The Rise Of Cognitive Agents in Artificial Intelligence

Cognitive assistants can already set your alarm, lower your thermostat at night, and tell you what movies are playing at the mall. And, as a new generation of personal, social robots becomes available to consumers in the coming years, they are likely to play a larger role in customer support as well.

 

One of the major concerns about artificial intelligence (AI) is that it will eventually replace humans. However, it is becoming clear that interacting with people is one of the most important tasks of AI. "Cognitive assistants," or systems that use cognitive technology to interact with people and make our lives easier, are one of the genre's fastest-growing subsets. Shortly, they are likely to transform many aspects of the business.

 

Of course, so far, the primary examples of this technology have primarily served consumers rather than businesses. Cognitive assistants can already set your alarm, lower your thermostat at night, and tell you what movies are playing at the mall. These applications are likely to become more common as a new generation of personal, social robots is developed.

 

However, the opportunities for cognitive assistants in business have thus far been underutilized. However, numerous business tasks and processes could benefit from them, and this category of application is just getting started. 

 

They can provide access to complex information, perform digital tasks such as opening a bank account or checking a patient into a hospital, make product or service recommendations, and bring context awareness to an interaction. Cognitive assistants can even recognize the emotional content of a conversation and respond appropriately.

 

What are Cognitive Agents? 

 

The concept of cognitive agents, also known as artificially intelligent agents, taking over the world is popular in art, entertainment, and public debate. The likelihood of robots one day ruling the world is, however, remote.

 

Why? The types of intelligence at which AI and humans excel differ greatly. When it comes to processing large amounts of data, artificially intelligent tools are inherently superior to humans. Humans, on the other hand, consistently outperform robots in tasks that require creative problem-solving, common sense, and human relationships.

 

Having said that, some cognitive agents are purposefully designed to become "smarter" than humans or to outperform us in specific tasks. These agents frequently make use of AI's inherent computational intelligence while also attempting to learn from certain types of human intelligence.

 

AlphaGo, the AI that defeated multiple Go champions, is one well-known example. Cognitive agents have also been developed to outwit humans at poker and other games, to lip-read human speech and identify visual imagery in photos more accurately than humans, and to create AI programs that are smarter than humans. Sentient Digital, Inc. is creating military training simulation software that uses artificial intelligence to learn from human player behavior and teach us new combat strategies.

 

Cognitive assistant technologies are based on decades of research in natural language understanding and generation, semantic decomposition, and even machine and deep learning. Companies can assemble these technologies as needed to address specific interaction needs with customers, suppliers, or employees because the underlying capabilities of these technologies are increasingly modular (provided as application program interfaces, or APIs).

 

Also Read | 5 Types of Intelligent Agents in Artificial Intelligence

 

Introduction to Cognitive Computing

 

Cognitive agents are the result of one of the most important aspects of artificial intelligence: cognitive computing. It is the computerized simulation of human thought processes. It also includes self-learning systems that use data mining, pattern recognition, and natural language processing (NLP) to mimic human brain patterns. These are the applications:

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Applications of Cognitive Computing

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1. Adaptive: 

 

Cognitive solutions adapt as information changes, goals shift, and requirements shift, preparing for and dealing with ambiguity. Cognitive computing solutions can often process data in real-time or near real-time.

 

2. Intelligent and stateful: 

 

If a problem statement appears ambiguous or incomplete, cognitive solutions can help define it or find additional sources. Previous interactions may also be used to define or finish problem statements.

 

3. Contextual: 

 

Contextual elements such as syntax, meaning, time, location, domain, regulations, user profile, process, task, and goal can be understood, identified, and extracted by cognitive solutions. They may also extract structured and unstructured information, as well as sensory inputs, in some cases.

 

While cognitive computing applications (such as cognitive agents) can simulate the human brain, they are not intended to replace human advisors. Instead, this technology can be viewed as a powerful assistant capable of handling time-consuming tasks and freeing up human resources to address more complex issues. And less time spent looking for information equals faster resolution times, which equals cost savings.

 

Cognitive agents can help you with your digital transformation in a variety of ways. Explore the stories below about modern workplaces that have used cognitive agent solutions to provide valuable insights and a better user experience for both customers and advisors.

 

Also Read | What is a Virtual Agent and How To Choose One?

 

Types of Intelligent Agents

 

Intelligent Agents can be any entity or object, including humans, software, and machines. These agents can make decisions based on environmental inputs using sensors and act on the environment using actuators. AI-enabled agents gather input from their surroundings via sensors such as cameras, microphones, and other sensing devices. 

 

The agents then perform real-time computation on the input and deliver output via actuators such as screens or speakers. These agents can solve problems in real-time, analyze error and success rates, and retrieve information.

 

1. Simple reflex agents:

 

Simple reflex agents disregard previous percepts and act solely based on the current percept. Percept history is the history of everything an agent has perceived up to that point. The condition-action rule underpins the agent function. A condition-action rule is a rule that maps a condition to a state. 

 

If the condition is met, the action is performed; otherwise, it is not. This agent function works only when the environment is fully visible. Infinite loops are frequently unavoidable for simple reflex agents operating in partially observable environments. If the agent can randomize its actions, it may be possible to escape from infinite loops.

 

2. Model-Based Agents:

 

It is a more sophisticated version of the Simple Reflex agent. It, like Simple Reflex Agents, can respond to events based on predefined conditions, and it can also store internal states (past information) based on previous events. 

 

At each step, Model-Based Agents update their internal state. These internal states help agents deal with the partially visible environment. Any action relies on both the internal state and current perception. When dealing with a partially observable environment, it is nearly impossible to determine the exact state.

 

3. Goal-based agents:

 

These agents make decisions based on how close they are to achieving their goal (description of desirable situations). Every action they take is designed to bring them closer to the goal. This gives the agent the ability to choose from multiple options, selecting the one that leads to the desired state. 

 

The knowledge that underpins its decisions is explicitly represented and modifiable, allowing these agents to be more adaptable. They usually necessitate some searching and planning. The goal-based agent's behavior is easily modifiable.

 

4. Utility Agents:

 

Because the actions of these agents are determined by the end goal, they are referred to as Utility Agents. Utility Agents are used when there are multiple solutions to a problem and the best option must be selected. The alternative chosen is based on the utility of each state. Then, they perform a cost-benefit analysis on each solution and choose the one with the lowest cost.

 

5. Learning Agent: 

 

In artificial intelligence, a learning agent is a type of agent that can learn from its past experiences or has learning capabilities. It begins by acting with basic knowledge and then learns to act and adapt automatically.

 

A learning agent consists primarily of four conceptual components, which are as follows:

 

• Responsible: It is responsible for making improvements by learning from the environment.

 

•  Critic: The learning element receives feedback from critics that describes how well the agent is performing in comparison to a predefined performance standard. It is in charge of deciding which external actions to take.

 

• Problem Generator: This component is in charge of suggesting actions that will lead to novel and educational experiences.

 

Conclusion:

 

The Cognitive Architecture Model serves as the foundation for designing cognitive agents, and cognitive agents serve as the foundation for cognitive machine learning. Cognitive agents can mimic human actions using machine learning algorithms, natural language processing, data mining, and other techniques. Data and previous experiences can help cognitive agents learn. 

 

However, it learns best when different algorithms are used. However, it is critical to understand the significance of cognitive agents and how they interact with cognitive architecture models, ML models, and NLP models. Before implementing any cognitive architecture models, it is critical to understand them. The model that best supports the organization's objectives should be chosen to ensure that the organization's goals are met.

 

The world of business cognitive assistants is a brave new one, with few organizations having extensive experience with it. There will undoubtedly be some surprises and disappointments. However, we are confident that the technology holds great promise for improving human-business interactions. We may soon discover that humans prefer to converse with machines due to their vast knowledge, ability to consider detailed contextual factors, and inability to become upset or befuddled. And never having to hear the phrase "All of our customer service agents are busy—please wait until the next one is available to take your call" would be a small blessing for humanity.

 

To summarize, cognitive agents, or artificially intelligent agents, are already being implemented in a variety of organizations, and their benefits outweigh the costs of implementation. The effects on these various organizations have been enormous and significant. It has changed the way businesses provide value to their customers. Furthermore, with the use of cognitive agents, businesses can work faster and smarter, lowering costs, increasing profits, and strengthening their competitive advantage.