Table of Contents
The applications of AI are a major driving force in the transformation and success of various industries. From healthcare to e-commerce, transportation to energy, and many more, AI is revolutionizing the way businesses operate.
In this article, we will delve into the exciting world of AI and explore its applications. You will have the opportunity to learn about the latest trends and advancements in AI and understand how it is changing the face of popular industries. You will gain insight into the potential benefits and challenges of implementing AI and see real-world examples of how it is being used to drive growth and innovation. With the rapid pace of AI development, there is no better time to explore this topic and understand its impact on users’ changing demands.
What does AI look like in 2023?
AI has continued to see significant growth and advancements in recent years, and 2023 is no exception. Some key trends and developments in AI include:
- Explainable AI (XAI): With the increasing use of AI in critical applications, there has been a growing concern about the lack of interpretability and transparency of AI models. In 2023, we can expect to see more research and development in XAI, aimed at creating AI models that can provide clear explanations of their decision-making processes.
- Ethical AI: Another application of AI that has been generating awareness is the impact the technology area will have on society and the environment. In 2023, without a doubt, we’ll see a growing focus on ethical AI, with a focus on creating AI systems that are fair, transparent, and responsible.
- Natural Language Processing (NLP): NLP has seen significant advancements in recent years, with models such as OpenAI and ChatGPT demonstrating impressive results in generating human-like text. In 2023, we will see improvements in NLP as more companies such as Google, Baidu, and Alibaba are joining the race. The focus now will be on even better context and sentimental understanding.
Overall, 2023 is shaping up to be an exciting year for AI, with continued advancements and growth in the field. There is much to be excited about, from new and improved AI technologies to their increasing adoption across various industries.
The applications of AI in the education sector can be extremely versatile and beneficial not only to the quality of education, and the personalized attention to everyone’s needs but also to the tasks and process management operations. Implementing AI in education simultaneously supports both students and teachers in improving education results. According to a recent report, in 2021 AI in education was valued at USD 1.82 billion, and from 2022 to 2030 is expected to expand at a compound annual growth rate of 36%. The AI market share in the education sector just in the US alone is projected to increase by USD 374.3 million in the period between 2021 and 2026.
One of the main applications of AI in education is the possibility of personal education. Something that can be physically impossible for teachers to do when managing a class of 30 children. AI, however, can analyze student data and provide customized learning experiences based on their strengths and weaknesses, interests, and learning styles. This can help to increase motivation and academic achievement. Advanced technologies can also adjust the difficulty of questions and assignments in real-time, based on a student’s performance, providing immediate feedback, support, or redirection to other topics or questions when needed. The goal is for AI to become capable of reading the students’ expressions and understanding if they are struggling or to develop a completely personalized curriculum taking each student into consideration.
Task automation in the education sector refers to the use of AI technology to automate routine tasks that are traditionally performed by teachers and administrative staff such as grading, record-keeping, and scheduling. A study done by Mckinsey in 2020 showed that 20% to 40% of teachers’ hours are spent on tasks that can be automated by using innovative technologies. By automating these processes, education professionals can save time and focus on higher-level responsibilities such as creating educational content, providing support to students, and engaging in professional development activities.
- Automated grading: AI-powered tools can grade assignments and provide feedback to students, freeing up teachers’ time.
- Record-keeping: Electronic student records systems can automate the process of storing, organizing, and retrieving student information, reducing the time and effort required to maintain these records.
- Scheduling: AI-powered scheduling tools can automatically create schedules for teachers, students, and facilities, reducing the time and effort required to manage scheduling manually.
Higher learning engagement
In recent years we can see a consistent trend when it comes to the increasing rate of dropouts, and the lack of interest, attendance, and participation in both schools and colleges. The implementation of AI has the potential to improve students’ learning engagement by providing a more captivating educational experience and communication. Many schools already use chatbots which allow students to easily gather information about courses, college admissions, and assignment deadlines. Additionally, technologies provide more interactive learning resources which also increase engagement and interest in young people.
CASE STUDY: MRI ONLINE MODERNIZATION
AI in Healthcare was valued at more than USD 11 billion in 2021 and is estimated to reach USD 188 billion by 2030. As we can see, the applications of AI in Healthcare are revolutionizing the industry by enabling more efficient, accurate, and personalized patient care. AI can help professionals to make data-driven decisions, improve patient outcomes, and reduce costs. Essentially, the growth of the healthcare AI market is driven by the increase in data volumes and complexity, improved computing power, lower hardware costs, and the imbalance in the health workforce-to-patient ratio.
Using AI in healthcare can lead to improved diagnostics by assisting healthcare providers in analyzing and interpreting large amounts of patient data, including medical images, lab results, and electronic health records. AI algorithms can detect patterns and anomalies in the data that might be missed by human observation and provide more accurate and timely diagnoses. This can result in earlier and more effective treatment, improved patient outcomes, and reduced costs for the healthcare system.
A very impressive and inspiring success story is the one of the University of Iowa Hospitals and Clinics which managed to decrease infections by 74% thanks to predictive analytics and real-time data. “Surgical site infections, after all, are probably the costliest complication, the most common healthcare-associated infection, the leading source of readmission for patients to the hospital,” said Cromwell. So, tackling this is a big goal for us.” Said John Cromwell, MD.
Better surgical accuracy
AI can also help improve surgical accuracy by providing real-time analysis and decision support during procedures. AI algorithms can assist in image analysis, detecting anatomy and structures in real-time, and providing guidance to the surgeon. Additionally, AI also has the capability to analyze data from previous surgeries to help predict outcomes and identify potential complications, helping to improve patient outcomes.
An innovative approach to getting a clear and accurate vessel visualization of a patient’s blood vessels during procedures is using a 3D map based on advanced AI technology. Prior to this, the traditional method included injecting a potentially harmful contrast dye. The newly adopted technology is cloud-based, allowing surgeons to collaborate with colleagues remotely, making it easier and more cost-effective to provide remote procedure support.
The applications of robotics in healthcare refer to the use of automated systems and robots in diagnosis, surgery, rehabilitation, and patient care. This field combines the advancements in robotics technology with the needs and demands of the healthcare industry to improve the quality of patient care and outcomes, reduce costs, streamline clinical workflows, and increase efficiency in healthcare delivery. By using AI and computer vision technologies, robots help surgeons achieve new levels of speed and accuracy. Another interesting application of AI in healthcare is therapeutic robots that help patients with multiple sclerosis, strokes, brain injuries, and more.
During the peak of the pandemic, a Mexican hospital started using a robot to check people’s medical history records, temperature, and blood oxygen levels. This not just increased efficiency and saving valuable time when admitting new patients, but also mitigated the need for more front-line workers.
The applications of AI in transportation achieve better efficiency, reduce costs, and enhance the overall travel experience by embracing technology innovations, such as data analytics and machine learning (ML). This allows both big and small cities and towns to lower the rate of road accidents, enhance traffic flow, and even aid in capturing criminals.
One of the hottest topics in transportation in the last few years, self-driving cars, have become from a dystopian idea to a real-life product. The concept is nothing new (existing since 1939), however, the proper execution and technology behind is still very exciting. Self-driving cars utilize AI and computer vision to perform various tasks, including object detection, lane detection, navigation, and decision-making. AI algorithms process huge amounts of data – from sensors, such as cameras, lidars, radars, and ultrasonic sensors, to understand the environment and make predictions about future events. This is how they get trained to take the correct action – slowing down, stopping, braking, etc.
Today many companies produce self-driving cars including Tesla, Mercedes, Ford, BMW, Cadillac, and more. In China for example, by 2040, it is estimated that 60% of all passenger-kilometer vehicles will be automated. Additionally, 40,000 people worldwide are expected to escape death in accidents due to the adoption of self-driving vehicles.
Free parking space detection
AI systems can use data from cameras and sensors in parking lots to provide real-time updates on the number of available parking spots, reducing the time and effort required for drivers to do so themselves. The data collected by computer vision is used to continuously update the inventory of empty parking spots. Additionally, the technology can improve parking lot management, as it can provide data on usage patterns, occupancy rates, and other metrics that can optimize parking space allocation and improve overall efficiency.
As part of the Accedia Innovation Development Center (IDC) our team has worked on two internal parking space detection projects. The first one is the Accedia Voice Assistant (AVA) – an ML-based voice assistant, which provides information when prompted by voice commands. One of the key functionalities is searching for and identifying free parking spots through an integration with the available video cameras in the parking garage. AVA can recognize images and figures from video recordings, distinguish the available parking spaces, and count their number. Additional functionalities include recording and submitting support tickets and providing weather forecasts. As AVA has access to sensitive information, thus the processing happens in a private network and only non-sensitive information gets to the outside world.
Smart Parking, the next IDC project, includes the development of a smart parking system, which informs users of available parking spots through a mobile application. It uses sensors, built into the ground within each parking space, which then send information wirelessly to the application. This allows users to access real-time monitoring of the available parking spaces. Once all spaces have been filled users receive a notification alerting them of the change.
Route optimization is another common application of AI. Genetic algorithms, simulated annealing, and reinforcement learning can be used to optimize the routes of vehicles or delivery systems. These algorithms take various constraints into account – time windows, capacity limits, and road networks, to find the most efficient routes. AI can also use real-time data, such as traffic conditions, to dynamically adjust routes. This leads to improved delivery times, reduced fuel consumption, and increased operational efficiency. Early adopters were UPS. They enhanced their On-Road Integrated Optimization and Navigation (ORION) platform with the addition of Dynamic Optimization. The update recalculates delivery routes throughout the day, in response to changing conditions, improves the accuracy of delivery time estimates, and reduces miles driven.
AI IN MANUFACTURING: RESHAPING THE FUTURE OF THE INDUSTRY
AI is increasingly being used in e-commerce to enhance the customer experience, streamline various business processes, and gain a competitive advantage. It is expected that by 2030 the AI e-commerce solution market will be worth USD 16.8 billion! And even today 78% of all brands have already implemented AI solutions or are planning to do so.
Virtual shop assistants
Virtual assistants are becoming more and more popular in e-commerce due to their ability to provide 24/7 support and handle multiple tasks efficiently, which can help improve a business’s scalability. They use natural language processing and ML to understand customer queries and offer personalized assistance, making it easy and convenient for customers to access information and get help.
Voice recognition has also seen tremendous growth in recent times. It allows customers to purchase products or perform searches using just their voice, making shopping easier as we all know from using solutions such as Amazon’s Alexa, Google Assistant, and Apple’s Siri.
Applications of AI in e-commerce also include product recommendations (Amazon, Instagram, Google eBay, Walmart). AI algorithms use data, such as customer behavior, purchase history, and browsing patterns to suggest products that are most likely to interest the customer. By analyzing this data, AI can personalize product recommendations and make them more relevant to each customer. This leads to improved customer satisfaction, increased sales, and reduced shopping cart abandonment. As customers expect more and more personalized experiences, many retail companies adopt this approach to answering their needs. Some of them include listing similar or trending products, best-sellers, new arrivals, “you may also like” sections, frequently purchased, and more, all depending on the stage of the customer journey.
CASE STUDY: CROSS PLATFORM E-COMMERCE APPLICATION FOR DAIRY COMPANY
The applications of AI in image recognition include processing images and recognizing objects, colors, patterns, and even text. Customers can take photos of a product they’re interested in, and the AI will recognize it and provide similar product options. It can also automatically categorize products based on their images, making it easier for customers to find what they’re looking for. Additionally, AI has the capability to detect fraudulent images, such as fake logos, protect e-commerce platforms from scams, and warn customers not to proceed with their orders.
Several marketplaces have implemented AI-powered image recognition to enhance shopping experiences. For instance, Facebook Marketplace uses an AI assistant to generate product information and labels from images, providing users with tailored recommendations. eBay also utilizes this technology to speed up item listing while Amazon has introduced an AI fashion search that uses ML to identify similar clothing and styles.
AI IN FINANCE: ADVANTAGES, USE CASES, AND CHALLENGES
AI significantly impacts the modern energy system, improving fault detection, forecasting, coordination, and supply management. The shift towards low-carbon energy and the rise of renewables requires smart consumption tools and decentralized power grids. Energy software providers play a key role in the implementation of AI and ML solutions, which can range from marketing strategies to predictive analytics models.
A smart grid refers to the modernized version of electricity grids that integrates digital technology to monitor and control the generation, distribution, and consumption of electricity in an efficient and reliable manner. AI plays a significant role in smart grids by providing advanced analytics and decision-making capabilities to optimize grid operations, improve system reliability and security, and increase energy efficiency. AI can also predict energy demand and adjust energy generation accordingly to reduce waste, detect and diagnose grid faults, optimize the integration of renewable energy sources, and more.
AI’s ability to predict and analyze data is extremely valuable for the renewable energy industry. One major advantage is its capability to gather real-world data, including historical weather patterns and energy consumption, allowing companies to make informed decisions about future energy requirements. According to the International Energy Agency (IEA), wind power is one of the most rapidly growing renewable energy technologies, which is expected to grow 11 times by 2050.
The downside of wind, however, is its unpredictability. DeepMind and Google have implemented ML algorithms to control 700MW of wind power capacity in the US as part of their efforts to address the issue. The wind farms, which generate enough electricity for a city of medium size, are part of Google’s renewable energy initiatives. The DeepMind system uses a neural network trained on weather forecasts and past turbine data to predict wind power output 36 hours in advance and suggest optimal grid delivery commitments a day ahead. This increases the predictability and value of wind power by 20% compared to not having time-based commitments.
Demand forecasting and supply
Demand forecasting is another crucial application of AI in the energy sector. ML algorithms such as decision trees, random forests, and neural networks can be trained on historical energy consumption data to generate demand forecasts. These algorithms can automatically identify patterns and relationships in the data and make predictions, based on those insights.
Accedia’s team has worked on a project building a Demand Forecasting solution for predicting the changing amount of petrol products needed for a company’s gas stations. This way, the solution makes it possible to properly distribute petrol and ensure the stability of product supply using data science. The project involves the development of trained regression predictive models based on ensembles of neural networks for demand forecasting, combining different data sources. As a result, achieving 5-8 times better performance.
WHAT IS MLOPS AND WHY WE NEED IT?
Some final words
In conclusion, the applications of AI have proven to bring numerous benefits to various industries. From improved efficiency and productivity to better decision-making and customer experience, AI is forever changing the way we do business. With the right approach, preparation, and skilled talent, AI can assure immense business growth.
If you’re interested in harnessing the power of AI for your own business, Accedia is here to help. Our dedicated AI team has the skills and expertise to help you design, build, and implement AI solutions that drive growth and success.