As stories of human-AI interactions continue to be highlighted in the news and research platforms, the challenges are becoming more pronounced, including potential risks of overreliance, cognitive offloading, social and emotional manipulation, and the nuanced degradation of human agency and judgment. This paper surveys recent research on these issues through the lens of the psychological triad: cognition, behavior, and emotion. Observations seem to suggest that while AI can substantially enhance memory, creativity, and engagement, it also introduces risks such as diminished critical thinking, skill erosion, and increased anxiety. Emotional outcomes are similarly mixed, with AI systems showing promise for support and stress reduction, but raising concerns about dependency, inappropriate attachments, and ethical oversight. This paper aims to underscore the need for responsible and context-aware AI design, highlighting gaps for longitudinal research and grounded evaluation frameworks to balance benefits with emerging human-centric risks.

This paper develops a distributed collaborative localization algorithm based on an extended kalman filter. This algorithm incorporates Ultra-Wideband (UWB) measurements for vehicle to vehicle ranging, and shows improvements in localization accuracy where GPS typically falls short. The algorithm was first tested in a newly created open-source simulation environment that emulates various numbers of vehicles and sensors while simultaneously testing multiple localization algorithms. Predicted error distributions for various algorithms are quickly producible using the Monte-Carlo method and optimization techniques within MatLab. The simulation results were validated experimentally in an outdoor, urban environment. Improvements of localization accuracy over a typical extended kalman filter ranged from 2.9% to 9.3% over 180 meter test runs. When GPS was denied, these improvements increased up to 83.3% over a standard kalman filter. In both simulation and experimentally, the DCL algorithm was shown to be a good approximation of a full state filter, while reducing required communication between vehicles. These results are promising in showing the efficacy of adding UWB ranging sensors to cars for collaborative and landmark localization, especially in GPS-denied environments. In the future, additional moving vehicles with additional tags will be tested in other challenging GPS denied environments.

Tracking multiple moving objects of interest (OOI) with multiple robot systems (MRS) has been addressed by active sensing that maintains a shared belief of OOIs and plans the motion of robots to maximize the information quality. Mobility of robots enables the behavior of pursuing better visibility, which is constrained by sensor field of view (FoV) and occlusion objects. We first extend prior work to detect, maintain and share occlusion information explicitly, allowing us to generate occlusion-aware planning even if a priori semantic occlusion information is unavailable. The efficacy of active sensing approaches is often evaluated according to estimation error and information gain metrics. However, these metrics do not directly explain the level of cooperative behavior engendered by the active sensing algorithms. Next, we extract different emergent cooperative behaviors that stem from the same underlying algorithms but manifest differently under differing scenarios. In particular, we highlight and demonstrate three emergent behavior patterns in active sensing MRS: (i) Change of tracking responsibility between agents when tracking trajectories with divergent directions or due to a re-allocation of the resource among heterogeneous agents; (ii) Awareness of occlusions to a trajectory and temporal leave-and-return of the sensing agent; (iii) Sharing of local occlusion objects in MRS that subsequently improves the awareness of occlusion.

Transportation equity is an interdisciplinary agenda that requires both transportation and social inputs. Traditionally, transportation equity information are sources from public libraries, conferences, televisions, social media, among other. Artificial intelligence (AI) tools including advanced language models such as ChatGPT are becoming favorite information sources. However, their credibility has not been well explored. This study explored the content and usefulness of ChatGPT-generated information related to transportation equity. It utilized 152 papers retrieved through the Web of Science (WoS) repository. The prompt was crafted for ChatGPT to provide an abstract given the title of the paper. The ChatGPT-based abstracts were then compared to human-written abstracts using statistical tools and unsupervised text mining. The results indicate that a weak similarity between ChatGPT and human-written abstracts. On average, the human-written abstracts and ChatGPT generated abstracts were about 58% similar, with a maximum and minimum of 97% and 1.4%, respectively. The keywords from the abstracts of papers with over the mean similarity score were more likely to be similar whereas those from below the average score were less likely to be similar. Themes with high similarity scores include access, public transit, and policy, among others. Further, clear differences in the key pattern of clusters for high and low similarity score abstracts was observed. Contrarily, the findings from collocated keywords were inconclusive. The study findings suggest that ChatGPT has the potential to be a source of transportation equity information. However, currently, a great amount of attention is needed before a user can utilize materials from ChatGPT

BRYAN, Texas, Jan. 05, 2023 (GLOBE NEWSWIRE) -- iBio, Inc. (NYSEA:IBIO) ("iBio" or the "Company"), an AI-driven innovator of precision antibody immunotherapies, today announced that the United States Patent and Trademark Office has issued U.S. Patent No. 11,545,238, entitled "Machine Learning Method For Protein Modelling To Design Engineered Peptides," which covers a machine learning model developed to design engineered epitopes which allow precise steering of therapeutic antibodies towards specific regions of a target protein. "We are thrilled to be issued this U.S. patent, which solidifies our position as a leader in AI-driven drug discovery, and whose claims guarantee broad coverage of our proprietary, epitope-steering antibody discovery engine," said Martin Brenner, DVM. "In addition to marking an important milestone as we transform iBio into an AI-powered biotech company, this patent provides us with a competitive advantage as we continue to build our differentiated pipeline, with benefits that extend to our potential future partners." It uses a combination of a proprietary epitope steering technology, a specialized antibody library, and AI-powered antibody optimization to quickly identify and optimize molecules that can effectively address challenging drug targets. This allows for faster discovery compared to traditional antibody discovery methods.

The estimated economic cost of all fatalities due to traffic crashes in 2018 was approximately $55 billion in the United States (CDC, 2020). Such a huge cost warrants continued investigation into the contributing factors of crash fatalities and the implementation of effective countermeasures for improving traffic safety. Traditional safety studies have generally focused on identifying correlations between crashes and roadway features. Due to a lack of substantial driving behavior information in conventional historical crash datasets, these studies can seldom identify driving behaviors that contribute to crashes. Moreover, traditional studies require crash data spanning an extended period of time.

A new report offers a five-point framework government agencies can use to maximize the benefits of artificial intelligence while minimizing the risks. "Risk Management in the AI Era," released by the IBM Center for the Business of Government April 16, proposes a risk management framework that can help agencies use AI to best suit their needs. "Public managers must carefully consider both potential positive and negative outcomes, opportunities, and challenges associated with the use of these tools," the report states, as well as the relative likelihood of positive or negative outcomes. The framework is based on five criteria. The first is efficiency, which the report defines as the ratio of output generated to input required.

Instead of a human grabbing the wheel to help a self-driving shuttle in a jam, a university is looking to remote drivers. Oregon-based teleoperations company Designated Driver is working with Texas A&M University's self-driving shuttles in downtown Bryan, Texas, to remotely control the ride on a fixed half-mile route through the town's shopping district. TAMU and the city of Bryan launched the shuttles last year with a safety driver and safety navigator in the front driver and passenger seats. With Designated Driver coming in with its cameras and communication system, only the safety navigator is needed – mostly for checking on passengers and making sure everyone's buckled up. SEE ALSO: This is what it's like to control an autonomous car from miles away Designated Driver CTO Walter Sullivan said in a phone call this week that with the new set-up it's "driving remotely but almost as if they're in the vehicle."

Texas A&M University is modifying its self-driving pilot program in the city of Bryan, Texas, to have humans remotely monitor and operate the shuttles starting in September, making it one of the first commercial deployments of teleoperation technology in the country. The teleoperation technology is being provided by a Portland, Oregon-based startup called Designated Driver. It will allow humans at Texas A&M to remotely control the shuttles in situations where the self-driving system may not be up to snuff, and they'll also be able to interact with passengers on board. The new functionality could help solve a problem that similarly nascent autonomous shuttle programs have run into: crashes. The low-speed autonomous shuttles currently whispering their way around a handful of downtown areas and campuses across the country are among the first real-world tests of self-driving technology.

At his keyboard in Austin, Texas, Bryan Bishop was writing quickly. A nationally ranked speed typist, he had drafted a polite inquiry to a prominent futurist in the UK. He wanted advice on his "designer baby startup." For a few years now, Bishop, a 29-year-old programmer and Bitcoin investor, has been leaving a trail of comments about human "enhancement" on the web. He's a transhumanist, which means he thinks humans can be improved in profound ways by technology. He'd long exhorted others to do something about the human condition. Now, he had decided to do it himself.