This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.

Drone strikes in Ethiopia's Tigray kill one amid fears of renewed conflict One person has been killed and another injured in drone strikes in Ethiopia's northern Tigray region, a senior Tigrayan official and a humanitarian worker said, in another sign of renewed conflict between regional and federal forces. The Tigrayan official on Saturday said the drone strikes hit two Isuzu trucks near Enticho and Gendebta, two places in Tigray about 20km (12 miles) apart. A local humanitarian worker confirmed the strikes had happened. Both asked not to be named, the Reuters news agency reported. It was not immediately clear what the trucks were carrying.

You'll get the best VPN experience by paying, but prices don't have to be steep. When talking about the best VPNs, I frequently warn about the dangers of trusting free VPNs without verifying them. Although there are a few free VPNs worth recommending, many other free providers are ineffective, malicious or looking to profit off their users (or sometimes all three). Even the best free VPNs work a lot better once you subscribe and access their full service. This can be frustrating if you want to enjoy the benefits of a VPN but don't have the budget for yet another subscription.

Apple's Siri AI will be powered by Gemini Reclaim stolen African artifacts with your crew on February 10. The intriguing Africanfuturist heist game,, is out on February 10. Developed by independent South African studio Nyamakop, the game focuses on a ragtag crew from Johannesburg that liberates real-life African artifacts from a series of fictionalized Western museums. You have to carefully plan each heist with your fellow teammates, knowing where to place each crew member and how you're going to get in and out in one piece. Once you've grabbed the artifact you're looking for in each mission, an alarm will sound and you have a limited amount of time to escape, so good preparation is vital.

Elon Musk walks to the White House after landing in Marine One on the South Lawn on 9 March in Washington DC. Elon Musk walks to the White House after landing in Marine One on the South Lawn on 9 March in Washington DC. We still don't really know what Elon Musk's Doge actually did W hen Elon Musk vowed late last year to lead a "department of government efficiency" (Doge), he claimed it would operate with "maximum transparency" as it set about saving $2tn worth of waste and exposing massive fraud. Today, with Musk out of the White House, Doge having cut only a tiny fraction of the waste it promised, and dozens of lawsuits alleging violations of privacy and transparency laws, much of what the agency has done remains a mystery. The effects of Doge's initial blitz through the federal government - which included dismantling the US Agency for International Development ( USAID), embedding staffers in almost every agency and illegally firing people en masse - are still playing out.

Sudan's air force has carried out bombings in which at least 1,700 civilians have died in attacks on residential neighbourhoods, markets, schools and camps for displaced people, according to an investigation into air raids in the country's civil war. The Sudan Witness Project says it has compiled the largest known dataset of military airstrikes in the conflict, which began in April 2023. Its analysis indicates that the air force has used unguided bombs in populated areas. The data focuses on attacks by warplanes, which only the Sudanese Armed Forces (SAF) is capable of operating. Its rival, the paramilitary Rapid Support Forces (RSF) does not have aircraft.

Large Language Models (LLMs) exhibit impressive capabilities but often hallucinate, confidently providing incorrect answers instead of admitting ignorance. Prior work has shown that models encode linear representations of their own knowledge and that activation steering can reduce hallucinations. These approaches, however, require real-time monitoring and intervention during inference. We introduce Contrastive Activation Steering for Amortized Learning (CASAL), an efficient algorithm that connects interpretability with amortized optimization. CASAL directly bakes the benefits of activation steering into model's weights. Once trained, LLMs answer questions they know while abstaining from answering those they do not. CASAL's light-weight design requires training only a submodule of a single transformer layer and yet reduces hallucination by 30%-40% across multiple short-form QA benchmarks. CASAL is 30x more compute-efficient and 20x more data-efficient than strong LoRA-based baselines such as SFT and DPO, boosting its practical applicability in data scarce domains. Importantly, CASAL also generalizes effectively to out-of-distribution (OOD) domains. We showcase CASAL's flexibility in mitigating hallucinations in both text-only and vision-language models. To our knowledge, CASAL is the first steering-based training method that has been shown to be effective for both dense and Mixture-of-Experts (MoE) models. CASAL represents a promising step forward for applying interpretability-inspired method for practical deployment in production systems.

Artificial Intelligence has witnessed remarkable progress over the past decades, both in its capabilities and its range of applications. Today, neural networks are present in a variety of fields. One classical application is function approximation, which is supported by the universal approximation theory [34]. In computer vision, convolutional neural networks form the backbone of most modern architectures [39, 38], while the framework of neural ordinary differential equations has contributed significantly to optimal control problems [17, 10]. In natural language processing and speech recognition, recurrent neural networks and the long short-term memory variants have yielded significant performance improvements [33, 51]. More recently, diffusion models have illustrated to be powerful generative models, with applications ranging from image denoising to video generation [56]. Neural networks have even found their way into scientific computing. The most notable example is physics-informed neural networks, which are capable of solving both forward and inverse problems governed by partial differential equations [50]. A neural network can be viewed, in general, as a function parametrized by a set of weights and biases, which we collectively refer to as parameters.

Low-resource African languages remain underrepresented in sentiment analysis research, resulting in limited lexical resources and reduced model performance in multilingual applications. This gap restricts equitable access to Natural Language Processing (NLP) technologies and hinders downstream tasks such as public-health monitoring, digital governance, and financial inclusion. To address this challenge, this paper introduces TriLex, a three-stage retrieval-augmented framework that integrates corpus-based extraction, cross-lingual mapping, and Retrieval-Augmented Generation (RAG) driven lexicon refinement for scalable sentiment lexicon expansion in low-resource languages. Using an expanded lexicon, we evaluate two leading African language models (AfroXLMR and AfriBERTa) across multiple case studies. Results show that AfroXLMR consistently achieves the strongest performance, with F1-scores exceeding 80% for isiXhosa and isiZulu, aligning with previously reported ranges (71-75%), and demonstrating high multilingual stability with narrow confidence intervals. AfriBERTa, despite lacking pre-training on the target languages, attains moderate but reliable F1-scores around 64%, confirming its effectiveness under constrained computational settings. Comparative analysis shows that both models outperform traditional machine learning baselines, while ensemble evaluation combining AfroXLMR variants indicates complementary improvements in precision and overall stability. These findings confirm that the TriLex framework, together with AfroXLMR and AfriBERTa, provides a robust and scalable approach for sentiment lexicon development and multilingual sentiment analysis in low-resource South African languages.