Approaches based on Koopman operators have shown great promise in forecasting time series data generated by complex nonlinear dynamical systems (NLDS). Although such approaches are able to capture the latent state representation of a NLDS, they still face difficulty in long term forecasting when applied to real world data. Specifically many real-world NLDS exhibit time-varying behavior, leading to nonstationarity that is hard to capture with such models. Furthermore they lack a systematic data-driven approach to perform data assimilation, that is, exploiting noisy measurements on the fly in the forecasting task. To alleviate the above issues, we propose a Koopman operator-based approach (named KODA - Koopman Operator with Data Assimilation) that integrates forecasting and data assimilation in NLDS. In particular we use a Fourier domain filter to disentangle the data into a physical component whose dynamics can be accurately represented by a Koopman operator, and residual dynamics that represents the local or time varying behavior that are captured by a flexible and learnable recursive model. We carefully design an architecture and training criterion that ensures this decomposition lead to stable and long-term forecasts. Moreover, we introduce a course correction strategy to perform data assimilation with new measurements at inference time. The proposed approach is completely data-driven and can be learned end-to-end. Through extensive experimental comparisons we show that KODA outperforms existing state of the art methods on multiple time series benchmarks such as electricity, temperature, weather, lorenz 63 and duffing oscillator demonstrating its superior performance and efficacy along the three tasks a) forecasting, b) data assimilation and c) state prediction.

Rather than subject us to useless grilles on its EVs, Škoda is experimenting with color-coded warnings that show when it's safe or dangerous to use the crosswalk. The Czech carmaker has built a crude prototype for now that hardly seems as cool as the renders, but the company says the device could be integrated on the Škoda Enyaq iV within a couple of years. That EV is built on the same platform as the Volkswagen ID.4, and while the VW doesn't suffer from a big grille, the technology would nonetheless be useful on the German EV, which is sold in the U.S. where pedestrian injuries and deaths are increasing at an alarming rate. The robot is known as the IPA2X, and it was designed to help kids, seniors, and people with disabilities cross roads safely. The 6.5-foot robot will be tall enough to look over rows of parked cars to detect oncoming traffic, and will be able to "talk" with modern cars, alerting drivers to the presence of pedestrians.

"We are finally seeing an inflection point in the industry", says Whipsaw CEO and Principal Designer, Dan Harden as he talks about how robots are slowly entering our households. Back at the beginning of the 2000s, the only robots you could find around the house were probably either toys (RC cars, RoboSapiens), or domestic cleaning robots like the vacuum cleaner or the lawn-mower. Today, home service robots are increasingly becoming an emerging trend, creating a unique new opportunity for designers to establish the identity, personality, form, function, and usability factors of these soon-to-emerge home service robots. "It is one of the most exciting design frontiers since the very founding of our profession", Harden tells Yanko Design. The west has been rather slow in adopting robots in domestic settings (something I often attribute to films like Terminator, iRobot, or Transformers, which haven't really made robots look too friendly), while countries in the east like Japan and China (who haven't been inherently exposed to'evil robots') have traditionally been much more accepting robots in their domestic lives.

KODA advising CTO John Suit discusses the skills and languages that are important for developers who want to build software and systems for modern robots.

AI today is fairly centralized and is limited to the ownership of a single entity, such as Facebook or Google. This presents a unique set of challenges that don't actually further additional advancements for the betterment of society. More importantly, there's no collaboration when things are centralized. The future of artificial intelligence (AI) will be determined by how much weight we put into collaboration. Fundamentally, collaboration relies on a group of people (or machines) who share their individual knowledge to solve a problem.

If you can't adopt a real dog, why not opt for this robot dog from Koda that uses artificial intelligence? Man's best friend has always been the domesticated dog, but mutts around the world could end up with some serious competition in the form of Koda's AI-powered robot dog. Unlike other robot dogs on the market, the Koda artificial intelligence dog is meant to interact socially with its human owners. The robot's AI helps it sense when its owner is sad, happy or excited so it can, over time, respond in an appropriate manner to human emotions. Get the latest science stories from CNET every week.

KODA, the artificial intelligence (AI) robotic dog company dedicated to helping people live better lives with robotic companions, announced today the pre-launch of its first model, the KODA.Unlike other robot dogs on the market, KODA is designed to be functional from pragmatic and emotional perspectives. KODA is a social robot. This is in part why KODA gave it a head. When a KODA cocks its ear to its owner's voice and runs over to be close, the consumer will know it's because KODA heard and understood them. KODA's blockchain-enabled decentralized AI infrastructure allows the robot dog to serve a multitude of purposes.

Founded in 2014 as a technology transfer venture company for the National Institute of Advanced Industrial Science and Technology, the Tsukuba, Ibaraki Prefecture-based Miraisens Inc. says its haptic technology would make it possible to get a realistic sensation of touch through subtle changes of vibration patterns traveling through the fingertips. Miraisens founder and Chief Technology Officer Norio Nakamura, the developer of the technology, says the important thing is "how we trick our brain, which means what stimulus patterns should be given." Similar devices using haptic technology, which creates the sense of touch through force, vibrations or motion, have been developed by other companies to achieve just that, but they are generally expensive and inconvenient. Miraisens says its technology, dubbed 3D Haptics, can eliminate these worries as it is lightweight and small enough to be embedded in a game controller or TV remote at an affordable price. Miraisens Chief Executive Officer Natsuo Koda notes that haptics itself is not a novel technology; it has already been used in the flat and stationary home button on some iPhone models, Koda explains, which provides the clicking sensation when we press the button. But the firm's own technology can re-create a variety of sensations of touch in a 3D world, rather than on a surface -- a technology Koda says is "the world's first."

While the Web of data is attracting increasing interest and rapidly growing in size, the major support of information on the surface Web are still multimedia documents. Semantic annotation of texts is one of the main processes that are intended to facilitate meaning-based information exchange between computational agents. However, such annotation faces several challenges such as the heterogeneity of natural language expressions, the heterogeneity of documents structure and context dependencies. While a broad range of annotation approaches rely mainly or partly on the target textual context to disambiguate the extracted entities, in this paper we present an approach that relies mainly on formalized-knowledge expressed in RDF datasets to categorize and disambiguate noun phrases. In the proposed method, we represent the reference knowledge bases as co-occurrence matrices and the disambiguation problem as a 0-1 Integer Linear Programming (ILP) problem. The proposed approach is unsupervised and can be ported to any RDF knowledge base. The system implementing this approach, called KODA, shows very promising results w.r.t. state-of-the-art annotation tools in cross-domain experimentations.