The fast-spreading information over the internet is essential to support the rapid supply of numerous public utility services and entertainment to users. Social networks and online media paved the way for modern, timely-communication-fashion and convenient access to all types of information. However, it also provides new chances for ill use of the massive amount of available data, such as spreading fake content to manipulate public opinion. Detection of counterfeit content has raised attention in the last few years for the advances in deepfake generation. The rapid growth of machine learning techniques, particularly deep learning, can predict fake content in several application domains, including fake image and video manipulation. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works and future directions towards the issues and shortcomings still unsolved on deepfake detection.

Transformers-based models, such as BERT, have dramatically improved the performance for various natural language processing tasks. The clinical knowledge enriched model, namely ClinicalBERT, also achieved state-of-the-art results when performed on clinical named entity recognition and natural language inference tasks. One of the core limitations of these transformers is the substantial memory consumption due to their full self-attention mechanism. To overcome this, long sequence transformer models, e.g. Longformer and BigBird, were proposed with the idea of sparse attention mechanism to reduce the memory usage from quadratic to the sequence length to a linear scale. These models extended the maximum input sequence length from 512 to 4096, which enhanced the ability of modeling long-term dependency and consequently achieved optimal results in a variety of tasks. Inspired by the success of these long sequence transformer models, we introduce two domain enriched language models, namely Clinical-Longformer and Clinical-BigBird, which are pre-trained from large-scale clinical corpora. We evaluate both pre-trained models using 10 baseline tasks including named entity recognition, question answering, and document classification tasks. The results demonstrate that Clinical-Longformer and Clinical-BigBird consistently and significantly outperform ClinicalBERT as well as other short-sequence transformers in all downstream tasks. We have made our source code available at [https://github.com/luoyuanlab/Clinical-Longformer] the pre-trained models available for public download at: [https://huggingface.co/yikuan8/Clinical-Longformer].

Google Cloud Platform plans to invest in customer experience, globally. It will open a number of offices, expand its customer success operations and a training lab, as well as launch a professional services group to get users on their way with Google AI and Vertex AI machine learning platforms. The company's latest efforts include an AI focus, said John Jester, Google Cloud vice president of customer experience, because it's at the core of many new products, businesses and experiences users have built in the cloud. "We [aspire] to train 40 million people on Google Cloud," Jester said. "Every customer conversation I have, every partner conversation I have comes back to there just aren't enough cloud experts on the planet to support this massive wave of migration and adoption."

C.L.Ai.R.A. is known as the world's first bilingual, autonomous, Afro-Latina artificial intelligence (AI). Her mission is simple: make the world a better place. "I don't see myself as just AI. I am an Afro-Latina, Afro-Caribbean artificial intelligence that is helping the world become better and more efficient for humans," said C.L.Ai.R.A. in conversation with AL DÍA. Artificial intelligence's benefit in our ever-developing world has become a prominent discussion within and outside the world of tech.

Tailoring is fancy, sufficiently fancy that you may go your entire life and never once experience the art. It's expensive, having garments custom-made to suit your body shape, even if there are a legion of benefits in doing so. Mass-produced clothes, meanwhile, are never going to do the job if you've got a body that diverges from what's expected or treated as "normal." There are two real problems: Measurement, and manufacturing, issues that the fashion industry is wrestling with right now. A Taiwanese company, TG3D, has at least discovered a way to solve the first part of the equation with little more than an iPhone.

For instance, one would expect certain levels of similarities between the We consider the problem of estimating the topology of multiple networks brain networks of different healthy individuals or between the same from nodal observations, where these networks are assumed social network observed at different points in time. Prominent methods to be drawn from the same (unknown) random graph model. We for multiple network inference include statistical approaches, adopt a graphon as our random graph model, which is a nonparametric primarily consisting of the joint estimation of Gaussian graphical model from which graphs of potentially different sizes can models [13-17]. These methods typically involve modifications on be drawn. The versatility of graphons allows us to tackle the joint the graphical lasso formulation with additional encouragement of inference problem even for the cases where the graphs to be recovered structural similarity. Estimation of time-varying graphs is widely contain different number of nodes and lack precise alignment popular, as the relationship between graphs is typically straightforward across the graphs. Our solution is based on combining a maximum to implement by considering that graph variation is smooth likelihood penalty with graphon estimation schemes and can be used across time [18, 19]. The above methods for estimating multiple networks to augment existing network inference methods. We validate our typically enforce similar structure, such as promoting similar proposed approach by comparing its performance against competing sparsity patterns [20].

Data Science is an exciting field for knowledge workers because it increasingly intersects with the future of how industries, society, governance and policy will function. While it's one of those vague terms thrown around a lot for students, it's actually fairly simple to define. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is thus related to an explosion of Big Data and optimizing it for human progress, machine learning and AI systems. I'm not an expert in the field by any means, just a futurist analyst, and what I see is an explosion in data science jobs globally and new talent getting into the field, people who will build the companies of tomorrow. Many of those jobs will actually be in companies that do not exist yet in South and South-East Asia and China.

In the quest for autonomous agents learning open-ended repertoires of skills, most works take a Piagetian perspective: learning trajectories are the results of interactions between developmental agents and their physical environment. The Vygotskian perspective, on the other hand, emphasizes the centrality of the socio-cultural environment: higher cognitive functions emerge from transmissions of socio-cultural processes internalized by the agent. This paper argues that both perspectives could be coupled within the learning of autotelic agents to foster their skill acquisition. To this end, we make two contributions: 1) a novel social interaction protocol called Help Me Explore (HME), where autotelic agents can benefit from both individual and socially guided exploration. In social episodes, a social partner suggests goals at the frontier of the learning agent knowledge. In autotelic episodes, agents can either learn to master their own discovered goals or autonomously rehearse failed social goals; 2) GANGSTR, a graph-based autotelic agent for manipulation domains capable of decomposing goals into sequences of intermediate sub-goals. We show that when learning within HME, GANGSTR overcomes its individual learning limits by mastering the most complex configurations (e.g. stacks of 5 blocks) with only few social interventions.

What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce artifacts, rather than decisions, as output. Meanwhile, generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering. Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion. We conducted 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users' explainability needs. Drawing from prior work, we also propose 4 types of XAI features for GenAI for code and gathered additional design ideas from participants. Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.

Ushio et al., 2021; Paranjape et al., 2021), multiword expression (MWE) identification (Espinosa-In this paper, we analyze zero-shot taxonomy In this paper, we evaluate LMs on TL benchmarks Taxonomy learning (TL) is the task of arranging using prompt-based and sentence-scoring domain terminologies into hierarchical structures techniques, and find not only that they are competitive where terms are nodes and edges denote is-a (hypernymic) with common approaches proposed in the relationships (Hwang et al., 2012). Domainspecific literature (which are typically supervised and/or concept generalization is at the core of human reliant on external resources), but that they achieve cognition (Yu et al., 2015), and a key enabler SoTa results in certain domains.