Waymo recently recalled 1,212 of its self-driving taxis, according to the Alphabet-owned company. The recalled cars, which comprised the entirety of the company's fleet at the time, received a software update in November designed to significantly decrease the likelihood that Waymos would collide with stationary objects. Last May, the Department of Transportation's National Highway Traffic Safety Administration (NHTSA) opened an investigation into Waymo for 22 reported incidents in which its AVs collided with objects like gates, chains, and parked vehicles. The cars also appeared to disobey traffic safety control devices. The accidents occurred at low speeds and didn't result in injuries.

Event cameras, offering high temporal resolution and high dynamic range, have brought a new perspective to addressing 3D reconstruction challenges in fast-motion and low-light scenarios. Most methods use the Neural Radiance Field (NeRF) for event-based photorealistic 3D reconstruction. However, these NeRF methods suffer from time-consuming training and inference, as well as limited scene-editing capabilities of implicit representations. To address these problems, we propose Event-3DGS, the first event-based reconstruction using 3D Gaussian splatting (3DGS) for synthesizing novel views freely from event streams. Technically, we first propose an event-based 3DGS framework that directly processes event data and reconstructs 3D scenes by simultaneously optimizing scenario and sensor parameters.

President Donald Trump has hailed deepening ties between the United States and the United Arab Emirates and said that the latter will invest 1.4 trillion in the former's artificial intelligence sector over the next decade. "I have absolutely no doubt that the relationship will only get bigger and better," Trump said on Thursday at a meeting with UAE President Sheikh Mohamed bin Zayed Al Nahyan, on the final leg of his three-country tour of the Gulf region that saw him strike a series of lucrative tech, business and military deals that he said amounted to 10 trillion. Sheikh Mohammed said the UAE remained "committed to working with the United States to advance peace and stability in our region and globally". The deal with UAE is expected to enable the Gulf country to build data centres vital to developing artificial intelligence models. The countries did not say which AI chips could be included in UAE data centres.

Over the course of a three-day trip to the Middle East, President Trump and his emissaries from Silicon Valley have transformed the Persian Gulf from an artificial-intelligence neophyte into an A.I. power broker. They have reached an enormous deal with the United Arab Emirates to deliver hundreds of thousands of today's most advanced chips from Nvidia annually to build one of the world's largest data center hubs in the region, three people familiar with the talks said. The shipments would begin this year, and include roughly 100,000 chips for G42, an Emirati A.I. firm, with the rest going to U.S. cloud service providers. The administration revealed the agreement on Thursday in an announcement unveiling a new A.I. campus in Abu Dhabi supported by 5 gigawatts of electrical power. It would the largest such project outside of the United States and help U.S. companies serve customers in Africa, Europe and Asia, the administration said.

The consequential report contained bad news for AI companies hoping to claim the fair use legal doctrine as a defense in court. And on May 12, the plaintiffs in Kadrey v Meta, which includes artists and authors such as Junot Diaz, Sarah Silverman, and Ta-Nehisi Coates, submitted the report as an exhibit in their class action lawsuit. The report addressed in detail the four factors of the fair use doctrine. The lengthy 113-page report spends around 50 pages delving into the nuances of fair use, citing historic legal cases that ruled for and against fair use. In general, President Trump has taken a pro-tech approach to AI regulation.

Donald Trump's jaunt to the Middle East featured an entourage of billionaire tech bros, a fighter-jet escort, and business deals designed to reshape the global landscape of artificial intelligence. On the final stop of the tour in Abu Dhabi, the US President announced that unnamed US companies would partner with the United Arab Emirates to create the largest AI datacenter cluster outside of America. Trump said that the US companies will help G42, an Emirati company, build five gigawatts of AI computing capacity in the UAE. Sheikh Tahnoon bin Zayed Al Nahyan, who leads the UAE's Artificial Intelligence and Advanced Technology Council, and is in charge of a 1.5 trillion fortune aimed at building AI capabilities, said the move will strengthen the UAE's position "as a hub for cutting-edge research and sustainable development, delivering transformative benefits for humanity." A few days earlier, as Trump arrived in Riyadh, Saudi Arabia announced Humain, an AI investment firm owned by the kingdom's Public Investment Fund.

In the summer of 2023, Ilya Sutskever, a co-founder and the chief scientist of OpenAI, was meeting with a group of new researchers at the company. By all traditional metrics, Sutskever should have felt invincible: He was the brain behind the large language models that helped build ChatGPT, then the fastest-growing app in history; his company's valuation had skyrocketed; and OpenAI was the unrivaled leader of the industry believed to power the future of Silicon Valley. But the chief scientist seemed to be at war with himself. Sutskever had long believed that artificial general intelligence, or AGI, was inevitable--now, as things accelerated in the generative-AI industry, he believed AGI's arrival was imminent, according to Geoff Hinton, an AI pioneer who was his Ph.D. adviser and mentor, and another person familiar with Sutskever's thinking. To people around him, Sutskever seemed consumed by thoughts of this impending civilizational transformation. What would the world look like when a supreme AGI emerged and surpassed humanity? And what responsibility did OpenAI have to ensure an end state of extraordinary prosperity, not extraordinary suffering?

Representation learning based on multi-task pretraining has become a powerful approach in many domains. In particular, task-aware representation learning aims to learn an optimal representation for a specific target task by sampling data from a set of source tasks, while task-agnostic representation learning seeks to learn a universal representation for a class of tasks. In this paper, we propose a general and versatile algorithmic and theoretic framework for \emph{active representation learning}, where the learner optimally chooses which source tasks to sample from. This framework, along with a tractable meta algorithm, allows most arbitrary target and source task spaces (from discrete to continuous), covers both task-aware and task-agnostic settings, and is compatible with deep representation learning practices. We provide several instantiations under this framework, from bilinear and feature-based nonlinear to general nonlinear cases.

The promising zero-shot generalization of vision-language models such as CLIP has led to their adoption using prompt learning for numerous downstream tasks. Previous works have shown test-time prompt tuning using entropy minimization to adapt text prompts for unseen domains. While effective, this overlooks the key cause for performance degradation to unseen domains -- distribution shift. In this work, we explicitly handle this problem by aligning the out-of-distribution (OOD) test sample statistics to those of the source data using prompt tuning. We use a single test sample to adapt multi-modal prompts at test time by minimizing the feature distribution shift to bridge the gap in the test domain.