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Probably when Siri appeared in our life for the first time, the idea of talking to your device sounded like a whim or a funny activity to test Artificial Intelligence. By now, the use of voice-activated assistants has become an everyday experience. Siri, Google Assistant, and Cortana from our mobile devices and Amazon Echo, Google Home and Apple’s HomePod in our house are always ready to answer our question of complete a task. Such spreading of voice-activated applications has changed the way we perform a search in the Internet shifting it from typing keywords to asking direct

Voice search is being increasingly used by customers to find businesses around them, complete tasks, or just help them go about their day-to-day life. By 2020, Gartner predicts that 30% of all searches will take place without a screen altogether, meaning voice or image search. Andrew Ng, then Chief Scientist at Baidu stated in September 2014 that “In five years’ time, at least 50% of all searches are going to be either through images or speech” — the prediction that has spread all over the market. …


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As we all know, tastes differ and change over time. However, each epoch tried to define its own criteria for beauty and aesthetics. As science was developing, so was the urge to measure beauty quantitatively. Not surprisingly, the recent advancements in Artificial Intelligence pushed forward the question of whether intelligent models can overcome what seems to be human subjectivity.

A separate subfield of artificial intelligence (AI), called ‘computational aesthetics’, was created to assess beauty in domains of human creative expression such as music, visual art, poetry, and chess problems. Typically, it uses mathematical formulas that represent aesthetic features or principles in conjunction with specialized algorithms and statistical techniques to provide numerical aesthetic assessments. …


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In the last decades, we’ve seen tremendous advancements in Artificial Intelligence (AI) and related fields. It is viewed not only as a ground-breaking technology, but as a step forward to the future having the means to change our society. We expect AI to use hardware and software to see and hear patterns, make predictions, learn and improve, and take action with this intelligence. …


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As soon as you start working on a data science task you realize the dependence of your results on the data quality. The initial step — data preparation — of any data science project sets the basis for effective performance of any sophisticated algorithm.

In textual data science tasks, this means that any raw text needs to be carefully preprocessed before the algorithm can digest it. …


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So, your team has fully moved to remote work. You are probably already used to working in your pajamas, having endless snacks and communicating with your colleagues via Slack or Zoom. …


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While in the previous article of the series we introduced the notion of White Box AI and explained different dimensions of interpretability, in this post we’ll be more practice-oriented and turn to techniques that can make algorithm output more explainable and the models more transparent, increasing trust in the applied models.

The two pillars of ML-driven predictive analysis are data and robust models, and these are the focus of attention in increasing interpretability. The first step towards White Box AI is data visualization because seeing your data will help you to get inside your dataset, which is a first step toward validating, explaining, and trusting models. …


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Background

As of March 31, 2020, 787,438 people have been confirmed with severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection from more than 200 countries and territories, and the total death toll reached 37,846 globally. The Coronavirus disease 2019 (COVID-19) pandemic is expected to continue spreading, so it is crucial for all nations to take timely measures to combat the looming crisis The disease is not only claiming thousands of lives, but also threatens the world with an economic crisis: in the median economy, the output might decline by 25%.

Challenge

Despite all the efforts of physicians, epidemiologists, and scientists, there is not enough evidence from real-world clinical data available. To generate evidence that can improve healthcare decisions and flatten the curve of COVID-19 outbreak worldwide, the Sciforce Medical Team, as a collaborator of The Observational Health Data Sciences and Informatics (OHDSI) international community, has taken part in COVID-19 virtual Study-a-thon (March 26–29). …


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Many courses and tutorials offer to guide you through building a deep learning project. Of course, from the educational point of view, it is worthwhile: try to implement a neural network from scratch, and you’ll understand a lot of things. However, such an approach does not prepare us for real life, where you are not supposed to spare weeks waiting for your new model to build. At this point, you can rely on a certain deep learning framework to help you.

A deep learning framework, just like a machine learning framework, is an interface, library or a tool which builds deep learning models quickly and with no evident effort, without getting into the details of underlying algorithms. Such frameworks help engineers define models from a collection of pre-built and optimized components. And from the practical side, this means that instead of writing hundreds of lines of code, you can choose a framework that will do most of the work for you. …


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Among many things that define us as humans, there is our ability to remember things such as images in great detail, and sometimes after a single view. What is even more interesting, humans tend to remember and forget the same things, suggesting that there might be some general internal capability to encode and discard the same types of information.

What makes certain images more memorable than others? Research suggests that pictures of people, salient actions and events are more memorable than natural landscapes and images that lack distinctiveness will soon be forgotten. We can conclude that memorable and forgettable images must have certain intrinsic visual features, making some information easier to remember than others. To prove this fact, a number of computer vision projects, such as Isola 2011, Khosla 2013, Dubey 2015 managed to reliably estimate the memorability ranks of novel pictures. However, the task to predict image memorability is quite complex: images that are memorable do not even look alike. A baby elephant, a kitchen, an abstract painting and an old man’s face can have the same level of memorability, but no visual recognition algorithm would cluster them together. …


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Each nation has its ancient monument or a pagan holiday — the relic of the days when our ancestors tried to persuade their gods to give them more rain, no rain, better harvest, fewer wars and many other things considered essential for survival. However, neither Stonehenge nor jumping over fire could predict the gods’ reaction. It was a totally reactive world with no forecast. However, as time passed, people started to look into the future more inquisitively trying to understand what would be waiting for them. The science of prediction has emerged.

In this article, we’ll see how prediction evolved over time shaping our technologies, expectations and the worldview. …

About

Sciforce

Ukraine-based IT company specialized in development of software solutions based on science-driven information technologies #AI #ML #IoT #NLP #Healthcare #DevOps

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