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AI and the big data revolution have already made a huge impact in sectors such as retail, healthcare, education, and autonomous driving. Now, manufacturing is experiencing rapid transformation thanks to a combination of AI and advanced technologies like the Internet of Things.
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Machine learning is one of the hottest topics in the world of technology. It makes sense then that so many companies are analysing whether they can benefit from it. Should you decide to enhance your business with ML, the question of finding a suitable team will arise immediately. Which is better: building an in-house machine learning team or outsourcing your needs to an industry specialist? You will need to think about many aspects before making a commitment to either option. Read on to see our take on whether you should go for an in-house or an outsourced ML team.
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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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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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As organizations strive to make the most out of the data they collect, they turn to different techniques for generating insights from vast data sets. The market for data science and analytics is on the rise – according to IMB, the number of jobs for all US data professionals will increase by 364,000 openings to reach smashing 2,720,000 by 2020!
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One of the major European banks has recently admitted that it has failed to spot money laundering in its books for years. As a result, the bank has agreed to pay $900 million in a settlement with prosecutors. While it was a man (the CFO) who stepped down for this negligence, would a machine (Machine Learning) be a better guardian to prevent such situations in the future?
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