Navigating the AI Landscape in 2024: Key Trends from Gartner’s Hype Cycle

* Gartner, Hype Cycle for Artificial Intelligence, 2024, Afraz Jaffri, Haritha Khandabattu, June 17, 2024

        * Gartner, Hype Cycle for Artificial Intelligence, 2024, Afraz Jaffri, Haritha Khandabattu, June 17, 2024

Gartner indicates that Generative AI (GenAI) has been passing the Peak of Inflated Expectations, even though the buzz around it persists. As of 2024, there are 10 technologies currently positioned within the Peak of Inflated Expectations phase. These technologies include the following: • AI TRISM • Prompt Engineering • Responsible AI • AI Engineering • Edge AI • Foundation Models • Synthetic Data • ModelOps • Generative AI

In this blog, I will explore the latest 2024 trends in surrounding these 10 technologies.

1. Sovereign AI

• Concept: Sovereign AI refers to the development and operation of AI technology by a specific country or region independently, aiming to protect its data and technological infrastructure from external influence. It focuses on maintaining data sovereignty, enhancing national security, and ensuring economic independence.

• Trend: The Indian government launched the 'Bharat AI' initiative in 2024, aimed at fostering domestic AI research and technology development while reducing reliance on foreign technology. This project is intended to strengthen India's digital sovereignty and economic self-reliance.

• More Infomation: Promises and Pitfalls of India’s AI Industrial Policy

  
  

2. AI TRiSM

• Concept: AI TRiSM (Trust, Risk, and Security Management) is a methodology that integrates the management of trust, risk, and security in AI systems. It focuses on ensuring the transparency, fairness, and security of AI systems, thereby enhancing their accountability.

• Trend: Microsoft integrated its AI TRiSM framework into enterprise solutions to improve the security and transparency of AI models. This allows companies to comply with legal and ethical requirements in AI system operations.

• More Infomation: AI TRiSM: Tackling Trust, Risk and Security in AI Models

  
  

3. Prompt Engineering

• Concept: Prompt Engineering is the technique of optimizing input prompts to a generative AI model to obtain desired outputs. It involves designing or adjusting input phrases so that the AI performs specific tasks accurately.

• Trend: In 2024, Google introduced new features in Google Bard that optimize user prompts, allowing for more sophisticated responses in education and research sectors.

• More Infomation: Master Gemini AI - Google Bard Prompt Engineering Bootcamp

  
  

4. Responsible AI

• Concept: Responsible AI refers to principles and practices ensuring that AI technology is developed and operated ethically, fairly, and transparently. It emphasizes preventing bias and discrimination, maintaining transparency, and ensuring accountability in AI's societal impact.

• Trend: IBM released a new Responsible AI toolkit that helps AI developers build models that maintain fairness, transparency, and accountability.

• More Infomation:  How your business can prioritize responsible AI with IBM watsonx 

  
  

5. AI Engineering

• Concept: AI Engineering encompasses the engineering principles and methodologies necessary to design, develop, deploy, and manage AI systems. It focuses on combining software engineering and AI approaches to enhance the performance, scalability, and maintainability of AI.

• Trend: Amazon launched a new cloud-based AI platform that incorporates best practices from AI Engineering, enabling rapid deployment and management of AI models.

• More Infomation:  https://www.aboutamazon.com/news/aws/aws-infrastructure-generative-ai

  
  

6. Edge AI

• Concept: Edge AI refers to AI technology that processes data locally on devices rather than on centralized servers. It is crucial for applications that require real-time data processing and rapid response.

• Trend: In 2024, NVIDIA launched a new Edge AI chip that provides real-time AI analytics capabilities for IoT devices and smart city infrastructure.

• More Infomation: https://www.nvidia.com/en-gb/deep-learning-ai/solutions/ai-at-the-edge/

  
  

7. Foundation Models

• Concept: Foundation Models are large-scale pre-trained AI models that can be fine-tuned for various downstream tasks. They serve as powerful general-purpose models that can be applied to a wide range of AI applications.

• Trend: In 2024, OpenAI released GPT-4o (&mini), the latest Foundation Model that has become a cornerstone for various natural language processing tasks.

• More Infomation:  Introducing GPT-4o and more tools to ChatGPT free users

  
  

8. Synthetic Data

• Concept: Synthetic Data refers to artificially generated data that mimics real data. It can be used to address data scarcity issues and protect privacy while training AI models.

• Trendmple: Synthetic Data are used in clinical studies to train AI models while safeguarding patient privacy.

• More Infomation:  Training AI requires more data than we have — generating synthetic data could help solve this challenge

  
  

9. ModelOps

• Concept: ModelOps is a methodology that automates and optimizes the deployment, monitoring, management, and maintenance of AI and machine learning models. It ensures efficient operation across the entire lifecycle of models.

• Trend: Google Cloud enhanced its ModelOps capabilities, launching new services that enable companies to rapidly and reliably deploy and manage AI models.

• More Infomation:  Introducing ModelOps to Operationalize AI

  
  

10. Generative AI

• Concept: Generative AI refers to AI technology that can create new data (such as text, images, or videos). It learns from existing data to generate content that follows similar patterns.

• Trend: Many companies are developing and and usding generative AI services for various businesses.

• More Infomation:  Top 10 Generative AI Trends to Watch in 2024

As we have explored, these 10 technologies are currently at the forefront of innovation, each playing a critical role in shaping the future of AI. As these technologies continue to develop, they hold the potential to revolutionize industries, enhance productivity, and address some of the most pressing challenges in our society.

Staying informed and adaptable to these trends will be key for organizations and individuals looking to leverage AI effectively in 2024 and beyond. The journey of these technologies through the Gartner Hype Cycle serves as a valuable guide, helping us anticipate both the opportunities and the challenges that lie ahead in the AI-driven future.