All Case Studies Design Development Interviews Machine Learning Project Management
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. And there is a plethora of insurtech startups, eager to take a slice of an insurance industry. 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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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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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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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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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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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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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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What Is Kaggle? An Overview

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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I have recently stumbled upon an article reporting that Facebook is using Artificial Intelligence to fight “revenge porn” - a phenomenon of sharing intimate photos on social networks without people’s permission. This is one of the downsides of the development of new technologies and social networks. This great TED talk from one of the victims explains why it is a serious problem that needs strong measures.
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Machine learning brings a wide range of new possibilities to nearly all industries. A number of tasks that can be automated or improved with machine learning-based algorithms is growing day to day. Let’s dive deeper and try to establish factors that enable a specific task to be improved with ML, so we can understand better how your business and processes can leverage artificial intelligence.
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AI-driven UX design is one of the hottest trends for 2019. That said, product designers are not among the jobs and specializations expected to be replaced by robots soon. After all, coming up with the right product idea and executing it is a complex process that combines creativity and advanced analytics.
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AI and machine learning have moved from the fringes to the very centre of the technology debate. But which industry has the huge datasets, along with the capital and staffing resources, to make the most of AI? As recently reported by both PwC and Gartner, it is the financial sector.
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The AI-powered solutions have a massive potential in healthcare. Computers equipped with software based on convolutional neural networks are better at detecting skin cancer than experienced dermatologists, and Deep Learning beats radiologists in diagnosing pneumonia.
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The final success of each and every business or project comes down to selling. It has always been like this, but nowadays - when we’re able to get ahold of massive amounts of data about our customers - it is just easier to focus on building relationships with them, and then make the most of this in terms of sales.
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Today’s web and app users demand personalized experiences. They expect the apps, news sites, social networks, and online stores they engage with to remember who they are and what they’re interested in, and make relevant, individualized, and accurate recommendations for new content and new products based on their previous activities. Any app or website that fails to deliver on these demands will quickly see its users flocking out the digital door.
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Artificial Intelligence (AI) can bring more benefits to healthcare than to any other industry. However, until recently the progress here has been slower than in other fields - such as finance or e-commerce. Luckily, this has changed lately. With significant investments, we can see an outburst of machine learning applications that should lead to medical revolutions in many fields.
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