Incorporating human expertise and domain knowledge is particularly important for medical image processing applications, marked with small datasets, and objects of interests in the form of organs or lesions not typically seen in traditional datasets. However, the incorporation of prior knowledge for breast tumor detection is challenging, since shape, boundary, curvature, intensity, or other common medical priors vary significantly across patients and cannot be employed. This work proposes an approach for integrating visual saliency into a deep learning model for breast tumor segmentation in ultrasound images. Visual saliency emphasizes regions that are more likely to attract radiologists' visual attention and stand out from its surrounding. Our approach is based on a U-Net model and employs attention blocks to introduce visual saliency. Such model forces learning feature representations that prioritize spatial regions with high levels of saliency. The approach is validated using a dataset of 510 breast ultrasound images.

Accurately learning from user data while providing quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. This paper presents a formal approach to carrying out privacy preserving text perturbation using the notion of dx-privacy designed to achieve geo-indistinguishability in location data. Our approach applies carefully calibrated noise to vector representation of words in a high dimension space as defined by word embedding models. We present a privacy proof that satisfies dx-privacy where the privacy parameter epsilon provides guarantees with respect to a distance metric defined by the word embedding space. We demonstrate how epsilon can be selected by analyzing plausible deniability statistics backed up by large scale analysis on GloVe and fastText embeddings. We conduct privacy audit experiments against 2 baseline models and utility experiments on 3 datasets to demonstrate the tradeoff between privacy and utility for varying values of epsilon on different task types. Our results demonstrate practical utility (< 2% utility loss for training binary classifiers) while providing better privacy guarantees than baseline models.

The Artificial Intelligence Robots Market report is a complete overview of the market, covering various aspects product definition, segmentation based on various parameters, and the prevailing vendor landscape. Analysis and discussion of important industry trends, market size, market share estimates are mentioned in the report. Artificial Intelligence Robots Market report includes historic data, present market trends, environment, technological innovation, upcoming technologies and the technical progress in the related industry. The Global Artificial Intelligence Robots Market accounted for USD 3.0 billion in 2017 and is projected to grow at a CAGR of 30.1% forecast to 2025. Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa and Brazil among others.

Every summer there is a mass exodus from New York City towards the white beach at Jones Beach State Park. Here, looking out over the Atlantic Ocean, you can sunbathe, catch a concert or play a game of mini-golf. And get away from the bustle of the city. But you have to get there first. And there's something odd about the route you take. The flyovers over the Southern State Parkway that leads to Jones Beach are low.

Effective coordination is crucial to solve multi-agent collaborative (MAC) problems. While centralized reinforcement learning methods can optimally solve small MAC instances, they do not scale to large problems and they fail to generalize to scenarios different from those seen during training. In this paper, we consider MAC problems with some intrinsic notion of locality (e.g., geographic proximity) such that interactions between agents and tasks are locally limited. By leveraging this property, we introduce a novel structured prediction approach to assign agents to tasks. At each step, the assignment is obtained by solving a centralized optimization problem (the inference procedure) whose objective function is parameterized by a learned scoring model. We propose different combinations of inference procedures and scoring models able to represent coordination patterns of increasing complexity. The resulting assignment policy can be efficiently learned on small problem instances and readily reused in problems with more agents and tasks (i.e., zero-shot generalization). We report experimental results on a toy search and rescue problem and on several target selection scenarios in StarCraft: Brood War, in which our model significantly outperforms strong rule-based baselines on instances with 5 times more agents and tasks than those seen during training.

In areas like the Caribbean that face considerable risk from natural hazards like earthquakes, hurricanes, and floods, these forces of nature can have a devastating effect. This is especially true where houses and buildings are not up to modern construction standards, often in poor and informal settlements. While buildings can be retrofit to better prepare them for disaster, the traditional method for identifying high-risk buildings involves going door to door by foot, taking weeks if not months and costing millions of dollars. This is where AI can help. WeRobotics and the World Bank Global Program for Resilient Housing have teamed up to prepare aerial drone imagery of buildings across the Caribbean annotated with characteristics that matter to building inspectors.

Is there a magic formula for brewing the perfect beer? If there is, then given the drink's timeless popularity, whoever finds it is likely to be very successful. It's a question that the world's largest brewer is hoping to answer with the help of artificial intelligence (AI). The Amazing Ways The Brewers of Budweiser Are Using Artificial Intelligence To Transform The Beer ... [ ] Industry AB InBev – producer of renowned brews including Budweiser, Stella Artois, and Corona - is building machine learning into key areas of its business, as it seeks to bring one of the world's oldest industries into the digital age. The company has invested in a raft of data-driven initiatives with the aim of improving everything from how it brews beer to how it manages its relationships with customers and markets its products to the public. It began its steps towards digital transformation several years ago by establishing what it refers to as its Beer Garage – a Silicon Valley-based hub of innovation, where it researches, develops, and tests technology-driven solutions.

Farmers in Colombia's maize-growing region of Córdoba had seen it all: too much rain one year, a searing drought the next. Yields were down and their livelihoods hung in the balance.

The demand for machine learning systems has soared over the past few years. This is majorly due to the success of Machine Learning techniques in a wide range of applications. AutoML is fundamentally changing the face of ML-based solutions today by enabling people from diverse backgrounds to use machine learning models to address complex scenarios. However, even with a clear indication that machine learning can provide a boost to certain businesses, a lot of companies today struggle to deploy ML models. This is because there is a shortage of experienced and seasoned data scientists in the industry.

At its core, technology exists to make our lives easier. Thanks to artificial intelligence, our tools have gotten smarter, and we're more productive as a result. According to a study released earlier today, workers around the world not only recognize AI's importance in the modern workplace – they embrace it. Conducted over the summer in partnership between Oracle and Future Workspace, the second annual AI at Work study asked 8,370 employees, managers and HR leaders from 10 countries about AI and its place in their work. Researchers found that AI is rapidly changing not only how we conduct business, but the very relationship between people and the tech they use every day.