Imagine you hire an assistant who does not complain to you, does every single thing following your commands without tiredness and does not sleep. It might sound interesting.
Well, the next generation of AI agents offers precisely that. These AI agents are tireless and loyal to handle every task accurately and with the utmost efficiency. However, a question can arise here are the AI agents creating a culture of autonomy or increasing liability concerns?
What are AI agents?
AI agent is a software that interacts with its environment, acquires data and autonomously performs tasks following the data to achieve goals set by humans. Apple’s Siri and Amazon’s Alexa have been known as AI assistants for over a decade. These AI assistants help complete normal tasks like setting alarms, scheduling appointments, setting reminders, playing songs, and many more.
With the advanced growth of the next GenAI, these AI assistants have become more developed, sophisticated, and capable and turned into AI agents. AI agent can autonomously handle the tasks set by humans. Humans are not required to put data again and again, rather AI agent continuously learn from past interactions and respond accordingly.
Categories of AI agents
The AI agent’s term is not new. AI assistants have turned into AI agent in this modern era. However, based on their performance, they can be categorised into 4 types. Which are-
Goal-based agents
These types of AI agent are mostly known for performing some specified tasks within a particular domain. These agents make decisions based on past interactions and knowledge. It takes help from algorithms to solve problems and predict some solutions.
Google Waymo can be a great example. Google Waymo is an autonomous driving technology company. They have their self-driving cars which have goal-based agents. These self-driving cars transport passengers from one location to another following the task provided by the passengers.
The goal-based agents can think beyond the present moment. Moreover, they can also employ search algorithms and based on the current situation can predict future scenarios. For instance, these agents are designed to be adaptable to changing circumstances to achieve the targets.
Learning agent
The learning agent is known for learning anything from their past experience and enhancing their performance. It mostly uses the sensory input and feedback mechanism as a learning element to train itself.
AlphaGo can be highlighted as an example of a learning agent. Google DeepMind has designed it for playing the board game Go. AlphaGo plays Go games against humans and learns from each game to improve its moves and understanding. By improving itself constantly, AlphaGo defeated Lee Sedol, the world-famous Go champion, in 2016 and made history.
The learning agents have used four elements to make decisions. One is the learning element, the second one is the performance element, the third one is the critic element and the fourth one is the problem generator.
The ‘learning’ element helps in performing improvements. The ‘performance’ elements help in choosing external actions. The ‘critic’ element helps in providing feedback and modification. The ‘problem generator’ helps in suggesting actions and making informative decisions.
Multi-domain agents
The multi-domain agents are experts in different knowledge areas. These domains overcome the restrictions of single-domain expertise by collaborating within a unified framework to improve knowledge. The collaboration helps to enhance their working efficiency, accuracy and decision-making process.
The multi-domain AI agent works in the customer services sector, healthcare sector, education sector, retail sector and many others. Starting from handling queries, resolving customer issues, providing recommendations, handling inventory management, providing sales strategies, and more have been handled by multi-domain AI agents.
Google Nest Smart Home Ecosystem can be a great example of a multi-domain AI agent. This can help with seamlessly multitasking starting from managing tasks, by using voice to play media, planning days and many more. The Smart Home Ecosystem includes Google Assistant, Nest Thermostat, Nest Cam and Nest Hello which are designed to handle multi-domain tasks.
The multi-domain AI agents have the characteristics of autonomy, reactivity and proactivity. It can operate independently without continuous human intervention, respond quickly through adaptability and by using patterns and trends to predict future scenarios.
Autonomous agents
Autonomous agents are referred to the advanced AI systems that do not require human intervention to perform a task. It is a combination of modern technologies of machine learning (ML), Natural Language Processing (NPL) and real-time data.
Auto-GPT is a perfect example of autonomous agents. It is an open-source AI agent that performs tasks independently. From the user, it receives high-level tasks and then autonomously executes data to reach the goal of the user. It performs goal-driven tasks, self-improvement, and multi-domain works and takes adaptable decisions.
BabyAGI can be another example of an autonomous agent. It mainly uses the Python script of OpenAI, Pinecone APIs, and LangChain framework to build, construct and prioritise tasks for better execution.
The difference between the BabyAGI and Auto-GPT can be highlighted as the design of BabyAGI is to use human-like intelligence in their assigned tasks; conversely, Auto-GPT is capable to understand and generate human-like texts.
Autonomous agents have the capability of adopting learning from each interaction with humans. Auto-GPT, BabyAGI and many other autonomous agents continuously update their knowledge and refine their tasks to deliver improved performance.
AI agents’ autonomy is increasing liability concerns.
Recent innovations in GenAI have increased people’s interest towards AI agents. The invention of BabyAGI by Yohei Nakajima using ChatGPT and LangChain create the concept of AI agents. Since that time, AI agents like Llamalndex, AgentGPT, Auto-GPT and many more started to emerge in the markets. Large and established companies like OpenAI, Microsoft and UiPath have made the path smoother to introduce AI agents.
Autonomy in technology started when AI agents performed complex tasks without any repetitive human intervention. However, these increased autonomy factors increased the liability concerns. AI agents usually generate results without human intervention because of that they can make decisions based on their programming and the knowledge they gather.
AI agents perform autonomously, henceforth maintaining accountability becomes complex. Several questions like if the AI agents are responsible for any harm to humans or fail to provide accurate results then who takes the liability of that?
Moreover, the decisions that AI agents make mostly depend on the algorithm. These steps can enhance efficiency but, on other sites, can increase the chances of unintended consequences. As an example, automated cars have been commanded to transport one person to their set locations. However, if any unforeseen obstacles arise, such as a pedestrian suddenly stepping onto the road, then AI agents have to make quick decisions to save the pedestrian as humans do. But if they failed to do that who will be liable for that, the person who designed the AI, the pedestrian or the passenger?
For instance, AI can generate biased results without interpreting the result’s future consequences. If a user has put false information in the AI agents again and again, then the AI agents can also generate a false result without comparing it with the actual data.
Steps to deal with AI agent’s liability issues
The developer of AI agents should cover all the possible scenarios including property damage, defamation, personal injury and many more. Moreover, several governments around the world have been introducing regulatory requirements for AI systems. The European Union’s General Data Protection Regulation (GDPR), California’s Consumer Privacy Act (CCPA) and many others have several regulations to perform an AI agent risk-free.
The AI system needs to be transparent, explainable and auditable to measure all the potential risks before launching it. Implementation of robust testing protocols can help the developers to validate the system and minimise liability concerns.
For instance, a collaborative culture can be a great strategy to reduce liability concerns. Collaborations between the users, developers, policymakers, companies and more can create ethical standards for the best practices of AI agents.
FAQ
What is an AI agent?
AI agent is a software that interacts with its environment, acquires data and autonomously performs tasks following the data to achieve goals set by humans.
Are AI assistants AI agents?
With the advanced growth of the next GenAI, these AI assistants have become more developed, sophisticated, and capable and turned into AI agents.
What are some examples of AI agents?
BabyAGI, Auto-GPT, Google Nest Smart Home Ecosystem, AlphaGo, Google Waymo and the like.
What are 4 types of AI agents?
The 4 types of AI agents are – goal-based agents, learning agents, multi-domain agents and autonomous agents.
What are the liability concerns of AI agents?
AI agents can generate biased results without interpreting the result’s future consequences. If a user has put false information in the AI agents again and again, then the AI agents can also generate a false result without comparing it with the actual data.
Is every data generated by AI agents are bias-free?
No. AI agents can generate biased results without interpreting the result’s future consequences.
How can the liability concerns of AI agents be reduced?
The AI system needs to be transparent, explainable and auditable to measure all the potential risks before launching it. Implementation of robust testing protocols can help the developers to validate the system and minimise liability concerns.
