When a technology has its breakthrough, it can often only be determined in hindsight. In the case of artificial intelligence (AI) and machine learning (ML), this is different.
Currently, both are everywhere and, personally, I think that there has never been a better time to develop smart applications and use AI and ML technologies. Why? Because three things are coming together.
First, users are capturing data digitally — whether this is in the physical world through sensors or GPS, or online through click stream data. As a result, there is a critical mass of data available.
Secondly, there is enough affordable computing capacity in the cloud for companies and organisations, no matter what their size, to use intelligent applications. Thirdly, an “algorithmic revolution” has taken place, meaning it is now possible to train trillions of algorithms simultaneously, making the whole ML process much faster.
This has allowed for more research, which has resulted in reaching a “critical mass” in knowledge that is needed to kick off an exponential growth in the development of new algorithms and architectures.
We may have come a relatively long way with AI and ML, but the progress came quietly. After all, over the last 50 years, AI and ML were fields that had only been accessible to an exclusive circle of researchers and scientists.
That is now changing as packages of AI and ML services, frameworks, and tools are today available to all sorts of companies and organisations, including those that don’t have dedicated research groups in this field.
A number of start-ups are using AI algorithms for all things imaginable — searching for tumours in medical images, helping people learn foreign languages, or automating claims handling at insurance companies. At the same time, entirely new categories of applications are being created whereby a natural conversation between man and machine is taking centre stage.
This means, with the help of digitisation and high-performance computers, we are able to replicate human intelligence in some areas, such as computer vision, and even surpass the intelligence of humans. We are creating very diverse algorithms for a wide range of application areas and turning these individual pieces into services so that AI and ML is available for everyone.
