The Future of AI: Beyond Transformer Architecture
Transformer architecture powers the leading AI models in both public and private sectors today. What lies ahead? Will this architecture enhance reasoning capabilities? What innovations follow transformers? Currently, implementing AI requires substantial data, GPU computing resources, and specialized talent, making development and maintenance costly.
AI deployment began with the advent of smarter chatbots. Now, startups and enterprises have evolved to create copilots that enhance human knowledge and skills. The next logical progression involves integrating multi-step workflows, memory, and personalization into agents capable of handling diverse tasks across functions such as sales and engineering. The goal is for a user prompt to enable an agent to comprehend intent, decompose the task into actionable steps, and execute it—whether through web searches, multi-tool authentication, or learning from prior behaviors.
Are You Ready for AI Agents?
Imagine personal AI agents akin to a digital Jarvis, intuitively managing tasks on your phone. Whether booking a trip to Hawaii, ordering your favorite meal, or overseeing personal finances, the potential for personalized agents is tantalizing. However, from a technological standpoint, we still have a long way to go.
Is Transformer Architecture the End of the Line?
The self-attention mechanism in transformers allows models to assess the significance of each input token concurrently, enhancing their grasp of language and computer vision by capturing long-range dependencies. Yet, this complexity results in high memory consumption and slow performance, especially for lengthy sequences (e.g., DNA).
To address these challenges, several research initiatives aim to optimize transformer performance:
1. Hardware Improvements: FlashAttention enhances transformer efficiency by optimizing read/write operations between various memory types on GPUs, minimizing data transfer.
2. Approximate Attention: Research seeks to reduce the O(n²) complexity of self-attention mechanisms to a linear scale, facilitating better handling of long sequences. Approaches include reformer and performers.
In addition to these optimizations, alternative models are emerging to challenge the dominance of transformers:
- State Space Models (SSMs): These models, related to recurrent and convolutional neural networks, offer linear or near-linear computation for long sequences. While SSMs like Mamba can manage long-distance relationships effectively, they still trail transformers in overall performance.
Recent advancements in model research are becoming publicly accessible and signal the evolving landscape of AI technologies.
Notable Model Releases
The latest model launches from industry leaders—OpenAI, Cohere, Anthropic, and Mistral—are noteworthy, particularly Meta’s foundation model focused on compiler optimization.
In addition to traditional transformers, we are witnessing the rise of state space models, hybrid models combining SSMs and transformers, mixture of experts (MoE), and composition of expert (CoE) models. Key models that have gained attention include:
- Databricks' DBRX Model: This MoE model has 132 billion parameters, utilizing 16 experts with four active during inference or training. It boasts a 32K context window and was trained on 12 trillion tokens, requiring significant resources for pre-training and refining.
- SambaNova Systems' Samba CoE v0.2: This CoE model consists of five 7 billion parameter experts, activating only one for inference. It boasts rapid performance at 330 tokens/second.
- AI21 Labs' Jamba: This hybrid model incorporates transformer elements with Mamba’s architecture, enhancing handling of long contexts while addressing the limitations of traditional transformers.
Challenges in Enterprise Adoption
Despite the promise of cutting-edge models, enterprises face significant technical challenges:
- Lack of Enterprise Features: Many models currently lack essential features such as role-based access control (RBAC) and single sign-on (SSO), hindering enterprise readiness. Organizations are allocating budgets specifically to avoid falling behind in the tech landscape.
- Security Complications: New AI features can complicate data and application security. For instance, video conferencing tools may introduce AI transcript features, which, while beneficial, necessitate further scrutiny to ensure compliance, particularly in regulated industries.
- Choosing Between RAG and Fine-Tuning: Retrieval-augmented generation (RAG) ensures factual accuracy but may not enhance model quality as effectively as fine-tuning—which presents challenges like overfitting. The evolving landscape favors RAG, particularly with Cohere's Command R+, the first open-weights model to outperform GPT-4 for chatbots and enterprise workflows.
I recently spoke with an AI leader at a large financial institution who suggested that the future belongs not to software engineers but to those skilled in crafting prompts. With simple sketches and multi-modal models, non-technical users can create applications with ease, turning tool usage into a career asset.
Researchers, practitioners, and founders now have a variety of architectures to explore in their quest for more efficient, cost-effective, and accurate models. Techniques such as fine-tuning and emerging alternatives like direct preference optimization (DPO) offer new avenues for innovation.
As the field of generative AI evolves rapidly, it can be daunting for startups and developers to navigate priorities. The future holds exciting potential for those willing to innovate and adapt.
Ashish Kakran is a principal at Thomvest Ventures, focusing on investments in early-stage cloud, data/machine learning, and cybersecurity startups.
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