Costa Rica


Machine learning algorithms and the art of hyperparameter selection

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Data Scientist and Principal Data Scientist, KNIME -- Mischa Lisovyi, Ph.D., is a data scientist in the customer care team at KNIME. He has an academic background in particle physics -- the scientific field in which data is so big that processing has to b… (show all) Mischa Lisovyi, Ph.D., is a data scientist in the customer care team at KNIME. He has an academic background in particle physics -- the scientific field in which data is so big that processing has to be distributed around the globe. Analysis of data is his passion, and his range of experience spans from identification of the hardest elementary particles in the known universe through assessing poverty levels in Costa Rica to adding an artistic touch to selfies of KNIMEers. Rosaria Silipo, Ph.D., principal data scientist at KNIME, is the author of 50 technical publications, including her most recent book "Practicing Data Science: A Collection of Case Studies."


Costa Rica Puts Time and Attention into AI Development - Nearshore Americas

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Artificial Intelligence (AI) is having a broad and deep impact on the way services are exported globally. Be it for good or bad, there is no getting away from the reality that AI is an agent of disruption. One of the perennial front-runners of Nearshore outsourcing, Costa Rica, appears to be adapting to the AI opportunity faster than most countries in the region. Local companies are intensifying their AI development operations and a number of AI technologies are gaining traction there – all of which will influence Costa Rica's positioning in the next-generation of services delivery. The Latin American nation of nearly five million has long been seen as a tech epicenter of Central America ever since Intel chose it to open the biggest microchip factory in the region in 1997, with an initial investment of US$800 million.


The AI Eye: Artificial Intelligence Innovation Alive and Well in Costa Rica

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Picking up steam in 1997 with Intel opening of a microchip factory and an $800 million USD investment, Costa Rica has since blossomed into a key tech hub in Latin America, according to an article from Nearshore Americas. But leaving the landmark Intel investment aside (the factory is now closed), the country is fostering growth through government spending in the space, high public funds devoted to education and tax-friendly technology parks that attract investors and talent from around the globe. With a population of five million inhabitants and 51,000 square kilometers, the number of companies in the country has reached over 546 IT companies, 3,447 manufacturing (including medical components), and performed 12,281 various commercial activities by 2018. This activity generated over 300,000 jobs, according to the National Institute of Statistics and Census. One of the major fields in the current technological revolution is artificial intelligence (AI), of which a high amount of development is occurring in Costa Rica.


Machine learning algorithms to infer trait matching and predict species interactions in ecological networks

arXiv.org Machine Learning

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be particularly likely if the proportions of a bee's tongue match flower shape in a beneficial way. Empirical evidence for trait matching, however, varies significantly in strength among different types of ecological networks. Here, we show that ambiguity among empirical trait matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional regression models with Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naive Bayes, and k-Nearest-Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We find that the best ML models can successfully predict species interactions in plant-pollinator networks (up to 0.93 AUC) and outperform conventional regression models. Our results also demonstrate that ML models can better identify the causally responsible trait matching combinations than GLMs. In two case studies, the best ML models could successfully predict species interactions in a global plant-pollinator database and infer ecologically plausible trait matching rules for a plant-hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other network types, such as trophic or competitive networks. More generally, our results highlight the potential of ML and artificial intelligence for inference beyond standard tasks such as pattern recognition.


Alexa can 'listen to users having sex' with some audio heard by Amazon staff, whistleblower claims

Daily Mail - Science & tech

Amazon staff review thousands of audio recordings made by Alexa each day -- including snippets of couples arguing and having sex -- an investigation claims. The clips were accidentally captured by the popular digital assistant -- confusing the noises for the commands it should be listening to -- and sent off for analysis. Staff at the tech firm review one in every five-hundred recordings made by Alexa, whether of deliberate commands to the assistant or accidental recordings. According to a privacy expert, the revelation is a reminder of the extent of the personal information that the tech firm has on its users. Amazon has an English-speaking team monitoring thousands of Alexa recordings daily based in Bucharest, Romania, the Sun claims, along with similar setups in Boston, Costa Rica and India.


Climate-driven statistical models as effective predictors of local dengue incidence in Costa Rica: A Generalized Additive Model and Random Forest approach

arXiv.org Machine Learning

Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repercussions. Using the number of dengue reported cases and climate data from 2007-2017, we fitted a prediction model applying a Generalized Additive Model (GAM) and Random Forest (RF) approach, which allowed us to retrospectively predict dengue occurrence in five climatological diverse municipalities around the country.


OECD Principles on Artificial Intelligence - Organisation for Economic Co-operation and Development

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The OECD Principles on Artificial Intelligence promote artificial intelligence (AI) that is innovative and trustworthy and that respects human rights and democratic values. They were adopted on 22 May 2019 by OECD member countries when they approved the OECD Council Recommendation on Artificial Intelligence. The OECD AI Principles are the first such principles signed up to by governments. Beyond OECD members, other countries including Argentina, Brazil, Colombia, Costa Rica, Peru and Romania have already adhered to the AI Principles, with further adherents welcomed. The OECD AI Principles set standards for AI that are practical and flexible enough to stand the test of time in a rapidly evolving field.


OECD Principles on Artificial Intelligence - Organisation for Economic Co-operation and Development

#artificialintelligence

The OECD Principles on Artificial Intelligence promote artificial intelligence (AI) that is innovative and trustworthy and that respects human rights and democratic values. They were adopted on 22 May 2019 by OECD member countries when they approved the OECD Council Recommendation on Artificial Intelligence. The OECD AI Principles are the first such principles signed up to by governments. Beyond OECD members, other countries including Argentina, Brazil, Colombia, Costa Rica, Peru and Romania have already adhered to the AI Principles, with further adherents welcomed. The OECD AI Principles set standards for AI that are practical and flexible enough to stand the test of time in a rapidly evolving field.


Sloth-inspired robot saves power by taking things slow

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Currently, the robot consists of two hinge-joined sections that both have a lot of protruding wires. Down the line, however, plans call for its workings to be encased within a protective outer shell. It may then be field-tested at a cacao plantation in Costa Rica, where it would utilize existing cables that are used to transport the cacao. Those cables are also utilized as a sort of "highway" by real sloths, which the SlothBot could observe unobtrusively.


History Made: OECD Adopts First Intergovernmental Standard on AI Tech

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According to OECD's Director of the Science, Technology and Innovation Directorate Andrew Wyckoff, the released document, titled "Recommendations of the Council on Artificial Intelligence," will hopefully establish a regulatory environment to promote AI technology in an ethical manner. "AI is what we would call a'general purpose technology.' It's going to change the way we do things in nearly every single sector of the economy -- that's part of the reason we give so much importance to its development," he told reporters Wednesday, according to Defense One. "Some have termed it as'the invention of a method of inventions,' and in fact we can see it already affecting the process of scientific discovery and science itself." The principles outlined in the document have been signed by the OECD's 36 member countries, as well as by the US, Argentina, Brazil, Colombia, Costa Rica, Peru and Romania.