Intent modifies an actor's culpability of many types wrongdoing. Autonomous Algorithmic Agents have the capability of causing harm, and whilst their current lack of legal personhood precludes them from committing crimes, it is useful for a number of parties to understand under what type of intentional mode an algorithm might transgress. From the perspective of the creator or owner they would like ensure that their algorithms never intend to cause harm by doing things that would otherwise be labelled criminal if committed by a legal person. Prosecutors might have an interest in understanding whether the actions of an algorithm were internally intended according to a transparent definition of the concept. The presence or absence of intention in the algorithmic agent might inform the court as to the complicity of its owner. This article introduces definitions for direct, oblique (or indirect) and ulterior intent which can be used to test for intent in an algorithmic actor.

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Network regression models, where the outcome comprises the valued edge in a network and the predictors are actor or dyad-level covariates, are used extensively in the social and biological sciences. Valid inference relies on accurately modeling the residual dependencies among the relations. Frequently homogeneity assumptions are placed on the errors which are commonly incorrect and ignore critical, natural clustering of the actors. In this work, we present a novel regression modeling framework that models the errors as resulting from a community-based dependence structure and exploits the subsequent exchangeability properties of the error distribution to obtain parsimonious standard errors for regression parameters.

This article is a follow-up to my piece last week, Data Sets, Fraud, and the Future. Let's say a minor religion emerges in Ohio. Its basis is a story about a miraculous tree growing in an arid desert. The only problem is, if the members of this Church bothered to check, they would discover the exact place where the tree supposedly grew was no desert. Instead, it was an ocean.

The philosophy of Artificial Intelligence is a riddle so confounding that it is unclear where, and how, one would even address the questions plaguing its era. Is AI a revolution or a war? Nowadays, A.I can write and analyse books, beat humans at about every game conceivable, make movies, compose classical songs and help magicians perform better tricks. Beyond the arts, it also has the potential to encourage better decision-making, make medical diagnoses, and even solve some of humanity's most pressing challenges. It's intertwining with criminal justice, education, retail, recruiting, healthcare, banking, farming, defense… These advances alone could lead many to end the conversation there and then, with overwhelming evidence that the benefits of AI reach far and wide within society, outweighing the risks associated with such a technology.

An interdisciplinary research team led by the University of Maryland, College Park (UMD) and in partnership with the University of Maryland, Baltimore County (UMBC) has entered into a cooperative agreement with the U.S. Army Research Laboratory(ARL) worth up to $68 million. The agreement brings together a large, diverse collaborative of researchers--leveraging the University System of Maryland's national leadership in engineering, robotics, computer science, operations research, modeling and simulation, and cybersecurity--to drive transformational advances in artificial intelligence (AI) and autonomy. The five-year agreement will accelerate the development and deployment of safe, effective, and resilient capabilities and technologies, from wearable devices to unmanned aircraft, that work intelligently and in cooperation with each other and with human actors across multiple environments. The robust effort encompasses three areas of research thrusts, each supported by a team of faculty, staff, and students. The new collaboration builds on a more than 25-year research partnership between UMD and ARL in AI, autonomy, and modeling and simulation to spur the development of technologies that reduce human workload and risk in complex environments such as the battlefield and search-and-rescue operations.

Artificial intelligence could lead to an Orwellian future if laws to protect the public aren't enacted soon, according to Microsoft President Brad Smith. Smith made the comments to the BBC news program "Panorama" on May 26, during an episode focused on the potential dangers of artificial intelligence (AI) and the race between the United States and China to develop the technology. The warning comes about a month after the European Union released draft regulations attempting to set limits on how AI can be used. There are few similar efforts in the United States, where legislation has largely focused on limiting regulation and promoting AI for national security purposes. "I'm constantly reminded of George Orwell's lessons in his book '1984,'" Smith said.

The College of Engineering Awards acknowledge the extraordinary efforts of the college's teaching and research assistants, staff and faculty members. Sam Burden is an expert in sensorimotor control and hybrid systems and their application to robotics, neuroengineering and cyber-physical systems. He is a founding co-director of the Laboratory for Amplifying Movement and Performance (AMP Lab), where his research focuses on developing mathematical and computational modeling tools to enable collaborative learning and control between humans and machines. As a first-generation college graduate and UW engineering alum, Burden is committed to broadening participation in engineering, a goal he pursues in his role as the first DEI coordinator for the ECE department, where he works to define and implement the department's diversity, equity and inclusion goals through the formation of an advisory committee and partnering with other department leaders on strategic planning, funding, hiring and recruiting. He is the recipient of an ARO Young Investigator Award, WRF Early Faculty Award and an NSF CAREER Award.

The Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-34) was to be held May 17-19, 2021, at the Double Tree Ocean Point Resort and Spa in North Miami Beach, Florida, USA. Due to COVID-19 pandemic and travel restriction, the conference held both virtual and in-person. The planned conference events included tutorials, invited speakers, special tracks, and presentations of papers, posters, and awards. The conference chair was Keith Brawner from the Army Research Laboratory. The program co-chairs were Roman Barták from Charles University, Prague, and Eric Bell, USA.

I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. AI (artificial intelligence) offers numerous opportunities to increase your business's value.