The Advantages of Specialized AI for business
The evolution of specialization traces back to the dawn of human civilization, marking a trajectory of increasing complexity and efficiency in every aspect of life. Specialization, the process by which individuals or entities focus on a narrow area of expertise,has been one of the cornerstones of economic development throughout history. From the age-old division of labour described by Adam Smith in his seminal work, "The Wealth of Nations," to the modern-day tailored solutions in technology, specialization has been a driving force of productivity and economic prosperity.
Smith's assertion that the division of labour significantly boosts production capabilities is as relevant today as it was in the 18th century. He argued that it leads to greater skill, time-saving, and the invention of specialized machinery. These principles laid the groundwork for the classical theory of economics and have influenced countless industries to push for specialized processes.
Fast forward to the 21st century, the advent of artificial intelligence (AI) has brought a new dimension to the application of intelligence. We now have access to AI systems capable of performing a wide range of tasks at a competent level, such as OpenAI's ChatGPT for natural language processing and basic computer vision. The breadth of ability of these systems is incredible and they certainly have their uses. However, these general systems, while versatile, often do not excel in specific tasks due to their broad focus.
Take, for example, the task of image segmentation in peatland conservation, where conservation experts want to quantify the extent of peatland erosion by mapping open and eroding peatland. General AI systems may be capable of identifying and classifying images, but they lack the fine-tuned precision required for such a specialized task. They consume more resources and provide less accurate results than a system specifically designed for this purpose.
At New Gradient as a leading machine learning developers and AI consultants we build bespoke AI solutions that address niche business needs. By developing specialized AI systems for businesses, we provide tailored services that far exceed the capabilities of off-the-shelf general AI tools in specific business use cases. One such example is our specialized Automatic Number Plate Recognition (ANPR) system designed for use in non typical ANPR scenarios, such as when cars are not head on or are faraway. Systems based on off shelf object detection algorithms such as YOLO (You Only Look Once) algorithm, struggle with accuracy in challenging scenarios where vehicles are not positioned head-on or occupy only a small portion of the image achieving only in the range on 85-90% accuracy. Our specialized ANPR system, developed with these scenarios in mind, boasts a remarkable 99.9% accuracy rate,significantly higher than systems adapted from more general purpose solutions.
This high level of accuracy is not just a technical achievement—it's a testament to the economic benefits of specialization in AI. A specialized system may require more investment in the development phase, as our graph indicates, but the long-term cost savings and performance enhancements are substantial. By focusing on the unique demands of a specific task, we can create systems that are not only more efficient but also more effective, ultimately providing a competitive edge and unlocking new business opportunities.
The tailored approach of New Gradient not only aligns with the economic theory of comparative advantage but also embodies the principle of efficiency in operation. Comparative advantage suggests that focusing on areas with the greatest proficiency not only maximizes output but also minimizes waste. In the context of AI, this efficiency is twofold: specialized systems not only outperform generalist models in task-specific accuracy but also in operational costs. Specialized AI systems can be developed to be leaner, requiring less computational power, thus allowing for faster inference times and lower server costs.
The precision of these systems means they can run on smaller, more cost-effective models without compromising performance. This leads to a cascade of economic benefits, including reduced server load, faster feedback loops, and even the potential for edge computing deployments, which bring processing closer to the point of data collection and action, like New Gradient’s AggCAM project. AggCAM leverages this cutting-edge approach for quarry rock segmentation, allowing for real-time analysis without the latency or expense associated with large-scale server farms.
Moreover,the ecological footprint of AI is an increasingly vital consideration. Smaller, specialized models contribute to reduced emissions from server farms, aligning technological advancement with environmental stewardship. By optimizing the size and efficiency of AI models, New Gradient not only enhances performance but also supports more sustainable operations.
In essence, the investment in specialized AI for business transcends the initial development costs, unfolding into a spectrum of operational benefits that reverberate across the economic and ecological facets of a business. The graph illustrates not just a cost trajectory but a narrative of innovation and foresight. As AI consultants we believe that by investing in specialized AI, businesses are not just saving on costs; they are investing in a future where speed, efficiency, and sustainability are not competing interests but complementary outcomes.
