With the growing role of artificial intelligence in climate and weather research, efficient model training and inference are in high demand. Current models like FourCastNet and AI-GOMS depend heavily on GPUs, limiting hardware independence, especially for Chinese domestic hardware and frameworks. To address this issue, we present a framework for migrating large-scale atmospheric and oceanic models from PyTorch to MindSpore and optimizing for Chinese chips, and evaluating their performance against GPUs. The framework focuses on software-hardware adaptation, memory optimization, and parallelism. Furthermore, the model's performance is evaluated across multiple metrics, including training speed, inference speed, model accuracy, and energy efficiency, with comparisons against GPU-based implementations. Experimental results demonstrate that the migration and optimization process preserves the models' original accuracy while significantly reducing system dependencies and improving operational efficiency by leveraging Chinese chips as a viable alternative for scientific computing. This work provides valuable insights and practical guidance for leveraging Chinese domestic chips and frameworks in atmospheric and oceanic AI model development, offering a pathway toward greater technological independence.

We investigate the emergent abilities of the recently proposed web-scale speech model Whisper, by adapting it to unseen tasks with prompt engineering. We selected three tasks: audio-visual speech recognition (AVSR), code-switched speech recognition (CS-ASR), and speech translation (ST) on unseen language pairs. We design task-specific prompts, by either leveraging another large-scale model, or simply manipulating the special tokens in the default prompts. Experiments show that compared to the default prompts, our proposed prompts improve performance by 10% to 45% on the three zero-shot tasks, and even outperform SotA supervised models on some datasets. In addition, our experiments reveal many interesting properties of Whisper, including its robustness to prompts, bias on accents, and the multilingual understanding in its latent space. Code is available at https://github.com/jasonppy/PromptingWhisper

Forbes Business Council Is an Invitation-Only Community for Successful Business Owners and Leaders Rakesh Soni, the founding CEO of LoginRadius, a cloud-based customer identity and access management platform (CIAM) based in San Francisco, California, has been accepted into the Forbes Business Council, the foremost growth and networking organization for successful business owners and leaders worldwide. Soni was vetted and selected by a review committee based on the depth and diversity of his executive and entrepreneurial experience. Criteria for acceptance include a track record of successfully impacting business growth metrics and personal and professional achievements and honors. "We are honored to welcome Rakesh Soni into the community," said Scott Gerber, founder of Forbes Councils, the collective that includes Forbes Business Council. "Our mission with Forbes Councils is to bring together proven leaders from every industry, creating a curated, social capital-driven network that helps every member grow professionally and make an even greater impact on the business world."

Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken language, most existing works collect code-switching data from read speech instead of spontaneous speech. ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong. We report ASCEND's design and procedure for collecting the speech data, including annotations. ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. Furthermore, we conduct baseline experiments using pre-trained wav2vec 2.0 models, achieving a best performance of 22.69\% character error rate and 27.05% mixed error rate.

Machine learning models have already mastered Chess, Go, Atari games and more, but in order for it to ascend to the next level, researchers at Facebook intend for AI to take on a different kind of game: the notoriously difficult and infinitely complex NetHack. "We wanted to construct what we think is the most accessible'grand challenge' with this game. It won't solve AI, but it will unlock pathways towards better AI," said Facebook AI Research's Edward Grefenstette. "Games are a good domain to find our assumptions about what makes machines intelligent and break them." You may not be familiar with NetHack, but it's one of the most influential games of all time.

You might be surprised by how many Americans are walking around these days with synthetic joints hidden in their pants. First performed in 1968, more than three-quarters of a million people in the US opted for knee replacement surgery in 2017 alone, according to the American Association of Orthopedic Surgeons. However, many knee replacement candidates (an estimated 300,000 in 2017) simply aren't yet ready to go under the knife -- whether that's due to health, financial, employment or other reasons -- but who could still benefit from some added support from a medical device. But rather than strap yourself into a P-5000 Powered Work Loader -- or even something slightly less extravagant like the Ekso NR -- Bay Area startup Roam Robotics has a less intensive and expensive means of getting folks with mobility issues back on their feet. It's called the Ascend and it's a sub-$10k exoskeletal knee brace for everybody.

Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due to their accessibility and interpretability. Previous works have targeted and demonstrated success on arcade, first person shooter (FPS), real-time strategy (RTS), and massive online battle arena (MOBA) games. Our work considers massively multiplayer online role-playing games (MMORPGs or MMOs), which capture several complexities of real-world learning that are not well modeled by any other game genre. We present Neural MMO, a massively multiagent game environment inspired by MMOs and discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO. We further demonstrate that standard policy gradient methods and simple baseline models can learn interesting emergent exploration and specialization behaviors in this setting.

The software is designed to meet these needs by offering businesses of all sizes access a wide range of anonymised Experian data in combination with industry-leading analytical tools and expert consultancy support, allowing users to identify the key insights that will best serve their customers and drive growth. Francesco Nazzarri, MD of Commercial Strategy for Experian EMEA, says: "To effectively compete in today's economy, businesses must be able to quickly use and understand a vast range of data assets, to move at speed with their market and consistently deliver outcomes that best serve their customers. It quickly converts vast quantities of data into smart, actionable insights by leveraging the latest analytical innovation, machine learning and artificial intelligence tools. Ascend is an important part of our suite of market-leading products, all of which will accelerate the ability of business to harness big data's full potential. Better still, Ascend can seamlessly integrate with PowerCurve, our leading decisioning and workflow software solution.

Traditional A/B testing can be biased in terms of results and limited in terms of designs available for testing. Ultimately, someone has to choose something, and if you end up choosing two ineffective sites for testing, the results will be meaningless. But not so for RateCity, which engaged an artificial intelligence (AI) solution via Sentient for a recent site testing, which resulted in a 51 per cent uplift in conversions. The solution was so successful, the home loan conversino website is applying the solution across its various verticals moving in the future. Like many businesses, RateCity could see the value in A/B testing, but didn't have the time or resources with its small team to undertake the amount of testing required in-house.

If, like most Game of Thrones fans, you're still looking for ways to fill the entertainment void until Thrones returns for its final season in 2019, then boy do we have some good news for you. Thanks to developer Nerial and game publisher Devolver Digital, fans will soon be able to sit the Iron Throne in the Tinder-inspired strategy game Reigns: Game of Thrones. In this spinoff of the original Reigns, players can rule Westeros as popular characters like Jon Snow, Daenerys Targaryen, and Cersei Lannister as they swipe left or right to make decisions that will determine their fate as the king or queen of the Seven Kingdoms. Just as is true in the show, all rulers must die. But the game allows for a variety of outcomes that could only take place in alternate Thrones timelines, such as Sansa Stark marrying Jaime Lannister.