AI that can accelerate research could drive a century of technological progress over just a few years. During such a period, new technological or political developments will raise consequential and hard-to-reverse decisions, in rapid succession. We call these developments grand challenges. These challenges include new weapons of mass destruction, AI-enabled autocracies, races to grab offworld resources, and digital beings worthy of moral consideration, as well as opportunities to dramatically improve quality of life and collective decision-making. We argue that these challenges cannot always be delegated to future AI systems, and suggest things we can do today to meaningfully improve our prospects. AGI preparedness is therefore not just about ensuring that advanced AI systems are aligned: we should be preparing, now, for the disorienting range of developments an intelligence explosion would bring.

Currently, a considerable research effort is devoted to comparing LLMs to a group of human experts, where the term "expert" is often ill-defined or variable, at best, in a state of constantly updating LLM releases. Without proper safeguards in place, LLMs will threaten to cause harm to the established structure of safe delivery of patient care which has been carefully developed throughout history to keep the safety of the patient at the forefront. A key driver of LLM innovation is founded on community research efforts which, if continuing to operate under "humans versus LLMs" principles, will expedite this trend. Therefore, research efforts moving forward must focus on effectively characterizing the safe use of LLMs in clinical settings that persist across the rapid development of novel LLM models. In this communication, we demonstrate that rather than comparing LLMs to humans, there is a need to develop strategies enabling efficient work of humans with LLMs in an almost symbiotic manner.

The annual meeting of the International Society for Autism Research took place in Seattle this week. The field's premiere scientific conference was scheduled to be held in the Emerald City five years ago, until COVID-19 dashed those plans. This time, U.S. autism researchers face a very different kind of crisis: massive cuts to federal funding, Cabinet members making false statements about the complex neurological condition they study, and a series of confusing and potentially worrisome policy announcements about autism research. In April, the U.S. Department of Health and Human Services disclosed that it's planning a 50-million "comprehensive research effort aimed at understanding the causes of [autism spectrum disorder] and improving treatments," a department spokesperson said. The effort was spurred by Secretary Robert F. Kennedy Jr.'s stated goal of determining the cause of autism, a neurological and developmental condition whose symptoms cluster around challenges with communication, social interaction and sensory processing.

Nebraska's Hau Chan has been selected to be part of an international research effort focused on the ethical deployment of artificial intelligence. The project is part of a broader collaboration between the National Science Foundation and CSIRO (Australia's national science agency) to fund groundbreaking artificial intelligence research that ultimately solves environmental and societal issues. The University of Nebraska–Lincoln will lead U.S. research efforts in collaboration with the New York-based Rensselaer Polytechnic Institute and the University of New South Wales. The work will concentrate on the development of AI-powered solutions to drought, harmful environmental emissions and infectious diseases. Chan, assistant professor in the School of Computing, will serve as principal investigator on a project that will use AI to determine appropriate allocation of resources such as water, vaccines, medical supplies and non-fossil fuel vehicle stations.

The Defense Advanced Research Projects Agency plans to conduct two workshops in 2023, aiming to convene academic, commercial and government experts to foster discussions on developing trustworthy artificial intelligence for national security purposes. DARPA's Information Innovation Office will host a virtual workshop from June 13 to 16 and an in-person workshop in Boston, Massachusetts, from July 31 to Aug. 2 as part of its AI Forward initiative. Each event will be limited to 100 attendees. Interested individuals are tasked with submitting an executive summary by Mar. According to DARPA, research efforts need to be directed toward foundational theory, engineering and human-AI teaming to delimit the scope of AI systems, ensure their real-world functionality and make them trustworthy partners for people.

The artificial intelligence for IT operations (AIOps) platform market is moving faster than can really be imagined, both in terms of vendors as well as the capabilities that make up AIOps solutions. This was driven by a continued increase of unmanageable data volumes and continued desire for business insights. To address these changes since the last Forrester Wave on AIOps two years ago, Forrester published the AIOps Reference Architecture, which defines the 18 functions required to deliver AIOps solutions. Additionally, we published guidance on the different perspectives of how to approach AIOps to address a growing organizational need to help put AIOps into practice. The Forrester Wave: Artificial Intelligence For IT Operations, Q4 2022 is focused on technology-centric AIOps solutions, which was a key element of the entry criteria in addition to single-code base and/or UI, stand-alone product, and domain-agnostic interoperability. Operationally aligned and process-centric AIOps vendors were not included in this research but will be highlighted in future Forrester research efforts.

Surrogate safety measures in the form of conflict indicators are indispensable components of the proactive traffic safety toolbox. Conflict indicators can be classified into past-trajectory-based conflicts and predicted-trajectory-based conflicts. While the calculation of the former class of conflicts is deterministic and unambiguous, the latter category is computed using predicted vehicle trajectories and is thus more stochastic. Consequently, the accuracy of prediction-based conflicts is contingent on the accuracy of the utilized trajectory prediction algorithm. Trajectory prediction can be a challenging task, particularly at intersections where vehicle maneuvers are diverse. Furthermore, due to limitations relating to the road user trajectory extraction pipelines, accurate geometric representation of vehicles during conflict analysis is a challenging task. Misrepresented geometries distort the real distances between vehicles under observation. In this research, a prediction-based conflict identification methodology was proposed. A sequence-to-sequence Recurrent Neural Network was developed to sequentially predict future vehicle trajectories for up to 3 seconds ahead. Furthermore, the proposed network was trained using the CitySim Dataset to forecast both future vehicle positions and headings to facilitate the prediction of future bounding boxes, thus maintaining accurate vehicle geometric representations. It was experimentally determined that the proposed method outperformed frequently used trajectory prediction models for conflict analysis at intersections. A comparison between Time-to-Collision (TTC) conflict identification using vehicle bounding boxes versus the commonly used vehicle center points for geometric representation was conducted. Compared to the bounding box method, the center point approach often failed to identify TTC conflicts or underestimated their severity.

Federal agencies that want to successfully scale and implement cloud computing systems into existing infrastructure can do so through several key practices, including designating expert teams, two-factor authentication, and enhanced education opportunities among users. Outlined in a White House report published earlier this month, officials documented how cloud computing systems can support further federal research and development in artificial intelligence, a goal within the broader Biden administration. Authored by the Machine Learning and Artificial Intelligence Subcommittee within the National Science and Technology Council, the report notes that leveraging cloud computing technology can enable better on-demand resources for researchers working with AI technologies. It went on to highlight opportunities for public agencies looking to bolster AI research efforts with advanced computing systems. "Agencies that have undertaken early efforts to leverage commercial cloud computing resources to advance AI R&D have commonly experienced benefits to their investments in terms of providing internal and external researchers persistent, on-demand access to cutting-edge capabilities, accelerating experimentation and the use of AI in new domains, and enabling reproducibility and scalability of the research activities and results," the report explains.

Modern educational systems have not really evolved enough to meet the needs of modern students.21 No wonder, the percentage of dropouts from university studies is quite high (40% in the U.S. and 10% in Europe7,9). The university student profile has changed over the years. While yesterday's students were mainly full-time, today's students face challenges such as work commitments, family obligations, financial constraints, physical impairments, and learning models that do not adequately engage students or help them understand core concepts.11 One might think that this issue concerns only those who fail to complete their studies, but this is view is shortsighted. Today's educational system deficiencies will affect the welfare of tomorrow's society. To improve current learning models, academic institutions around the world agree that the time has come to improve the world of education, moving from a traditional approach--where learning is standardized and available only to those with access to educational buildings--to a new paradigm that enables students to personalize their educational pathway, so they can progress at their own pace.19,21

Thanks to rapid technological developments in terms of Computational Intelligence - smart tools have been taking active roles in daily life. It is clear that the 21st century has brought many advantages of using high level computation and communication solutions to deal with real world problems. However, more technologies bring more changes to society. In this sense, the concept of smart city has been a widely discussed topic in terms of society and Artificial Intelligence oriented research efforts. The rise of smart cities is somewhat a transformation of both communities and technology use habits and surely, there are many different research orientations to shape a better future.