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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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The insurance industry has always relied on data to calculate risk and come up with personalized ratings. Today, the sector is undergoing a profound digital transformation thanks to technologies such as machine learning.  Insurers are using machine learning to increase their operational efficiency, boost customer service, and even detect fraud. Here are 6 ways machine learning is transforming the insurance industry. ---------------------------------------------------------------------------------------- Note: this post has been updated, to reflect the progress of new technologies and business moves by companies mentioned in the article.  ----------------------------------------------------------------------------------------
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Sustainable agriculture is a simple and pragmatic idea that there does not have to be a trade-off between agricultural productivity and environmental protection. The conventional approach, however, has been to view agricultural yields and environmental protection as some sort of a zero-sum game in which the latter was constantly out-prioritised by the former. As a result, we have only managed to exacerbate the already adverse environmental impact of agriculture in terms of groundwater depletion, water pollution from fertilizer runoffs, biodiversity loss, soil erosion, and high rates of greenhouse gas emissions.
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The financial services sector is on the eve of a major transformation, and the driving force behind it is AI. Innovative applications for AI have already been found across areas such as credit scoring, regulatory compliance, customer experience, and portfolio management. Thanks to rapid advancements in technology, tasks that once took employees hours to complete manually, can now be done in a matter of seconds.
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Audio classification is one of the most common and most explored tasks in the field of audio processing. It’s the foundation of many apps that enable users to automatically identify artists, instruments, or simply recognize someone’s voice. How does the algorithm work in practice? We had a chance to implement audio processing with machine learning on iOS and Android mobile devices. See how our journey went.
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