There has never been a better time than now to develop smart applications. Users across the globe are capturing data digitally, whether this is in the physical world through sensors or GPS, or online through clickstream data.

This means there is a critical mass of data available, and there is also enough affordable computing capacity in the cloud for companies and organisations of all sizes to use it effectively. Furthermore, an algorithmic revolution has taken place, because of which it is now possible to train trillions of algorithms simultaneously, making the whole machine learning process much faster.

During the past 50 years, artificial intelligence (AI) and machine learning (ML) were fields that were only 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.

Startups are using AI algorithms for amazing tasks, like searching for tumours in medical images, helping people learn foreign languages, or automating claims-handling at insurance companies. Entirely new categories of applications are being created, where a natural conversation between humans and machines is taking centre-stage, including building chatbots for everyday consumer requests, such as accessing the latest news updates, game scores, or weather.

At Amazon, we work with billions of historical order information data, which allows us to create AI/ML-based models for many different kinds of functionalities. For example, programming interfaces that developers can use to analyse images, recognise faces and objects, or convert text to speech. Ultimately, there is something to be found for everyone who wants to define models, train them, and then scale.

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Matchmaking site Shaadi.com uses Amazon Rekognition, which adds image and video analysis to applications by identifying objects, people, text, scenes, and activities. Great photos are critical in helping Shaadi.com users find their right match. Amazon Rekognition helps the company quickly and affordably automate its highly complex process of identifying incorrect photos and inappropriate content.Shaadi.com estimates this has cut the team’s manual work by half.

One of the most advanced areas of application is e-commerce. AI supported pre-selection mechanisms help companies to remove complexity from their customers’ decision-making. The more possibilities there are, the more difficult it becomes for the customer. Our best-known algorithms come from this field: filtering product suggestions based on one’s purchase history of products with similar attributes, or on the behaviour of other customers who were interested in similar things.

In B2B and B2C businesses, it is critical that goods are available quickly, so there are algorithms today that can predict the daily demand for goods. This is particularly complex for fashion goods, which are available in many different sizes and variations, and for which reorder possibilities are very limited. Amazon retail tackled this issue and has now made the technology available to other companies as a web service.

Applied in robots, AI can free people from routine activities that are physically difficult and often stressful. For example, letting intelligent robots learn from humans how to identify the right goods, take on various orders and navigate their way autonomously through the warehouse on the most efficient route. In medicine, AI supports doctors in analysing X-ray CTs or MRT images. The World Bank uses AI to implement infrastructure programmes, development aid and other measures in a more targeted manner.

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What we need is a pragmatic optimistic view of the emerging possibilities. AI enables us to reduce tasks in our work where machines can do a better job than us. Not with the goal of making ourselves redundant. Rather, to refocus our skills on more meaningful tasks and projects in the workplace and for everything that we humans can do better than machines. That is what we should strive for. If we don’t, we will ultimately forego the economic and societal opportunities that we could have grasped.





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