Many people are worried that Artificial Intelligence (AI) will eventually replace humans in the workforce, rendering us all obsolete. The future of AI is not all doom and gloom. In fact, there are many exciting possibilities for the technology in the coming years, and we can all look forward to seeing the positive impact it will have on our lives.

Over the next decade, AI is expected to revolutionize various industries, from healthcare and finance to education and entertainment. We can expect to see more personalized experiences and tailored recommendations, thanks to AI algorithms that can learn our preferences and habits.

But what about the job market? Will AI really take over all our jobs? This fear is not new; in fact, it echoes the apprehension felt during the Industrial Revolution, when machines and new manufacturing processes threatened to render craftsmen and laborers outdated. As we look to the future of AI and its potential impact on the workforce, it's worth considering the lessons of the past and what they might tell us about what's to come. As AI technology continues to evolve, we will need more people with the skills to develop and manage these systems.

The evolution of AI

AI is a significant research area in academia and various industries that has dramatically changed our daily lives. Its development has been full of twists and turns and can be divided into four stages, with the last one being established in the last decade with the modern deep learning era.

Early 50s are generally viewed as the time of AI's inception. Even then, concerns about people losing their jobs were present, but at the time, the technology was not yet powerful enough to bring those fears to the forefront. As a result, the future implications of AI were somewhat overlooked for several decades. An earlier generation of scientists, physicists, and intellectuals had conceptualized the idea of AI, but it was not until British polymath Alan Turing proposed that people solve problems and make decisions using available information and reason; shortly after the earliest successful AI program was written in 1951 by Christopher Strachey, later director of the Programming Research Group at the University of Oxford. Strachey’s checkers (draughts) program ran on the Ferranti Mark I computer at the University of Manchester, England. By the summer of 1952 this program could play a complete game of checkers at a reasonable speed.

The biggest obstacle to AI's further expansion was the limitations of computers, which were unable to store information and therefore needed to undergo fundamental adaptation. The issue of funding also posed a significant challenge until 1974. By this time, however, computers had become more popular due to their increased speed, decreased cost, and improved storage capacity.

As we move towards the next decade, there is no doubt that AI will continue to shape and transform various industries. From healthcare to business sustainability, trust ethics and governance, and service innovation, AI is set to play a crucial role in driving growth and development.

Subsets of AI

AI refers to the ability of a machine to make intelligent decisions. When a machine possesses this ability, it can be considered intelligent in an artificial sense. Although people often use the terms machine learning, deep learning, and AI synonymously, it is important to note that deep learning is a subset of machine learning, and machine learning is a subset of AI.

Types of AI

AI is categorized based on how closely it replicates human abilities. The more human-like functions an AI system can perform, the more advanced it is considered. Two common ways of categorizing AI are based on their similarity to human thinking and feeling, and their ability to perform tasks. The four types of AI or AI-based systems are reactive machines, limited memory machines, theory of mind, and self-aware AI.

In this blog post, we will explore the future of AI and predictions regarding it from industry experts for the next decade affecting some of the most important industries.

AI in Healthcare

The healthcare industry has been one of the earliest adopters of AI, and it is expected to continue to make significant progress in the next decade. AI has the potential to revolutionize the way we diagnose and treat diseases, making healthcare more efficient and accessible. With the help of AI-powered devices, medical professionals can gather and analyze patient data in real-time, enabling them to make more accurate diagnoses and treatment decisions. In addition, AI can help to improve patient outcomes by identifying potential health risks before they become severe. By analyzing data from various sources, AI can help to predict which patients are at higher risk of developing certain diseases, allowing medical professionals to intervene early and prevent the progression of the disease.

One example can be Radiology AI solutions which are being developed to automate image analysis and diagnosis. The use of technology such as AI can assist radiologists in identifying areas of interest on scans, which can lead to increased efficiency and reduced chances of human error. Additionally, there is potential for fully automated solutions that can read and interpret scans without human oversight. Alongside predictive care comes another breakthrough - Networked hospitals with connected care. In the future, hospitals will no longer be one large building that caters to a broad range of illnesses. Instead, they will focus on treating the acutely ill and performing complex procedures, while less urgent cases will be treated at smaller hubs and clinics such as retail clinics, same-day surgery centers, specialist treatment clinics, and even patients' homes. These locations will be connected through a single digital infrastructure.

Centralized command centers will analyze clinical and location data in real-time to monitor supply and demand across the network. By utilizing AI to identify at-risk patients and removing bottlenecks, the system will ensure that patients receive care where it is needed most, improving the overall efficiency and effectiveness of the healthcare system.

AI in Education

In the past, people used to go to the library to make copies of encyclopedia pages for their school projects. However, nowadays with technology available, younger generations have the internet as their main source of education, information, and entertainment.

The utilization of AI in the field of education is set to revolutionize the way people of all ages acquire knowledge. AI incorporates machine learning, natural language processing, and facial recognition to digitize textbooks, detect plagiarism and gauge the emotions of students to help determine who’s struggling or bored. Both presently and in the future, AI tailors the experience of learning to student’s individual needs.

The implementation of AI in education enables a tailored approach to learning where the technology analyzes and comprehends each student's distinctive needs, interests, learning history, and abilities. Based on these factors, AI systems adapt and modify educational programs, providing relevant content that effectively engages students. For example, Metaverse Learning is an online learning platform that provides interactive content and exposes learners to situations to help them develop the specific skills they want to learn.

On the other hand, the Khan Academy has introduced a new tool called Khanmigo that utilizes GPT-4 technology. This tool will be integrated into the lessons and videos provided by Khan Academy. Khanmigo will act as a virtual tutor, asking questions and encouraging students to provide answers rather than giving them outright. It will provide suggestions on how to improve students' essays, rather than writing them for them. Khanmigo will work in real-time with students who are struggling in the classroom. Teachers who use Khan Academy will have access to records of Khanmigo's conversations with individual students, allowing them to monitor their progress. Parents will also have access to their children's work at home. With Khanmigo, students can even have conversations with historical figures they are studying in their history class. Furthermore, Khanmigo can act as the opposition in debate exercises.

AI in Customer Service

Although many people dislike receiving automated phone calls, AI technology applied in customer service can offer the industry valuable data-driven tools that provide valuable insights for both the customer and the service provider.

Customer service is on the verge of a major transformation with the integration of AI, which has the potential to resolve one of the biggest issues faced by many businesses, - the management of extensive data volumes.

Enterprises can benefit from top-notch customer service through outsourcing. Kosbit's expertise puts them in an excellent position to help implement AI-powered data analysis tools that can help improve customer service. By analyzing customer data, Kosbit can help businesses identify the products and services that are most relevant to each customer. With this information, businesses can provide personalized recommendations to each customer, increasing the likelihood of a purchase and improving customer satisfaction.

Also read: Why your company should consider data science outsourcing?

A good example of eliminating the Customer Service challenges is to apply AI in reducing the customer handling time. Providing a genuine human connection can be a challenge for agents when they are occupied with tasks like searching for customer information or addresses. AI can be a valuable tool for agents to locate crucial data, carry out repetitive low-priority tasks, and assist them in navigating through different workflows and customer service tools used by a brand. By utilizing AI in this way, agents can free up time to focus on addressing clients' requirements, resulting in a significant improvement in the quality of customer service provided.

Over the next decade, the use of AI in the workplace is expected to increase as machine learning and natural language processing technologies become more efficient. Apart from carrying out routine support tasks, these programs will start generating significant consumer data on their behavior and preferences. This information can be leveraged by companies to enhance their marketing strategies and branding efforts, resulting in higher returns on their investments.

AI in Transportation

The transportation industry is poised for a significant transformation with the integration of AI. Self-driving vehicles and AI-powered travel planning are just a few examples of how AI will revolutionize the way we commute. While autonomous vehicles are still a work in progress, they are expected to become more reliable and efficient in the future, potentially revolutionizing the way we travel.

Autonomous vehicles have the potential to improve road safety by taking automatic measures to protect both drivers and pedestrians, even in situations where a driver may react too slowly. In the US, autonomous trucks are increasingly being used for the transportation of goods worldwide, which could significantly reduce costs and maintenance expenses associated with supply chain management. By reducing the reliance on human decision-making while driving, AI can enhance road safety. Furthermore, AI can help address daily traffic issues, such as congestion and blocked roads, by employing cameras and sensors integrated into roads to collect extensive traffic data.

The traffic data collected can be sent to the cloud and analyzed with algorithms that provide insights through apps and systems that guide people to reduce their congestion issues. Traffic predictions can be gathered through comprehensive data processing, and commuters can get much more accurate insights into what’s going on in the roads around them.

AI in Manufacturing

For many years, AI has had a significant impact on the manufacturing industry. Since the 1960s and 1970s, AI-powered robotic arms and other manufacturing bots have been implemented, allowing the industry to effectively utilize AI technology. These robots work in conjunction with humans to perform tasks like assembly and stacking, while predictive analysis sensors help keep equipment running smoothly. Robots are capable of performing repetitive tasks, designing production models, increasing efficiency, automating processes, eliminating human error, and providing high levels of quality assurance.

Humans need to work in three separate shifts to ensure continuous production, while robots can work 24/7 on the production line. By implementing AI, the manufacturing industry can reduce the need for human resources to engage in hazardous and labor-intensive tasks, as errors on the manufacturing plant are reduced.

What about business sustainability?

One area where AI is already making a significant impact is automation. By automating routine tasks, businesses can save time and increase efficiency, freeing up employees to focus on more complex and high-value tasks.

Kosbit's Managed IT Services can help businesses identify areas where automation can be most beneficial, as well as implement AI-powered solutions to streamline their operations.

With the help of AI-powered systems, businesses can optimize their operations and reduce their carbon footprint. For example, AI can help to automate various processes, reducing the need for manual labor and increasing efficiency. This can lead to cost savings for businesses while also reducing their impact on the environment. AI can also help businesses to make more informed decisions by analyzing large amounts of data. This can lead to better resource allocation, reduced waste, and increased productivity. With the help of AI, businesses can become more sustainable and ensure long-term success.

One example can be the reduce of the defective production which according to Forbes around 17 billion purchased items are returned globally. Computer vision-enabled quality control systems installed on the conveyor belt or production line can inspect the quality of the product more accurately and efficiently than manual inspection.

Another example can be the reduced energy consumption in which AI can study the patterns of energy consumption and provide insights on reducing and improving consumption while not compromising the company’s productivity. Google uses DeepmindAI to reduce its data center energy consumption by 30%.

What will be the biggest challenges for AI in the next 10 years?

One of the biggest challenges facing AI is the issue of data bias. This is when data used to train AI algorithms is skewed in a way that results in inaccurate or unfair decisions being made by the AI system.

Another challenge that needs to be addressed is the issue of safety. As AI systems become more advanced, they will be given increasingly complex tasks to perform. In certain instances, AI systems may be responsible for critical real-world mistakes due to errors made by them. For instance, if an autonomous vehicle were to be involved in an accident, it would be crucial to establish whether the fault lay with the AI system or the human driver.

Currently, there is a risk that AI will deteriorate inequality as only those who can afford expensive hardware and software will be able to take advantage of it. This could lead to a two-tier society where the rich get richer and the poor get left behind.

Conclusion:

As we conclude our investigation of AI advancement estimates for the next decade, it is clear that the sector is moving in exciting and potentially disruptive directions. Although certain forecasts may appear unrealistic, it is critical to recognize that even the most outlandish ideas can come to fulfillment under the appropriate conditions. As a result, we must anticipate and watch what the next ten years will reveal for AI, which will undoubtedly be a fascinating adventure.