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.

On May 14, the National Oceanic and Atmospheric Administration (NOAA) ship Okeanos Explorer will depart from Port Canaveral in Florida on a two-week expedition led by NOAA Ocean Exploration, featuring the technology demonstration of an autonomous underwater vehicle. Called Orpheus, this new class of submersible robot will showcase a system that will help it find its way and identify interesting scientific features on the seafloor. Terrain-relative navigation was instrumental in helping NASA's Mars 2020 Perseverance Mars rover make its precision touch down on the Red Planet on Feb. 18. The system allowed the descending robot to visually map the Martian landscape, identify hazards, and then choose a safe place to land without human assistance. In a similar way, the agency's Ingenuity Mars Helicopter uses a vision-based navigation system to track surface features on the ground during flight in order to estimate its movements across the Martian surface.

Welcome back to the "Life in 2050" series! So far, we've looked at how ongoing developments in science, technology, and geopolitics will be reflected in terms of warfare and the economy. Today, we are shifting gears a little and looking at how the turbulence of this century will affect the way people live from day to day. As noted in the previous two installments, changes in the 21st century will be driven by two major factors. These include the disruption caused by rapidly accelerating technological progress, and the disruption caused by rising global temperatures, and the environmental impact this will have (aka. These factors will be pulling the world in opposite directions, and simultaneously at that.

The US Air Force conducted a flight test that paves the way for AI-piloted fighter jets to man the skies. The military group flew its Skyborg Autonomy Core System (ACS) for two hours and 10 minutes over Florida and Gulf of Mexico on April 29. The technology is a combination of hardware and software designed to act as a brain for a drone, allowing it to conduct operations without human interference. Fitted to a Kratos UTAP-22 tactical unmanned vehicle, the ACS demonstrated basic aviation capabilities and responded to navigational commands, while reacting to geo-fences, adhering to aircraft flight envelopes and demonstrating coordinated maneuvering. The US Air Force conducted a flight test that paves the way for AI-piloted fighter jets to man the skies.