Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpetuate existing biases against racial minorities, as bias can be implicitly embedded in data. In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes. Our work makes three key contributions. First, we find that models can identify patient self-reported race from clinical notes even when the notes are stripped of explicit indicators of race. Second, we determine that human experts are not able to accurately predict patient race from the same redacted clinical notes. Finally, we demonstrate the potential harm of this implicit information in a simulation study, and show that models trained on these race-redacted clinical notes can still perpetuate existing biases in clinical treatment decisions.

Food protein digestibility and bioavailability are critical aspects in addressing human nutritional demands, particularly when seeking sustainable alternatives to animal-based proteins. In this study, we propose a machine learning approach to predict the true ileal digestibility coefficient of food items. The model makes use of a unique curated dataset that combines nutritional information from different foods with FASTA sequences of some of their protein families. We extracted the biochemical properties of the proteins and combined these properties with embeddings from a Transformer-based protein Language Model (pLM). In addition, we used SHAP to identify features that contribute most to the model prediction and provide interpretability. This first AI-based model for predicting food protein digestibility has an accuracy of 90% compared to existing experimental techniques. With this accuracy, our model can eliminate the need for lengthy in-vivo or in-vitro experiments, making the process of creating new foods faster, cheaper, and more ethical.

Recent reports from the Intergovernmental Panel on Climate Change (IPCC) show that climate change is affecting every region of our planet and that some of the changes -- such as rising temperatures and sea levels -- can only be arrested, but are irreversible. Part of the problem stems from how complex an issue climate change is. It has scientific and economic elements and sociopolitical and ethical ones, and it requires cooperation on a scale hitherto unseen. Halting climate change and responding to the effects of the damage already wrought requires two approaches. The first is mitigation, namely trying to remove carbon dioxide from the atmosphere while reducing emissions.

How do you see the growth of artificial intelligence in the Middle East? The Middle East region has always delivered on ambitious visions. This region has realised that artificial intelligence (AI) will be ubiquitous in every part of life in the next five to 10 years, will bring huge cost savings and/or efficiency gains and the governments are looking at harnessing the power of AI as quickly as possible. What are the new trends that you have seen? Upskilling Industry wide upskilling programmes to have the basic mathematical skills of data science to understand AI.

Turkey says it is determined that Ukraine continues its food exports despite Russia announcing its withdrawal from a UN-brokered grain deal, a move that has heightened concerns for nations desperate for food assistance. Russia pulled out of the deal on Saturday after what it said was a major Ukrainian drone attack on its naval fleet in annexed Crimea. Despite Moscow's decision, cargo ships set sail carrying 354,500 tonnes of grain, the most dispatched in one day since the programme began in August. Turkey, which helped broker the agreement, remained committed to the deal. "Even if Russia behaves hesitantly because it didn't receive the same benefits, we will continue decisively our efforts to serve humanity," President Recep Tayyip Erdogan said.

Several blasts shook Kyiv on Monday, days after Russia blamed Ukraine for drone attacks on its Crimea fleet in the Black Sea. At least five explosions were heard in the Ukrainian capital between 8:00 am (0600 GMT) and 8:20 am, according to AFP journalists. Kyiv had already been hit on October 10 and 17 by drones. After Monday's blasts, mayor Vitali Klitschko said in a Telegram message: "An area of Kyiv is without electricity and certain areas without water following Russian strikes." Monday's attack on the Ukrainian capital comes after Russia pulled out of a landmark agreement that allowed vital grain shipments via a maritime safety corridor.

Nellore: The conference on Futuristic Nursing being held at Narayana Nursing College here has discussed at length aspects of patient safety as also use of artificial intelligence and tele-medicine, apart from mobile health and sensor-based technologies (smartphones, smartphone apps and wearable technologies). More than 800 nurses are participating in the meet and around 40 eminent nursing leaders across the globe discussing the latest in nursing practices during the 3-day conference from Saturday. In a paper on'Artificial Intelligence in Nursing' presented jointly by Dr Ramesh M.Sc Phd, HoD Medical Surgical Nursing, St Paul's Hospital Millennium Medical College, Ethiopia, and Dr S. Indira, Dean of Narayana Nursing College, said AI offers three advantages over traditional methods -- the ability to quickly consider large volumes of data in risk prediction, increased intervention specificity (accurately flagging patients most at-risk) and automated adjustments in variable selection and calculation. "AI can help detect which patient features are most important in public health applications, allowing for more focused preventive interventions. Robots may aid nursing care tasks in hazardous clinical environments and they have the potential to automate some tasks."

Artificial intelligence in healthcare diagnosis market comprises North America, Europe, Asia Pacific (APAC), Middle East & Africa (MEA), …

Since its original appearance in 1991, the Perso-Arabic script representation in Unicode has grown from 169 to over 440 atomic isolated characters spread over several code pages representing standard letters, various diacritics and punctuation for the original Arabic and numerous other regional orthographic traditions. This paper documents the challenges that Perso-Arabic presents beyond the best-documented languages, such as Arabic and Persian, building on earlier work by the expert community. We particularly focus on the situation in natural language processing (NLP), which is affected by multiple, often neglected, issues such as the use of visually ambiguous yet canonically nonequivalent letters and the mixing of letters from different orthographies. Among the contributing conflating factors are the lack of input methods, the instability of modern orthographies, insufficient literacy, and loss or lack of orthographic tradition. We evaluate the effects of script normalization on eight languages from diverse language families in the Perso-Arabic script diaspora on machine translation and statistical language modeling tasks. Our results indicate statistically significant improvements in performance in most conditions for all the languages considered when normalization is applied. We argue that better understanding and representation of Perso-Arabic script variation within regional orthographic traditions, where those are present, is crucial for further progress of modern computational NLP techniques especially for languages with a paucity of resources.

Reinforcement learning (rl) is a popular paradigm for sequential decision making problems. The past decade's advances in rl have led to breakthroughs in many challenging domains such as video games, board games, robotics, and chip design. The sample inefficiency of deep reinforcement learning methods is a significant obstacle when applying rl to real-world problems. Transfer learning has been applied to reinforcement learning such that the knowledge gained in one task can be applied when training in a new task. Curriculum learning is concerned with sequencing tasks or data samples such that knowledge can be transferred between those tasks to learn a target task that would otherwise be too difficult to solve. Designing a curriculum that improves sample efficiency is a complex problem. In this thesis, we propose a teacher-student curriculum learning setting where we simultaneously train a teacher that selects tasks for the student while the student learns how to solve the selected task. Our method is independent of human domain knowledge and manual curriculum design. We evaluated our methods on two reinforcement learning benchmarks: grid world and the challenging Google Football environment. With our method, we can improve the sample efficiency and generality of the student compared to tabula-rasa reinforcement learning.