The next-generation AI assistants are called AI agents and are set to surpass their predecessors in ability as well as efficiency.

About AI Agents

• Definition of AI Assistant: As per Google DeepMind “An AI assistant is an artificial agent with a natural language interface. Its function 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.” 
  • Example: Apple’s Siri and Amazon’s Alexa
• AI Agents: The next-generation AI assistants are called AI agents (AIA) 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
• Categories of AI Agents:
  • Reactive agents: These are the first-generation AI agents developed to respond to specific inputs or commands. They follow predefined rules and perform tasks limited in scope as they cannot learn anything new and lack the ability to adapt. 
  • Learning agents: They were enabled by machine learning to learn from their experiences. They have better abilities, such as pattern detection and data analysis, and can improve their performance over time.
  • Cognitive agents: They can reason, analyze, and plan. They have cognitive skills because they can learn from their environment, and adapt and make decisions based on algorithms and their own ‘knowledge’. 
    • These agents use techniques including natural language processing, computer vision, and deep learning to perform tasks.
• The present generation of AIAs are cognitive agents. 

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Capabilities of Cognitive AIA

• Multi-Functionality: They are capable of performing a wide range of tasks both autonomously and as user agents. 
  • They can be integrated with the ‘Internet of Things’ to analyze data in real-time.
• Understanding Human Language: They can comprehend human speech and text to perform tasks that require understanding multiple domains. 
  • For example, they can plan a trip after listening in on a user’s phone calls and reading their emails and understanding their preferences, 
• Autonomous Decision-Making: Capable of making real-time decisions, such as handling items in a warehouse or driving autonomous vehicles, by integrating various data inputs and using advanced algorithms.
  • Example: Bengaluru-based startup launched an AIA that could autonomously handle items in a warehouse
  • A tool called Orby AI automates repetitive tasks while 4149 AI collaborates with humans inside apps like Slack and Notion to improve their productivity.
• Personalization: These agents can acquire new knowledge and tailor their responses based on user preferences and needs, enhancing personalization in services.

Challenges Posed by AIAs

• Privacy Concerns: As AIAs access and process vast amounts of personal data, protecting user privacy becomes a challenges.
• Dependency Risks: Increased reliance on AIAs may make users more vulnerable if the systems are compromised.
• Moral and Ethical Issues: AIAs can learn and adapt, potentially impacting user safety and autonomy. Their ability to exercise hindsight must align with moral principles without infringing on human creativity.
• Manipulation and Security: AIAs must be safeguarded against manipulation by malicious actors. Mechanisms should be in place to prevent compromised AIAs from harming users.
• Legal Liability: The use of AIAs raises complex legal and ethical questions about accountability and liability.

Legal Liability of AIAs

• Lack of Legal Agency: AIAs do not possess legal personhood. Hence, their actions are not distinct from those of their users in the eyes of the law.
• Liability on Creators: The responsibility for an AIA’s actions typically falls on the developers or service providers. For example, a court held Air Canada liable for misinformation provided by its chatbot.
• Need for Regulation: Legal scholars argue for holding AI developers and companies to standards of liability and reasonable care, emphasising that the costs of potential risks imposed by AI should be internalised by these entities.
• Ethical Programming: It is unfair to hold users fully responsible for AIAs’ actions; programmers and companies should share the blame, especially when their algorithms influence decisions.

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