In the last 24 hours alone, we've had hoaxes, FTC complaints and… ads. We'll get into how Microsoft is bringing ads to its Bing chatbot – bound to happen – while OpenAI may have to halt ChatGPT releases in the face of FTC complaints. The nonprofit research organization, Center for AI and Digital Policy (CAIDP), says OpenAI's models are "biased, deceptive" and threaten privacy and public safety. The CAIDP says OpenAI also fails to meet Commission guidelines calling for AI to be transparent, fair and easy to explain. There's no guarantee the FTC will act on the complaint.

Forget the collapse of employment, forget the spam and misinformation, forget human obsolescence and the upending of society. Some believe AI is flat-out going to wipe out all of biological life at its earliest opportunity. Here's how and why.

Former President Trump appeared to shoot down a New York Times report that he has workshopped "Meatball Ron" as a disparaging nickname for Florida Gov. Ron DeSantis. Subscribe now to get Fox News First in your email. And here's what you need to know to start your day ... 'UN-AMERICAN' - DeSantis' statement on possible Trump extradition leaves no room for doubt. BLURRED LINES - Dire warning issued about the dangers of artificial intelligence in your kid's hands. RIVALS' REACTIONS - Trump's 2024 opponents weigh in after former president is charged.

For instance, according to the Centers for Medicare & Medicaid Services, in December 2021, it was reported that the total national health expenditure in the U.S. increased to USD 4.1 trillion in 2020, which was a growth of 9.7% as compared to the previous year. Thus, a significant number of insurance providers are relying on the service providers to negotiate the drug price with retail pharmacy units and lower the price of the listed drugs in the insurance coverage. Furthermore, increasing initiatives, such as extending mail order delivery services and strengthening distribution network in remote areas, were responsible for the growing adoption of these services. Hence, these initiatives by the major players coupled with increasing demand for specialty drugs boosted the pharmacy benefit management market growth during the COVID-19 pandemic. Request a Free sample to learn more about this report.

As tech experts sound the alarm on advanced artificial intelligence, congressional lawmakers were split on the extent to which the federal government is capable of regulating AI platforms. WASHINGTON, D.C. – As tech experts sound the alarm on advanced artificial intelligence, congressional lawmakers were split on the extent to which the federal government is capable of regulating AI platforms. "I think it's important that the government regulate these platforms," Democratic Rep. Maxwell Frost said. "That's one of the major functions of the federal government, to help protect consumers and data and privacy of our citizens." Rep. Maxwell Frost said it's important that the government regulate artificial intelligence platforms, though he also acknowledged he's not "super briefed" on the platforms.

Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classification, overlooking the social nature of the task. The stance detection task becomes particularly challenging in cross-target classification scenarios, where even in few-shot training settings the model needs to predict the stance towards new targets for which the model has only seen few relevant samples during training. To address the cross-target stance detection in social media by leveraging the social nature of the task, we introduce CT-TN, a novel model that aggregates multimodal embeddings derived from both textual and network features of the data. We conduct experiments in a few-shot cross-target scenario on six different combinations of source-destination target pairs. By comparing CT-TN with state-of-the-art cross-target stance detection models, we demonstrate the effectiveness of our model by achieving average performance improvements ranging from 11% to 21% across different baseline models. Experiments with different numbers of shots show that CT-TN can outperform other models after seeing 300 instances of the destination target. Further, ablation experiments demonstrate the positive contribution of each of the components of CT-TN towards the final performance. We further analyse the network interactions between social media users, which reveal the potential of using social features for cross-target stance detection.

As machine learning systems have been increasingly applied in social fields, it is imperative that machine learning models do not reflect real-world discrimination. However, machine learning models have shown biased predictions against disadvantaged groups on several real-world tasks (Larson et al., 2016; Dressel and Farid, 2018; Mehrabi et al., 2021a). In order to improve fairness and reduce discrimination of machine learning systems, a variety of work has been proposed to quantify and rectify bias (Hardt et al., 2016; Kleinberg et al., 2016; Mitchell et al., 2018). Despite the emerging interest in fairness, the topic of adversarial fairness attack and robustness against such attack have not yet been properly discussed. Most of current literature on adversarial training has been focusing on improving robustness against accuracy attack (Chakraborty et al., 2018), while the problem of adversarial attack and adversarial training w.r.t.

In recent years, the robotics community has witnessed the development of several software stacks for ground and articulated robots, such as Navigation2 and MoveIt. However, the same level of collaboration and standardization is yet to be achieved in the field of aerial robotics, where each research group has developed their own frameworks. This work presents Aerostack2, a framework for the development of autonomous aerial robotics systems that aims to address the lack of standardization and fragmentation of efforts in the field. Built on ROS 2 middleware and featuring an efficient modular software architecture and multi-robot orientation, Aerostack2 is a versatile and platform-independent environment that covers a wide range of robot capabilities for autonomous operation. Its major contributions include providing a logical level for specifying missions, reusing components and sub-systems for aerial robotics, and enabling the development of complete control architectures. All major contributions have been tested in simulation and real flights with multiple heterogeneous swarms. Aerostack2 is open source and community oriented, democratizing the access to its technology by autonomous drone systems developers.

This paper introduces RiskCards, a framework for structured assessment and documentation of risks associated with an application of language models. As with all language, text generated by language models can be harmful, or used to bring about harm. Automating language generation adds both an element of scale and also more subtle or emergent undesirable tendencies to the generated text. Prior work establishes a wide variety of language model harms to many different actors: existing taxonomies identify categories of harms posed by language models; benchmarks establish automated tests of these harms; and documentation standards for models, tasks and datasets encourage transparent reporting. However, there is no risk-centric framework for documenting the complexity of a landscape in which some risks are shared across models and contexts, while others are specific, and where certain conditions may be required for risks to manifest as harms. RiskCards address this methodological gap by providing a generic framework for assessing the use of a given language model in a given scenario. Each RiskCard makes clear the routes for the risk to manifest harm, their placement in harm taxonomies, and example prompt-output pairs. While RiskCards are designed to be open-source, dynamic and participatory, we present a "starter set" of RiskCards taken from a broad literature survey, each of which details a concrete risk presentation. Language model RiskCards initiate a community knowledge base which permits the mapping of risks and harms to a specific model or its application scenario, ultimately contributing to a better, safer and shared understanding of the risk landscape.

English is the most widely spoken language in the world, used daily by millions of people as a first or second language in many different contexts. As a result, there are many varieties of English. Although the great many advances in English automatic speech recognition (ASR) over the past decades, results are usually reported based on test datasets which fail to represent the diversity of English as spoken today around the globe. We present the first release of The Edinburgh International Accents of English Corpus (EdAcc). This dataset attempts to better represent the wide diversity of English, encompassing almost 40 hours of dyadic video call conversations between friends. Unlike other datasets, EdAcc includes a wide range of first and second-language varieties of English and a linguistic background profile of each speaker. Results on latest public, and commercial models show that EdAcc highlights shortcomings of current English ASR models. The best performing model, trained on 680 thousand hours of transcribed data, obtains an average of 19.7% word error rate (WER) -- in contrast to the 2.7% WER obtained when evaluated on US English clean read speech. Across all models, we observe a drop in performance on Indian, Jamaican, and Nigerian English speakers. Recordings, linguistic backgrounds, data statement, and evaluation scripts are released on our website (https://groups.inf.ed.ac.uk/edacc/) under CC-BY-SA license.