Generative Agents
Generative AI agents are often seen as the future, but in some ways, they are already here. Agents in generative AI applications are essentially independent in that they can learn, adapt, and even communicate with other agents, all without human intervention. This technology holds the promise of taking on increasingly complex tasks and reaching new heights in artificial intelligence.
What Are Generative Agents?
The concept of generative agents combines two ideas: “generative artificial intelligence” and “agents.”
Generative AI
This term is based on the word “generate”, or “create”. In response to a query or command, generative AI technology looks at statistical data patterns to determine an answer. The technology accesses its own database and/or the internet to find examples of the information it needs, decides on the most relevant answer, and creates a response delivered as text, voice, imagery, or a combination of all three.
Agents
An agent acts on behalf of something else as a kind of representative. In terms of a generative agents’ simulation, this means a technology that takes the role of a person when answering a question or performing a task. Some of the older types of agents include chatbots that provide output only through a synthesized voice or text-only. But advanced agents are forms of simulacra, which look and behave like people.
Are Generative Agents Already Here?
Applications of AI are a sort of continuum. For instance, chatbots can process simple requests and are still popular. However, they are gradually being replaced by interactive agents, which can understand context and sentiment while generating responses to complicated queries. On a visual level, generative agent code enables AI-powered avatars that demonstrate facial expressions and body movements related to the conversation.
The future of generative agents will be even more sophisticated. According to McKinsey Consulting, “agentic” technology is building agents that are interactive and independent. They will be able to conduct complex interactions with people and each other, learn from their experience, and improve their performance.
How Generative Agents Work
The technology behind generative agents is complicated and evolving. Simply put, it allows the agent to interact, process queries, and provide a response through the following:
Interaction
The classic form of providing a query to an AI agent is through text. More recently, voice-powered systems like Alexa and Siri enable verbal intercommunication, while the touch screens of phones and computers are tactile. Currently, the cutting-edge of interaction is through a natural user interface (NUI) that allows a person to communicate through their usual mode of speech. Future developments include gesture and facial expression recognition.
Processing
Artificial intelligence translates the query into machine language that can be understood by the computer, which then looks up an answer using large language models that reference massive amounts of data and/or small language models that are for specific tasks. Both models are supported by retrieval augmented generation technology, which enhances the accuracy of the answer.
Response
Using generative AI, the answer is translated back into something a person can understand, such as text, voice, or imagery. An AI agent delivers the response by simulating a person’s appearance and behavior. Through AI programming, a generative agent turns the text of an answer into verbal communication along with lip-synching, facial expressions, and gestures.
Applications of Generative Agents
The use of generative agents is new, so the number of applications today is limited compared to future predictions, but it’s still growing all the time. The goal of developers is to create generative AI agents that are visually indistinguishable from people but with a level of performance that is greater than humans. Below is a sample of the areas where we can expect AI agent applications to become even more lifelike and efficient as the technology improves.
Retail
Through generative agents, people will enjoy personalized shopping experiences that allow them to make more informed choices. For instance, AI agents will consist of:
- Virtual assistants that interact with the customer about what they want to buy and then search for ideal matches and best prices
- Personal shopping agents that provide suggestions based on the user’s shopping history, budget, and preferences
- Brand representatives that will customize offers according to the shopper’s profile and enable loyalty programs
Finance
Generative AI is already essential for processing massive amounts of financial data. Soon, this will be combined with interaction so that people can personalize financial services and investments instead of being subject to the high fees and long waits that come with using human representatives.
Healthcare
Generative agents will affect the healthcare industry in significant ways. For example, they can be part of pre-and post-treatment functions where they interview patients, answer their queries, and monitor their condition. In the training sphere, AI agents in the form of virtual patients or mannequins will act as training devices for students and doctors. Generative agents can also handle administrative tasks by helping with data processing, logistics, and scheduling.
Hospitality
Much like the retail industry, generative agents will offer fast, efficient, and personalized service for those searching for hotel bookings and entertainment. Agents will automate reservation processes while delivering customized suggestions for attractions, dining, movies, and so on.
Benefits of Generative Agents
Generative agents come with an enormous array of benefits. They are a specific application of artificial intelligence, which already provides us with services that (when compared to human output) are more:
- Scalable
- Accurate
- Fast
- Widely informed
- Easily updated
- Consistent
What is different about agents is that they put a very human face on complex technological functions.
Why is this important? One of the challenges of AI development is the ability to use its capabilities fully. To transfer the benefits of AI to the broader population, this requires a method of interactivity that feels more natural. Early computer applications required coding, and ever since, technology has been developed to be easier to use. With the increasing realism, speed, and human-like behavior of generative agents, we are one step closer to fully realizing the advantages of artificial intelligence.
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