It is a challenging and complex task to acquire information from different regions of a disaster-affected area in a timely fashion. The extensive spread and reach of social media and networks allow people to share information in real-time. However, the processing of social media data and gathering of valuable information require a series of operations such as (1) processing each specific tweet for a text classification, (2) possible location determination of people needing help based on tweets, and (3) priority calculations of rescue tasks based on the classification of tweets. These are three primary challenges in developing an effective rescue scheduling operation using social media data. In this paper, first, we propose a deep learning model combining attention based Bi-directional Long Short-Term Memory (BLSTM) and Convolutional Neural Network (CNN) to classify the tweets under different categories. We use pre-trained crisis word vectors and global vectors for word representation (GLoVe) for capturing semantic meaning from tweets. Next, we perform feature engineering to create an auxiliary feature map which dramatically increases the model accuracy. In our experiments using real data sets from Hurricanes Harvey and Irma, it is observed that our proposed approach performs better compared to other classification methods based on Precision, Recall, F1-score, and Accuracy, and is highly effective to determine the correct priority of a tweet. Furthermore, to evaluate the effectiveness and robustness of the proposed classification model a merged dataset comprises of 4 different datasets from CrisisNLP and another 15 different disasters data from CrisisLex are used. Finally, we develop an adaptive multitask hybrid scheduling algorithm considering resource constraints to perform an effective rescue scheduling operation considering different rescue priorities.

I participated in an amazing AI challenge through Omdena's community where we built a classification model for trees to prevent fires and save lives using satellite imagery. Omdena brings together AI enthusiasts from around the world to address real-world challenges through AI models. My primary responsibility was to manage the labeling task team. Afterward, I had the chance to take on another responsibility and build an AI model that delivered results beyond expectations. I am Leo from Rio de Janeiro, Brazil and I m a mechanical aeronautics engineer who currently works as a data scientist and management consultant in Brazil helping several companies to achieve better business results.

Today, thousands of organizations around the world use Artificial Intelligence (AI) as a key to growing their companies or as a complement to this strategy. On all continents, a boom in new projects based on Artificial Intelligence are transforming business models. This movement is driven primarily by the ecosystems of startups and entrepreneurship. As a result of this movement, everis and Endeavor launched a pioneering study that traces an overview of AI in Latin American entrepreneurship. The study gathered information from more than 240 companies on 70 AI projects developed in Argentina, Brazil, Chile, Colombia, Mexico and Peru.

Conditional independence testing is a key problem required by many machine learning and statistics tools. In particular, it is one way of evaluating the usefulness of some features on a supervised prediction problem. We propose a novel conditional independence test in a predictive setting, and show that it achieves better power than competing approaches in several settings. Our approach consists in deriving a p-value using a permutation test where the predictive power using the unpermuted dataset is compared with the predictive power of using dataset where the feature(s) of interest are permuted. We conclude that the method achives sensible results on simulated and real datasets.

In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of intelligence about military and government movements. Typically these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category. armasuisse Science + Technology, the R&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than two million aircraft. We show that we can obtain the correct aircraft category with an accuracy of over 88%. In cases, where no metadata is available, this approach can be used to create the data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect sensitive aircraft such as police and surveillance aircraft using this method.

We present here the Temporal Clustering Algorithm (TCA), an incremental learning algorithm applicable to problems of anticipatory computing in the context of the Internet of Things. This algorithm was tested in a specific prediction scenario of consumption of an electric water dispenser typically used in tropical countries, in which the ambient temperature is around 30-degree Celsius. In this context, the user typically wants to drinking iced water therefore uses the cooler function of the dispenser. Real and synthetic water consumption data was used to test a forecasting capacity on how much energy can be saved by predicting the pattern of use of the equipment. In addition to using a small constant amount of memory, which allows the algorithm to be implemented at the lowest cost, while using microcontrollers with a small amount of memory (less than 1Kbyte) available on the market. The algorithm can also be configured according to user preference, prioritizing comfort, keeping the water at the desired temperature longer, or prioritizing energy savings. The main result is that the TCA achieved energy savings of up to 40% compared to the conventional mode of operation of the dispenser with an average success rate higher than 90% in its times of use.

While it's not unimportant, a lot of the groundwork when developing a new fragrance is done by evaluating data, and that's something artificial intelligence is highly qualified to do. In a partnership between IBM Research and Symrise, a global producer of fragrances and flavors based in Germany with clients such as Estee Lauder, Donna Karan, Avon, Coty and more, the first AI-developed scent is now available for purchase in Brazil. Philyra became the artificial intelligence (AI) apprentice IBM created that perfumer David Apel worked alongside to create two new fragrances for Brazilian cosmetics company O Boticário in time for the country's Valentine's Day holiday this year. They were specifically looking for a fragrance to sell to Generation Z and millennials who they knew would be intrigued by a fragrance created by AI. This collaboration officially launched AI into the fragrance industry.

While it's not unimportant, a lot of the groundwork when developing a new fragrance is done by evaluating data, and that's something artificial intelligence is highly qualified to do. In a partnership between IBM Research and Symrise, a global producer of fragrances and flavors based in Germany with clients such as Estee Lauder, Donna Karan, Avon, Coty and more, the first AI-developed scent is now available for purchase in Brazil. Philyra became the artificial intelligence (AI) apprentice IBM created that perfumer David Apel worked alongside to create two new fragrances for Brazilian cosmetics company O Boticário in time for the country's Valentine's Day holiday this year. They were specifically looking for a fragrance to sell to Generation Z and millennials who they knew would be intrigued by a fragrance created by AI. This collaboration officially launched AI into the fragrance industry.

Natural language understanding(NLU) is one of the richest areas in deep learning which includes highly diverse tasks such as reaching comprehension, question-answering or machine translation. Traditionally, NLU models focus on solving only of those tasks and are useless when applied to other NLU-domains. Also, NLU models have mostly evolved as supervised learning architectures that require expensive training exercises. Recently, researchers from OpenAI challenged both assumptions in a paper that introduces a single unsupervised NLU model that is able to achieve state-of-the-art performance in many NLU tasks. The idea of using unsupervised learning for different NLU tasks has been gaining traction in the last few months.

At first glance, the Berlin startup doesn't seem so different from others: a factory floor in the rear courtyard of a building in the city's Neukölln district, stacked preserving jars filled with muesli in the kitchen, a discarded ping-pong surface repurposed as a conference table. The employees are young, relaxed and very international. The company's head and founder, Christian Kroll, is 35 years old, the same age as Mark Zuckerberg. The two men also share a quirk: To avoid wasting time in the mornings choosing an outfit, he always wears the same thing -- in his case, blank white T-shirts made from organic cotton. Zuckerberg's favorite color, by contrast, is gray.