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 concepts 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.
[caption id="attachment_31278" align="alignright" width="226"] Werner Vogels, Vice-President and CTO of Amazon.com.[/caption]
First: Users across the globe 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 organizations, 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.
Progress through AI and ML
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 organizations, 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 tumors 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 center-stage.
This means, with the help of digitization 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.
Removing barriers to entry
At Amazon, we know that innovative technologies always take off whenever barriers to entry fall for market participants. That is the case right now with AI and ML. In the past, anyone who wanted to use this technology had to start from scratch: develop algorithms and feed them with enormous amounts of data – even if an application was only needed for a strictly confined context. AI technologies were also so expensive that it made them prohibitive to use. Today, AI and ML technologies are available in the cloud, at the click of a mouse, and they can be called up according to individual requirements. Even users who are not specialists can very easily and affordably incorporate the building blocks into their own services. In particular small and medium-sized companies can benefit as they do not have to learn any complex AI or ML algorithms or technologies and they can start experimenting straight away without incurring high costs.
This is why today many consumer interfaces that everyone is familiar with, such as recommendations, similarities, or autofill functions for search prediction, are all ML driven. This application of ML is also in other areas such as predicting inventory levels or vendor lead times, detecting customer problems and automatically deducting how to solve them, or discovering counterfeit goods and abusive reviews, thereby protecting customers from fraud.
New division of labor
In the field of fulfillment, which is relevant for numerous industry sectors, we are thinking of ideas of how AI can contribute. Applied in robots, AI can free people from routine activities that are physically difficult and often stressful. Machines are very good at, and sometimes even outperform, tasks that are complicated for a human to do, such as finding the optimal route in a warehouse or transporting heavy goods. For supposedly easy tasks, by contrast, robots can be overwhelmed; an example is recognizing a box has landed on the wrong shelf. So how about bringing together the best of both?
AI and ML enable us to get rid of tasks in our work which damage our health or where machines are better than we are. This does not make ourselves redundant but, rather, helps us to gain more personal and economic freedom – for interpersonal relationships, for our creativity, and for everything that we humans can do better than machines. That is what we should strive for.