5 AI Trends to Know in 2024

GenAI may be receiving the bulk of media attention recently, but AI advancements continue to emerge without cessation.

Among the numerous developments gaining traction, five AI trends stand out for their potential to address critical privacy, accessibility, and transparency challenges:

• Federated learning
• Small Language Models (SMLs)
• Explainable AI (XAI)
• Causal AI
• Multimodal AI 

As AI technologies evolve, organizations must stay informed about the various types of AI and their unique applications. This blog covers five emerging AI trends shaping FY25 AI investments and strategies.

Federated Learning

Federated learning is a decentralized machine learning technique where multiple clients, such as devices or servers, collaborate to train a shared model while keeping their data locally stored and private. Only model updates, not raw data, are shared and aggregated to enhance the central model. 

This approach involves a federated learning server coordinating the efforts of multiple clients by initially distributing a base model to the clients. Each client trains this model on its own local dataset before sending the resulting model updates back to the federated learning server, which aggregates these updates to refine the global model. A strong global model is eventually developed through multiple iterations of this cycle.

Globally, the federated learning market is projected to reach $311.4 billion by 2032, up from $144.9 billion in 2024. The recent surge in adoption is due to increased regulatory scrutiny and corresponding business emphasis on data security and privacy. 

In financial services, for instance, federated learning enables a consortium of financial institutions to improve their fraud detection systems by leveraging diverse datasets to gain a more comprehensive view of fraud patterns without compromising regulatory standards that prevent customer data from being shared directly. 

Key Benefits:

• Local data processing reduces risks, such as data breaches and unauthorized access, and facilitates regulatory compliance. 
• Lower data transfer requirements save on bandwidth and storage costs, and local computational resources minimize the need for extensive centralized computing infrastructure.
• Diverse data sources and local training improve model robustness and personalization.
• Models trained and updated locally on devices as new data is generated ensure fast system adaptation to new information without waiting for centralized data processing, supporting immediate model improvement.
  
  

Causal AI

Causal AI focuses on understanding cause-and-effect relationships within data to uncover why certain outcomes occur rather than just predicting outcomes based on correlations. While traditional machine learning models excel at identifying patterns in data, causal AI aims to uncover the fundamental mechanisms that drive these patterns. This is a critical distinction because correlation doesn’t imply causation.

Causal AI is considered a type of white box model because it provides transparency and interpretability in its decision-making processes. Unlike black box models, which operate without revealing their internal workings, causal AI models explicitly demonstrate how different variables influence outcomes by mapping out cause-and-effect relationships. Users can then understand, trust, and verify the reasoning behind the model’s predictions and decisions. 

The causal AI market is projected to reach $543.73 million by 2032, driven by the need to uncover richer insights, enhance decision-making, and resolve the constraints of correlation-based analytics. 

Causal AI can be leveraged in supply chain management, for example, to analyze how weather patterns, geopolitical events, or economic indicators affect supply chain operations. By understanding these causal relationships, an organization can proactively modify inventory levels, transportation routes, and procurement strategies to mitigate risks and optimize efficiency.

Key Benefits:

• Uncover previously unknown causal relationships in data to foster innovation and facilitate the development of new products, services, and strategies.
• Optimization based on causal insights improves operational efficiency, reduces costs, and improves ROI by enabling targeted resource allocation and strategic planning.
• Clear explanations of how conclusions are derived help organizations prioritize transparency to foster trust and promote understanding among stakeholders.
• Predict outcomes under various scenarios and simulate interventions to support actionable decision-making throughout different domains, like marketing, healthcare, and supply chain management.
  
  

Explainable AI

Explainable AI (XAI) addresses the need for transparency and interpretability in AI decision-making by clearly explaining how AI models reach their conclusions.

XAI provides insights into how different features and inputs contribute to the model’s output, contributing to more predictable and reliable system behavior. Additionally, explainability holds AI systems accountable, simplifying how errors or biases are identified and rectified. These capabilities can help improve user acceptance in critical applications and promote ethical AI practices. 

The Explainable AI market is forecasted to reach $16.6 billion in 2028, an increase from $6.2 billion in 2023. This rapid growth is attributed to the need for interpretable machine learning models that offer more insights into their decision-making processes. 

XAI applications are expanding across numerous fields, including healthcare, manufacturing, and finance, where explainability is vital for understanding system recommendations, ensuring regulatory compliance, and promoting trust in automated decision systems. For example, XAI can assist doctors in comprehending why an AI system suggested a specific diagnosis or treatment plan, enabling them to make better-informed medical decisions.

Key benefits:

• Exposes factors that influence AI decisions to help identify and address biases, enabling fair outcomes.
• Insights into decision-making processes enable developers to debug AI models and rectify issues such as feature importance for improved model performance.
• Understanding the nuances of AI models can lead to optimization opportunities, such as fine-tuning hyperparameters or restructuring model architectures. 
• Helps organizations meet regulatory requirements for transparency and accountability in automated decision-making processes. 
• Detailed model explanations streamline auditing processes, making conducting comprehensive evaluations easier and ensuring model integrity.
  
  

Small Language Models (SLMs)

Small Language Models (SMLs) are a subset of natural language processing (NLP) models designed with a reduced number of parameters compared to large language models (LLMs) like GPT-3. LLMs are computationally intensive, which can be a barrier to adoption and lead to escalating operational costs and higher energy consumption.

SMLs are emerging as an exciting alternative to LLMs as they can perform various language-related tasks effectively with fewer computational demands. SMLs parameters typically range from millions to a few hundred million, in contrast to billions of LLM parameters, enabling them to run on devices with minimal resources, like a smartphone.

SML models have simplified architectures that can maintain a balance between performance and resource efficiency. In some cases, they’ve surpassed LLMs in terms of performance, such as Microsoft’s recently released Phi and Orca SMLs. 

An emerging application for SMLs is a customer support chatbot that can operate effectively on an organization’s existing infrastructure without requiring extensive computational power or specialized hardware. The SML can provide a more responsive customer experience via reduced latency and faster inference times. Simultaneously, compact size enables the chatbot to be deployed across various channels, including messaging apps.

Key benefits:

• Reduced computational requirements result in lower operational costs and increase accessibility to a wider audience, including smaller businesses and developers without access to high-end computing resources.
• Smaller models can process data faster, supporting real-time applications like virtual assistants, chatbots, and real-time language translation.
• Lower latency in generating responses or predictions enhances the user experience, such as in time-sensitive applications.
• SMLs consume less energy, which is ideal for environmental sustainability and for use in battery-powered devices.
• Simpler architecture can facilitate more robust models that are less prone to overfitting and easier to interpret.
  
  

Multimodal AI

Multimodal AI are AI systems that can process and integrate information from multiple types of data simultaneously, like images, text, audio, and video. The approach of combining these diverse data types enables multimodal AI systems to achieve richer, more accurate outputs based on a more nuanced understanding of context. 

Multimodal AI essentially mimics human perception, as we use multiple senses to interpret the world around us. It can support complex interactions between different types of data, improving its ability to respond to multifaceted inputs, and cross-validate and enrich information.

This approach leverages representation learning, which captures the relationships and dependencies between various data types. It also uses transfer learning to apply knowledge gained from one modality to improve the performance of another, enabling the model to leverage shared features and patterns. Cross-modal attention mechanisms allow the model to target the most relevant aspects of each modality to enhance overall understanding and performance.

Globally, the multimodal AI market is anticipated to reach $4.5 billion in 2028, an increase from $1 billion in 2023. This rapid escalation is largely attributed to increasing demand for models that can analyze unstructured data in numerous formats, and the proliferation of GenAI techniques that have accelerated the multimodal ecosystem.

Multimodal AI is still in the nascent stages, but offers attractive potential across numerous applications. For example, multimodal AI could be deployed in medical diagnostics, merging data from patient records, clinical notes, and genetic testing to improve diagnostic support and personalized treatment plans. Or it could be leveraged in cybersecurity applications to enhance threat detection and situational awareness by integrating data from audio sensors, surveillance cameras, and online activity. 

Key benefits:

• Enables more intuitive interactions, such as voice-activated commands combined with visual feedback, for an improved user experience.
• Data combined from different modalities helps mitigate the limitations or biases inherent in single data sources and reduce errors to improve output reliability.
• Provides timely and relevant outputs by dynamically adapting to changing inputs and contexts, enabling immediate feedback that is critical for real-time analytics.
• Appropriate for large-scale applications due to its ability to handle increasing amounts of diverse data.
• Achieves better performance with optimized use of computational resources and integration of different data sources that minimizes the need for redundant data processing.
  
  

Empower Your AI Story with Market-Leading Content 

The potential benefits and applications of AI trends like Federated Learning, Causal AI, Explainable AI, Small Language Models, and Multimodal AI are vast and transformative. However, efficiently communicating the complexities of these technologies and developing marketing assets that explore use cases, advantages and applications, is often challenging. 

Orange Bridge is a recognized top 1% technology writing agency that provides comprehensive coverage for all of your content development needs. Our team of accomplished writers specializes in AI, helping your organization simplify complicated concepts and ensure they’re more accessible to your audience - whether they’re tech savvy or new to AI.

Transform your business narrative with compelling content that highlights the benefits of AI and positions your company as a leader in innovation. 

Contact us today to discuss how we can help you create content that informs and inspires action.