Over the past years, Machine Learning-as-a-Service (MLaaS) has received a surging demand for supporting Machine Learning-driven services to offer revolutionized user experience across diverse application areas. MLaaS provides inference service with low inference latency based on an ML model trained using a dataset collected from numerous individual data owners. Recently, for the sake of data owners' privacy and to comply with the "right to be forgotten (RTBF)" as enacted by data protection legislation, many machine unlearning methods have been proposed to remove data owners' data from trained models upon their unlearning requests. However, despite their promising efficiency, almost all existing machine unlearning methods handle unlearning requests independently from inference requests, which unfortunately introduces a new security issue of inference service obsolescence and a privacy vulnerability of undesirable exposure for machine unlearning in MLaaS. In this paper, we propose the ERASER framework for machinE unleaRning in MLaAS via an inferencE seRving-aware approach. ERASER strategically choose appropriate unlearning execution timing to address the inference service obsolescence issue. A novel inference consistency certification mechanism is proposed to avoid the violation of RTBF principle caused by postponed unlearning executions, thereby mitigating the undesirable exposure vulnerability. ERASER offers three groups of design choices to allow for tailor-made variants that best suit the specific environments and preferences of various MLaaS systems. Extensive empirical evaluations across various settings confirm ERASER's effectiveness, e.g., it can effectively save up to 99% of inference latency and 31% of computation overhead over the inference-oblivion baseline.

Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more efficient and accurate, the importance of unlearning previously learned information has become increasingly significant in fields such as privacy, security, and fairness. This paper presents a comprehensive survey of MU, covering current state-of-the-art techniques and approaches, including data deletion, perturbation, and model updates. In addition, commonly used metrics and datasets are also presented. The paper also highlights the challenges that need to be addressed, including attack sophistication, standardization, transferability, interpretability, training data, and resource constraints. The contributions of this paper include discussions about the potential benefits of MU and its future directions. Additionally, the paper emphasizes the need for researchers and practitioners to continue exploring and refining unlearning techniques to ensure that ML models can adapt to changing circumstances while maintaining user trust. The importance of unlearning is further highlighted in making Artificial Intelligence (AI) more trustworthy and transparent, especially with the increasing importance of AI in various domains that involve large amounts of personal user data.

Alphabet stock slid more than 5% in after-hours trading Tuesday despite narrowly beating overall revenue predictions for quarter four of 2023 after the tech giant fell short in its key advertising sector. The Google parent company reported a miss on predicted advertising revenue at 65.52bn compared to 65.8bn, but beat predictions for overall revenue at 86.31bn compared to 85.36bn – up 13% year over year. Referencing the overall revenue beat, Alphabet chief financial officer called the results "very strong". "We remain committed to our work to durably re-engineer our cost base as we invest to support our growth opportunities," she said. The lukewarm response to the report comes after the Google parent company laid off 1,000 employees in January, according to the Alphabet Workers Union.

A Microsoft manager claims OpenAI's DALL-E 3 has security vulnerabilities that could allow users to generate violent or explicit images (similar to those that recently targeted Taylor Swift). GeekWire reported Tuesday the company's legal team blocked Microsoft engineering leader Shane Jones' attempts to alert the public about the exploit. The self-described whistleblower is now taking his message to Capitol Hill. "I reached the conclusion that DALL·E 3 posed a public safety risk and should be removed from public use until OpenAI could address the risks associated with this model," Jones wrote to US Senators Patty Murray (D-WA) and Maria Cantwell (D-WA), Rep. Adam Smith (D-WA 9th District), and Washington state Attorney General Bob Ferguson (D). GeekWire published Jones' full letter. Jones claims he discovered an exploit allowing him to bypass DALL-E 3's security guardrails in early December.

Fury has erupted over deepfake porn images of Taylor Swift that have circulated widely on social media in recent days - but the problem is also traumatising scores of real life women in the UK. New AI technology has made it easier for misogynistic trolls to steal photos of real women before transplanting some of their features - such as their face - onto pornographic footage before sharing it online without their consent. The boom in deepfake porn is widely recognised as a growing problem, but slow progress in formulating new laws to tackle it means victims are often left without any legal recourse. Researcher Kate Isaacs was scrolling through X when a video popped up on her notifications. When she clicked play, she realised the footage showed a woman in the middle of a sex act with her face superimposed onto the woman's body.

Fox News host Greg Gutfeld goes over this weeks leftovers and Gutfeld! reacts to the resurfacing of an old training video on DEI by former Navy DEI director Dr. Charles Chuck Barber. A city agency in Austin, Texas invited employees to racially segregated "anti-racist" meetings where "white folks" were asked not to attend a meeting that was only for "people of color." A January email obtained by Fox News Digital reveals Austin's Parks & Recreation Department's equity and inclusion coordinator invited employees to attend "Antiracist Affinity Spaces," consisting of two separate trainings segregated by race as part of an "Equity and Inclusion program." "For People of Color*: Once a month, PARD employees of color will meet up at various city sites," the email says. "The first 1.5 hours will be for fostering dialogue and the last 30 minutes will be for networking. This monthly space will offer folks the opportunities to gather and connect with other PARD employees of color, share about our personal and professional experiences with racism, and learn about mentoring and job opportunities for professional development."

Large Vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natural images transfer to Earth observation (EO) data, which are predominantly satellite and aerial images less common in VLM training data. In this work, we propose a comprehensive benchmark to gauge the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection tasks. Motivated by real-world applications, our benchmark includes scenarios like urban monitoring, disaster relief, land use, and conservation. We discover that, although state-of-the-art VLMs like GPT-4V possess extensive world knowledge that leads to strong performance on open-ended tasks like location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting tasks. Our benchmark will be made publicly available on this website and on Hugging Face for easy model evaluation.

This paper offers an insightful examination of how currently top-trending AI technologies, i.e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology, including video generation, understanding, and streaming. It highlights the innovative use of these technologies in producing highly realistic videos, a significant leap in bridging the gap between real-world dynamics and digital creation. The study also delves into the advanced capabilities of LLMs in video understanding, demonstrating their effectiveness in extracting meaningful information from visual content, thereby enhancing our interaction with videos. In the realm of video streaming, the paper discusses how LLMs contribute to more efficient and user-centric streaming experiences, adapting content delivery to individual viewer preferences. This comprehensive review navigates through the current achievements, ongoing challenges, and future possibilities of applying Generative AI and LLMs to video-related tasks, underscoring the immense potential these technologies hold for advancing the field of video technology related to multimedia, networking, and AI communities.

Large language models (LLMs) have exhibited an array of reasoning capabilities but face challenges like error propagation and hallucination, particularly in specialised areas like finance, where data is heterogeneous, and precision is paramount. We explore the potential of language model augmentation with external tools to mitigate these limitations and offload certain reasoning steps to external tools that are more suited for the task, instead of solely depending on the LLM's inherent abilities. More concretely, using financial domain question-answering datasets, we apply supervised fine-tuning on a LLaMA-2 13B Chat model to act both as a 'task router' and 'task solver'. The 'task router' dynamically directs a question to either be answered internally by the LLM or externally via the right tool from the tool set. Our tool-equipped SFT model, Raven, demonstrates an improvement of 35.2% and 5.06% over the base model and SFT-only baselines, respectively, and is highly competitive with strong GPT-3.5 results. To the best of our knowledge, our work is the first that investigates tool augmentation of language models for the finance domain.

Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to become a significant advancement in the field of computing. From GPS and location-based services to healthcare, spatial computing technologies have influenced and improved our interactions with the digital world. The use of spatial computing in creating interactive digital environments has become increasingly popular and effective. This is explained by its increasing significance among researchers and industrial organisations, which motivated us to conduct this review. This review provides a detailed overview of spatial computing, including its enabling technologies and its impact on various applications. Projects related to spatial computing are also discussed. In this review, we also explored the potential challenges and limitations of spatial computing. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of spatial computing, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions.