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Artificial Intelligence (AI) has since moved from an abstract concept or theory to actual practical usage.
This article will review these two concepts to help you understand how they work and why they matter.
What is Predictive AI?
Predictive AI is artificial intelligence that collects and analyzes data to predict future occurrences.
Predictive AI aims to understand patterns in data and make informed predictions.
How does Predictive AI work?
This gives organizations an edge to plan ahead of certain events to ensure maximum utilization of every market condition.
It uses machine learning algorithms to analyze historical data and predict the future.
These algorithms identify patterns and relationships between data to help businesses make informed and fast decisions.
The Human expert involved in this process also plays an important role.
An adequate forecast of future occurrences helps companies to plan and maximize every opportunity.
Decision-making
Predictive AI help in fast-tracking the process of decision-making.
In business, data backing for every decision made is very important.
This helps increase the efficiency of individuals and businesses alike.
Hence it has only as much knowledge as it is given.
It will create a false pattern that will lead to an output that cannot be proven.
Hence, running an analysis and continuously updating the model will be necessary.
Forecasting of possible weather has become more accurate over time with the help of predictive AI.
Industries such as aviation depend on weather conditions.
This has helped boost operation efficiency and reduce the risk involved.
What is Generative AI?
Generative AI is a key in of AI used to generate content based on prompts.
Generative AI undergoes a series of dataset feeding, analyzing, and outputting results.
How does Generative AI work?
Generative AI has several models, each with its use cases and capabilities.
As the name implies, generative means generating, and adversarial means training a model by comparing opposite data.
GANs can be applied in various areas such as image synthesis, image-to-text generation or text-to-image generation, etc.
These autoencoders consist of two networks: the encoder and decoder connection.
The vector serves as a representation of the input sample data, which is understandable by the model.
Lets take, for example, To train a generative model to detect a dog.
It is important to know that the autoencoder cannot generate data independently.
That is where the variational autoencoder comes to play.
Then the models learn to recover the data by removing the noise from the sample data.
For example, a text-to-image generation model that generates a poor image already defeats the aim of the model.
The model output should have very close similarities to the real data.
Speed
Time is essential.
Suppose a model fails to produce output in a record time compared to a humans output.
Then the model has little advantage.
Hence the time complexity of the model must be very low to produce a quality result.
Increased efficiency
Automation of tasks can be made possible with AI.
Generative AI can generate content faster than humans.
Making the task of content creation faster and easier.
This help boosts the productivity of teams by helping them accomplish more task within a limited time.
Increased creativity
Generative AI can be used for generating aesthetically pleasing content.
There are also concerns about the generation of inappropriate or biased content.
Since these models are only limited to the amount of data given, this could lead to serious issues.
Training data-dependent
Generative AI models do not have a mind of their own.
Generative AI could be used to create this fake content and exploit people.
Applications of Generative AI
Generative AI has played a big part in this aspect.
Developers could also give instructions and get sample code for implementation.
Customer service inquiries are mostly handled using chatbots in todays business world, unlike previously when humans were involved.
It is used in creating content such as images, music, text, and more.
In comparison, predictive AI is centered around analyzing data and making future predictions from historical data.
Both generative AI and predictive AI use machine learning, but how they yield results differs.
While one creates data, the other simulates results.
