Washington, DC – The United States is sending a "dangerous and misleading message" by failing to hold any US military personnel responsible for a Kabul drone attack that killed 10 civilians, including seven children, human rights advocates have said. Calls for accountability for the deadly bombing on August 29 grew on Tuesday, a day after US media outlets first reported that US Defense Secretary Lloyd Austin had accepted a recommendation from top commanders not to punish any members of the military. Rights groups also urged President Joe Biden's administration to do more to help the survivors of the attack in the Afghan capital to relocate to the US. The bombing targeted the car of Zemari Ahmadi, who worked for US-based aid organisation Nutrition and Education International (NEI), killing him and nine of his family members. "I've been beseeching the US government to evacuate directly-impacted family members and NEI employees for months because their security situation is so dire," Steven Kwon, founder and president of NEI, said in a statement.

Welcome to The TechCrunch Exchange, a weekly startups-and-markets newsletter. It's inspired by the daily TechCrunch column where it gets its name. If you aren't in the United States, it's a little hard to explain. In short, certain deficiencies in our policing and judicial systems flared brightly as the week came to a close. So, today's Exchange newsletter will be shorter than intended. Hug the people you love, and everyone else.

Organizations are just starting to tap the incredible computational powers of AI for creativity, human productivity, and business results. If you thought judgment, ethics, and even creativity were the unique purview of humans, think again. The latest industry analyst predictions about artificial intelligence (AI) are out, and they're certain to oust a ton of assumptions we've made to date. Read on to find out just how smart, creative, and sincere AI will become during the next few years. Noting that South Africa granted the first patent to a creative AI system in 2021, Forrester researchers predicted creative AI systems will win dozens of patents in 2022.

Enthusiastic and inquisitive healthcare professionals are significantly excited for healthtech summits taking place around the world. Perhaps when it is about rapidly evolving technologies influencing healthcare operations like robotics in healthcare. Lately, the introduction of artificial intelligence has considerably fueled surgical robots and other robotic applications in the healthcare industry. Thus, concerns relating to the future of healthcare and scrutinization of past healthtech events are analysed in these healthtech summits. Here are some of the most helpful healthtech summits listed that cordially invite healthcare enthusiasts to join.

Digital health provider Babylon has launched its AI-powered triage tool in Rwanda to further digitise the country's healthcare system. The tool is now being used by Babylon (known locally as Babyl) call centre nurses in Rwanda to help them work more efficiently and make improved, faster decisions for their patients. It will help nurses ask patients the right questions, collect relevant information about a patient's symptoms and provide them with insights to help choose the correct triage path. If a follow-up appointment is required, the patient information collected on the triage call is passed on to the doctor, saving both the clinician and the patient time. Shivon Byamukama, CEO of Babyl Rwanda, said: "Rwandans have embraced digital healthcare that allows them to access clinicians from wherever they are. With the introduction of the AI triage tool in our call centre, we are effectively placing doctors' brains in the hands of our nurses in the digital triage."

The graph represents a network of 3,049 Twitter users whose tweets in the requested range contained "#cx", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 13 December 2021 at 01:26 UTC. The requested start date was Sunday, 12 December 2021 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 21-hour, 47-minute period from Wednesday, 08 December 2021 at 03:11 UTC to Sunday, 12 December 2021 at 00:59 UTC.

The AI Robotics Ethics Society (AIRES) is a non-profit organization founded in 2018 by Aaron Hui to promote awareness and the importance of ethical implementation and regulation of AI. AIRES is now an organization with chapters at universities such as UCLA (Los Angeles), USC (University of Southern California), Caltech (California Institute of Technology), Stanford University, Cornell University, Brown University, and the Pontifical Catholic University of Rio Grande do Sul (Brazil). AIRES at PUCRS is the first international chapter of AIRES, and as such, we are committed to promoting and enhancing the AIRES Mission. Our mission is to focus on educating the AI leaders of tomorrow in ethical principles to ensure that AI is created ethically and responsibly. As there are still few proposals for how we should implement ethical principles and normative guidelines in the practice of AI system development, the goal of this work is to try to bridge this gap between discourse and praxis. Between abstract principles and technical implementation. In this work, we seek to introduce the reader to the topic of AI Ethics and Safety. At the same time, we present several tools to help developers of intelligent systems develop "good" models. This work is a developing guide published in English and Portuguese. Contributions and suggestions are welcome.

Detecting social bias in text is challenging due to nuance, subjectivity, and difficulty in obtaining good quality labeled datasets at scale, especially given the evolving nature of social biases and society. To address these challenges, we propose a few-shot instruction-based method for prompting pre-trained language models (LMs). We select a few label-balanced exemplars from a small support repository that are closest to the query to be labeled in the embedding space. We then provide the LM with instruction that consists of this subset of labeled exemplars, the query text to be classified, a definition of bias, and prompt it to make a decision. We demonstrate that large LMs used in a few-shot context can detect different types of fine-grained biases with similar and sometimes superior accuracy to fine-tuned models. We observe that the largest 530B parameter model is significantly more effective in detecting social bias compared to smaller models (achieving at least 20% improvement in AUC metric compared to other models). It also maintains a high AUC (dropping less than 5%) in a few-shot setting with a labeled repository reduced to as few as 100 samples. Large pretrained language models thus make it easier and quicker to build new bias detectors.

While knowledge graphs contain rich semantic knowledge of various entities and the relational information among them, temporal knowledge graphs (TKGs) further indicate the interactions of the entities over time. To study how to better model TKGs, automatic temporal knowledge graph completion (TKGC) has gained great interest. Recent TKGC methods aim to integrate advanced deep learning techniques, e.g., attention mechanism and Transformer, to boost model performance. However, we find that compared to adopting various kinds of complex modules, it is more beneficial to better utilize the whole amount of temporal information along the time axis. In this paper, we propose a simple but powerful graph encoder TARGCN for TKGC. TARGCN is parameter-efficient, and it extensively utilizes the information from the whole temporal context. We perform experiments on three benchmark datasets. Our model can achieve a more than 42% relative improvement on GDELT dataset compared with the state-of-the-art model. Meanwhile, it outperforms the strongest baseline on ICEWS05-15 dataset with around 18.5% fewer parameters.

Reconfiguring two shortest paths in a graph means modifying one shortest path to the other by changing one vertex at a time so that all the intermediate paths are also shortest paths. This problem has several natural applications, namely: (a) revamping road networks, (b) rerouting data packets in synchronous multiprocessing setting, (c) the shipping container stowage problem, and (d) the train marshalling problem. When modelled as graph problems, (a) is the most general case while (b), (c) and (d) are restrictions to different graph classes. We show that (a) is intractable, even for relaxed variants of the problem. For (b), (c) and (d), we present efficient algorithms to solve the respective problems. We also generalize the problem to when at most $k$ (for a fixed integer $k\geq 2$) contiguous vertices on a shortest path can be changed at a time.