Smart homes are becoming more and more popular. In 2017 there were about 41.2 million smart home devices in the US alone, and that number is growing rapidly. It’s clear that the smart home is not something just for eccentrics anymore, but an improvement that everybody can take advantage of.
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Chatbots, from smart home assistants to automatic bank assistants, have become a very common way to quickly respond to users’ questions and needs. Their main benefit is that they help businesses scale effectively and support customer service with no harm to the user experience. Implementing a chatbot into your customer communication strategy can save you time and money. They have some limitations too - most of which result from the fact that they simply are not human. Let’s look at DialogFlow - a great solution for all your chatbot needs.
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At Netguru, our biggest asset is the talent. On a day-to-day basis, Netguru’s professionals are busy delivering top-notch products to companies from all around the globe. In their spare time they travel the world to share their experience. Piotr Śliwa, Senior UX Designer, spoke at four UXDX Europe conferences; Marek Talarczyk, COO at Netguru, presented at Dreamforce 17 conference in San Francisco, and Bruno Mańczak, QA Manager, has just got back from IP EXPO London, where he spoke about “crimes against testability”. We aim to give even more to external communities. But we also decided to create a space for the team where they could share knowledge with other workmates. That’s how our very first internal conference, Netguru Tech Talks, came to be.
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"Fintech? We Are Topping the League", Says Head of Level39, One of the Best Tech Community in the World
Tomasz Grynkiewicz
How to create a fintech product that people will use daily? To get an answer to this question, we invited the top experts from the fintech industry – Revolut, Starling Bank, Funding Circle, Monese, Tide, and OakNorth – to our Disruption Forum. To spice our event up, we've also invited Lloyds Banking to take part in the discussion.
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Some time ago I talked with my colleague Mateusz Jankowski, who is a Senior UI designer. I was telling him that I’m going to write a post about the truth of working as UI designer. The deeper you dig, the darker it gets, but maybe there’s one specific term that describes your daily duties? - I asked. His answer made me cry with laughter - sure, it’s like a visual OCD (obsessive-compulsive disorder). Well, it’s hard to disagree. But it’s just the tip of an iceberg.
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It’s no secret that big data and advanced analytics are changing the face of the business world. Insights gained from big data are revolutionising foundational business processes such as sales and marketing, supply chain management, and R&D; giving companies the information they need to increase revenues, reduce costs, and improve customer experience.
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The telecommunications industry is riding high on the waves of the tech revolution and digital transformation. Their business is doing so well that, unlike in many other sectors, Artificial Intelligence (AI) and Machine Learning (ML) is more of a solution to their problems than a challenge. Telecoms are not far behind such big tech companies as Google, Amazon, or Microsoft and could use the 5G transition as an opportunity to cut the distance.
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With artificial intelligence becoming smarter and smarter, the interest in NLP (natural language processing) has also been growing rapidly. Utilizing and developing the technology that helps machines understand and process human language has become a hot topic in many industries, because it has the potential to finally fill the gap in human-computer interactions.
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In the machine vision area, image recognition refers to the ability of software to identify people, objects, places, or actions in images. Machine vision technologies combine device cameras and artificial intelligence algorithms to achieve accurate image recognition to guide autonomous robots and vehicles or perform other tasks (for example, searching image content).
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Machine Learning algorithms are being used more often than we can imagine and there is a good reason for that. Let's see what kind of different Machine Learning algorithms exist and how they can help us in solving everyday life problems.
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