Currently, the technology industry and others such as medicine, retailing, sales, eCommerce and more, have shown interest in learning more about AI. Therefore, it is necessary to know what is the difference between artificial intelligence and generative artificial intelligence (GenAI). Well, at Codster we are here to help you understand these types of new technologies so that you can get the most out of them for you and your company.
So, the first thing we will do is explain what these two technological tools are in general terms and what their main implementations are in various branches of work.
The difference between Traditional Artificial Intelligence, also known as Narrow AI or Weak AI, is that this is a subset of artificial intelligence focused on performing specific tasks using predetermined algorithms and rules. Unlike General AI, which seeks to display human-like intelligence across a wide range of activities, traditional AI is designed to excel at a single activity or a limited set of tasks.
Traditional artificial intelligence systems generally operate in a programmed manner, following explicit rules and instructions set by humans. Think of a video game character with a single instruction or a chatbot with predetermined responses and major limitations. All of these implementations are rule-based and depend on well-defined algorithms derived from structured data.
For this same reason, The difference between artificial intelligence and generative artificial intelligence lies in this simplicity. Fortunately, traditional AI has found wide application in numerous everyday industries and technologies. Some common examples include:
- Spam filters in email services: These filters use predefined rules to identify and separate spam emails from genuine ones.
- Recommendation systems on e-commerce platforms: Based on user behavior and preferences, they suggest products or content that align with users' interests.
- Virtual assistants like Siri or Google Assistant: These wizards use predefined algorithms to understand and respond to user queries.
- Puzzle solving programs: Traditional AI has been successful in creating algorithms to solve different types of puzzles or, for example, follow specific orders with predefined strategies to face human opponents like in chess.
There is also Analytical AI, which is used to classify, group or predict using models specifically adjusted for the particular data and uses of a business. It is usually applied to very specific solutions, some so common that they have been encapsulated in a AWS service.
Some of its uses include fraud detection, text-to-speech or speech-to-text conversion, among other forms of artificial intelligence or machine learning that are consumed without the complexity of training and maintaining underlying models.
For its part, GenAI refers to powerful language models that generate innovative content by receiving stimuli such as a request, message, audio or multimedia content and produce new products, text or content in general. For example, they can generate images, compose poems or, in terms of automation and assistance, facilitate the creation of code. These models tend to be generic by default, trained with a wide range of information available on the internet.
Generative AI is a branch of artificial intelligence dedicated to the creation and development of new material. It is sometimes called Creative AI or Strong AI.
The main difference between artificial intelligence and generative artificial intelligence lies in their objectives and operation. While Traditional AI seeks to perform specific tasks based on predefined rules and patterns, Generative AI goes beyond this limitation and strives to create entirely new data that resembles human-created content.
Generative AI has numerous applications in various industries and areas. Some notable cases include:
- Image generation: Generative Adversarial Networks (GANs) can create realistic images of objects, landscapes or even human faces that do not exist in reality.
- Text generation: Language models like GPT-3 can create human-like text, including stories, poems, and articles, from a specific stimulus.
- Musical composition: Generative AI can create original musical compositions in various styles and genres, as well as imitate human voices.
- Video synthesis: AI-powered systems can generate realistic videos, such as deepfake technology, which can raise ethical concerns.
- Drug discovery: Generative AI can be used in drug discovery to design new molecules with desired properties.
Difference between artificial intelligence and generative artificial intelligence
The particularity of GenAI lies in the fact that the large language models have been trained primarily on the public internet. This leads to them being less specific for a particular business use. Although the direct use of these models may not differentiate a company from its competitors, it could boost productivity.
Currently, GenAI is primarily used to accelerate internal use cases such as content generation or data synthesis. Additionally, companies are combining Recovery Augmented Generation (RAG) with GenAI. This involves integrating private information using services such as Amazon Bedrock or Amazon SageMaker JumpStart, allowing models to generate responses in natural language and then search for answers and generate summaries through conversational interfaces.
For example, this technology could help an account executive compose a message in response to a proposal for a client, based on the company's internal documentation. While still very specific to the company's internal use case, it leverages the linguistic capabilities that large language models have been trained on.
The key to success with these models is not to completely retrain them, but rather to mix in the optimal number of specific adjustments. These models lack awareness and intention; They simply know the data they have been fed. Leveraging its ability to understand and produce language combined with business data enables responses tailored to business use cases.
For example in Google, a subsidiary of Alphabet, this tool is key in its search engine, in Waymo's self-driving cars and in Google Brain, creator of the transformer neural network architecture that has driven recent advances in natural language processing.
The difference between Generative Artificial Intelligence and Traditional Artificial Intelligence is evident in several key aspects:
- Methodological approach
- Traditional AI: Based on predefined rules and algorithms, it follows explicit instructions for specific tasks.
- Generative AI: It is data-driven, using machine learning techniques to learn patterns and structures from large data sets.
- Learning
- Traditional AI: Mainly employs supervised learning, training with labeled data and relying on human supervision.
- Generative AI: Can use both supervised and unsupervised learning, excelling in unsupervised environments, learning patterns and structures without explicit human guidance.
- Models Used:
- Traditional AI: Mainly uses discriminative models that classify data into specific categories.
- Generative AI: It is based on generative models that learn the underlying probability distribution of the data and can generate new samples that resemble the original data.
- Creativity and Adaptability:
- Traditional AI: Limited to specific tasks and lacks creativity beyond its predefined rules.
- Generative AI:Exhibits creativity and adaptability, generating innovative content and adapting to changes in input data.
The advantages of Generative AI are notable:
- Expanded Creativity: Ability to produce creative and innovative content, exploring infinite possibilities in art, design and entertainment.
- Uncertainty Management: It can fill data gaps and manage uncertainties, applicable in fields such as medical imaging to improve diagnostic accuracy.
- New Industrial Applications: Offers opportunities in architecture, design, video games, virtual reality, and more, allowing immersive and interactive experiences.
- Revolution in Art and Media: Transform the creative arts and media, composing music, creating paintings, and personalizing content for a more engaging and relevant experience.
- Data and Sample Generation: Generates synthetic data, benefiting fields such as natural language processing and computer vision.
Knowing the difference between artificial intelligence and generative artificial intelligence is key to knowing which is the perfect tool for you. If you have even more questions, do not hesitate to request a consultation with Codster to learn more about the benefits and what Artificial Intelligence is for. Well, many of them depend on the specifications and needs that your own firm has. But, don't worry, we will surely find the perfect and ideal solution for you.
