In 1965, the political scientist and Nobel laureate Herbert Simon declared: "Machines will be capable, within 20 years, of doing any work a man can do." Today, in what is increasingly referred to as the fourth industrial revolution, the arrival of artificial intelligence (AI) in the workplace is igniting similar concerns. The European parliament's forthcoming Artificial Intelligence Act is likely to deem the use of AI across education, law enforcement and worker management to be "high risk". Geoffrey Hinton, known as the "godfather of AI", recently resigned from his position at Google, citing concerns about the technology's impact on the job market. And, in early May, striking members of the Writers Guild of America promised executives: "AI will replace you before it replaces us."

PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. The accessibility of artificial intelligence (AI) will change the international landscape to empower "bad actor" strongman regimes and lead to unprecedented social disruptions, a risk analysis expert told Fox News Digital. "We know that when you have a bad actor, and all they have is a single-shot rifle as opposed to an AR-15, they can't kill as many people, and the AR-15 is nothing compared to what we are going to see from artificial intelligence, from the disruptive uses of these tools," said Ian Bremmer, founder and president of political risk research firm Eurasia Group. In referencing improved capabilities for autonomous drones and the ability to develop new viruses, among others, Bremmer said that "we've never seen this level of malevolent power that will be in the hands of bad actors."

Senator Pete Ricketts of Nebraska told Fox News Digital on Thursday that he's concerned about China's use of Artificial Intelligence (AI) after a report claimed pro-Chinese groups were spreading CCP propaganda using AI-generated news anchors. EXCLUSIVE: China's expansive artificial intelligence (AI) operations could play a concerning role in the 2024 election cycle, Sen. Pete Ricketts warned on Thursday. "There's absolutely a possibility that they could do that for the 2024 election, and that's what we have to be on guard [for]," Ricketts told Fox News Digital in an interview in his Senate office. During a Senate Foreign Relations subcommittee hearing earlier this month, Ricketts referenced China and its use of AI technology to create "deepfakes," which are fabricated videos and images that can look and sound like real people and events. A report released earlier this year by a U.S.-based research firm claimed a "pro-Chinese spam operation" was using AI deepfakes technology to create videos of fake news anchors reciting Beijing's propaganda.

Cognitive scientist and AI expert Gary Marcus advocates for the formation of an international body to govern emerging artificial intelligence technologies. The world needs a United Nations-like agency to regulate rapidly advancing artificial intelligence technology, particularly since governments are starting to pass laws that put varying demands on AI companies. "Right now, we have 37 countries that passed laws about artificial intelligence last year, each of them doing their own thing," said Gary Marcus, who hosts the AI-themed podcast, "Humans vs Machines with Gary Marcus." "But there's no coordination between what all of these countries are doing." Without a shared regulatory body, AI companies might be forced to modify their software and offer different versions from country to country -- or even state to state -- to comply with each unique law, according to Marcus.

In this review, we explore the historical development and future prospects of artificial intelligence (AI) and deep learning in astronomy. We trace the evolution of connectionism in astronomy through its three waves, from the early use of multilayer perceptrons, to the rise of convolutional and recurrent neural networks, and finally to the current era of unsupervised and generative deep learning methods. With the exponential growth of astronomical data, deep learning techniques offer an unprecedented opportunity to uncover valuable insights and tackle previously intractable problems. As we enter the anticipated fourth wave of astronomical connectionism, we argue for the adoption of GPT-like foundation models fine-tuned for astronomical applications. Such models could harness the wealth of high-quality, multimodal astronomical data to serve state-of-the-art downstream tasks. To keep pace with advancements driven by Big Tech, we propose a collaborative, open-source approach within the astronomy community to develop and maintain these foundation models, fostering a symbiotic relationship between AI and astronomy that capitalizes on the unique strengths of both fields.

Automatic discourse processing is bottlenecked by data: current discourse formalisms pose highly demanding annotation tasks involving large taxonomies of discourse relations, making them inaccessible to lay annotators. This work instead adopts the linguistic framework of Questions Under Discussion (QUD) for discourse analysis and seeks to derive QUD structures automatically. QUD views each sentence as an answer to a question triggered in prior context; thus, we characterize relationships between sentences as free-form questions, in contrast to exhaustive fine-grained taxonomies. We develop the first-of-its-kind QUD parser that derives a dependency structure of questions over full documents, trained using a large, crowdsourced question-answering dataset DCQA (Ko et al., 2022). Human evaluation results show that QUD dependency parsing is possible for language models trained with this crowdsourced, generalizable annotation scheme. We illustrate how our QUD structure is distinct from RST trees, and demonstrate the utility of QUD analysis in the context of document simplification. Our findings show that QUD parsing is an appealing alternative for automatic discourse processing.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Regulatory stress tests have become one of the main tools for setting capital requirements at the largest U.S. banks. The Federal Reserve uses confidential models to evaluate bank-specific outcomes for bank-specific portfolios in shared stress scenarios. As a matter of policy, the same models are used for all banks, despite considerable heterogeneity across institutions; individual banks have contended that some models are not suited to their businesses. Motivated by this debate, we ask, what is a fair aggregation of individually tailored models into a common model? We argue that simply pooling data across banks treats banks equally but is subject to two deficiencies: it may distort the impact of legitimate portfolio features, and it is vulnerable to implicit misdirection of legitimate information to infer bank identity. We compare various notions of regression fairness to address these deficiencies, considering both forecast accuracy and equal treatment. In the setting of linear models, we argue for estimating and then discarding centered bank fixed effects as preferable to simply ignoring differences across banks. We present evidence that the overall impact can be material. We also discuss extensions to nonlinear models.

Value-alignment in normative multi-agent systems is used to promote a certain value and to ensure the consistent behavior of agents in autonomous intelligent systems with human values. However, the current literature is limited to incorporation of effective norms for single value alignment with no consideration of agents' heterogeneity and the requirement of simultaneous promotion and alignment of multiple values. This research proposes a multi-value promotion model that uses multi-objective evolutionary algorithms to produce the optimum parametric set of norms that is aligned with multiple simultaneous values of heterogeneous agents and the system. To understand various aspects of this complex problem, several evolutionary algorithms were used to find a set of optimised norm parameters considering two toy tax scenarios with two and five values are considered. The results are analysed from different perspectives to show the impact of a selected evolutionary algorithm on the solution, and the importance of understanding the relation between values when prioritising them.

Word embeddings, i.e., semantically meaningful vector representation of words, are largely influenced by the distributional hypothesis "You shall know a word by the company it keeps" (Harris, 1954), whereas modern prediction-based neural network embeddings rely on design choices and hyperparameter optimization. Word embeddings like Word2Vec, GloVe etc. well capture the contextuality and real-world analogies but contemporary convolution-based image embeddings such as VGGNet, AlexNet, etc. do not capture contextual knowledge. The popular king-queen analogy does not hold true for most commonly used vision embeddings. In this paper, we introduce a pre-trained joint embedding (JE), named IMAGINATOR, trained on 21K distinct image objects level from 1M image+text pairs. JE is a way to encode multimodal data into a vector space where the text modality serves as the ground-ing key, which the complementary modality (in this case, the image) is anchored with. IMAGINATOR encapsulates three individual representations: (i) object-object co-location, (ii) word-object co-location, and (iii) word-object correlation. These three ways capture complementary aspects of the two modalities which are further combined to obtain the final JEs. Generated JEs are intrinsically evaluated to assess how well they capture the contextuality and real-world analogies. We also evaluate pre-trained IMAGINATOR JEs on three downstream tasks: (i) image captioning, (ii) Image2Tweet, and (iii) text-based image retrieval. IMAGINATOR establishes a new standard on the aforementioned down-stream tasks by outperforming the current SoTA on all the selected tasks. IMAGINATOR will be made publicly available. The codes are available at https://github.com/varunakk/IMAGINATOR