AI, Machine Learning and Data Analysis: Potential Future Applications
It wasn’t too long ago that novelists were writing speculative fiction regarding what was then seen as impossible. The idea of thinking machines and smart devices that could make accurate predictions is not new. Although it took a while to become a reality, it’s finally here.
Artificial intelligence (AI), machine learning (ML), and data analysis are rapidly advancing technologies that are poised to revolutionize a wide range of industries in the near future. From healthcare and finance to transportation and logistics, these technologies have the potential to improve efficiency, accuracy, and personalization in ways that were once unimaginable.
This article will explore the potential future applications of AI, ML, and data analysis and the impact they may have on various industries, including healthcare, finance, transportation, manufacturing, and energy efficiency.
What can AI and machine learning do?
Today, we have smart home devices that perform automated functions. In 2021 alone, the size of the global smart home market was $99.41 billion.
Voice recognition technology makes it easy to control these smart home devices, and it is designed such that the controls respond only to your voice. With the number of digital voice assistants globally estimated to reach 8.4 units by 2024, all evidence points to even more growth in this sector.
Many mobile applications and services have gotten better through the use of machine learning models. For instance, Netflix, the popular video streaming service, has been able to improve the quality of its content recommendation to users. The built-in machine learning mechanism curates various kinds of user data, such as the region you’re streaming from, thumbs down and thumbs up clicks, and the time you watch content.
All of these data are processed by AI and analyzed using machine learning algorithms to make remarkably accurate predictions and recommend your next watch.
Insights and potential future applications of AI, data analysis, and machine learning
The impact of these evolving technologies in modern-day industries is truly significant. Here are some market insights that demonstrate this:
- The AI global software market is expected to reach $126 billion by 2025.
- 77% of people use an AI-powered device or service.
- AI application in agriculture is tipped to grow at a compound annual growth rate of 20% and reach $5 billion in 2026.
- The machine learning segment is the largest in the AI industry.
- The global machine-learning market is expected to have grown at a CAGR of 39.2% by 2027.
- The global big data analytics market is expected to grow from $271.83 billion to $655.53 billion between 2022 and 2029.
The future market projections of these technological disciplines point to their accelerated adoption in various industries. Here are some of the potential future applications of AI, machine learning, and data analysis:
Banking and Finance
Banking and finance are one of the biggest fields where machine learning is strongly in use. Machine learning algorithms’ capacity to learn and carry out predictive analysis of complex, ever-changing data makes it possible for fintech providers to identify new business opportunities and draw up efficient strategies.
For instance, machine learning automates fraud detection processes, helping to prevent threats in insurance, payments, and banking processes. Additionally, the capacity to process big data means that machine learning algorithms can give lenders and banks insights into customers’ credit history and risk scores before the approval of loans.
Several business applications and tools exist today. Many of them are based on a computer program’s ability to curate, understand, and analyze data. For context, 25% of businesses incorporate AI technologies into their processes.
Machine learning algorithms can help individuals curate and analyze data regarding consumer habits and market forecasts. Additionally, these algorithms can help to internally identify manual errors in business logistics and optimize corporate procedures.
Since the first industrial robot was developed in 1954, this technological field has progressed in leaps and bounds.
Today, the robotics field is one of the biggest playgrounds for AI applications, and the future holds even brighter prospects than the present. As a matter of fact, the global AI robots market is expected to be worth about $55 billion in 2030, growing at a CAGR of 21.81% between 2022 and 2030.
With AI, robots can work as more efficient machines, beyond merely carrying goods in factories and warehouses and cleaning large equipment and offices. In the future, machine learning models will help innovate a new breed of robots with near-cognitive intelligence to help carry out crucial human operations.
Very soon, recruitment specialists will be able to carry out blind hiring operations better. They’ll spend less on identifying suitable candidates and save more time. The reason for this? AI, ML, and data analytics technologies.
Recent research reveals that almost 82% of human resource teams will integrate more AI solutions into their processes between 2021 and 2025.
With machine learning software, it’ll be possible to analyze bulk job applications and narrow down the list based on preset parameters. The algorithm can efficiently scan resumes and candidate profiles and make informed, accurate inferences via a comprehensive data analysis of the talent pool.
In the health sector, AI’s applications are diverse, with each more promising than the last. For context, the AI-in-healthcare global market is expected to reach $174.4 billion by 2030.
AI can help to develop highly cognitive machines specially trained and programmed to detect terminal diseases and cancer cells. Early diagnosis is often key to the survival of an ailing individual. As such, AI’s capacity for analyzing chronic conditions using available medical data can help ensure early diagnosis.
Another future AI application in the healthcare sector is the ability to curate historical medical data in the discovery of new drugs. Additionally, virtual nursing assistants can help monitor patients remotely, while big data analysis of medical records can help personalize each patient’s experience.
Already, virtual assistants and chatbots are replacing physical customer service agents. However, there’s still much work to be done before these tools work optimally.
Natural Language Processing (NLP), a subfield of AI, makes it easy to communicate in entire, humane phrases rather than clipped, curt robotic lingo.
In the future, customer support channels will be fully equipped and optimized with bots that have NLP integration. Over time, this will lead to more accurate language translation, either via voice or text.
The future of intelligent software borne of AI technologies, machine learning models, and data analytics is quite vast, and their potential is much bigger than what most people speculate. Tech giants like Oracle, IBM, Amazon, Google, Microsoft, and Apple spend billions of dollars annually to develop relevant services and products.
These disruptive projects are sure to be groundbreaking, with various industries and humanity sure to benefit to various extents. Indeed, data analytics, machine learning, and artificial intelligence will only continue to grow and evolve. The future is bright with endless possibilities!