Distributed Artificial Intelligence - A New Paradigm for AI

by John Heinrichs John Heinrichs | Dec 8, 2022 8:00:00 AM


The landscape of artificial intelligence (AI) is rapidly changing. We are seeing a shift from the traditional, monolithic approach to AI (purpose-built models deployed for dedicated tasks) to a more distributed approach which allows you to scale your data and a wide variety of applications across distributed cloud environments. This new paradigm is called Distributed Artificial Intelligence (DAI).

Distributed AI

In the past few years, we have seen a dramatic shift in the way that artificial intelligence is deployed and used. Traditionally, AI has been deployed in centralized architectures, with a single point of control and management. However, this model is no longer feasible at scale. With the recent advances in distributed computing, it is now possible to deploy AI across multiple distributed nodes, allowing for greater flexibility and scalability. In this article, we will take a look at distributed AI, its evolution from the standard cloud-based and edge AI models, to the new DAI model, and discuss the capabilities of DAI.

The Evolution of AI

In the early days of AI, all computing was done on centralized mainframes. This meant that if you wanted to use AI, you needed access to a mainframe computer. However, as personal computers became more powerful, AI began to move off of mainframes and onto personal computers. This ushered in the era of cloud-based AI, where instead of needing access to a physical computer, you could simply access an AI platform via the internet. While this made AI more accessible to businesses and individuals, it also created a bottleneck in terms of scalability. As data began to grow exponentially, it became clear that the centralized approach to AI was not sustainable.

AI Life Cycle 

The life cycle of an artificial intelligence system can be divided into four main phases: data collection, training, inference, and feedback. Data collection is the process of gathering data from various sources. This data is then used to train the AI system. Training is the process of using this data to tune the parameters of the AI model so that it can accurately solve the task at hand. Inference is the process of using the trained model to make predictions on new data. Feedback is the process of using these predictions to improve the performance of the system by collecting more data or changing the training methodology. 


Edge Computing 

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. Edge nodes are typically located at the perimeter of the network, near where data is being generated. This allows for reducing latency and increasing security and privacy. For example, instead of having one central server processing all the data from thousands of sensors, each sensor can have its processor which can then send its processed data to the central server. This approach is much more scalable and efficient than the traditional cloud-based approach. Edge nodes are often resource-constrained, so they need to be able to execute computationally intensive tasks efficiently. This has traditionally been a challenge for artificial intelligence algorithms which are often computationally intensive. However, with recent advances in hardware and software optimization techniques, it is now possible to run these algorithms efficiently on edge nodes.

What Is Distributed AI? 

Distributed AI (DAI) is an approach that is very similar to edge AI in solving complex learning, planning, and decision-making problems. DAI allows for breaking down these problems into smaller sub-problems that can be solved in parallel by multiple nodes. This type of architecture is well suited for problems that are too large to be solved by a single node, or when there is a need for real-time decisions. By distributing the computation across multiple nodes, it is possible to achieve much faster results than with a traditional centralized approach. So, the data is kept at or stored at the 'spoke' and the application is at and the analysis is performed at the 'hub' where the control is located.

DAI Challenges

Implementing distributed AI can be challenging due to the need for parallel processing and coordination among nodes. Furthermore, not all AI algorithms are suitable for distributed execution, so careful selection is required.

Data Gravity

One of the main challenges of implementing distributed AI is the issue of data gravity. Data gravity is the tendency of data to accumulate at certain points in a network. This can be due to a variety of factors, such as the limited bandwidth or storage capacity of nodes. When data accumulates at a certain point, it can create a bottleneck and slow down the overall system. Distributed AI algorithms need to be able to efficiently process data in a decentralized manner to avoid this problem. Thus, there is pressure at the hub to control data. A potential solution is to only collect needed or important data. This requires an intelligent data collection process that can be adapted and monitored at each spoke location.


Another challenge of implementing distributed AI is the issue of heterogeneity among nodes. Each node in a distributed system can have a different configuration and be running different software. This can lead to problems such as inconsistency and incompatibility. For a distributed AI system to be effective, all nodes need to be able to communicate with each other and work together seamlessly. This can be difficult to achieve in practice due to the heterogeneity of nodes or spokes requiring different AI models.


Another challenge in implementing distributed AI is the issue of scale. Distributed AI systems can be very large and difficult to manage. This can be due to the number of nodes in the system or the amount of data that needs to be processed. Furthermore, distributing the computation across multiple nodes can lead to increased latency and decreased efficiency. Distributed AI algorithms need to be able to efficiently process data in a decentralized manner to avoid these problems. Yet, it is important to also understand the types of data required. These eata types could be images, sounds, sensors, lidar, network information, time-series information, and many others.

Thus, solving scale issues require a greater amount of automation and a clear data policy for the various stages of the data lifecycle.

Resource Constraints

Another challenge of implementing distributed AI is the issue of resource constraints. Distributed AI systems can be very large and difficult to manage due to the limited bandwidth or storage capacity of spokes/nodes.

Traditional AI vs. Edge AI vs. Distributed AI

So how does distributed AI compare to traditional artificial intelligence (AI) and edge AI? Traditional AI generally relies on a central server for all computations, while edge AI offloads some computations to devices at the edge of the network. However, even with edge computing, there are still challenges when it comes to training and deploying AI models. This is where Distributed artificial intelligence (DAI) comes in. Distributed AI takes this one step further by distributing computations across multiple devices, both at the edge and in the cloud. By distributing the computational load across multiple devices, DAI can scale much more efficiently than traditional approaches. Additionally, DAI allows for continuous learning by keeping models up-to-date with the latest data. All three types of AI have their strengths and weaknesses, but Distributed AI has emerged as a powerful solution for many real-world problems due to its ability to effectively utilize resources both at the edge and in the cloud. This means that businesses can deploy AI models faster and with less resource investment than before.

A typical day in the life cycle of an application powered by distributed artificial intelligence might look like this:  

  1. Data is collected from various sources,
  2. The data is processed and stored on multiple machines,
  3. Machine learning models are trained on the data,
  4. The models are deployed on multiple machines, and
  5. The models make predictions The results are fed back into the system so that they can be used to improve future predictions. 


The DAI paradigm represents a major shift in how we think about AI. No longer are we limited by the processing power of a single device or the scalability of a centralized architecture. With DAI, we can distribute computation across a wide variety of devices and keep our models up-to-date with the latest data to deploy them faster and with less resource investment. If you are thinking about implementing AI in your business, keep DAI in mind as it may be the best option for you.


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