AI can now generate breathtaking original images based on simple text prompts. Depicted here: "a ... [ ] cute corgi lives in a house made out of sushi." A powerful new form of artificial intelligence has burst onto the scene and captured the public's imagination in recent months: text-to-image AI. Text-to-image AI models generate original images based solely on simple written inputs. Users can input any text prompt they like--say, "a cute corgi lives in a house made out of sushi"--and, as if by magic, the AI will produce a corresponding image. These models produce images that have never existed in the world nor in anyone's imagination.

If you apply for this opportunity we will get you resume and its contain personal data whose treatment has been authorized by its owner for Digital OnUs, S. de RL de CV (the "Company"). If you are not the owner of this information or have no relation whatsoever with the subjects treated in it, you are requested in the most attentive way not to make copies of it and / or its attached files and delete it immediately, under the risk of being considered as responsible for the unauthorized treatment of personal data in accordance with the Federal Law on Protection of Personal Data Held by Private Parties, its Regulations, and other applicable regulations. Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Advances in language modeling are changing how American lawyers and administrators envision the practice of law [13]. In transactional settings, computational language tools are already being used in document review [18], and have illustrated promise for more sophisticated tasks like due diligence [4]. In administrative and civil settings [12, 14], many have identified the potential for computational tools to improve the accessibility of legal services [10, 26, 27, 30], thereby alleviating the United States' long standing access-to-justice crisis [8]. Unsurprisingly, the high risk nature of these tools--and their position in society--has inspired calls for better, law specific evaluation and auditing regimes [11]. The potential for impactful computational legal language tools has been magnified by the development of language Foundation Models (FM)--large scale models trained on massive corpora of text [2].

Continuous software engineering has become commonplace in numerous fields. However, in regulating intensive sectors, where additional concerns needs to be taken into account, it is often considered difficult to apply continuous development approaches, such as devops. In this paper, we present an approach for using pull requests as design controls, and apply this approach to machine learning in certified medical systems leveraging model cards, a novel technique developed to add explainability to machine learning systems, as a regulatory audit trail. The approach is demonstrated with an industrial system that we have used previously to show how medical systems can be developed in a continuous fashion.

In the Indian court system, pending cases have long been a problem. There are more than 4 crore cases outstanding. Manually summarising hundreds of documents is a time-consuming and tedious task for legal stakeholders. Many state-of-the-art models for text summarization have emerged as machine learning has progressed. Domain-independent models don't do well with legal texts, and fine-tuning those models for the Indian Legal System is problematic due to a lack of publicly available datasets. To improve the performance of domain-independent models, the authors have proposed a methodology for normalising legal texts in the Indian context. The authors experimented with two state-of-the-art domain-independent models for legal text summarization, namely BART and PEGASUS. BART and PEGASUS are put through their paces in terms of extractive and abstractive summarization to understand the effectiveness of the text normalisation approach. Summarised texts are evaluated by domain experts on multiple parameters and using ROUGE metrics. It shows the proposed text normalisation approach is effective in legal texts with domain-independent models.

Language is not only used to transmit neutral information; we often seek to persuade by arguing in favor of a particular view. Persuasion raises a number of challenges for classical accounts of belief updating, as information cannot be taken at face value. How should listeners account for a speaker's "hidden agenda" when incorporating new information? Here, we extend recent probabilistic models of recursive social reasoning to allow for persuasive goals and show that our model provides a pragmatic account for why weakly favorable arguments may backfire, a phenomenon known as the weak evidence effect. Critically, this model predicts a systematic relationship between belief updates and expectations about the information source: weak evidence should only backfire when speakers are expected to act under persuasive goals and prefer the strongest evidence. We introduce a simple experimental paradigm called the Stick Contest to measure the extent to which the weak evidence effect depends on speaker expectations, and show that a pragmatic listener model accounts for the empirical data better than alternative models. Our findings suggest further avenues for rational models of social reasoning to illuminate classical decision-making phenomena.

The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the "big picture"--the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime, Disasters, Finance, Physical Environment, Politics, and Health and Population. We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions.

Predicting the outcome of a process requires modeling the system dynamic and observing the states. In the context of social behaviors, sentiments characterize the states of the system. Affect Control Theory (ACT) uses sentiments to manifest potential interaction. ACT is a generative theory of culture and behavior based on a three-dimensional sentiment lexicon. Traditionally, the sentiments are quantified using survey data which is fed into a regression model to explain social behavior. The lexicons used in the survey are limited due to prohibitive cost. This paper uses a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model to develop a replacement for these surveys. This model achieves state-of-the-art accuracy in estimating affective meanings, expanding the affective lexicon, and allowing more behaviors to be explained.

Speaker verification (SV) provides billions of voice-enabled devices with access control, and ensures the security of voice-driven technologies. As a type of biometrics, it is necessary that SV is unbiased, with consistent and reliable performance across speakers irrespective of their demographic, social and economic attributes. Current SV evaluation practices are insufficient for evaluating bias: they are over-simplified and aggregate users, not representative of real-life usage scenarios, and consequences of errors are not accounted for. This paper proposes design guidelines for constructing SV evaluation datasets that address these short-comings. We propose a schema for grading the difficulty of utterance pairs, and present an algorithm for generating inclusive SV datasets. We empirically validate our proposed method in a set of experiments on the VoxCeleb1 dataset. Our results confirm that the count of utterance pairs/speaker, and the difficulty grading of utterance pairs have a significant effect on evaluation performance and variability. Our work contributes to the development of SV evaluation practices that are inclusive and fair.

In an era of mass personalisation and technological innovation, organisations increasingly need to make consideration of the way they use consumer data a part of their organisational culture. Since the GDPR's inception back in May 2018, there have been some encouraging findings (as I have discussed before) indicating that consumers are increasingly willing to share their data in exchange for personalised services and improved experiences. In addition, marketers are more confident about their reputation in the eyes of consumers. However, there is still a long way to go to improve consumer trust in marketing and highlight how data can be used as a force for good. Recent Adobe research reveals that over 75 per cent of UK consumers are concerned about how companies use their data.