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.

Recent works for Open-domain Question Answering refer to an external knowledge base using a retriever model, optionally rerank the passages with a separate reranker model and generate an answer using an another reader model. Despite performing related tasks, the models have separate parameters and are weakly-coupled during training. In this work, we propose casting the retriever and the reranker as hard-attention mechanisms applied sequentially within the transformer architecture and feeding the resulting computed representations to the reader. In this singular model architecture the hidden representations are progressively refined from the retriever to the reranker to the reader, which is more efficient use of model capacity and also leads to better gradient flow when we train it in an end-to-end manner. We also propose a pre-training methodology to effectively train this architecture. We evaluate our model on Natural Questions and TriviaQA open datasets and for a fixed parameter budget, our model outperforms the previous state-of-the-art model by 1.0 and 0.7 exact match scores.

A phrase like "It's cold today" would suggest a very different temperature if it were uttered in Nairobi or Montreal, while words like "troll" and "tweet" referred to totally different things just a generation ago. Although contemporary large-scale pretrained language models are very effective at learning linguistic representations, they are not as well equipped at capturing speaker/author-related temporal, geographical, social and other contextual aspects. In the new paper LMSOC: An Approach for Socially Sensitive Pretraining, a Twitter Cortex research team proposes LMSOC, a simple but effective approach for learning both linguistically contextualized and socially sensitive representations in large-scale language models. An implicit assumption in most pretrained language models (PLMs) is that language is independent of extra-linguistic contexts such as speaker/author identity and social settings. Despite the impressive achievements of PLMs, this remains a critical weakness, as there is strong evidence that socio-linguistics can significantly impact social context processing performance.

The writer is co-founder and head of research and development at Qure.ai, an AI developer for medical images In a medical artificial intelligence business, the quality of your algorithms -- and therefore the value of your company -- depends on your access to data. In this, the health tech sector is in some ways similar to advertising and internet search industries: it has quickly learnt that data is immensely valuable. However, on the internet, most user-generated data is used to train algorithms that encourage consumption, commerce or engagement. Health data is vastly different -- it can be used for the global public good. It can help us track epidemics and prevent their spread, discover new drugs and diagnostics, and advance medical research that can help us live healthier, longer lives.

DUBAI, 12th December, 2021 (WAM) -- A prominent line-up of researchers, practitioners and stakeholders from academia, industry and governments from 38 countries will gather tomorrow in Dubai for the IEEE Global Conference on Artificial Intelligence & Internet of Things (2021 IEEE GCAIoT) to share their latest research contributions, and exchange knowledge with the common goal of shaping the future of the interaction among Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), 5G, and related technologies to foster digital transformation and develop sustainable, smart cities. An introductory talk will focus on 50 Years of Architecting: How the UAE Became The Country For the Future? Organised by Institute of Electrical and Electronics Engineers, Inc. (IEEE) in partnership with Dubai University, the conference will include paper presentations, poster sessions, and project demonstrations, along with prominent keynote speakers and industry-focused workshops to address the unprecedented COVID-19 and future similar epidemics, life after COVID-19 and how to use AI and IoT to fight altogether and cope with consequences. Other events along with the conference will include IEEE 5G Summit, Women in Technology, Industry Summit, Africa AI and IoT Challenge, Triple Helix Exhibition, Poster Presentation, Workshops on IoT, Arab AIoT Challenge, Industry Exhibition and Student Poster Contest.