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It has been two years since we started our data science team. At the beginning, we have built a strong group focused mostly on machine learning. In the meantime, the team has naturally evolved to its present form of two subgroups: data engineering and machine learning. Both focused on the two ends of successful AI adoption, working hand to hand.
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The key to the successful implementation of digital healthcare solutions is patient engagement. Without mutual trust between healthcare providers and patients, even the most sophisticated technological solutions won’t improve patients’ medical outcomes. We gathered experts from across digital healthcare – both from dynamic startups like Keto-Mojo, as well as enterprises like Roche – and with this patient-centered approach in mind, discussed the essential aspects of patient engagement:
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The age of generic services is dropping off. Today's customers expect more, and they want you to produce software tailored to their needs. This can be done with machine learning. Its solutions are a revolution happening right before our eyes, and those late to the party might find themselves at a huge disadvantage.
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Rapid advances in technology allow us to observe our environment on a larger scale and in greater detail. Due to this fact, the popularity of hyperspectral imaging is increasing, especially in agriculture, ecology and environmental monitoring, urban planning, and many more. Applications are limitless, such as detecting concentration of greenhouse gases, monitoring crop irrigation levels to prevent crop waste, or detecting and classifying pollution of water bodies.
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Booking a room can sometimes be a challenge, especially when you are in a hurry and your meetings pop up unexpectedly. We all have situations in which we try to make a room reservation on the phone while moving from one place to another, which is not the most convenient option. That’s why at one of our hackathons we came up with an app which will allow us to make room reservations instantly, without having to open our computers and with minimal interaction with our phones.
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Application that monitors your child - the idea Being a parent to a newborn is always stressful. There are many questions popping up: will I manage with all the new duties? Should I watch the baby all the time? What if the baby needs me and I am not there? 
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Since many businesses want to make use of AI in order to scale up or take their start-up off the ground, it is crucial to realize one thing: the technology they choose to work with must be paired with an adequate deep learning framework, especially because each framework serves a different purpose. Finding that perfect fit is essential in terms of smooth and fast business development, as well as successful deployment.
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Machine Learning is a trending field of Computer Science turning computer’s computations into usable data and giving a number of unique opportunities. It’s rapidly gaining in popularity – it’s common in modern web application as well as in services such as Netflix, Spotify, Amazon and Facebook. Machine Learning is a good solution for apps based on recommendations or some kind of predictions. If you want to build this kind of app you will need an efficient backend technology to support it – is Ruby on Rails the right choice?
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Ever since Apple introduced Siri a few years ago to rival Android’s voice assistant, speech-to-text has been a staple tool in Apple’s and Google’s mobile ecosystems. After the initial introduction, Apple opened up the API to developers, allowing them to write apps that make heavy use of speech-to-text transcription. As part of a project for one of our clients, we implemented a speech-to-text transcription feature that takes advantage of the Apple transcription API. To our client’s delight, we were able to successfully integrate transcription into the app. The only shortcoming was the lack of any punctuation marks in the transcriptions produced by Apple’s SFSpeechRecognizer.
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Machine learning solutions have been used in healthcare for decades, but they only became popular a few years ago, mostly due to developments in deep learning. According to PwC, in the next four years, the market for AI in healthcare will grow from $760 million to $6 billion.
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