Partner POV | 2024 AI Trends

This article was written by Adam Wolf, AI Software Marketing Engineer at Intel. 

In the podcast episode of Intel's "Code Together" series, 2024 AI Trends - Part 1, host Tony Mongkolsmai interviews Rahul and Ryan about the top trends in AI that startups in the Intel Liftoff for Startups program see coming. From multi-modal AI models to commoditization of LLM APIs, they discuss what to look out for in 2024.

Multimodal AI Takes Center Stage

A significant shift highlighted for 2024 is the emergence of multimodal AI, which transcends traditional language models to encompass images, videos, and text. This development promises a unified model capable of understanding and processing different data types cohesively. As businesses seek to create more sophisticated AI-driven applications, the ability to handle multimodal data seamlessly opens up new possibilities for innovation. 

The Democratization of AI through Open Source

Another pivotal trend is the progression towards open-source AI models that parallel the capabilities of proprietary giants like GPT-4. These models, particularly those tailored for specialized tasks, are expected to offer competitive alternatives without the steep costs associated with large-scale proprietary models. This move towards open sourcing could democratize access to high-quality AI tools, enabling a wider range of developers and startups to incorporate advanced AI functionalities into their solutions.

API Commoditization and Specialized Model Deployment

The podcast discussion also touched upon the commoditization of AI through APIs, which simplifies the integration of AI capabilities into various applications. This trend is particularly beneficial for non-AI specialists, allowing them to leverage AI's power without the complexities of model development and deployment. Furthermore, startups are anticipated to increasingly fine-tune specialized models to meet specific needs, ensuring more efficient and cost-effective deployments tailored to their target markets. 

The Economic and Strategic Implications of AI Trends

Beyond technical considerations, the conversation ventured into the economic and strategic dimensions of these AI trends. The potential consolidation within the AI API market, driven by the commoditization of AI services, could lead to a more streamlined ecosystem dominated by a few vertically integrated players. This scenario underscores the importance of startups thinking beyond commoditization to explore how they can add unique value atop widely available AI capabilities.

Looking Ahead: The AI Landscape in 2024 and Beyond

As 2024 unfolds, the AI landscape is poised for further transformation, driven by advances in multimodal AI, the expansion of open-source models, and the strategic shifts in how AI services are delivered and consumed. For startups and developers within the Intel Liftoff program and beyond, these trends represent both opportunities and challenges. Navigating this evolving terrain will require a blend of technical prowess, strategic foresight, and a keen understanding of the broader economic forces at play in the AI domain.

In the podcast episode of Intel's "Code Together" series, 2024 AI Trends—Part 2, host Tony Mongkolsmai interviews Rahul and Ryan again to discuss how Generative AI is affecting society and strategies to ensure secure and productive AI solutions. They also discuss the current state of AI code generation tools and whether such solutions are ready to replace developers. 

AI Everywhere: From Concept to Ubiquitous Integration

The concept of "AI everywhere" encapsulates the vision of seamlessly integrating AI across various platforms and industries, making it an indispensable element of technological innovation. This integration aims to infuse AI where it can add significant value, optimizing processes and enhancing user experiences without the redundancy of forced implementation. As startups look towards the horizon, the opportunity to innovate by embedding AI into their offerings represents a pathway to success in an increasingly competitive landscape. 

Narrow Task AI to General AI: Broadening the Spectrum

The evolution from narrow, task-specific AI towards more general AI applications marks a pivotal trend. This shift promises a future where AI models not only excel in specific tasks but also exhibit a broader understanding and adaptability across multiple domains. Such versatility is crucial for deploying AI across varied hardware environments and for crafting AI solutions that are truly responsive to diverse user needs and contexts. 

Interactivity and Personalization: Rethinking Human-AI Interaction

They then segue into discussions about the future of human-AI interaction, highlighting the potential for AI to revolutionize how we engage with technology. Beyond traditional input methods like keyboards, emerging modalities—including voice commands and even brainwave detection—suggest a future where AI can cater to a wider array of users, including those in non-English speaking regions and diverse cultural contexts. This inclusivity, driven by personalized AI assistants, has the potential to democratize access to technology, breaking down barriers imposed by conventional programming and interaction paradigms. 

Here, privacy and the ability to run local large language models (LLMs) and multimodal models on-device become important. That's why Intel is focusing not only on the data center for large-scale pretraining, fine-tuning, and scalable inference but also on our AI PC product line featuring Intel®
Core™ Ultra processors.

AI in Programming: The Rise of AI-Assisted Development

A significant area of interest is the role of AI in programming, particularly through tools like GitHub Copilot. While the current iterations of AI coding assistants offer valuable support, there's a consensus on the need for more personalized and advanced AI copilots that can elevate programming practices. This includes not just generating code but fostering better programming habits, suggesting clean and elegant solutions, and seamlessly integrating with the developer's workflow for a truly augmented coding experience. 

Customization of coding assistants to align with a particular codebase is crucial for making copilots more helpful. Here, small language models (SLMs) and local knowledge graphs/embeddings come into play. With these models running on Neural Processing Units (NPUs) in Intel AI PCs featuring Intel  Ultra processors, we can provide real-time, customized feedback without significantly affecting battery life.  

Security and Ethics: Navigating the Challenges of AI Innovation

The discussion also touched upon the critical issues of security and ethical considerations in the era of AI everywhere. The emergence of deepfakes and other AI-generated content raises concerns about the veracity of digital media, necessitating innovative solutions for authentication and the detection of AI-generated falsities. As AI becomes more integrated into daily life, the responsibility to ensure its ethical use and the protection of privacy and security becomes paramount, requiring collective efforts from developers, regulators, and the broader community.

Looking Forward: Envisioning the Future of AI 

As the conversation concluded, it was evident that while AI's potential to transform technology and society is immense, so too are its challenges and ethical considerations. The anticipation for a part three discussion underscores the community's eagerness to explore not only the immediate applications of AI but also the foundational shifts in AI technology that will define its future.

In 2024 and beyond, AI's trajectory promises a landscape where innovation, ethics, and inclusivity converge, offering unprecedented opportunities for startups and technologists to shape a future where AI not only augments our capabilities but does so in a manner that is secure, equitable, and aligned with society's broader interests.

There is a significant need to build new open foundation models and task-specific fine-tunes that are safe and maintain a trust boundary. Here the Intel® Gaudi® AI Accelerator architecture comes into play; its unmatched scale-out capabilities and cost-effective performance give customers more options making the future of Gen AI development and products built using Gen AI more equitable.

We encourage you to check out Intel's other AI Tools and Framework optimizations and learn about the unified, open, standards-based oneAPI programming model that forms the foundation of Intel's AI Software Portfolio. 

The Guests: 

Rahul Nair - Engineering Lead, Intel® Liftoff for Startups

Ryan Metz - Head of AI product at Intel Developer Cloud

References - ChatGPT & Wolf, A. (2023, March 12). Summarize the main points from the following transcript. https://chat.openai.com