why is ai such a big deal right now?
Artificial intelligence as a field of study has been around for a fairly long time. Its roots can be traced back at least as far as a series of papers by computing pioneer Alan Turing which paved the way for the concept of “machine learning” that sits at the heart of modern AI.
This is the idea that rather than attempting to create an AI system by specifying up front all the rules that will determine its operation, we instead mirror the way we actually develop as humans, by creating algorithms that have the ability to “learn” through a process of repeated trial and self-modification.
As an interesting historical side-note for those interested in the intersection of charity and AI, philanthropy actually played a key role in the early development of the technology. Between 1932 and 1955, the Rockefeller Foundation employed as its Director of Natural Sciences (or “Chief Philanthropoid” as he preferred to style himself) a man named Warren Weaver. Weaver was a high-quality mathematician in his own right, but he also played a hugely important catalytic role in his time at Rockefeller by using the foundation’s grantmaking to support scientific research in a range of areas, including the nascent field of computer science. In 1956, Rockefeller foundation gave a grant of $7,500 to support a conference in Dartmouth that is now widely recognised as the birthplace of modern AI, and it was in fact in the grant application for this conference that the first recorded usage of the term “Artificial Intelligence” can be found.
Given this long history, why has AI all of a sudden come to an unprecedented level of prominence in recent years? There are a number of factors that can help explain this, including:
- New types of algorithms: there have been huge leaps forward in the field of machine learning (ML) in the last decade; most notably the development of a type of powerful new algorithm known as a “deep neural network”.
- Data explosion: ML algorithms require vast quantities of data on which they can be trained; and historically this was a major limiting factor. However, recent years have seen an exponential increase in the amount of data being generated in all areas of our lives, and this has provided the fuel for the rapid growth and development of ML.
- Processing power: The leap forward in software terms represented by the development of deep learning algorithms has only been possible because of an associated leap forward in hardware. In particular, the development of fast, cheap Graphical Processing Units (GPUs), originally driven by computer gaming market, has made the kind of processing required for ML affordable at scale.
- Investment: As the potential of AI has become clear (given the factors outlined above), it is no surprise that investors are piling in to the field and putting up huge sums of capital to fund new developments and applications. In particular, the Chinese government has started funding AI very aggressively, which has led many experts to suggest that we may be in the early stages of an “AI arms race”.