Riot engineers bring deep knowledge of specific technical areas but also value the opportunity to work in many broader domains. As a Senior Software Engineer, you'll also dive into projects that focus on team cohesiveness and cross-team goals. You'll lead without authority and provide other engineers with a clear illustration of extraordinary engineering. The Technology Research team performs applied research to accelerate the pace of building amazing player experiences at Riot Games. As a Senior Software Engineer, you'll contribute to a research team whose focus is to leverage NLP technologies to simplify and amplify game development.

There is little doubt that artificial intelligence and machine learning will revolutionise decision-making. But how these new technologies make decisions is a mystery and the black art that goes on behind the scenes to deliver those decisions is based on mathematical models that cannot easily be explained. AI relies on accurate data, but data protection regulations can sometimes act as a barrier to prevent the access required to train algorithms with more diverse use cases. Without this diversity, the dataset is stymied by only including data from individuals who have opted in to sharing their personal information. Such data mining could improve the accuracy of the data models used in machine learning.

This week, OpenAI granted users of its image-generating AI system, DALL-E 2, the right to use their generations for commercial projects, like illustrations for children's books and art for newsletters. DALL-E 2 "trained" on approximately 650 million image-text pairs scraped from the Internet, learning from that data set the relationships between images and the words used to describe them. But while OpenAI filtered out images for specific content (e.g. As the AI community creates open source implementations of DALL-E 2 and its predecessor, DALL-E, both free and paid services are launching atop models trained on less-carefully-filtered data sets. When contacted for comment, the Pixelz.ai

In the intensifying race for global competitiveness in artificial intelligence (AI), the United States, China and the European Union are vying to be the home of what could be the most important technological revolution of our lifetimes. AI governance proposals are also developing rapidly, with the EU proposing an aggressive regulatory approach to add to its already-onerous regulatory regime. It would be imprudent for the U.S. to adopt Europe's more top-down regulatory model, however, which already decimated digital technology innovation in the past and now will do the same for AI. The key to competitive advantage in AI will be openness to entrepreneurialism, investment and talent, plus a flexible governance framework to address risks. The International Economyjournal recently asked 11 experts from Europe and the U.S. where the EU currently stood in global tech competition.

Canopy, the leading cloud-based practice management platform for accounting professionals, announced the availability of new automation enhancements to its Workflow software. Canopy's Workflow automation helps firms streamline the delivery of accounting services by reducing manual, repetitive processes leading to increased efficiency and accuracy. "As accounting firms grow, they experience more complex workflows due to more clients, more internal staff, more projects, and ever-changing regulations. Managing these complexities can be time-consuming, but these new features make that experience significantly easier for firms" By incorporating automation across accounting firms' workflows, professionals have better visibility of their processes, optimize their time to get more done, and are able to use the time saved to focus on higher-value and more profitable efforts. The robust capabilities of Workflow automation are also extremely effective as a holistic practice management solution, working across the firm to ensure information is retained and the user experience is simplified.

The use of artificial intelligence (AI) and newer technologies can help the judiciary dispose of thousands of pending cases, Aravind Kumar, Gujarat High Court Chief Justice, said. "Consider Motor Vehicle (MV) cases that account for highest in any court…They can be broadly classified into death cases, grievous injuries cases, simple injuries and for insurance claims," he added.

The UK government has regularly stated its ambition to turn the UK into a hub for AI products and services, as have other jurisdictions. And regulation has emerged as a crucial element in ensuring a thriving AI industry given that the technology's advancement has led to an increased concern about how it is used. Indeed the ethical debate over AI use and ensuring that algorithms are explainable in some way has even involved the Pope. The UK's AI rulebook takes a principles-based approach that will enable regulators in different industries to apply the rules as they see fit. According to digital minister Damian Collins, this will enable a "flexible approach [that will] help us shape the future of AI".

Video recording is a widely used method for documenting infant and child behaviours in research and clinical practice. Video data has rarely been shared due to ethical concerns of confidentiality, although the need of shared large-scaled datasets remains increasing. This demand is even more imperative when data-driven computer-based approaches are involved, such as screening tools to complement clinical assessments. To share data while abiding by privacy protection rules, a critical question arises whether efforts at data de-identification reduce data utility? We addressed this question by showcasing the Prechtl's general movements assessment (GMA), an established and globally practised video-based diagnostic tool in early infancy for detecting neurological deficits, such as cerebral palsy. To date, no shared expert-annotated large data repositories for infant movement analyses exist. Such datasets would massively benefit training and recalibration of human assessors and the development of computer-based approaches. In the current study, sequences from a prospective longitudinal infant cohort with a total of 19451 available general movements video snippets were randomly selected for human clinical reasoning and computer-based analysis. We demonstrated for the first time that pseudonymisation by face-blurring video recordings is a viable approach. The video redaction did not affect classification accuracy for either human assessors or computer vision methods, suggesting an adequate and easy-to-apply solution for sharing movement video data. We call for further explorations into efficient and privacy rule-conforming approaches for deidentifying video data in scientific and clinical fields beyond movement assessments. These approaches shall enable sharing and merging stand-alone video datasets into large data pools to advance science and public health.

Natural language generation models are computer systems that generate coherent language when prompted with a sequence of words as context. Despite their ubiquity and many beneficial applications, language generation models also have the potential to inflict social harms by generating discriminatory language, hateful speech, profane content, and other harmful material. Ethical assessment of these models is therefore critical. But it is also a challenging task, requiring an expertise in several specialized domains, such as computational linguistics and social justice. While significant strides have been made by the research community in this domain, accessibility of such ethical assessments to the wider population is limited due to the high entry barriers. This article introduces a new tool to democratize and standardize ethical assessment of natural language generation models: Tool for Ethical Assessment of Language generation models (TEAL), a component of Credo AI Lens, an open-source assessment framework.

Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorithmic fairness in regression and classification however there has been little exploration of the field for survival analysis. Survival analysis is the prediction task in which one attempts to predict the probability of an event occurring over time. Survival predictions are particularly important in sensitive settings such as when utilising machine learning for diagnosis and prognosis of patients. In this paper we explore how to utilise existing survival metrics to measure bias with group fairness metrics. We explore this in an empirical experiment with 29 survival datasets and 8 measures. We find that measures of discrimination are able to capture bias well whereas there is less clarity with measures of calibration and scoring rules. We suggest further areas for research including prediction-based fairness metrics for distribution predictions.