Opera announced today that its Aria AI assistant has made its way to iOS. The feature launched on desktop in June and stems from a partnership with ChatGPT creator OpenAI. Opera says Aria, now available on all major desktop and mobile platforms, has tallied over a million users on desktop and Android. Like Microsoft's Bing Copilot and Google's Search Generative Experience, Aria can answer questions and respond to context around active web pages. "As an expert in both web navigation and browser functions, Aria facilitates AI collaboration in tasks such as information retrieval, text or code generation, and product inquiries," Opera's Kseniia Sycheva wrote in the company's announcement post today.

Ford announced today that it's expanding the availability of its BlueCruise hands-free driving tech. Previously, customers had to decide whether to add the option on available models at purchase -- and that decision was final. Now, the service will be installed as standard on all supported vehicles. In addition to enabling it at purchase, owners can add the service later or only activate it for months when needed (like for road trips). You'll still have the option of buying the feature at purchase and folding it into your financing.

CEO says OpenAI CEO Sam Altman said language and cultural inclusivity is "very important" to his company's mission as it builds and trains powerful artificial intelligence systems. OpenAI has launched web crawler GPTBot to improve artificial intelligence models. "Web pages crawled with the GPTBot user agent may potentially be used to improve future models and are filtered to remove sources that require paywall access, are known to gather personally identifiable information (PII) or have text that violates our policies," the company said in a post on its website. "Allowing GPTBot to access your site can help AI models become more accurate and improve their general capabilities and safety," OpenAI wrote. A web crawler is a type of bot.

When Major League Soccer (MLS) announced plans to deploy AI-powered tools in its recruiting program starting at the tail end of this year, the eyebrows of skeptics were raised. The MLS will be working with London-based startup ai.io, and its'aiScout' app to help the league discover amateur players around the world. This unprecedented collaboration is the first time the MLS will use artificial intelligence in its previously gatekept recruiting program, forcing many soccer enthusiasts and AI fans to reckon with the question: has artificial intelligence finally entered the mainstream in the professional soccer industry? There is no doubt that professional sports have been primed for the potential impact of artificial intelligence. Innovations have the potential to transform the way we consume and analyze games from both an administrative and fan standpoint.

A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory forecasting of the detected objects, or predict dense occupancy and flow grids for the whole scene. The former poses a safety concern as the number of detections needs to be kept low for efficiency reasons, sacrificing object recall. The latter is computationally expensive due to the high-dimensionality of the output grid, and suffers from the limited receptive field inherent to fully convolutional networks. Furthermore, both approaches employ many computational resources predicting areas or objects that might never be queried by the motion planner. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network. Our method avoids unnecessary computation, as it can be directly queried by the motion planner at continuous spatio-temporal locations. Moreover, we design an architecture that overcomes the limited receptive field of previous explicit occupancy prediction methods by adding an efficient yet effective global attention mechanism. Through extensive experiments in both urban and highway settings, we demonstrate that our implicit model outperforms the current state-of-the-art. For more information, visit the project website: https://waabi.ai/research/implicito.

Japanese education services company Benesse will offer a new service to help elementary school students with their research projects using generative artificial intelligence during the summer break. The service, which will be provided for free on its website for parents, will make suggestions and offer tips to help students search research themes and compile their findings, the company said in a recent press release. For example, if one asks "How can I study the biology of dinosaurs?" the AI would give such advice as "How about finding out what they ate?" without giving exact answers, the company said. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Dukaan CEO and founder Suumit Shah explains why he laid of 90% of his customer support staff after saying AI outperformed them. The real estate market might feel the shifts and impacts of artificial intelligence (AI). But when it comes to making perhaps the most significant purchase of one's life, homebuyers will want that human touch, experts told Fox News Digital. "I don't think it's going to replace how we necessarily do business and automate it, but I think it's going to enhance the amount of data that we have available," Pierre Debbas, co-managing partner of Rommer Debbas LLP, said. He argued it will allow Realtors to "provide the consumer with more accurate information and maybe a broader scope of data in making their decisions when buying a home." AI has already reshaped a number of industries, including the very industry that created it, leading to rapidly decreasing work opportunities and doomsday prophecies of no more tech jobs in five years.

Ben Goertzel said the sky's'not even the limit' when it comes to the potential impact of artificial general intelligence. A panel of robots told reporters in Switzerland Friday that they could be more efficient leaders than human beings, among other statements. The nine artificial intelligence-enabled humanoid social robots also explained at a Geneva conference center that they wouldn't take anyone's jobs or stage a rebellion. Conference organizers at the United Nations-driven AI for Good Global Summit did not specify to what extent their responses were scripted or programmed. Some of the robots are capable of producing preprogrammed responses and the United Nations Development Program's first robot innovation ambassador, Sophia, sometimes relies on responses scripted by a team of writers at Hanson Robotics.

High-quality datasets can speed up breakthroughs and reveal potential developing directions in SLAM research. To support the research on corner cases of visual SLAM systems, this paper presents Ground-Challenge: a challenging dataset comprising 36 trajectories with diverse corner cases such as aggressive motion, severe occlusion, changing illumination, few textures, pure rotation, motion blur, wheel suspension, etc. The dataset was collected by a ground robot with multiple sensors including an RGB-D camera, an inertial measurement unit (IMU), a wheel odometer and a 3D LiDAR. All of these sensors were well-calibrated and synchronized, and their data were recorded simultaneously. To evaluate the performance of cutting-edge SLAM systems, we tested them on our dataset and demonstrated that these systems are prone to drift and fail on specific sequences. We will release the full dataset and relevant materials upon paper publication to benefit the research community. For more information, visit our project website at https://github.com/sjtuyinjie/Ground-Challenge.

Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment and allowing training to be scaled both safely and cheaply. However, there is a lack of understanding of how to build effective training benchmarks for closed-loop training. In this work, we present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents, such as how to design traffic scenarios and scale training environments. Furthermore, we show that many popular RL algorithms cannot achieve satisfactory performance in the context of autonomous driving, as they lack long-term planning and take an extremely long time to train. To address these issues, we propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead and exploits cheaply generated imagined data for efficient learning. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines. For more information, visit the project website: https://waabi.ai/research/travl