Built-in decision thresholds for AI diagnostics are ethically problematic, as patients may differ in their attitudes about the risk of false-positive and false-negative results, which will require that clinicians assess patient values. 

Clinical decisions using AI must consider patient values

Collective behavior, and swarm formation in particular, has been studied from several perspectives within a large variety of fields, ranging from biology to physics. In this work, we apply Projective Simulation to model each individual as an artificial learning agent that interacts with its neighbors and surroundings in order to make decisions and learn from them. Within a reinforcement learning framework, we discuss one-dimensional learning scenarios where agents need to get to food resources to be rewarded. We observe how different types of collective motion emerge depending on the distance the agents need to travel to reach the resources. For instance, strongly aligned swarms emerge when the food source is placed far away from the region where agents are situated initially. In addition, we study the properties of the individual trajectories that occur within the different types of emergent collective dynamics. Agents trained to find distant resources exhibit individual trajectories that are in most cases best fit by composite correlated random walks with features that resemble Levy walks. This composite motion emerges from the collective behavior developed under the specific foraging selection pressures. On the other hand, agents trained to reach nearby resources predominantly exhibit Brownian trajectories.

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Development of swarm behavior in artificial learning agents that adapt to different foraging environments

First, this paper reviews the work on the ethics of artificial intelligence in Africa by researchers from Africa and the diaspora in a concept marked by a majority occidental contribution. Many African and diaspora researchers have proposed solutions for the development of ethical and responsible AI in Africa, but the proposed solutions are difficult to implement without the integration of ethical concepts in the training of actors. We show how education can be used to facilitate the development of responsible artificial intelligence in Africa. Through the introduction of ethics courses in academic training and capacity building of artificial intelligence development actors through research on responsible artificial intelligence in Africa, education will facilitate the integration of African ethical values and the development of responsible artificial intelligence through the diversification of artificial intelligence teams.

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Towards an ethics of AI in Africa: rule of education

Complex global behavior patterns can emerge from very simple local interactions between many agents However, no local interaction rules have been identified that generate some patterns observed in nature, for example the rotating balls, rotating tornadoes and the full-core rotating mills observed in fish collectives Here we show that locally interacting agents modelled with a minimal cognitive system can produce these collective patterns We obtained this result by using recent advances in reinforcement learning to systematically solve the inverse modelling problem: given an observed collective behavior, we automatically find a policy generating it Our agents are modelled as processing the information from neighbour agents to choose actions with a neural network and move in an environment of simulated physics Even though every agent is equipped with its own neural network, all agents have the same network architecture and parameter values, ensuring in this way that a single policy is responsible for the emergence of a given pattern We find the final policies by tuning the neural network weights until the produced collective behaviour approaches the desired one By using modular neural networks with modules using a small number of inputs and outputs, we built an interpretable model of collective motion This enabled us to analyse the policies obtained We found a similar general structure for the four different collective patterns, not dissimilar to the one we have previously inferred from experimental zebrafish trajectories; but we also found consistent differences between policies generating the different collective pattern, for example, repulsion in the vertical direction for the more three-dimensional structures of the sphere and tornado Our results illustrate how new advances in artificial intelligence, and specifically in reinforcement learning, allow new approaches to analysis and modelling of collective behavior

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Automated Discovery of Local Rules for Desired Collective-Level Behavior Through Reinforcement Learning

Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study human hepatic tissue samples, developing and validating a workflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues from adjacent non-tumour tissues in a rapid, non-disruptive, and label-free manner by using Raman spectroscopy combined with deep learning, which is validated by tissue metabolomics. This technique allows for detailed pathological identification of the cancer tissues, including subtype, differentiation grade, and tumour stage. 2D/3D Raman images of unprocessed human tissue slices with submicrometric resolution are also acquired based on visualization of molecular composition, which could assist in tumour boundary recognition and clinicopathologic diagnosis. Lastly, the potential for a portable handheld Raman system is illustrated during surgery for real-time intraoperative human liver cancer diagnosis. 

/pdf/rapid-label-free-histopathological-diagnosis-of-liver-cancer-2q128p1w.pdf

Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans—experienced nurses and newly trained community health worker staff—and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning–enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics1, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections. In a pilot field study conducted in rural South Africa, deep learning algorithms can accurately classify rapid HIV tests as positive or negative, highlighting the potential of deep learning–enabled diagnostics for use in low- and middle-income countries.

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Deep learning of HIV field-based rapid tests.

This work proposes a method for predicting the internal mechanisms of individual agents using observed collective behaviours by multi-agent reinforcement learning (MARL). Since the emergence of group behaviour among many agents can undergo phase transitions, and the action space will not in general be smooth, natural evolution strategies were adopted for updating a policy function. We tested the approach using a well-known flocking algorithm as a target model for our system to learn. With the data obtained from this rule-based model, the MARL model was trained, and its acquired behaviour was compared to the original. In the process, we discovered that agents trained by MARL can self-organize flow patterns using only local information. The expressed pattern is robust to changes in the initial positions of agents, whilst being sensitive to the training conditions used.

Learning how to flock: deriving individual behaviour from collective behaviour with multi-agent reinforcement learning and natural evolution strategies

Koki Shimada

Papers

Deep learning of HIV field-based rapid tests.

Learning how to flock: deriving individual behaviour from collective behaviour with multi-agent reinforcement learning and natural evolution strategies