Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF; EC for short) can provide a basis for the establishment of maritime-disaster warning systems, but they contain some systematic biases.The fifth-generation EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by about 5 days. To overcome this issue, a spatiotemporal deep-learning method could be used for nonlinear mapping between EC and ERA5 data, which would improve the quality of EC wind forecast data in real time. In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved double-encoder forecaster architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model. The study area was the western North Pacific (WNP), and real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021, divided into four seasons. Compared with the original EC forecasts, after correction using the MT-DETrajGRU model the wind speed and wind direction biases in the four seasons were reduced by 8-11% and 9-14%, respectively. In addition, the proposed method modelled the data uniformly under different weather conditions. The correction performance under normal and typhoon conditions was comparable, indicating that the data-driven mode constructed here is robust and generalizable.

Perched on the edge of a curb, squinting down the length of Market Street in the US city of San Francisco, I find myself twitchily tapping my phone, only to wince as the time ticks away. And up the Market Street rail line rumbles my deliverance: a 1928 wood-panelled tram in green-and-white trim. Oh, the irony: I'm headed to test out one of the city's newest transportation options while creaking down the road on one of its oldest. San Francisco has long been a hub for transportation innovation. It was here that the first cable car system was put into use.

The driver of a 2021 Tesla Model S told California authorities the vehicle was in "full self-driving mode" when the technology malfunctioned, causing an eight-vehicle crash on the San Francisco Bay bridge last month. The crash on Thanksgiving Day resulted in two juveniles being transported to hospital and led to lengthy delays on the bridge. The incident was made public in a police report on Wednesday. It is the latest in a series of accidents blamed on Tesla technology. The electric automaker's chief executive, Elon Musk, has heavily promoted "Full Self-Driving" (FSD) software, sold as $15,000 add-on to Tesla vehicles, but it faces legal, regulatory and public scrutiny.

Music discovery services let users identify songs from short mobile recordings. These solutions are often based on Audio Fingerprinting, and rely more specifically on the extraction of spectral peaks in order to be robust to a number of distortions. Few works have been done to study the robustness of these algorithms to background noise captured in real environments. In particular, AFP systems still struggle when the signal to noise ratio is low, i.e when the background noise is strong. In this project, we tackle this problematic with Deep Learning. We test a new hybrid strategy which consists of inserting a denoising DL model in front of a peak-based AFP algorithm. We simulate noisy music recordings using a realistic data augmentation pipeline, and train a DL model to denoise them. The denoising model limits the impact of background noise on the AFP system's extracted peaks, improving its robustness to noise. We further propose a novel loss function to adapt the DL model to the considered AFP system, increasing its precision in terms of retrieved spectral peaks. To the best of our knowledge, this hybrid strategy has not been tested before.

Dec 15 (Reuters) - ChatGPT, the new chatbot that is the talk of Silicon Valley, can spit out haikus, crack jokes in Italian and may soon be the scourge of teachers everywhere facing fake essays generated by the AI-powered technology. But a question it can't fully answer is this: How will OpenAI make money? The research organization, co-founded by Elon Musk and investor Sam Altman and backed by $1 billion in funding from Microsoft Corp (MSFT.O), is expecting its business to surge. Three sources briefed on OpenAI's recent pitch to investors said the organization expects $200 million in revenue next year and $1 billion by 2024. The forecast, first reported by Reuters, represents how some in Silicon Valley are betting the underlying technology will go far beyond splashy and sometimes flawed public demos.

A look at the venture capitalists who are currently investing in AI (artificial intelligence) firms. Although the idea of robots used to be somewhat unsettling, a lot of money is currently being invested in the architecture and systems that enable machines to learn and grow on their own without the assistance of humans. According to PwC's 2018 Moneytree Report, which was just released, $9.3 billion was invested in AI firms in 2017. This enormous number demonstrates the growing interest in technology and the growing understanding of its potential among public and commercial sector donors. Artificial intelligence crosses all business areas, from self-driving vehicle AI operating systems to self-learning language processing platforms. Venture capital companies are hopping on the bandwagon to finance the minds behind the initiatives since they have the potential to disrupt so many industries, putting them ahead of the curve. The most well-known venture capital firms investing in AI technology are listed below, with an overview of their goals.

Ammaar Reshi was playing around with ChatGPT, an AI-powered chatbot from OpenAI when he started thinking about the ways artificial intelligence could be used to make a simple children's book to give to his friends. Just a couple of days later, he published a 12-page picture book, printed it, and started selling it on Amazon without ever picking up a pen and paper. The feat, which Reshi publicized in a viral Twitter thread, is a testament to the incredible advances in AI-powered tools like ChatGPT--which took the internet by storm two weeks ago with its uncanny ability to mimic human thought and writing. But the book, Alice and Sparkle, also renewed a fierce debate about the ethics of AI-generated art. Many argued that the technology preys on artists and other creatives--using their hard work as source material, while raising the specter of replacing them.

SAN FRANCISCO, Dec 13 (Reuters) - Alphabet Inc's (GOOGL.O) Waymo has applied for the final permit it needs in California before it can sell fully autonomous rides, the company told Reuters on Tuesday. A decision on its application, which was submitted Monday to the California Public Utilities Commission, could take months. General Motors Co's (GM.N) Cruise is the only company with the permit so far and has charged for driverless rides in San Francisco since June. The two rivals are frontrunners in the slow-moving effort to demonstrate that autonomous transport can become a widely available and profitable service, with San Francisco's hills, weather and clogged roads making it a key proving ground. GM plans to expand to more cities next year.

As research into artificial intelligence (AI) continues its march forward, computers are becoming more and more human-like all the time. Making headlines of late has been the new ChatGPT, developed by OpenAI - an artificial intelligence research and deployment company that says its mission is "to ensure that artificial general intelligence benefits all of humanity." OpenAI already took the world by storm with its DALL-E project, which, using AI, created new images based on human input, such as: "show me an astronaut riding a horse." But now, ChatGPT is moving into the text-based world of AI, allowing users to carry on human-like conversations but with a (mostly) know-it-all computer that is ever-learning. Of course, we're all weather geeks here at FOX Weather, so I had to test its meteorological chops.

We are searching for a Backend Software Engineer for Machine Learning to help us develop AI-based autonomous industrial robotic solutions and integrate them with our customers' complex software management environments. As a Backend Software Engineer for Machine Learning, you will design, develop, manage, and deploy the critical infrastructure needed to support a range of industrial automation applications. In this role, you will primarily pair with the Machine Learning team to handle creating an efficient software platform and data pipeline that supports our core backend systems. At OSARO we develop solutions to endow industrial robots with the level of autonomy needed to perform an unprecedented variety of complex pick and place tasks leveraging sophisticated robot control and neural network-based perception algorithms. We value candidates who are passionate about what they build, feel a strong sense of ownership over their work, and love being continually challenged.