Artificial intelligence seems to be everywhere in our lives, even in areas or forms that we only think of in science-fiction stories. Alan Turing, the pioneer of computer science, wrote at the conclusion of his 1950 paper titled “Computing Machinery and Intelligence” that he hoped machine intelligence would be able to compete with human intelligence in every field. Since 1950, developments in computer science and artificial intelligence show that Alan Turing’s expectations have come true.
Combining cognitive insight and artificial intelligence has the potential to greatly enhance problem-solving capabilities and create more efficient and effective solutions. The goal is to develop AI systems that can mimic the human brain’s ability to think creatively and find innovative solutions to problems. This can result in more efficient decision-making and improved outcomes in various fields such as healthcare, finance, and education.
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Also Read: Can An AI Be Smarter Than A Human.
What Is Artificial Intelligence?
Before we get into all machine intelligence developments, it would be useful to define what artificial intelligence is. Defining artificial intelligence as just “machines with intelligence” would be insufficient to understand these systems that are at the center of our lives.
Artificial intelligence is a discipline that combines computer science and data sets to provide the key elements required to solve a particular problem. Artificial intelligence utilizes sub-fields such as deep learning and machine learning to solve the given problem. It is an umbrella term that covers many concepts such as neural networks, deep learning and machine learning (sequenced from the least to the most comprehensive). Artificial intelligence uses different algorithms to create predictive models or pre-trained models from large data sets. It is also used in smart cities where AI is used to collect data from devices and buildings in urban areas to predict the cities’ future needs.
Although the definitions of artificial intelligence are approximately similar, the functions vary considerably. The critical thinking processes in which artificial intelligence can assist us depend on the field.
Business is one of the main fields in which artificial intelligence is increasing its impact. Artificial intelligence technologies are increasingly being used to facilitate business functions. According to a study in the Harvard Business Review, 152 artificial intelligence projects were analyzed within the scope of business. The European Union even proposed a law called the AI Act that categorized CV-scanning tools, which are used to rank applicants, as “high-risk applications” that “are subject to specific legal requirements.”
In this research, the use of artificial intelligence in business is frequently used in three areas, such as automating business processes, obtaining cognitive insights through data analysis, and cognitive engagement. This article will briefly review each of these areas and how they work.
The study found that almost half of these projects used the robotic automation process system. Process automation is known as robotic process automation. Software used for robotic process automation systems performs tasks without human intervention. Its purpose is to automate office work such as editing employee or customer data, filling out necessary documents, and moving files without the use of manpower. In particular, it integrates APIs and user interface interactions to embed and implement tasks in its algorithm that need to be done repetitively over and over again. Process automation enables the systematic execution of tasks and operations by mimicking employees.
In business, process automation includes updating and submitting data to mail and registration systems, recording residence changes, making the necessary updates as a result of service changes, accessing the necessary systems to provide communication with customers; reporting and replacing lost bank cards, optimizing financial systems by obtaining information from multiple data, detecting errors in customers’ financial systemsProcess automation can be used in a business setting as a cheap, flexible, and useful system that requires less intelligence and can be used to improve customer satisfaction.
What Is Cognitive Insight?
The results of the study show that the second most frequently used artificial intelligence algorithm is cognitive insight through machine learning. Cognitive insight is defined as a cognitive process that involves reevaluating the decisions made. Although it seems to be a metacognitive process specific to humans, machines can also perform this function through artificial intelligence.
To achieve cognitive machine insight, machine learning models are used. Machine learning involves three stages – the decision process, the error function, and the optimization process. Predicting or categorizing data is a common application of machine learning algorithms during the decision process. The algorithm generally makes a pattern prediction of data from input that may be labeled or unlabeled.
Machine learning uses an error function to evaluate the performance of a prediction model made in a decision process. An error function can compare the predicted value to the actual value, providing a measure of the model’s precision if the data follows established patterns.
Lastly, machine learning makes modifications to the algorithms such that the gap between the observed pattern and the model’s prediction is less if the model can better match the data points in the training set. The algorithm updates the data until a target accuracy is obtained by multiple repetitions of the “evaluating and optimizing” process. The cognitive insight that results from these processes can be used for various business functions.
In the business world, cognitive insight can be used for predicting the number of purchases a customer is likely to make, detecting fraudulent activity related to credit and insurance in real time, accelerating the recruitment process through automated job search and onboarding, and spotting and conducting analyses of fraud cases.
The benefits of this technology can be observed in the data of several companies. For example, a company named GE managed to save $80 million in redundancies and negotiation of contracts that were previously controlled at the business unit level.
Similarly, a big bank utilized this technology to extract data on conditions from supplier contracts and match it with invoice numbers, uncovering undeliverable goods and services worth tens of millions of dollars. GE used cognitive insight technology to consolidate customer data, reduce expenses and negotiate pre-signed contracts at the business unit level. Similarly, a bank used cognitive insight to analyze data on specific terms in contracts and match it to billing numbers. This method revealed the existence of products and services worth millions of dollars that could not be delivered.
Deloitte’s audit application, another application that uses this technology, uses cognitive insight technology to identify which items should be removed from contracts. In this way, the application enables the review of all contracts without the need for human intelligence and abilities.
Finally, the study found that the least common artificial intelligence method in the use of business is cognitive engagement. Cognitive engagement is a system that uses natural language processing bots and autonomous vehicles through machine learning. Cognitive engagement provides work that enables communication or interaction with employees and customers.
Natural Language Processing (NLP) is a system that integrates important sciences such as linguistics and statistics, as well as artificial intelligence subfields such as machine learning and deep learning. This system allows a computer to encode written and spoken human language as data. It also aims to analyze the intention or emotion of the person who is the source of human language.
Therefore, natural language processing provides a full comprehension of human language including nonverbal cues. You’ve probably interacted with natural language processing in the form of voice-over navigation systems, virtual assistants, speech-to-text transcription tools, customer experience platforms, self-driving cars and other modern conveniences.
Natural language processing technologies are also playing an increasingly important part in corporate systems, which help businesses optimize their operations, enhance employee efficiency, and simplify tasks that are tasks to the organization.
In the business world, cognitive engagement can be used for customer service applications such as changing passwords and smart systems that provide 24/7 service to respond to the increasing number of customer questions in the customer’s own language; sites to meet the demands of employees in employee rights and human resources processes; verbal or visual recommendation systems for goods and service to increase sales, and treatment recommendation systems that generate personalized plans that consider patients’ health and prior medical history.
Cognitive Insight and Artificial Intelligence
Cognitive insight is defined as a human ability to reassess thoughts, actions, or decisions in healthy people. In psychiatric disorders, it can be defined as awareness of one’s own illness. However, in the context of artificial intelligence, it is the detection and categorization of patterns in the available data sets through machine learning or, more specifically, deep learning.
To understand cognitive insight in the context of artificial intelligence, it is useful to know the basic principle of deep learning models. Deep learning models use multilayered neural networks compared to machine learning. The goal of these neural networks is to enable “learning” from enormous volumes of data in a manner analogous to that of the human brain, but with much lower performance.
A neural network with just one layer can still produce predictions but adding more hidden layers improves accuracy. Cognitive insight in the context of AI is a function that requires multiple neural associations, as found in neuroanatomical studies in humans. Deep learning models improve the efficiency of processing, recognizing and categorizing patterns in large data sets.
Cognitive insight can help companies in three areas, in particular,
1. Cognitive insight provides important algorithms for identifying business opportunities. Using cognitive insight, a business can analyze its existing information and text archive to determine where it can benefit the most.
2. Cognitive insight helps companies to identify sufficient firepower in the business. The use of cognitive insight can collect greater data than a computer can efficiently analyze. Thus, it can have vast amounts of data about consumer behavior and take action on what that data means or how it can be strategically applied.
3. Cognitive insight helps companies to find bottlenecks. The lack of cognitive insight as a result of the interruption in the flow of information can cause the information available in the company not to be distributed optimally. This can create a bottleneck situation in the company.
Also Read: How Can AI Improve Cognitive Engagement
Scaling With Cognitive Insight
Scaling is the process of standardizing independent data components within a range. Data preprocessing is concerned with magnitudes, values, and units that differ significantly. A machine learning algorithm without data scaling prioritizes larger values over smaller values, regardless of the unit.
There have been several businesses that have attempted to launch artificial intelligence pilots but have been unsuccessful in their attempts. To maximize the benefits of machine learning or deep learning models, businesses need to have detailed scaling strategies, which require coordination between company owners and developers.
Because these technologies frequently support individual activities rather than a complete or an entire process, scale-up mostly requires integration with existing processes and systems. Businesses need to begin the process of scaling up by determining whether or not the needed integration is possible or practical.
For example, if the integration depends on proprietary technology that is difficult to procure, this will limit scale-up. It is important for the scaling of AI applications that business owners conduct a pilot and discuss scaling before or during the pilot.
However, if it is possible, the return on artificial intelligence investments is typically three times better for businesses that are successfully scaling AI compared to those that are exploring proof of concepts in isolation. For example, in the bottleneck situations mentioned above where information is available but cannot be optimized, another way out is through financial advisors.
However, the knowledge generated by financial advisors is either time-consuming or costly to scale. Due to all these disadvantages, many companies offer their customers AI-supported “robo-advice” systems that provide affordable and quick results in routine financial matters.
Artificial intelligence algorithms have gained widespread use as a problem-solving tool in the business world. According to the findings of a study that analyzed 152 different projects, businesses have a better chance of success when they develop and implement artificial intelligence using an incremental rather than a transformative strategy.
This strategy focuses on enhancing human ability rather than removing or replacing it altogether. The findings indicate that artificial intelligence can provide virtual assistance for three crucial business functions. These include automating business activities (including the ongoing administrative and financial office labor), gaining cognitive insights via the use of machine learning or, more particularly, deep learning models, and cognitive engagements with both customers and employees.
To make the best use of artificial intelligence, companies must comprehend which technologies do what tasks, categorize those technologies in accordance with the requirements of the company, and devise strategies for scaling those strategies across the company.
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