The idea of artificial intelligence (AI) has been around since time immemorial in science fiction, philosophy and literature, but it first really emerged in the mid-20th century. Alan Turing’s 1950 paper “Can Machines Think?” and its accompanying “Turing Test” to determine whether a machine has human-level intelligence can be considered the milestone of AI evolution. The main goal of AI was to develop algorithms and systems that mimic some aspects of human intelligence. This included human-like intelligence and competencies such as problem solving, learning, perception, perception, language, and even feeling.
In the early 60s, rule-based systems were the simplest form of artificial intelligence. In the 80s, machine learning algorithms gained momentum and started to evolve from simple rule-based structures to more complex algorithms. However, the most popular period of artificial intelligence in the past was undoubtedly in 1997 when IBM’s Deep Blue defeated world chess champion Garry Kasparov. The main reason behind this popularity was that it broke a situation that no one at the corporate level or in research and development laboratories knew about or could get information about through the media.
The main reason why Generative AI has become so popular in the world so quickly was the opening of the ChatGPT application developed by OpenAI to the whole world. Artificial intelligence studies, which have always been B2B (Business to Business) until today, were opened to B2C (Business to Consumer) general consumers for the first time. The main reason for its popularity is that everyone can access it with a simple interface and start using its capabilities easily, such as the Google search engine or Facebook, Twitter, Tiktok in the social media revolution. The speed at which it became popular: ChatGPT reached 100 million monthly active users in just 2 months. Considering that this speed is 9 months for TikTok and 2.5 years for Instagram, the difference can be understood more easily.
The content that Gen-AI can produce “surprisingly”, its ability to easily understand what is being written, and its interactive and iterative progress in a conversational manner have undoubtedly led to its enduring popularity.
To summarize, using huge language models without establishing the necessary governance mechanisms has led to problems in many areas such as trust, performance and competence. When we look at the solution approach we have developed as KPMG to overcome these problems:
- Instead of huge multimodal/unimodal language models in corporate use, the “Base Model” is the translation of models with a basic level of competence into “Customized”-Language Models by training them with corporate data. It is the use of “medium/small/micro” language models with “fewer parameters” but “deepened expertise” such as ERP-LM that has learned ERP data very well, HR-LM that has learned HR data very well, Customer-LM that has learned Customer data very well.
- In the case where specialized small language models only have expertise in some subjects, it is necessary to have more than one customized LM working and to use orchestrating/aggregating language models that aggregate and filter the results into a single truth.
- A “routing” routing language model is needed that determines which custom-LM the incoming request should go to.
- There is a need for a “control” language model that can check whether the user / system making the request is suitable for the scope of the custom-LM ecosystem (eliminating out-of-competence / security breach requests), authenticate the user, and check whether the user is authorized to make the request and receive the result of the request.
- In an ecosystem with many custom-small LMs, there is a need for “data governance” practices in data access and management. In the event that an LM’s own data sets are accessed by another LM, it is necessary to be able to make permission / disallowance decisions with a correct data governance approach.
- In order to manage simultaneous client or busy client situations well, it is necessary to micro-architect custom-LMs as much as possible and thus facilitate both horizontal and scaling, and to benefit from cloud computing and similar container architectures. To give an example from a telecom company, it is necessary to divide the campaign-LM into smaller LMs in order to respond to a campaign-LM developed to answer which campaign, which channel and which offer details should be given to 35 million subscribers. By dividing it into micro-LMs such as finding the right campaign, finding the right channel, finding the right product, resources should be used effectively and scalable as infrastructure.
As professionals who have been involved in software development and architecture roles in the IT industry for years, it can be seen how well the evolution from monolithic applications to micro-service and data-mesh architectures will support the solution here.
In fact, typical operational system application development architectures and AI application development approaches, which seem to diverge at many points, can create a synergy at this point.
To explain the main reason for the divergence with an anology: classic enterprise applications (such as CRM, ERP) are actually similar to our organs and the tissues and cells underneath. We can liken the methods developed at the atomic level to cells, object-oriented business logics consisting of methods to tissues, and applications consisting of business logics to organs. What they have in common is that they perform “specific tasks”. The heart pumps blood, the stomach grinds the food we eat. The heart does not suddenly decide on its own to grind food instead of pumping blood. It has no consciousness of its own, no autonomous decision-making. But the technology that comes with generative intelligence and AGI (general artificial intelligence) is similar to tumor cells, which we expect to have good intentions but cannot be 100% sure. Since we provide the ability to develop itself autonomously, it has dimensions that surprise us a lot and can increase productivity very seriously with its creativity. Therefore, an organ such as the “brain” must constantly monitor these cells and take them under control at points where they are “going in the wrong direction”. In order to prevent this difference from creating a “risk”, it is necessary to build a system suitable for two different cell types. If we go back to the application architecture, we need to build Gen-AI applications that we need to allow to develop in a controlled manner, without hindering their efficiency and performance, but with architectures that we can control.
In order to explain the right architectural solution, it is first necessary to explain the logic of generative intelligence, customized multimodal language models, micro-service and data-mesh architectures:
Multimodal Language Models (MLLM)
Multimodal Language Models are artificial intelligence systems trained on various datasets, including text, images, video, audio, structured database data. They can understand and generate information across these different modalities, thus providing a level of interaction that more closely mirrors human communication.
In enterprise applications, MLLMs can offer transformative capabilities:
– Customer Interaction: Improving customer service by interpreting and responding to queries with both text and visual content.
– Content Creation: Automating the creation of marketing materials that combine written and visual elements.
– Training and Development: Providing rich, interactive training materials that combine educational text with supporting images and videos.
Specific Multimodal Language Models with Special Training (CMLLM)
Based on deep learning and natural language processing technologies, these models are capable of understanding and making connections between various types of data, including text, images and audio. Specific MLLMs are trained for a particular domain or functionality, for example, they may be optimized for medical imaging and reporting, customer service dialogues or creative design processes.
Unlike MLLMs for general use, custom-trained models are customized in terms of training data sets, algorithms and targeted outputs. This enables them to better understand and interpret the jargon, visual aesthetics or tone of voice of a particular industry. For example, a custom MLLM developed for a law firm will be much more capable than a standard model in understanding and rendering legal terms and documents.
This customization enables models to produce results with higher accuracy and in context, so they can be directly integrated into workflows and provide solutions to the specific challenges organizations face. This deepened understanding brought by specific MLLMs allows businesses to personalize customer interactions, improve decision-making processes of automated systems, and make data analytics results more precise.
The most important feature of CMLLMs in our solution is that they are competent, flexible and manageable enough to be loaded into microservices instead of complex, monolithic, huge language models consisting of billions of parameters. The key approach in the solution architecture is to transform huge language models into micro-language models, creating a CMLLM pool of atomic-level competencies.
The Role of Microservices
Microservices architecture involves structuring an application as a collection of loosely coupled services, each responsible for executing a specific business function. This design principle is modular in nature and fosters an agile development environment where services can be updated, deployed and scaled independently.
The microservice architecture is ideal for deploying MLLMs because of its inherent scalability and flexibility. Each microservice can be independently developed, deployed and scaled, which is crucial for MLLMs that require significant computational resources.
This architecture supports continuous integration and delivery, enabling rapid iteration and deployment of AI capabilities. It also facilitates resilience, as the failure of one microservice will not disable the entire application.
The Role of Data Mesh
Data-mesh is a socio-technical approach to data architecture and organizational design. It emphasizes the decentralized distributed nature of data ownership and architecture, with individual teams acting as stewards of their own data as a product. The concept is built on the principles of domain-oriented ownership, data as a product, self-serving data infrastructure and federated informatics governance.
Benefits of Data Mesh for Data Governance
– Autonomy: Teams have the autonomy to manage and optimize their data while ensuring that it is discoverable and interoperable with the rest of the organization’s data ecosystem.
– Quality: Data products are built with quality by design, with clear accountability for the accuracy, format and use of data.
– Speed: By treating data as a product, teams can iterate and deliver data improvements quickly and gain faster access to trusted data.
– Compliance: Data-mesh facilitates compliance with governance standards and regulations by clearly defining ownership and control mechanisms for data products.
A data-mesh complements microservices by organizing decentralized data ownership and management. It essentially acts as connective tissue between the various microservices and ensures that they can access the data they need while adhering to the organization’s data policies and standards.
In a microservice architecture, each service manages its own data, but Data-mesh provides an interoperable platform that provides consistent, managed access to that data across the entire system. This is particularly important for MLLMs, which require the integration of different types of data from various sources to operate effectively.
With microservices and Data-mesh working together, enterprises can build a robust, scalable and flexible AI infrastructure that can adapt to the changing needs of the business.
Artificial Intelligence, Microservices and Data-Mesh Future Trends
– Advances in Artificial Intelligence and Machine Learning: As computational power and algorithms improve, we can expect MLLMs to become even more sophisticated with improved understanding and generative capabilities across modalities.
– Expansion of Microservices: Microservices are likely to become more granular, leading to even more specialized services that can operate with greater efficiency and flexibility.
– Advancement of Data-mesh: The concept of data-mesh will mature and more organizations will adopt it as a standard for data architecture. This will include improvements in automated governance and quality control tools.
– Convergence of AI and Operations: The integration of AI into operational processes, known as AIOps, will become more widespread, enabling real-time data processing and decision making.
– Edge-AI: With the growth of IoT and edge computing, AI processing is expected to move closer to the data source, reducing latency and bandwidth utilization.
– CoPilot & AutoPilot: One of the next steps in Gen-AI is thought to be “capabilities” built on language models. A capability can be thought of as connecting to an ERP system to learn the latest status of an order, or performing the line opening operation of a newly subscribed customer. It is envisioned that Gen-AI applications with these capabilities will automatically perform some tasks and RPA / Intelligent Automation technologies will radically evolve.
– General Artificial Intelligence (AGI): The goal of AGI is the ability to demonstrate flexible and general-purpose intelligence, going beyond systems specialized in narrow areas. In simple language, it can be thought of as a form of super-artificial intelligence that knows everything best. Studies on this subject are ongoing in secret and developments are expected in the near future.
