Difference between AI and generative AI is distinguishable. While artificial intelligence (AI) encompasses a broader concept involving machines, generative AI generates explicit content. The machines with AI perform functions as if humans were operating them. There are various kinds of devices that AI can manage, including smart assistants and smart robots. Some examples of AI-driven devices or software that we encounter in our daily lives include chatbots, Alexa, self-driving cars, and robotic vacuum cleaners. On the other hand, generative AI is a sub-branch of artificial intelligence that can create ideas and content with intelligence and meaning.
Difference between AI and Generative AI: In Origin
Although AI had been in use since the 1950s, only a select few scientists were able to access this technology. A lack of sophisticated technology and limited resources have been the leading causes of the unavailability of access to AI technology over the past few years. On the other hand, the emergence of generative AI dates back to around the 2010s.
Advancements in the area of deep learning led to the creation of generative AI. However, due to insufficient resources, there were roadblocks to commercialization. Only after the broad adoption of cloud computing technologies was the commercialization of generative AI made viable. It all started in 2022. It is another difference between AI and generative Artificial Intelligence.
Why is there a Sudden Shift in Interest for Artificial Intelligence Technologies?
Although the concepts of AI have been in use since the 1950s, interest in and application of these technologies have grown rapidly in recent years. So, what is leading to the sudden increase in interest? Previously, due to limited resource availability, there was a restriction on using AI. As a result, only select data scientists and researchers had access to these technologies.
However, with significant progress in technology and an increasing understanding of AI technologies, AI and its associated technologies are growing at a rapid pace. It’s all because of the sophistication of technology, along with the massive proliferation of data. Additionally, access to highly scalable computing resources with large capacity is made possible with the introduction of cloud computing technologies. At the same time, new developments emerged within machine learning technologies. All these factors are driving forces behind the increasing focus and extensive use of generative AI technologies.
Scope of Work:
AI with basic machine learning functionality did not allow the input of complex values. Only the scope of intake was limited to simple numeric values while mapping to simple output values (with all predictions). Playing chess against a computer is a simple example.
With the emergence of deep learning technologies, along with advanced ML techniques, there is a provision to intake complicated inputs as well. They include images and videos, while mapping them to simple outputs. Although this method is an advanced version of the basic model, it does not offer the option to generate a new entity.
However, with the introduction of generative AI, there was a drastic change in the technology sphere, especially as it widened the scope of work possibilities. With the introduction of generative AI, the possibility of creating new things widened. Primarily, it became easy to leverage massive data as well as map complicated input and output data. As a result, it enables the creation of new content.
Task Capabilities:
While AI combined with traditional ML models can perform task-specific functions, in contrast, generative AI using deep learning models can perform several additional, more complicated tasks. Generative AI is widely known for creating new things. For example, while ML models can only translate Spanish to German, generative AI can generate new content in addition to translation. Like creating recipes in German and others.
The examples above illustrate the difference between AI and generative AI.