With the advent of AI agents, a lot of our jobs will become easier. Similar to people, these bots are multilingual, but they also pose serious privacy issues.
AI is entering a new age with the rise of ChatGPT-4o and Google Project Astra. With voice or visual inputs, these two generative AI platforms can deliver intelligent replies in real time. They differ from earlier AI agents like Google Voice Assistant, Siri, and Alexa in that they can respond to inquiries based on information found online. These sophisticated features do, however, raise serious privacy issues.
An AI agent: what is it?
A virtual assistant that utilizes artificial intelligence technology to engage in real-time communication is known as an AI agent. It is frequently used to answer customer inquiries in sectors including banking, telecommunications, and insurance. It can communicate via text, graphics, or voice.
Speaking with an AI assistant is more like having a conversation with a human than it is like putting text into a text box, as Google CEO Sundar Pichai highlighted during the Project Astra launch. These new AI bots use sensors to collect data from their surroundings and use AI algorithms to respond in real time.
What sets them apart from LLM? It is noteworthy that generative AI AI agents differ from large language models (LLM) such as GPT-3 or GPT-4 in that the former enables natural interactions while the latter does not generate text. LLMs react in real-time in text format, but AI agents use their AI algorithms to handle complicated queries and respond contextually. Project Astra and GPT-4o are two instances of this.
What kind of privacy danger do they pose?
Despite their advantages, AI agents’ access to users’ personal information and surroundings raises privacy concerns. They can train AI models using this data as well. Regulations for these agents must be established, given their growing popularity.
Apart from privacy concerns, AI agents also have to deal with issues of dependability, technical intricacy, and possible biases in the data they offer. As a result, it’s crucial to approach their output critically and cautiously.
