The Federal Trade Commission is launching an inquiry into massive investments made by Microsoft, Amazon and Alphabet into generative AI startups OpenAI and Anthropic, the agency announced on Thursday. The FTC said that it had issued "compulsory orders" to the companies and would scrutinize their relationships with AI startups to understand their impact on competition. "History shows that new technologies can create new markets and healthy competition," FTC Chair Lina Khan said in a statement. "As companies race to develop and monetize AI, we must guard against tactics that foreclose this opportunity. Our study will shed light on whether investments and partnerships pursued by dominant companies risk distorting innovation and undermining fair competition."

Under the Biden administration, federal regulators have stepped up their scrutiny of Big Tech companies' acquisitions of smaller rivals, bringing lengthy and costly legal challenges against Meta's acquisition of the virtual reality company Within and Microsoft's purchase of the game maker Activision. In the age of generative AI, Silicon Valley giants have to date sidestepped such legal obstacles by instead funneling investments into younger AI companies and striking deals to ensure those start-ups are giving preference to their computing services.

The United States trade regulator launched an inquiry on Thursday into generative artificial intelligence investments and partnerships. The Federal Trade Commission (FTC) said in a statement that it issued orders to five companies requiring them to provide information on the matter. The companies were Google's parent company Alphabet, Amazon, Anthropic, Microsoft, and ChatGPT maker OpenAI, the agency's statement said. The inquiry will focus on what authority and rights the tech giants' investments in the fledgling AI companies have conferred and whether those deals harm competition. "Our study will shed light on whether investments and partnerships pursued by dominant companies risk distorting innovation and undermining fair competition," FTC chair Lina Khan said in a statement.

Human Rights Watch said Thursday that Burkina Faso's security forces last year killed at least 60 civilians in three different drone strikes, which the group says may have constituted war crimes. The West African nation's government claimed the strikes targeted extremists, including jihadi fighters and rebel groups that have been operating in many remote communities. The accusation by the New York-based watchdog were the latest in a string of similar charges raised by various rights groups. "The government should urgently and impartially investigate these apparent war crimes, hold those responsible to account, and provide adequate support for the victims and their families," HRW said in a new report. A mural is seen in Ouagadougou, Burkina Faso, on March 1, 2023.

Humans are racing to harness the ocean's vast potential to power global economic growth. Worldwide, ocean-based industries such as fishing, shipping and energy production generate at least US 1.5 trillion in economic activity each year and support 31 million jobs. This value has been increasing exponentially over the past 50 years and is expected to double by 2030. Transparency in monitoring this "blue acceleration" is crucial to prevent environmental degradation, overexploitation of fisheries and marine resources, and lawless behavior such as illegal fishing and human trafficking. Open information also will make countries better able to manage vital ocean resources effectively. But the sheer size of the ocean has made tracking industrial activities at a broad scale impractical – until now.

Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This paper explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi-criteria decision-making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics-informed neural networks (PINNs), and generative pre-trained transformer (GPT)-based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super-resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data-driven modeling and uncertainty quantification.

Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods like least squares, minimum Hellinger distance, and optimal bounded influence function available. This paper introduces a novel robust estimation technique, the Method of Truncated Moments (MTuM), specifically designed to estimate the tail index of a Pareto distribution from grouped data. Inferential justification of MTuM is established by employing the central limit theorem and validating them through a comprehensive simulation study.

The rapid and wide-scale adoption of AI to generate human speech poses a range of significant ethical and safety risks to society that need to be addressed. For example, a growing number of speech generation incidents are associated with swatting attacks in the United States, where anonymous perpetrators create synthetic voices that call police officers to close down schools and hospitals, or to violently gain access to innocent citizens' homes. Incidents like this demonstrate that multimodal generative AI risks and harms do not exist in isolation, but arise from the interactions of multiple stakeholders and technical AI systems. In this paper we analyse speech generation incidents to study how patterns of specific harms arise. We find that specific harms can be categorised according to the exposure of affected individuals, that is to say whether they are a subject of, interact with, suffer due to, or are excluded from speech generation systems. Similarly, specific harms are also a consequence of the motives of the creators and deployers of the systems. Based on these insights we propose a conceptual framework for modelling pathways to ethical and safety harms of AI, which we use to develop a taxonomy of harms of speech generators. Our relational approach captures the complexity of risks and harms in sociotechnical AI systems, and yields an extensible taxonomy that can support appropriate policy interventions and decision making for responsible multimodal model development and release of speech generators.

We introduce the concept of "Alternative Speech" as a new way to directly combat hate speech and complement the limitations of counter-narrative. An alternative speech provides practical alternatives to hate speech in real-world scenarios by offering speech-level corrections to speakers while considering the surrounding context and promoting speakers to reform. Further, an alternative speech can combat hate speech alongside counter-narratives, offering a useful tool to address social issues such as racial discrimination and gender inequality. We propose the new concept and provide detailed guidelines for constructing the necessary dataset. Through discussion, we demonstrate that combining alternative speech and counter-narrative can be a more effective strategy for combating hate speech by complementing specificity and guiding capacity of counter-narrative. This paper presents another perspective for dealing with hate speech, offering viable remedies to complement the constraints of current approaches to mitigating harmful bias.

Consistency is a key requirement of high-quality translation. It is especially important to adhere to pre-approved terminology and adapt to corrected translations in domain-specific projects. Machine translation (MT) has achieved significant progress in the area of domain adaptation. However, in-domain data scarcity is common in translation settings, due to the lack of specialised datasets and terminology, or inconsistency and inaccuracy of available in-domain translations. In such scenarios where there is insufficient in-domain data to fine-tune MT models, producing translations that are consistent with the relevant context is challenging. While real-time adaptation can make use of smaller amounts of in-domain data to improve the translation on the fly, it remains challenging due to supported context limitations and efficiency constraints. Large language models (LLMs) have recently shown interesting capabilities of in-context learning, where they learn to replicate certain input-output text generation patterns, without further fine-tuning. Such capabilities have opened new horizons for domain-specific data augmentation and real-time adaptive MT. This work attempts to address two main relevant questions: 1) in scenarios involving human interaction and continuous feedback, can we employ language models to improve the quality of adaptive MT at inference time? and 2) in the absence of sufficient in-domain data, can we use pre-trained large-scale language models to improve the process of MT domain adaptation?