10 AI Trends Revolutionizing Industries and Society in 2024

Artificial intelligence trends continue to rapidly and dramatically redefine the technological landscape, driving innovations that enhance software capabilities and influence human activities across business and culture. As we power through 2024, staying ahead of today’s key AI trends is essential for IT professionals and businesses who want to keep current with the dynamic technology. Here are our expert opinions on 10 current AI trends and their potential impacts on business and the world at large.

KEY TAKEAWAYS

•As AI gains adoption, there’s a greater push for AI companies to make their data collection more transparent so users know how their data is being used. (Jump to Section)
•Artificial intelligence is playing an increasingly leading role in creative content production, from graphics to video. (Jump to Section)
•
AI-driven models are being used by governments and organizations to monitor environmental changes in real-time. (Jump to Section)

TABLE OF CONTENTS
ToggleAI Expands in Creative Arts and Content GenerationAI-Powered Autonomous Vehicles Gain AdoptionAI Fights Climate ChangeMultimodal AI Bridges Data Types for Richer InteractionsOpen Source’s Role in AI ExpandsCompliance and Ethics Face Higher ExpectationsQuantum Computing Boosts AIRetrieval-Augmented Generation (RAG) Reshapes AI ContentAI-Driven Healthcare Improves Patient CareAI Assists Workforce Management and OptimizationFuture Directions and Opportunities in AIFrequently Asked Questions (FAQs)Bottom Line: As AI Grows, It Must Be Monitored

AI Expands in Creative Arts and Content Generation

AI is playing an increasing role in the creative arts, pushing the boundaries of what’s possible in music, visual arts, and filmmaking. Generative AI tools are now fully capable of generating a remarkable array of original content, challenging traditional notions of creativity and authorship.

Currently, artists are using tools like OpenAI’s DALL-E, Stable Diffusion, Midjourney, and Google’s DeepDream to create novel works that blend human creativity with machine-generated outputs. These AI systems can generate everything from paintings to musical compositions, offering new avenues for creative expression. In filmmaking, AI is being used to generate scripts, edit footage, and even create entirely new characters. A tool like OpenAI’s Sora is even going further to generate life-like video content.

AI’s role in the creative industries is expected to grow. Future developments could see AI becoming a collaborator rather than just a tool, working alongside human artists to co-create works that neither could produce alone. This shift may end up redefining the creative process and might lead to a new era of art and entertainment in which artificial intelligence and human creativity intersect to generate ever higher levels of quality.

A still taken from a video by OpenAI’s Sora shows impressive video generation capabilities.

AI-Powered Autonomous Vehicles Gain Adoption

Autonomous vehicles rely heavily on AI for navigation, safety, and real-time decision-making. As these technologies advance, we are moving closer to a future where self-driving cars are a common sight on our roads.

Companies like Tesla, Waymo, and Cruise are leading the development of AI-powered autonomous vehicles. These vehicles use AI to process vast amounts of sensor data in real time, allowing them to navigate complex environments, avoid obstacles, and make split-second decisions that ensure passengers stay safe. The growing adoption of AI in this field is also creating advances in vehicle-to-vehicle communication and predictive maintenance, further enhancing the reliability of autonomous vehicles.

Greater sophistication in the AI and transportation industry through autonomous vehicles will offer greater efficiency, reduce traffic congestion, and—in theory—decrease the number of accidents. Additionally, we can expect much more integration of AI with smart city infrastructure for more seamless interactions between autonomous vehicles and urban environments. The outcome may well be a future where transportation is safer, more efficient, and more sustainable.

Waymo’s Waymax platform simulates examples for autonomous driving research.

AI Fights Climate Change

AI is proving to be an asset in the fight against climate change by offering critical insights into environmental monitoring and helping to optimize resource use. By analyzing vast datasets, AI can predict climate patterns, keep track of deforestation, and even optimize the energy efficiency of buildings.

Today, AI-driven models are being used by governments and organizations to monitor environmental changes in real time. For instance, AI is helping to track illegal deforestation in the Amazon and predict the spread of wildfires in California. A number of businesses are developing AI solutions that optimize energy consumption in buildings, contributing to a reduction in carbon footprints.

As we move forward, future applications could include AI-driven systems that manage renewable energy grids, predict and mitigate the effects of natural disasters, and even help design new materials that reduce environmental impact. These advances will be important to global efforts to combat climate change and create a more sustainable future.

Imagery by University of Leeds researchers shows AI being used to measure the size of icebergs and track changes across time.

Multimodal AI Bridges Data Types for Richer Interactions

Multimodal AI processes and integrates multiple types of data inputs, such as text, images, and audio, to create better output than single-source AI generation. This AI trend has gained traction as businesses realize the value of creating more nuanced and contextually aware AI systems that can understand and interact in ways that mirror human sensory and cognitive capabilities​.

The integration of various data types allows for a more comprehensive understanding and responsiveness from AI systems. The benefit? Improved user experiences and decision-making support. However, the complexity of designing these systems introduces challenges in terms of data integration, processing power, and maintaining privacy across different data streams​.

With the growth of multimodal AI, we can expect to see significantly enhanced customer service interfaces and product interaction capabilities. Multimodal AI will make them more intuitive and responsive, as it can better take advantage of multiple human senses simultaneously. We’ll see technologies such as creativity applications and search tools become more seamless and precise. Additionally, multimodal AI can help give businesses more robust security postures, as combining data types leads to more comprehensive threat detection.

Google Gemini suggests improvements to a flowchart as it handles image and text input simultaneously.

Open Source’s Role in AI Expands

Open source AI consists of AI technologies developed on open, publicly accessible platforms that allow developers to modify and distribute their code. The movement toward open source AI has democratized AI development, meaning that a broader range of developers contribute to and innovate with AI technologies. A great example is the geospatial AI foundation model by NASA and IBM, which helps users track and adapt to landscape changes.

Open source AI accelerates innovation, reduces the costs of developing AI software, and involves the community in development. Such collaborations enable AI to develop rapidly and grow in effectiveness. But it’s not without its challenges, as open source AI faces issues with varying levels of support and maintenance, potential security vulnerabilities, and the complexity of integrating open source AI into proprietary systems.

Businesses using open source AI can drastically reduce costs and become more agile in deploying AI solutions. Even so, they must carefully manage these tools to ensure they meet enterprise standards for security and reliability. Organizations need to establish rigorous evaluation and integration processes to leverage open source AI in a compliance-focused corporate environment.

TensorFlow is a popular open source AI platform.

Compliance and Ethics Face Higher Expectations

Artificial intelligence tools continue to mature and reach into new areas of our lives, harvesting massive amounts of personal and sensitive data. Businesses and individuals alike are growing concerned about what data is collected, how it’s used, and whether it’s appropriately secured during use and deleted after use.

As a result, there’s currently a push for AI companies to make their data collection and model training processes more transparent so users know how their data is being used. Many customers are also pushing for explainable AI. On a related note, there’s an increasing focus on developing stronger compliance and ethical frameworks to govern AI’s use. Strong compliance and ethical standards help build trust and reliability in AI systems, ensuring that they are used responsibly. On the flip side, implementing these frameworks can be costly and complex, possibly slowing down the deployment of new AI technologies.

Adhering to stringent compliance and ethics guidelines cannot be overlooked if businesses want to maintain their reputations and fully adhere to regulatory requirements. Such commitment not only mitigates risks but also enhances consumer and stakeholder trust in the company’s AI applications​.

Compliance.ai monitors the regulatory environment and aligns financial organizations to the regulations.

Quantum Computing Boosts AI

Quantum computing shows great potential to transform industries, and AI is at the heart of it. AI can use quantum algorithms to solve problems that are currently impossible for traditional computers, which struggle with tasks that involve vast amounts of data and require immense computational power, such as simulating molecular structures or optimizing large-scale logistical operations.

Companies like IBM and Google are making significant strides in integrating AI with quantum computing. These advancements are relevant in fields that require massive computational power, such as cryptography and material science. Clearly, the capacity to process information exponentially faster than traditional computers opens up new possibilities for AI applications.

The expectation is that quantum computing will unlock even more groundbreaking innovations across industries ranging from finance to pharmaceuticals. These sectors will benefit from the increased processing power, leading to faster discoveries and more sophisticated AI models that can tackle complex challenges with previously unknown levels of efficiency.

A data page from IBM’s Quantum platform.

Retrieval-Augmented Generation (RAG) Reshapes AI Content

Large language models often generate content that sounds coherent. This can create a risk of users trusting misleading, outdated, or incorrect content because it seems plausible. As a result, retrieval-augmented generation (RAG) is becoming an increasingly popular way to reshape how AI models generate content.

It combines the strengths of generative models with the precision of retrieval-based systems. Instead of relying solely on a model’s internal data, RAG enhances the output by retrieving relevant information from external sources to ensure that the generated content is both accurate and contextually relevant. You’ll find RAG being used to improve the performance of large language models like GPT-4, particularly in applications that require up-to-date or highly specific information. For example, in customer service, RAG systems can generate responses that incorporate the most current data from a company’s knowledgebase. This improves the accuracy and relevance of customer interactions.

Looking ahead, we expect RAG to become a standard feature in many AI applications, particularly those requiring high reliability and specificity, such as legal research, academic writing, and real-time decision support systems. By integrating retrieval mechanisms with generative capabilities, RAG offers a powerful tool for creating content that is not only creative but also grounded in real-world data, which improves the utility and trustworthiness of AI-generated content. Organizations will be more comfortable using AI if they can minimize the risks associated with misinformation.

A RAG flow diagram by AWS.

AI-Driven Healthcare Improves Patient Care

AI is transforming healthcare by enabling predictive analytics that can foresee health outcomes and recommend early interventions. This shift toward preventive care increases the level of patient outcomes and reduces healthcare costs across the board. 

There are a variety of use cases for AI in healthcare settings, from predicting patient readmissions to identifying individuals at high risk for chronic diseases. Leading vendors in AI healthcare use AI to analyze patient data and develop personalized treatment plans. These innovations are helping healthcare providers make more informed decisions, ultimately leading to far better patient care.

Looking ahead, AI-driven predictive healthcare will become even more integral to medical practice. With advancements in AI and machine learning, healthcare providers will be able to offer increasingly personalized care geared for the unique genetic and environmental factors of each patient. The hope is that this trend will lead to a future where AI plays a central role in maintaining and improving public health, with a focus on prevention.

Micromedex by Merative uses an AI-based platform to offer healthcare providers current and reliable drug information.

AI Assists Workforce Management and Optimization

AI is transforming workforce management by automating HR functions, improving employee engagement, and optimizing team performance. This trend is helping companies enhance productivity while reducing operational costs. AI tools are now being used to streamline recruitment processes, predict employee turnover, and personalize training programs.

Companies like Workday and SAP are industry leaders in developing AI-powered HR platforms that offer insights into employee performance and help managers make data-driven decisions. These tools are also being used to improve employee engagement by identifying factors that contribute to job satisfaction and addressing them proactively.

In the years to come, AI is expected to play an even larger role in workforce management. As AI technologies become more advanced, they will provide more accurate predictions about employee behavior and performance, which will allow companies to optimize their workforce strategies further. This will not only enhance productivity but also improve employee satisfaction and retention, making AI a critical tool for managing the workforce of the future.

Some of the AI-specific features found in Workpay’s platform.

Future Directions and Opportunities in AI

AI is developing at a remarkable rate, and you can expect to see even more impactful trends in the coming years. The future of AI will hinge on innovations in AI software architectures, the emergence of new applications, and a stronger focus on ethical considerations like bias and fairness.

Advances in AI Architectures and Algorithms

AI is going beyond traditional neural networks to focus on more sophisticated architectures like transformers. Future AI architectures are expected to focus on enhancing model efficiency, scalability, and interpretability. Techniques like federated learning and neuromorphic computing are gaining traction as they promise to make AI more adaptable and energy-efficient, allowing for broader deployment across industries.

Recent developments in self-supervised learning and reinforcement learning are also setting the stage for AI systems that need less labeled data and can learn more autonomously. This would reduce the costs and time required to train AI models, making sophisticated AI more accessible to businesses and researchers.

Growing Applications and Use Cases

As AI evolves, it is unlocking new applications across various fields. In healthcare, AI is being used to predict patient outcomes, personalize treatments, and even assist in complex surgeries. In finance, AI is enhancing fraud detection and enabling more accurate predictive analytics.

Industries such as agriculture, education, and entertainment are also seeing the rise of AI-driven innovations. For example, AI in agriculture is improving crop yield predictions and optimizing resource use, while in education, AI is being used to create personalized learning experiences. The entertainment industry is leveraging AI for content creation, audience engagement, and more realistic virtual environments.

Additional emerging use cases include AI-driven climate modeling, smart cities, and autonomous vehicles. AI’s applications in energy optimization, smart grids, and environmental monitoring will support more efficient management of resources and disaster response, significantly contributing to sustainability efforts​​.

Addressing Bias and Fairness in AI

As AI becomes more pervasive, the importance of ensuring fairness and minimizing bias in AI systems becomes all the more necessary. AI models often not only include but also amplify the biases present in the data they are trained on, which can lead to discriminatory outcomes. We need to address these biases if we’re going to see trustworthy AI systems.

To mitigate bias in AI, we need more diverse and representative datasets, to create algorithms that can detect and correct bias, and to execute more effective fairness checks during model development. Additionally, organizations must adopt frameworks for responsible AI that emphasize transparency, accountability, and inclusivity. AI ethics is gaining prominence as a field of study. You’ll find more researchers exploring the societal impacts of AI as well as proposing guidelines for ethical AI development and deployment.

Frequently Asked Questions (FAQs)

What is the Next Big Trend in AI?

The next big trend in AI is likely to be the advancement of self-supervised learning and more efficient AI architectures, such as those based on transformers and neuromorphic computing. Self-supervised learning allows models to learn from unlabeled data, which can reduce the need for extensive data annotation and enhance the adaptability of AI systems. Additionally, the integration of AI with edge computing and federated learning is expected to improve real-time processing and privacy by enabling models to learn and infer locally on devices without centralizing data.

What is the Next AI Breakthrough?

One of the anticipated breakthroughs in AI is the development of more advanced generative AI models that can create realistic content such as text, images, and even synthetic data with minimal human intervention. Advances in large language models (LLMs) and multimodal models that can understand and generate text, images, and audio are also on the horizon. These breakthroughs could lead to significant improvements in AI’s ability to handle complex tasks, create content, and interact more naturally with humans.

What Fields will AI Transform?

AI is expected to transform a variety of fields, with automation potentially displacing certain roles in industries like manufacturing, logistics, and customer service. Tasks that involve routine and repetitive activities are particularly susceptible to automation. However, rather than outright replacement, AI is more likely to augment human roles by handling repetitive tasks and enabling workers to focus on more complex and creative aspects of their jobs. Fields such as healthcare and finance may experience particularly significant changes, with AI supporting decision-making and enhancing efficiency rather than fully replacing human roles.

Bottom Line: As AI Grows, It Must Be Monitored

AI is set to make a significant impact in 2024 and beyond, with powerful developments on the horizon. Advances in AI architectures that are driving innovative applications across diverse fields, from climate action to transportation to media, will transform how we live and work. This progress means it’s crucial to address concerns about bias and fairness in AI to ensure that these technologies benefit everyone; AI must be monitored as it grows more powerful. By focusing on ethical practices, we can make the most of AI’s potential while tackling its challenges.

Read our take on the current generative AI landscape to learn more about the direction the industry is taking and the key themes driving the technology forward.
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