TRENDS ACCELERATING AI
Core technological advances are central to the continued development of AI. Significant progress has been made with all core AI technologies, and the levels of investment and demand for ongoing improvement give good reason to expect this growth will continue well into the future. Technological advances can be classified into three broad categories: improving computer processing speed and power, increasing AI system access to big data, and using algorithmic improvements to enable more complex AI applications.
• Computing Power &Speed: AI is a computer processing intensive technology – breakthroughs in computing power and efficiency have enabled the expansion and complexity of AI applications.
In the technology industry, Moore’s Law is used to show the relationship between the cost and speed of computer processing power over time, the trajectory of which results in an exponential curve.
Until recently, a computing device’s CPU, or central processing unit, typically provided the core function of processing.
In recent years, GPUs, or graphical processing units, have begun to partially take over computer processing workloads, contributing significantly to the rise of AI.
Originally designed for the much larger and more complex computational workloads of rendering computer gaming graphics in real time, GPUs are designed to handle hundreds of tasks in parallel, and today are successfully being used to enable AI applications.
Advances in computer chip technology are an important part of the AI developmental story. Given the consistency of chip improvements and the likelihood that chip design will continue to improve, this is not the primary reason for the existence of AI but just one of the essential enablers.
• Big Data: The existence of plentiful and easily accessible data is not a new phenomenon, however its ever-increasing volume, velocity, and variety is a key part of the AI story. Even though AI could exist on a smaller scale without these advances, AI requires data to demonstrate its full power. While new types of data have emerged in the past few years, and while there is a significant increase in the pace at which data is created and changes, AI systems are currently consuming only a tiny fraction of available data. This has been true for a long time. So even if data quantities were to stagnate, and the rates of data volume and velocity were to remain constant, AI would still have a lot of data to ingest, contextualise, and understand.
• Algorithmic Improvements: The increasing abundance of data being created every day has invited researchers, data scientists, and software engineers to conceptualise sophisticated new algorithms capable of ingesting large volumes of complex data.
Because of this, today AI is not merely capable of handling the rapid assembly of large and quickly changing datasets but, in fact, thrives on this. These big datasets make the best contribution to AI’s ability to learn when they are complex, so the more diversity in the data domain the better. This is an advantage AI systems have over other data-processing methods: whereas standard systems get bogged down with large complex datasets, algorithmic improvements in recent years have improved significantly to be able to handle large volumes of heterogeneous data, enabling the detection of patterns and discovery of correlations that might not be obvious to humans or to standard rule-based systems. While these three technology advances are the main drivers of AI, considering the future of AI reveals not just a single trend, but the confluence of many underlying technology trends.
Simultaneously, other important technology trends are developing along a path that complements AI, namely cloud computing and connectivity. As cloud computing advances to become a new industry standard, it allows centralized processing of large datasets. And as connectivity (through the internet and cellular networks) increases, it enables transmission and control of large datasets in real time.
This means more and more datasets are being stored, processed, and accessed through the cloud, and connectivity to that information no longer limits overall system performance. As a result, the accelerating advancement of data storage, accessibility, and transmission speed is catalyzing the further development of AI.
Technological factors provide essential support to the progress of AI, but their contribution pales in comparison to the way that social and commercial factors influence the viability of AI technology.