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.

Machine learning has the potential to support aspects of teaching and learning that are currently time consuming and difficult to manage, such as individual project work, collaboration, tutorials and self-directed learning. In 2018, the global Machine Learning in Education market size was xx million US$ and it is expected to reach xx million US$ by the end of 2025, with a CAGR of xx% during 2019-2025. This report focuses on the global Machine Learning in Education status, future forecast, growth opportunity, key market and key players. The study objectives are to present the Machine Learning in Education development in United States, Europe and China. The key players covered in this study IBM Microsoft Google Amazon Cognizan Pearson Bridge-U DreamBox Learning Fishtree Jellynote Quantum Adaptive Learning Market segment by Type, the product can be split into Cloud-Based On-Premise Market segment by Application, split into Intelligent Tutoring Systems Virtual Facilitators Content Delivery Systems Interactive Websites Others Market segment by Regions/Countries, this report covers United States Europe China Japan Southeast Asia India Central & South America The study objectives of this report are: To analyze global Machine Learning in Education status, future forecast, growth opportunity, key market and key players.

Workers place more trust in robots and AI than their managers according to the second annual AI at Work study conducted by Oracle and Future Workplace. To compile the study, the two firms surveyed 8,370 employees, managers and HR leaders across 10 countries to find that AI has changed the relationship between people and technology in the workplace and is reshaping the role HR teams and managers need to play when it comes to attracting, retaining and developing talent. In contrast to common fears that AI and robots will take workers jobs, the AI at Work study found that employees, managers and HR leaders across the globe are reporting increased adoption of AI in the workplace and many are welcoming the emerging technology with enthusiasm. AI is becoming more prominent in workplaces with 50 percent of workers currently using some form of AI at work compared to only 32 percent last year. Workers in China (77%) and India (78%) have adopted AI over two times more than those in France (32%) and Japan (29%).

The first food product invented by an artificial intelligence (AI) system can already be obtained in Argentina. And with this specific meal, AI shows that it can already revolutionize a productive item that, until now, was conservative: the food industry. Dictating an email to the smartphone, driverless cars or combat drones that choose a target and fire without a commander's order are already common. The novelty is that, in laboratories and companies, computational techniques of Machine Learning and Big Data are already used to create new -and recreate old- foods using absolutely novel ingredients. For example, a Chilean startup has just presented in the local market a mayonnaise that has the same taste, smell, color and texture as the traditional one.