Do AI agents foretell the next wave of autonomy or liability?

Context: An AI assistant can be defined in
many ways, according to an April 2024 report, Google DeepMind defines an AI
assistant as an artificial agent with a natural language interface, the function
of which is to plan and execute sequences of actions on the user’s behalf
across one or more domains and in line with the user’s expectations.

AI agents

·       About: The next-generation AI assistants are called AI agents and are set
to surpass their predecessors in ability as well as efficiency. These
agents can perform multiple functions as users’ agents or autonomously, that
is, without instructions or user intervention.

·       Functionality: They
perceive their environment via sensors, process this information with AI
algorithms, and take actions based on this data. This allows them to adapt to
new situations and handle a variety of scenarios.

·       Potential uses: Versatile Assistants-
They can manage tasks ranging from giving personalized recommendations to scheduling
appointments, ideally suited for customer service. Educational Tools- They can
act as personal tutors, adapting to different learning styles and offering
customized instructions. Healthcare Support- They
can assist medical professionals by providing diagnostic support and real-time
patient monitoring.

·       Risk & challenges: Privacy and Security-
As they can access and use a lot of personal and environmental data, there is a
significant concern about how this information is protected and used. Bias-
They might also replicate biases present in the data or algorithms they were
trained on. This can result in unfair or harmful decisions. Regulation Needs-
As AI agents become more widespread, there needs to be strong rules and
guidelines to ensure they are used responsibly and safely.

Replacing
the Large language Models

·       The large
language models (LLMs) like GPT-3 and GPT-4 have the ability to only generate
human-like text, AI agents make interactions more natural and
immersive with the help of voice, vision, and environmental
sensors.

·       Unlike
LLMs, AI agents are designed for instantaneous, real-time conversations with
responses much similar to humans.

·       LLMs lack contextual awareness,
while AI agents can understand and learn from the context of interactions,
allowing them to provide more relevant and personalized responses.

·       Also,
language models do not have any autonomy
since they only generate text output. AI agents, however, can perform complex
tasks autonomously such as coding, data analysis, etc. When integrated with
robotic systems, AI agents can even perform physical actions.