Another ship called the Mayflower is set to make its way across the Atlantic Ocean this week, but it won't be carrying English pilgrims -- or any people -- at all. When the Mayflower Autonomous Ship leaves its home port in Plymouth, England to attempt the world's first fully autonomous transatlantic voyage, it will have a highly trained "captain" and a "navigator" versed in the rules of avoiding collisions at sea on board, both controlled by artificial intelligence (AI). The ship's AI captain was developed by Marine AI and is guided by an expert system based on IBM technologies, including automation software widely used by the financial sector. The technology could someday help crewed vessels navigate difficult situations and facilitate low-cost exploration of the oceans that cover 70 percent of the Earth's surface. Over its roughly three-week trip, the Mayflower sea drone will sail through the Isles of Scilly and over the site of the lost Titanic to land in Plymouth, Massachusetts, as the colonists on the first Mayflower did more than 400 years ago.

"If we told clinicians, 'we will use advanced math to help you improve care,' they would probably be fine with it. But the term'artificial intelligence' raises natural skepticism about what it really means." "First, do no harm" is a promise many of us make when becoming clinicians. To me it means that, for as long as I practice medicine, I must carefully weigh the risks and benefits of my decisions for patients. This principle underpins a healthy skepticism from some clinicians when learning about new approaches and technologies, such as artificial intelligence (AI), that claim to improve patient care.

Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points. A recent addition to this family, Convolutional Conditional Neural Processes (ConvCNP), have shown remarkable improvement in performance over prior art, but we find that they sometimes struggle to generalize when applied to time series data. In particular, they are not robust to distribution shifts and fail to extrapolate observed patterns into the future. By incorporating a Gaussian Process into the model, we are able to remedy this and at the same time improve performance within distribution. As an added benefit, the Gaussian Process reintroduces the possibility to sample from the model, a key feature of other members in the NP family.

Razer has partnered with marine waste cleaning startup ClearBot to advance the use of AI and robotics to reduce ocean pollution. The pair announced their partnership in celebration of World Oceans Day and is part of Razer's 10-year #GoGreenWithRazer campaign that will see the company make green investments to support environment- and sustainability-focused startups. "We are extremely happy to have the opportunity to work with a startup focused on saving the environment. ClearBot's unique AI and advanced machine learning technology will enable and empower governments and organisations around the world to broaden their sustainability efforts. We urge other innovative startups to reach out to Razer for collaboration opportunities as we strive to make the world a safer place for future generations."

Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. However, the options are much more limited when the prediction target is multivariate and a joint measure of uncertainty is required. For example, in predicting a 2D velocity vector a joint uncertainty would quantify the probability of any vector in the plane, which would be more expressive than two separate uncertainties on the x- and y- components. To enable joint probabilistic regression, we propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches. We demonstrate these claims in simulation and with a case study predicting two-dimensional oceanographic velocity data. An implementation of our method is available at https://github.com/stanfordmlgroup/ngboost.

Elon Musk is determined to reshape the world by transforming his excellent outside-the-box ideas into viable products and services. His AI inventions are providing top-notch services to the global market efficiently. Genius is always in search of new ideas using AI and its sub-fields to strive for success. Let's dig into the latest AI innovations that can transform the world into a better place. Elon Musk achieved success by implementing Neuralink in a Gyek pig known as Gertrude in August 2020.

Hello, today I'd like to explain briefly how we use artificial intelligence to count sunflower seeds in a photo taken with a mobile device. Agenda: 1. Business needs 2. Data preparation 3. Model structure 4. Used libs and tools 5. Results 6. Error analysis 7. Fails/Hypotheses 8. Conclusion 9. References Fortunately for me, I am working at Kernel. Where I am developing Computer Vision (CV) and other models to solve business problems and challenges. One of them is to count seeds on sunflower.

We propose a novel approach to the problem of semantic heterogeneity where data are organized into a set of stratified and independent representation layers, namely: conceptual (where a set of unique alinguistic identifiers are connected inside a graph codifying their meaning), language (where sets of synonyms, possibly from multiple languages, annotate concepts), knowledge (in the form of a graph where nodes are entity types and links are properties), and data (in the form of a graph of entities populating the previous knowledge graph). This allows us to state the problem of semantic heterogeneity as a problem of Representation Diversity where the different types of heterogeneity, viz. Conceptual, Language, Knowledge, and Data, are uniformly dealt within each single layer, independently from the others. In this paper we describe the proposed stratified representation of data and the process by which data are first transformed into the target representation, then suitably integrated and then, finally, presented to the user in her preferred format. The proposed framework has been evaluated in various pilot case studies and in a number of industrial data integration problems.

We assume that substances in the world are represented by two types of concepts, namely substance concepts and classification concepts, the former instrumental to (visual) perception, the latter to (language based) classification. Based on this distinction, we introduce a general methodology for building lexico-semantic hierarchies of substance concepts, where nodes are annotated with the media, e.g., videos or photos, from which substance concepts are extracted, and are associated with the corresponding classification concepts. The methodology is based on Ranganathan's original faceted approach, contextualized to the problem of classifying substance concepts. The key novelty is that the hierarchy is built exploiting the visual properties of substance concepts, while the linguistically defined properties of classification concepts are only used to describe substance concepts. The validity of the approach is exemplified by providing some highlights of an ongoing project whose goal is to build a large scale multimedia multilingual concept hierarchy.

As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.