Augmented Intelligence Vs Artificial Intelligence
Technology is used for almost everything, be it work, entertainment, or lifestyle. From Jarvis of the Marvel Universe to the Metaverse of the real world, we have seen rapid progress in technology. Automation has always fascinated humankind, and a handful of options have enabled business enterprises to automate their processes such as Artificial Intelligence and Intelligence Augmentation.
Both technologies are popular and are used by different industries to streamline their workflows, improve productivity, save cost, and minimize manual error. These technologies play a different role based on their application, industry type, and requirements of the enterprise.
Let's learn more about these technologies and learn how Intelligence Augmentation can change the game.
Artificial Intelligence and Intelligence Augmentation
Let's learn more about these technologies and learn how Intelligence Augmentation can change the game.
Artificial Intelligence (AI) is one of the most powerful fields, aiming to leverage computers to create intelligent machines that can work and react like humans to enable problem-solving and decision-making capabilities.
To augment means "to improve." Also known as cognitive augmentation, Intelligence Augmentation (IA) is more of an alternative conceptualization of AI with a different perspective on technological advances. It is designed to complement and enhance human intelligence rather than replace it. IA focuses on helping humans perform tasks faster and smarter, which makes it even more desirable for businesses.
How is Intelligence Augmentation Different from Artificial Intelligence?
Both AI and IA are closely related and share the same goals but different require different approaches and techniques. The key difference between artificial intelligence and intelligence augmentation is how these technologies operate. AI is intended to perform tasks without human intervention and take mundane and repetitive tasks from humans. IA does not have to participate in decision-making. It can analyze data, study patterns, and create reports based on those patterns, allowing humans to make the final decisions. Let's break down the difference even more.
Artificial Intelligence (AI)
Generally, AI can mimic humans and enable machines to work and make intelligent decisions like humans.
- Retain data and learn from past observations
- Combine computers and machine-based systems
- Processes and results are machine-dependent
- Will compete with human jobs in the future
- Faster task completion
Intelligence Augmentation (IA)
Intelligence augmentation works with both humans and machines to provide better outputs. It is more of a helper for humans rather than a replacement.
- Combines humans and machines
- Processes and results are partially machine-dependent
- Will bring better opportunities for humans in the future
- Slower task completion due to human involvement
How Does Intelligence Augmentation Work?
Here is a consumer goods industry use case for intelligence augmentation. IA gathers both structured and unstructured data from different sources and presents data to employees to provide complete customer information. This helps employees understand what is going on in the industry, what's new, what can affect their customers, and the new opportunities or threats. With augmented intelligence, companies can use information under the supervision of humans so that technology doesn't have complete control over it.
Where Can You Apply Intelligence Augmentation?
The nature of technology is that it learns and adapts faster. Augmented intelligence improves over time which is a significant advantage for its users. IA also helps companies transform how they interact with their customers and supports onboarding, customer support, and consulting services. Let's look at some popular industries to apply intelligence augmentation.
Healthcare
Augmented intelligence is popular in the healthcare sector. It improves patient care quality and provides information on how healthcare vendors can improve too. IA also reduces the chances of medical errors, which usually occurs during manual data entry, and accelerates many time-consuming processes such as invoice, payments, and insurance claims.
Financial
Augmented intelligence in financial services has incredible benefits. Financial services are complicated and even a tiny error can affect the entire calculation of an enterprise. Using smart technologies such as IA can help companies manage their finances. With augmented intelligence, financial planners can apply personalized services based on their customer data, objectives, and risks. IA also ensures no manual errors.
Manufacturing
Manufacturing involves complex processes requiring human intervention, but technology can ease the burden of manual processes. Adopting IA in the manufacturing industry helps accelerate the design process. Workers can provide input parameters, and the machine can design objects in multiple ways. IA boosts efficiency by providing an abundance of design options quickly, and human workers can choose the best options to deliver value to customers.
Oil and Gas
The oil and gas industry also involves several complicated processes, such as drilling. With augmented intelligence-enabled systems, companies can optimize the drilling process and get the correct coordinates and measurements. Workers can check for favorable environmental conditions to make operating decisions and IA can start working on the processes, leading to less damage, faster work, and greater accuracy.
The advantages of augmented intelligence are incredible across many other industries that rely on big data for various purposes, including:
- Predict machine maintenance
- Predict customer behavior and preferences
- Identify efficient treatment options
- Identify market patterns
- Generate autopilot options
Intelligence augmentation has endless possibilities, and its ultimate goal is to improve the knowledge, expertise, and experience of all workers. It also aims to empower employees to work with machines to understand, access, and interpret all data types and manage risks during automation decision-making.
