Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (i.e., state changes), and their causal dependencies. These challenges are further aggravated by the natural parallelism from multi-tasking and partial observations in multi-agent collaboration. Most prior works leverage action localization or future prediction as an indirect metric for evaluating such task understanding from videos. To make a direct evaluation, we introduce the EgoTaskQA benchmark that provides a single home for the crucial dimensions of task understanding through question-answering on real-world egocentric videos. We meticulously design questions that target the understanding of (1) action dependencies and effects, (2) intents and goals, and (3) agents' beliefs about others. These questions are divided into four types, including descriptive (what status?), predictive (what will?), explanatory (what caused?), and counterfactual (what if?) to provide diagnostic analyses on spatial, temporal, and causal understandings of goal-oriented tasks. We evaluate state-of-the-art video reasoning models on our benchmark and show their significant gaps between humans in understanding complex goal-oriented egocentric videos. We hope this effort will drive the vision community to move onward with goal-oriented video understanding and reasoning.

Developed and used responsibly Artificial Intelligence (AI) is a force for global sustainable development. Given this opportunity, we expect that the many of the existing guidelines and recommendations for trustworthy or responsible AI will provide explicit guidance on how AI can contribute to the achievement of United Nations' Sustainable Development Goals (SDGs). This would in particular be the case for the AI strategies of the Nordic countries, at least given their high ranking and overall political focus when it comes to the achievement of the SDGs. In this paper, we present an analysis of existing AI recommendations from 10 different countries or organisations based on topic modelling techniques to identify how much these strategy documents refer to the SDGs. The analysis shows no significant difference on how much these documents refer to SDGs. Moreover, the Nordic countries are not different from the others albeit their long-term commitment to SDGs. More importantly, references to \textit{gender equality} (SDG 5) and \textit{inequality} (SDG 10), as well as references to environmental impact of AI development and use, and in particular the consequences for life on earth, are notably missing from the guidelines.

The minimum salary for this role is $85,000. The total compensation package includes eligibility for performance-based bonuses as well as a 1-time equity grant based on level. The actual offer, reflecting the total compensation package and benefits, will be at the company's sole discretion, and determined by a myriad of factors including, but not limited to, years of experience, depth of experience, and other relevant business considerations. OppFi offers a flexible remote environment, 401(k) matching program, and flexible paid vacation. Other benefits include medical benefits, dental and vision coverage, and tuition reimbursement.

The White House issued a call for artificial intelligence systems to be developed with built-in protections Tuesday, even as the tech industry barrels forward in an AI free-for-all. Why it matters: Automated systems can influence or even determine important aspects of Americans' lives, including healthcare, employment, housing and education. In the U.S., government regulations covering the new technology remain minimal or nonexistent. Driving the news: The Blueprint for an AI Bill of Rights, released Tuesday by the Office of Science & Technology Policy, describes 5 principles that should be incorporated into AI systems to insure their safety and transparency, limit the impact of algorithmic discrimination, and give users control over data. The report details real-world consequences of failures to put such principles into practice.

Early-stage companies are innovating new artificial intelligence-based solutions, but they often face questions as to whether such technology can be protected and the best strategy for doing so. Without an understanding of how to protect their R&D investment and claim technology as proprietary, startup companies are leaving a tool behind, possibly forfeiting market share and investments as a result. The considerations below will be useful for companies trying to understand the opportunities to protect their innovation. AI software is patentable, and applicants are seeking protection at a remarkable rate. In 2000, the U.S. Patent and Trademark Office (USPTO) had received about 10,000 applications directed to artificial intelligence, and by 2020, that number reached about 80,000 applications, of which 77% were approved.

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Rebecca Finlay about data privacy and women's rights. What is the reality of data privacy after the overruling of Roe v. Wade? Rebecca Finlay is the CEO of the non-profit Partnership on AI, overseeing the organization's mission and strategy. In this role, Rebecca ensures that the Partnership on AI and their global community of partners work together so that developments in AI advance positive outcomes for people and society.

EU lawmakers held their first political debate on the AI Act on Wednesday (5 October) as the discussion moved to more sensitive topics like the highly debated issue of biometric recognition. The AI Act is a landmark EU legislation intended to regulate Artificial Intelligence introducing a series of obligations proportional to the potential harm of the technologies' applications. So far, the co-rapporteurs of the European Parliament, the social democrat Brando Benifei and the liberal Dragoș Tudorache, have limited the discussion to the more technical aspects, hoping to build momentum before addressing the more political hurdles. This approach was not without its successes since the file progressed in several parts. In the meeting, the MEPs formally agreed on the first two batches of compromises on administrative procedures, conformity assessment, standards, and certificates.

The most important competency is the ability to learn quickly in a fast paced startup environment.

Legislation is an inherently human endeavor. But just as organizations across industries are unlocking new capabilities and efficiencies through artificial intelligence (AI), governments also can aid their legislative processes through the application of AI. For the past five years, we've studied the potential impact of AI on government. We've looked at everything from how much time AI could save workers in each US federal agency to the rate of AI adoption in US federal, state, and local governments.2 While AI can help many different areas of the legislative process--from AI assistants answering members' questions about legislation to natural language processing analyzing the US Code for contradictions--two key applications stand out.

We consider quantitative analyses of spectral data using laser-induced breakdown spectroscopy. We address the small size of training data available, and the validation of the predictions during inference on unknown data. For the purpose, we build robust calibration models using deep convolutional multitask learning architectures to predict the concentration of the analyte, alongside additional spectral information as auxiliary outputs. These secondary predictions can be used to validate the trustworthiness of the model by taking advantage of the mutual dependencies of the parameters of the multitask neural networks. Due to the experimental lack of training samples, we introduce a simulation-based data augmentation process to synthesise an arbitrary number of spectra, statistically representative of the experimental data. Given the nature of the deep learning model, no dimensionality reduction or data selection processes are required. The procedure is an end-to-end pipeline including the process of synthetic data augmentation, the construction of a suitable robust, homoscedastic, deep learning model, and the validation of its predictions. In the article, we compare the performance of the multitask model with traditional univariate and multivariate analyses, to highlight the separate contributions of each element introduced in the process.