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.

Artificial neural networks (ANNs) have demonstrated remarkable utility in a variety of challenging machine learning applications. However, their complex architecture makes asserting any formal guarantees about their behavior difficult. Existing approaches to this problem typically consider verification as a post facto white-box process, one that reasons about the safety of an existing network through exploration of its internal structure, rather than via a methodology that ensures the network is correct-by-construction. In this paper, we present a novel learning framework that takes an important first step towards realizing such a methodology. Our technique enables the construction of provably correct networks with respect to a broad class of safety properties, a capability that goes well-beyond existing approaches. Overcoming the challenge of general safety property enforcement within the network training process in a supervised learning pipeline, however, requires a fundamental shift in how we architect and build ANNs. Our key insight is that we can integrate an optimization-based abstraction refinement loop into the learning process that iteratively splits the input space from which training data is drawn, based on the efficacy with which such a partition enables safety verification. To do so, our approach enables training to take place over an abstraction of a concrete network that operates over dynamically constructed partitions of the input space. We provide theoretical results that show that classical gradient descent methods used to optimize these networks can be seamlessly adopted to this framework to ensure soundness of our approach. Moreover, we empirically demonstrate that realizing soundness does not come at the price of accuracy, giving us a meaningful pathway for building both precise and correct networks.

Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. However, none of these methods outperforms the others for all problems. This motivates the development of automatic approachesto recommend the most suitable multi-target regression method. In this paper, we propose a meta-learning system to recommend the best predictive method for a given multi-target regression problem. We performed experiments with a meta-dataset generated by a total of 648 synthetic datasets. These datasets were created to explore distinct inter-targets characteristics toward recommending the most promising method. In experiments, we evaluated four different algorithms with different biases as meta-learners. Our meta-dataset is composed of 58 meta-features, based on: statistical information, correlation characteristics, linear landmarking, from the distribution and smoothness of the data, and has four different meta-labels. Results showed that induced meta-models were able to recommend the best methodfor different base level datasets with a balanced accuracy superior to 70% using a Random Forest meta-model, which statistically outperformed the meta-learning baselines.

This article was written by Kate Crawford & Vladan Joler. Below is an extract, featuring the first three sections of this long article (21 sections total.) Link to the full article is provided at the bottom. A cylinder sits in a room. It is impassive, smooth, simple and small.

We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.

Transportation systems can be conceptualized as an instrument of spreading people and resources over the territory, playing an important role in developing sustainable cities. The current rationale of transport provision is based on population demand, disregarding land use and socioeconomic information. To meet the challenge to promote a more equitable resource distribution, this work aims at identifying and describing patterns of urban services supply, their accessibility, and household income. By using a multidimensional approach, the spatial inequalities of a large city of the global south reveal that the low-income population has low access mainly to hospitals and cultural centers. A low-income group presents an intermediate level of accessibility to public schools and sports centers, evidencing the diverse condition of citizens in the peripheries. These complex outcomes generated by the interaction of land use and public transportation emphasize the importance of comprehensive methodological approaches to support decisions of urban projects, plans and programs. Reducing spatial inequalities, especially providing services for deprived groups, is fundamental to promote the sustainable use of resources and optimize the daily commuting.

Global Artificial Intelligence (AI) in Agriculture Market 2019 by Company, Regions, Type and Application, Forecast to 2024 presents a detailed competitive outlook and systematic framework of Artificial Intelligence (AI) in Agriculture market at a global uniform platform. The report begins with the market summary, chain structure, past and present market size in conjunction with business opportunities in coming back years, demand and lack, various drivers and restrainers. The research study exhibits the historical data that analyzes respective analytical tools including porters five forces analysis, supply chain analysis, pricing analysis, and regulatory analysis. It offers a detailed analysis of top-line vendors along with revenue and cost profit analysis. The research covers a crucial market segmentation analysis that is a rich source of all essential segments including Artificial Intelligence (AI) in Agriculture types, applications, technologies, end-users, and regions.