acquired data is utilized to train machine learning models.

What is data storage in AI?

In AI, organizing and storing huge amounts of data that are utilized to train machine learning models is known as data storage. Databases, data warehouses, or cloud storage options can all be used for this.

What are data and types of data?

All information that can be studied and used to learn something new or make judgments is referred to as data. Data come in many forms, including both qualitative and quantitative data.

What are the two types of data?

Quantitative and qualitative data are the two types. Quantitative data can be measured or counted, but qualitative data describe features or characteristics.

Are there 3 types of data?

There are more forms of data than simply three. There are many different ways to categorize data, such as source, format, quality, or purpose. Structured, semi-structured, and unstructured data are the three forms of data most frequently mentioned in AI, however, there are many additional types of data as well.

Summary

  • Continuous variables – measurable data points, such as weight and temperature
  • Categories – such as fixed/broken, acceptable/unacceptable
  • Features – data points for an example (event/object/person) such as hair color or height. Features can be continuous variables or categories.
  • Labels – categories we assign to data points.

Hope this article helps to shed some light on the topic “FUEL To AI MAGIC is Data”.

Check out the table of contents for Product Management and Data Science to explore those topics.

Curious about how product managers can utilize Bhagwad Gita’s principles to tackle difficulties? Give this super short book a shot. This will certainly support my work.

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