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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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Some of the Deadliest Diseases Can Be Prevented by Using Modern Technology Solutions – Interview with Michał Sosiński, CEO at Soma Healthcare
Natalia Chrzanowska
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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How Machine Learning and Hyperspectral Imaging Contribute to Sustainable Agriculture and Environmental Protection
Michał Marcinkiewicz
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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Fraud Detection with Machine Learning: How Banks and Financial Institutions Leverage AI
Michał Dyzma
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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Increasing the Accuracy of the Machine Learning Model in the CarLens Mobile App
Anna-Mariia Shkarlinska
25,000 analyzed photos, 10 more weeks, 6 new Machine Learning models, and even more hard work of development teams, topped with AR engines and Machine Learning technology. That's our recipe for improving our Machine Learning model! Welcome to the second part of our journey with CarLens, where we are going through all the methods that we used for improving our Machine Learning model. How did the model’s performance change? Read on to see our results with detailed charts. In case you missed the first one, make sure you read this article too.
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Have you ever wondered what steps you should take first to dive deep into the Machine Learning world? You can consider various scenarios – including academia classes, online tutorials, and reading books. All of these approaches have their special benefits and may help you become an AI Engineer. On the other hand, it is often said that the learning process is at its best when done hands-on.
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