We present sustain.AI, an intelligent, context-aware recommender system that assists auditors and financial investors as well as the general public to efficiently analyze companies' sustainability reports. The tool leverages an end-to-end trainable architecture that couples a BERT-based encoding module with a multi-label classification head to match relevant text passages from sustainability reports to their respective law regulations from the Global Reporting Initiative (GRI) standards. We evaluate our model on two novel German sustainability reporting data sets and consistently achieve a significantly higher recommendation performance compared to multiple strong baselines. Furthermore, sustain.AI is publicly available Figure 1: A screenshot of the sustain.AI recommender tool.

As AI is integrated into day-to-day lives, justifiable concerns over its fairness, power, and effects on privacy, speech, and autonomy grow. Join this VB Live event for an in-depth look at why ethical AI is essential, and how we can ensure our AI future is a just one. "AI is only biased because humans are biased. And there are lots of different types of bias and studies around that," says Daniela Braga, Founder and CEO of Defined.ai. "All of our human biases are transported into the way we build AI. So how do we work around preventing AI from having bias?"

In this work, we deal with a relatively new statistical tool in machine learning: the estimation of the ratio of two probability densities, or density ratio estimation for short. As a side piece of research that gained its own traction, we also tackle the task of parameter selection in learning algorithms based on kernel methods.

Classification on imbalanced datasets is a challenging task in real-world applications. Training conventional classification algorithms directly by minimizing classification error in this scenario can compromise model performance for minority class while optimizing performance for majority class. Traditional approaches to the imbalance problem include re-sampling and cost-sensitive methods. In this paper, we propose a neural network model with novel loss function, CRCEN, for imbalanced classification. Based on the weighted version of cross entropy loss, we provide a theoretical relation for model predicted probability, imbalance ratio and the weighting mechanism. To demonstrate the effectiveness of our proposed model, CRCEN is tested on several benchmark datasets and compared with baseline models.

This paper describes the Autonomous Architecture Over Restricted Domains project. It begins with the description of the context upon which the project is focused, and in the sequence describes the project and implementation models. It finish by presenting the environment conceptual model, showing where stand the components, inputs and facilities required to interact among the intelligent agents of the various implementations in their respective and restricted, routing domains (Autonomous Systems) which together make the Internet work.

Amazon Go, a brick-and-mortar convenience store featuring automatic checkout, opened to the public in Seattle on Monday. Today for lunch, I stopped by a newly opened convenience store at the north end of Seattle's downtown. I grabbed a tuna wrap from the refrigerated display, shoved it into my pocket and made straight for the exit as casually as I could. On my way out, a store employee stopped me. I paused for a long second.

With all the hype surrounding artificial intelligence, you would be forgiven for thinking that developing the algorithms powering deep learning are where the toughest challenges in the industry are. The actual challenge for most algorithms though is not their mathematics, but rather their inputs -- collating high-quality data that is well-labeled and allows for the training of these models as quickly and efficiently as possible. That's where DefinedCrowd comes in. The company, which is based in Seattle and Portugal, was founded in 2015 by Daniela Braga, a data scientist and natural language processing expert, and Amy Du, who has since moved on from the company to start a global entrepreneurship network. We've talked about the company back when it participated in Microsoft's startup accelerator and also when it was featured in the Battlefield at TechCrunch Disrupt New York this past year.

Part of Microsoft Accelerator's batch 3 of startups, DefinedCrowd is filling a niche in the big data and machine learning community, providing near-real-time feeds of rich language data, checked by actual well-informed humans all over the world. The need comes from the Catch-22 that often arrests deep data analysis, in that you have to understand the data to analyze it, but you must analyze it to understand it. The vast landscape of the spoken and written word and its big data counterpart in natural language processing is especially troublesome in this way. "In the artificial intelligence space, to develop virtual assistants like Cortana, or Apple's Siri and things like that, you need large amounts of voice recordings, you need transcriptions of those voices, you need intents and empathy labeling of those voices," said Daniela Braga, co-founder and chief scientist, in an interview with TechCrunch. "The crowd input provides the extra refinement of the data that basically no machine can do."