Significant uncertainty in climate prediction and cloud physics is tied to observational gaps relating to shallow scattered clouds. Addressing these challenges requires remote sensing of their three-dimensional (3D) heterogeneous volumetric scattering content. This calls for passive scattering computed tomography (CT). We design a learning-based model (ProbCT) to achieve CT of such clouds, based on noisy multi-view spaceborne images. ProbCT infers - for the first time - the posterior probability distribution of the heterogeneous extinction coefficient, per 3D location. This yields arbitrary valuable statistics, e.g., the 3D field of the most probable extinction and its uncertainty. ProbCT uses a neural-field representation, making essentially real-time inference. ProbCT undergoes supervised training by a new labeled multi-class database of physics-based volumetric fields of clouds and their corresponding images. To improve out-of-distribution inference, we incorporate self-supervised learning through differential rendering. We demonstrate the approach in simulations and on real-world data, and indicate the relevance of 3D recovery and uncertainty to precipitation and renewable energy.

Recently, influence functions present an apparatus for achieving explainability for deep neural models by quantifying the perturbation of individual train instances that might impact a test prediction. Our objectives in this paper are twofold. First we incorporate influence functions as a feedback into the model to improve its performance. Second, in a dataset extension exercise, using influence functions to automatically identify data points that have been initially `silver' annotated by some existing method and need to be cross-checked (and corrected) by annotators to improve the model performance. To meet these objectives, in this paper, we introduce InfFeed, which uses influence functions to compute the influential instances for a target instance. Toward the first objective, we adjust the label of the target instance based on its influencer(s) label. In doing this, InfFeed outperforms the state-of-the-art baselines (including LLMs) by a maximum macro F1-score margin of almost 4% for hate speech classification, 3.5% for stance classification, and 3% for irony and 2% for sarcasm detection. Toward the second objective we show that manually re-annotating only those silver annotated data points in the extension set that have a negative influence can immensely improve the model performance bringing it very close to the scenario where all the data points in the extension set have gold labels. This allows for huge reduction of the number of data points that need to be manually annotated since out of the silver annotated extension dataset, the influence function scheme picks up ~1/1000 points that need manual correction.

Microsoft appears to have blocked several prompts in its Copilot tool that led the generative AI tool to spit out violent, sexual and other illicit images. The changes seem to have been implemented just after an engineer at the company wrote to the Federal Trade Commission to lay out severe concerns he had with Microsoft's GAI tech. When entering terms such as "pro choice," "four twenty" (a weed reference) or "pro life," Copilot now displays a message saying those prompts are blocked. It warns that repeated policy violations could lead to a user being suspended, according to CNBC. Users were also reportedly able to enter prompts related to children playing with assault rifles until earlier this week.

Los Angeles commuters: Don't be alarmed, but driverless taxis may soon become a more common site on local streets. On March 1, state regulators gave Waymo, the self-driving taxi company owned by Google's parent, Alphabet, the green light to expand its robotaxi service to Los Angeles County, clearing the way for the company's expansion into one of the biggest markets in the country. While local transportation agencies deal with day-to-day traffic operations in their respective jurisdictions, the California Public Utilities Commission oversees the regulation of driverless vehicles across the state, superseding local governments. Waymo has not disclosed a timeline for when its service will become widely available, but a handful of Waymo vehicles are already roaming about the county, including around the USC campus, as part of its ongoing testing and promotion program. Under its new approval agreement, Waymo's driverless fleet can operate in Los Angeles, Santa Monica, Beverly Hills, Inglewood, East Los Angeles, Compton and many more locales.

Google's co-founder Sergey Brin has kept a low profile since quietly returning to work at the company. But the troubled launch of Google's artificial intelligence model Gemini resulted in a rare public utterance recently: "We definitely messed up." Brin's comments, at an AI "hackathon" event on 2 March, follow a slew of social media posts showing Gemini's image generation tool depicting a variety of historical figures – including popes, founding fathers of the US and, most excruciatingly, German second world war soldiers – as people of colour. The pictures, as well as Gemini chatbot responses that vacillated over whether libertarians or Stalin had caused the greater harm, led to an explosion of negative commentary from figures such as Elon Musk who saw it as another front in the culture wars. But criticism has also come from other sources including Google's chief executive, Sundar Pichai, who described some of the responses produced by Gemini as "completely unacceptable".

Swarms of killer AI drones might sound like the plot of a dystopian science-fiction thriller. But in a terrifying glimpse of the future, one scientist has shown just how easy it already is to build an'assassination drone' that can hunt down and kill people. In just a few hours, Luis Wenus, an engineer and entrepreneur, converted a 115 ( 89.99) drone into the basis of a deadly weapon. Using AI facial recognition the drone was programmed to recognise individuals and race towards them at full speed. Although Mr Wenus says he built the drone'for a game' he also says he wanted to raise awareness for how easily this could be used for a deadly terrorist attack.

Bengaluru, India – On February 20, India's chief opposition party, the Indian National Congress (INC), uploaded a video parodying Prime Minister Narendra Modi on Instagram that has amassed over 1.5 million views. It is a short clip from a new Hindi music album named "Chor" (thief), where Modi's digital likeness is grafted onto the lead singer. The song's lyrics were humorously reworked to describe a thief's – in this case, a business tycoon's – attempt to steal, and Modi handing over coal mines, ports, power lines and ultimately, the country. The video isn't hyperrealistic, but a pithy AI meme that uses Modi's voice and face clones, to drive home the nagging criticism of his close ties to Indian business moguls. That same day, the official Bharatiya Janata Party (BJP) handle on Instagram, with over seven million followers, uploaded its own video.

Transformer-based models have made remarkable advancements in various NLP areas. Nevertheless, these models often exhibit vulnerabilities when confronted with adversarial attacks. In this paper, we explore the effect of quantization on the robustness of Transformer-based models. Quantization usually involves mapping a high-precision real number to a lower-precision value, aiming at reducing the size of the model at hand. To the best of our knowledge, this work is the first application of quantization on the robustness of NLP models. In our experiments, we evaluate the impact of quantization on BERT and DistilBERT models in text classification using SST-2, Emotion, and MR datasets. We also evaluate the performance of these models against TextFooler, PWWS, and PSO adversarial attacks. Our findings show that quantization significantly improves (by an average of 18.68%) the adversarial accuracy of the models. Furthermore, we compare the effect of quantization versus that of the adversarial training approach on robustness. Our experiments indicate that quantization increases the robustness of the model by 18.80% on average compared to adversarial training without imposing any extra computational overhead during training. Therefore, our results highlight the effectiveness of quantization in improving the robustness of NLP models.

While chain-of-thought prompting (CoT) has the potential to improve the explainability of language model reasoning, it can systematically misrepresent the factors influencing models' behavior--for example, rationalizing answers in line with a user's opinion without mentioning this bias. To mitigate this biased reasoning problem, we introduce bias-augmented consistency training (BCT), an unsupervised fine-tuning scheme that trains models to give consistent reasoning across prompts with and without biasing features. We construct a suite testing nine forms of biased reasoning on seven question-answering tasks, and find that applying BCT to GPT-3.5-Turbo with one bias reduces the rate of biased reasoning by 86% on held-out tasks. Moreover, this model generalizes to other forms of bias, reducing biased reasoning on held-out biases by an average of 37%. As BCT generalizes to held-out biases and does not require gold labels, this method may hold promise for reducing biased reasoning from as-of-yet unknown biases and on tasks where supervision for ground truth reasoning is unavailable.

Social media platforms offer a goldmine to collect data for analyzing opinions and attitudes expressed by large numbers of users (Alturayeif, Luqman and Ahmed, 2023a), which because of the large volume requires development of automated tools to support stance detection from textual content (AlDayel and Magdy, 2021; Küçük and Can, 2020). Stance detection is the process of automatically determining a user's viewpoint or position as favor or against regarding a particular subject of interest, often known as the target (Alturayeif, Luqman and Ahmed, 2023b; Khiabani and Zubiaga, 2023). In particular, there is a notable interest within the Natural Language Processing (NLP) community for examining the identification of attitudes expressed towards political figures on Twitter (Mohammad, Kiritchenko, Sobhani, Zhu and Cherry, 2016; Sobhani, Inkpen and Zhu, 2017). Much of the previous research in stance detection has generally assumed that there is sufficient training data to develop a model that determines the stance towards a particular target. In a realistic scenario, however, one may have access to limited training data when new targets emerge for which sufficient data could not be labeled.