Generative AI in Software Architecture: Dont Replace Your Architects Yet! by Karthik Chandraraj inspiringbrilliance

Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. This approach leverages the analytical and communication powers of LLMs and the wealth of pre-trained task-specific machine learning (ML) models to solve tasks across four stages — task planning, Yakov Livshits model selection, task execution and response generation. Demonstrating his process and ideologies for this project, Coorlas provides an introductory level video tutorial for architects and designers wishing to take the journey toward AI assisted design and documentation. A follow up design enhancement video tutorial also explains how the 2D Midjourney images, as well as other image types, can be brought to life as 3D renderings using depth maps and online animation tools.

This inclusivity enables more effective communication right from the early stages of planning. Another important aspect of this layer is ensuring that the model is optimized for performance and scalability. This may involve using cloud-based services or other technologies to ensure that the model can handle large volumes of data and is able to scale up or down as needed. New tools, specifically those optimized by artificial intelligence, have often been labeled threats to new generations. However, from the perspective of viewing and understanding them as instruments with specific applications, they represent significant opportunities for architects and designers. When it comes to designing a building, architects and designers have a lot to consider.

‘It’s already way beyond what humans can do’: will AI wipe out architects?

Collaborate with other users or beginners to exchange knowledge, tips, and creative ideas related to generative AI. Stay updated with the latest advancements and updates in the field of generative AI through online forums, blogs, and research papers. Generative AI will significantly alter their jobs, whether it be by creating text, images, hardware designs, music, video or something else. In response, workers will need to become content editors, which requires a different set of skills than content creation. The field of generative AI will progress rapidly in both scientific discovery and technology commercialization, but use cases are emerging quickly in creative content, content improvement, synthetic data, generative engineering and generative design.

• And by industrializing the process, you can build up a corpus of efficient, well-designed prompts and templates that are aligned to specific business functions or domains.
• This does not mean bolting some security system on your architecture as a final step; security must be architected into the systems at every step.
• This article delves into the effects of the digital transformation, exploring how it is reshaping the architectural industry and raising important questions about the future role of architects.
• With the right documents for context, a custom Q&A chatbot can provide users an easy and informative experience.

For instance, automotive, aerospace, and machinery organizations can improve product quality, sustainability and success, while life sciences, healthcare and consumer products companies can improve patient outcomes and customer experiences. Generative AI improves marketing and CX applications by enhancing customer interactions, enabling greater personalization and providing more advanced analytics. Early versions of generative AI have been used in AI-driven chatbots and agents for contact centers and customer self-service but with mixed results. However, the next generation of generative AI capabilities will offer a broader range of interactions, more accurate answers and reduced need for human interaction, leading to higher adoption and more training data for the models. Purpose-built GenAI models have played a significant role in the widespread adoption of generative AI.

Federated learning

To achieve the best possible performance, fine-tuning the model involves adjusting the various hyperparameters, such as learning rate, batch size and network architecture. Additionally, the optimization process may involve extensive experimentation and testing to identify the optimal settings for the model. Selecting and optimizing the right generative AI model for a given use case can be challenging, requiring expertise in data science, machine learning, statistics and significant computational resources. With numerous models and algorithms, each with its strengths and weaknesses, choosing the right one for a particular use case is challenging and needs a thorough understanding of the model. The optimal model for a given use case will depend on various factors, such as the type of data being generated, the level of accuracy required, the size and complexity of the data and the desired speed of generation. Generative AI is highly dependent on data, and one of the major challenges in implementing an architecture of generative AI for enterprises is obtaining a large amount of high-quality data.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

This includes ensuring that the data is accurate, complete, and relevant to the problem being addressed. For example, suppose a company has a legacy system for managing inventory built using an outdated technology stack. The company wants to integrate a generative AI model that can generate 3D models of products based on images to help with inventory management. However, integrating the generative AI model into the legacy system may require significant modifications to the existing codebase, which can be time-consuming and expensive. Many of these vendors have attached their generative design offerings to additive manufacturing capabilities needed to realize these unique products.

This is because generative AI can generate thousands of design options in a matter of seconds, whereas a human designer would take days or even weeks to explore the same number of options. This can lead to more innovative and unique designs that are optimized for specific requirements. This layer focuses on continuously improving the generative model’s accuracy and efficiency. It involves collecting user feedback, analyzing generated data, and using insights to drive improvements in the model. Start with beginner-friendly tutorials or guides provided by the chosen tool or platform.

Edge computing can improve the performance and speed of generative AI models for enterprises that require real-time processing, such as manufacturing or autonomous vehicles. By moving the processing power closer to the data source, edge computing can reduce latency and improve responsiveness, leading to more efficient and accurate decision-making. Organizations must also ensure appropriate security measures are in place to protect sensitive data, including implementing appropriate access controls, encryption and data retention policies. Additionally, organizations must ensure they have the necessary consent or legal basis to use the data in the generative AI models.

Performance considerations

We also provide complementary design for OVX L40S configuration powered by BlueField-3 and NVIDIA L40S GPUs. Included in this architecture are the required VMware components and NVIDIA AI Enterprise. At the heart of the AI inference system is the Triton Inference Server, part of NVIDIA AI Enterprise, that handles AI models and processes inference requests. Triton is a powerful inference server software that efficiently serves AI models with low latency and high throughput. Its integration with the compute infrastructure, GPU accelerators, and networking ensures smooth and optimized inferencing operations. NeMo offers pretrained models that address various categories, including Automatic Speech Recognition (ASR), NLP, and Text-to-Speech (TTS).

Before the Transformer took the AI world by storm, several other models attempted to tackle the challenges of generative AI. The most notable among these were Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), and Gated Recurrent Units (GRUs). HomeByMe is a web-based application for imagining future design changes to your house from a three-dimensional perspective. The app also lets you let your loved ones keep tabs on your choices and offer input from the comfort of their own devices. A diverse digital database that acts as a valuable guide in gaining insight and information about a product directly from the manufacturer, and serves as a rich reference point in developing a project or scheme.