Demystifying AI Agents: Your guide to understanding intelligent virtual assistants & why Silicon Valley is excited about it

Virtual assistants such as Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana were initially introduced at various points in time. However, now a new generation of more autonomous AI assistants is already appearing in Silicon Valley.

The industry is investing billions of dollars in a new tool called 'AI Agents.' These are advanced AI tools backed by cutting-edge technologies like ChatGPT, in an effort to take advantage of breakthroughs in AI. These machines promise to do more difficult jobs without requiring constant supervision by humans. But the question arises what is an AI Agent?

What is an AI agent?

A software programme called an ‘AI agent’ is made to interact with its environment, understand the data it gets, and behave based on that information to accomplish particular goals.

 

With the versatile AI Agent, you can create your own agents by selecting a name, a goal, and the AI model it is supposed to employ (GPT-3.5 Turbo and GPT-4). You can change and add your own tasks once it initialises the objective and generates the initial work list.

For example, an AI agent called ‘AutoGPT’ can produce text replies that resemble those of a person. It can understand the conversation's context and produce relevant replies.

An autonomous AI agent called ‘BabyAGI’ is capable of learning on its own and carrying out tasks including comprehending natural language, analysing photos, recognising objects etc.

While there aren’t any existing models to give context, we are sure you must be familiar with conversational AI like Jarvis in the Iron Man series or Cortana in the popular Microsoft game, Halo.

AI Agents of the future would be similar to these fictional tech assistants and you can bet that they will be as good as you have seen in the movies.

Features of AI Agents

An AI Agent is a type of software program that possesses certain characteristics which sets it apart from basic AI tools. These characteristics include autonomy, reactivity, reasoning and decision-making abilities, learning capabilities, communication skills, and goal-oriented behaviour.

Autonomy: It means that an AI agent can perform tasks independently without constant human intervention. It has the ability to sense and interpret its environment through sensors such as cameras or microphones, which enables it to perceive its surroundings.

Reactivity: Reactivity refers to the agent's capability to assess the environment and respond accordingly to achieve its goals.

Reasoning and decision-making: It can analyse data using algorithms and reasoning techniques to make informed decisions in order to accomplish its objectives. Learning is another important characteristic, as AI agents can enhance their performance through various learning techniques such as machine learning, deep learning, and reinforcement learning.

Communication: It is the most prominent feature of AI agents, allowing them to interact with other agents or humans. They can understand and respond to natural language, recognise speech, and exchange messages through text or other communication methods.

Goal-oriented behaviour: Lastly, AI agents are goal-oriented. They are designed to achieve specific goals, which can be either pre-defined or learned through interactions with the environment.

How does it work?

Autonomous AI agents like AutoGPT and BabyAGI operate by perceiving their environment, processing information, making decisions, taking actions, and learning from feedback. These agents utilise sensors and data sources to gather information about their surroundings. They then process this data, organising it and creating internal representations for understanding.

Using reasoning techniques such as logic or statistical analysis, the agents make informed decisions based on their knowledge base and goals. They formulate plans or strategies to achieve their objectives, considering factors like resource allocation and limitations.

Throughout this process, the agents continuously learn and improve. They receive feedback from the environment or from human interactions, allowing them to adjust their actions, update their knowledge base, and enhance their performance.

How AI agents differ from Chatbots?

- The main communication channel for chatbots is typically live chat, although some chatbots can also interact with customers through SMS or within mobile applications.

On the other hand, an AI Agent is capable of providing support across various text-based communication channels, including email. Email support is particularly well-suited for an AI Agent, as it excels in efficiently addressing customer inquiries.

 

- Typically, a chatbot works independently from a helpdesk system. However, AI Agent performs similar duties to a human agent within your help desk. It effortlessly connects with your present company processing tools and e-commerce engine to accept, understand, and even act on support tickets on its own. This implies that the AI Agent may handle support requests without the need for human involvement, helping clients effectively and efficiently.

- The purpose of chatbots is frequently to divert customers to resources or tools for self-service. An AI Agent, on the other hand, goes beyond it and actively completes tasks to meet consumer requests. An AI agent acts to completely address a variety of service requests, encompassing more than 50 categories of issues.

Virtual assistants to AI agents

The competition for artificial intelligence, or the ability for machines to do cognitive tasks on level with or better than humans, is what is driving the creation of advanced and autonomous agents. Startups and major corporations like Microsoft and Alphabet are funding AI agents that are driven by GPT-4-type foundation models.

For instance, Inflection AI funded $1.3 billion to create a personal assistant that can serve as a mentor and manage responsibilities connected to delays in transportation.

Another AI firm, Adept, received $415 million and demonstrated its technology's capacity to autonomously browse a company's client database.

Ethical concerns in AI

Yoshua Bengios, a well-known AI researcher, says concerns about ethical issues in AI development reflect a growing recognition within the field of the potential risks associated with advanced AI systems. The ethical concerns raised by Yoshua include misinformation, a lack of human oversight, ethical decision-making, and privacy concerns.

To address these concerns, there is an increasing call for regulation and responsible AI development. This includes developing transparent and explainable AI algorithms, conducting testing and evaluation, and establishing legal and ethical frameworks to ensure accountability.

Overall, the objective is to find a balance between the risks that must be reduced and the potential advantages of AI, while also ensuring that AI systems are in line with human values and advance the well-being of individuals as well as society.

AI Agents could revolutionise various sectors

Indeed, the commercial potential of AI agents is significant, which has led to increased interest and investment from various companies. AI agents built on foundation models like GPT-3 have demonstrated impressive capabilities in performing advanced tasks such as natural language processing, text generation, and even image recognition. These agents can be trained on vast amounts of data to develop a broad understanding of different domains.

Microsoft, on the other hand, aims to leverage AI agents for specific tasks. Their 'copilot for work' initiative focuses on developing AI agents that can assist users in drafting documents and presentations effectively. The goal is to enhance productivity and streamline work processes by leveraging the capabilities of AI agents.

In summary, Silicon Valley is pursuing autonomous AI agents with more autonomy that can carry out challenging jobs. The industry is making significant investments in creating AI agents that are driven by cutting-edge models, even though technology is still growing.

While challenges related to robust reasoning (the process of learning to solve new problems based on the research and the application of AI) and ethical concerns persist, the potential commercial benefits are driving significant investments in AI research and development.