The administration of United States President Joe Biden has imposed new sanctions on Iran in response to its missile and drone attack on Israel, as tensions mount over the possibility of further escalation in the Middle East. In a statement on Thursday, Biden said the sanctions targeted "leaders and entities connected to the Islamic Revolutionary Guard Corps, Iran's Defense Ministry, and the Iranian government's missile and drone program that enabled" the April 13 attack on Israel. "As I discussed with my fellow G7 [Group of Seven] leaders the morning after the attack, we are committed to acting collectively to increase economic pressure on Iran," the US president said. "And our allies and partners have or will issue additional sanctions and measures to restrict Iran's destabilizing military programs." Iran launched hundreds of missiles and drones at Israel in the early hours on Sunday, in retaliation for the deadly bombing of the Iranian consulate in Syria's capital, Damascus, earlier this month.

Meta just announced a major update for its AI assistant platform, Meta AI, which has been built using the long-awaited open source Llama 3 large language model (LLM). The company says it's "now the most intelligent AI assistant you can use for free." As for use case scenarios, the company touts the ability to help users study for tests, plan dinners and schedule nights out. Meta AI, however, has expanded into just about every nook and cranny throughout the company's entire portfolio, after a test run with Instagram DMs last week. It's still available with Instagram, but now users can access it on Messenger, Facebook feeds and Whatsapp.

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The compliments start flowing as soon as she answers the video call. "Wow, you so pretty, honey," says the man on the other side of the screen. His video feed shows he's white, with short hair, likely a few years younger than her, and is sitting in front of his camera wearing a plaid shirt. "You're looking different with that beard and stuff gone," the woman says in an American accent as the conversation gets going. The man doesn't miss a beat. "I told you I was going to shave my beard so I will look good."

Her research focuses on developing privacy-preserving generative models for tabular data and speech models tailored for African-accented English. She is also the co-founder of AISaturdays Lagos whose aim is to make AI knowledge easily accessible in Africa; free courses on data science, machine learning and deep learning are offered. She is among the few people working to make African data accessible on the internet. She shares her story about how she balances the multiple roles she plays to maintain a healthy work/life balance. My journey into the realm of technology began in my second year of undergraduate studies, ignited by the captivating images of NASA's Curiosity rover exploring Mars.

Israel Defense Forces spokesperson Rear Adm. Daniel Hagari discusses Iran's attack on Israel, saying the attacks proved that Iran seeks to "escalate the region." JERUSALEM -- Some of Israel's most advanced military technology was on display over the weekend when its multi-level aerial defense array led the way in striking down an estimated 99% of the more than 350 drones, rockets and missiles that were fired by Iran in an unprecedented attack on the Jewish state. From the Iron Dome, which in its latest format uses artificial intelligence (AI) to improve accuracy when shooting short-range surface-to-surface rockets, to David's Sling, which intercepts short- to medium-range and medium- to long-range surface-to-surface missiles, to the Arrow 2 and 3 systems, which is used for longer-range ballistic and cruise missiles, as well as AI-driven aircraft and other technology, Israel's defensive operation proved it was far superior to the offensive capabilities of the Islamic Republic. In a press briefing following the attack, Israel Defense Forces spokesperson Rear Adm. Daniel Hagari hailed Israel's defensive operation, which was carried out together with partners from U.S. Central Command (CENTCOM), as a "very significant strategic achievement." He said it demonstrated the "exceptional professionalism" of Israel's Aerial Defense Array and the "defensive abilities of the air force as well as the army's military and technological superiority."

Language models (LMs) trained on vast quantities of text data can acquire sophisticated skills such as generating summaries, answering questions or generating code. However, they also manifest behaviors that violate human preferences, e.g., they can generate offensive content, falsehoods or perpetuate social biases. In this thesis, I explore several approaches to aligning LMs with human preferences. First, I argue that aligning LMs can be seen as Bayesian inference: conditioning a prior (base, pretrained LM) on evidence about human preferences (Chapter 2). Conditioning on human preferences can be implemented in numerous ways. In Chapter 3, I investigate the relation between two approaches to finetuning pretrained LMs using feedback given by a scoring function: reinforcement learning from human feedback (RLHF) and distribution matching. I show that RLHF can be seen as a special case of distribution matching but distributional matching is strictly more general. In chapter 4, I show how to extend the distribution matching to conditional language models. Finally, in chapter 5 I explore a different root: conditioning an LM on human preferences already during pretraining. I show that involving human feedback from the very start tends to be more effective than using it only during supervised finetuning. Overall, these results highlight the room for alignment techniques different from and complementary to RLHF.

Multi-modal relation extraction (MMRE) is a challenging task that aims to identify relations between entities in text leveraging image information. Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing very similar contextual information (ie, the same text and image), resulting in increased difficulty in the MMRE task. To address this limitation, we propose the Variational Multi-Modal Hypergraph Attention Network (VM-HAN) for multi-modal relation extraction. Specifically, we first construct a multi-modal hypergraph for each sentence with the corresponding image, to establish different high-order intra-/inter-modal correlations for different entity pairs in each sentence. We further design the Variational Hypergraph Attention Networks (V-HAN) to obtain representational diversity among different entity pairs using Gaussian distribution and learn a better hypergraph structure via variational attention. VM-HAN achieves state-of-the-art performance on the multi-modal relation extraction task, outperforming existing methods in terms of accuracy and efficiency.

Large Language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for high-resource languages (HRLs). A few of them have been trained on low-resource languages (LRLs) and give decent performance. Owing to the prohibitive costs of training LLMs, they are usually used as a network service, with the client charged by the count of input and output tokens. The number of tokens strongly depends on the script and language, as well as the LLM's subword vocabulary. We show that LRLs are at a pricing disadvantage, because the well-known LLMs produce more tokens for LRLs than HRLs. This is because most currently popular LLMs are optimized for HRL vocabularies. Our objective is to level the playing field: reduce the cost of processing LRLs in contemporary LLMs while ensuring that predictive and generative qualities are not compromised. As means to reduce the number of tokens processed by the LLM, we consider code-mixing, translation, and transliteration of LRLs to HRLs. We perform an extensive study using the IndicXTREME classification and six generative tasks dataset, covering 15 Indic and 3 other languages, while using GPT-4 (one of the costliest LLM services released so far) as a commercial LLM. We observe and analyze interesting patterns involving token count, cost, and quality across a multitude of languages and tasks. We show that choosing the best policy to interact with the LLM can reduce cost by 90% while giving better or comparable performance compared to communicating with the LLM in the original LRL.

The prevalence of digital media and evolving sociopolitical dynamics have significantly amplified the dissemination of hateful content. Existing studies mainly focus on classifying texts into binary categories, often overlooking the continuous spectrum of offensiveness and hatefulness inherent in the text. In this research, we present an extensive benchmark dataset for Amharic, comprising 8,258 tweets annotated for three distinct tasks: category classification, identification of hate targets, and rating offensiveness and hatefulness intensities. Our study highlights that a considerable majority of tweets belong to the less offensive and less hate intensity levels, underscoring the need for early interventions by stakeholders. The prevalence of ethnic and political hatred targets, with significant overlaps in our dataset, emphasizes the complex relationships within Ethiopia's sociopolitical landscape. We build classification and regression models and investigate the efficacy of models in handling these tasks. Our results reveal that hate and offensive speech can not be addressed by a simplistic binary classification, instead manifesting as variables across a continuous range of values. The Afro-XLMR-large model exhibits the best performances achieving F1-scores of 75.30%, 70.59%, and 29.42% for the category, target, and regression tasks, respectively. The 80.22% correlation coefficient of the Afro-XLMR-large model indicates strong alignments.