TOKYO, July 07, 2022 (GLOBE NEWSWIRE) -- The Global Artificial Intelligence in Genomics Market size accounted for USD 471 Million in 2021 and is estimated to reach USD 18,213 Million by 2030. According to a 2021 study, 62% of healthcare organizations were considering investing in artificial intelligence (AI) and machine learning (ML), and 72% of companies believe AI will be critical to how they do work in the future. Furthermore, 50% of organizations intend to implement and adopt AI strategies by 2025. This trend in artificial intelligence in genomics market will spur the industry demand in the coming years. Likewise, the surging adoption of AI in precision medicine is another trend that is likely to boost the AI in genomics market revenue.

When using medical images for diagnosis, either by clinicians or artificial intelligence (AI) systems, it is important that the images are of high quality. When an image is of low quality, the medical exam that produced the image often needs to be redone. In telemedicine, a common problem is that the quality issue is only flagged once the patient has left the clinic, meaning they must return in order to have the exam redone. This can be especially difficult for people living in remote regions, who make up a substantial portion of the patients at Portal Telemedicina, a digital healthcare organization based in Brazil. In this paper, we report on ongoing work regarding (i) the development of an AI system for flagging and explaining low-quality medical images in real-time, (ii) an interview study to understand the explanation needs of stakeholders using the AI system at OurCompany, and, (iii) a longitudinal user study design to examine the effect of including explanations on the workflow of the technicians in our clinics.

Neural networks have had discernible achievements in a wide range of applications. The wide-spread adoption also raises the concern of their dependability and reliability. Similar to traditional decision-making programs, neural networks can have defects that need to be repaired. The defects may cause unsafe behaviors, raise security concerns or unjust societal impacts. In this work, we address the problem of repairing a neural network for desirable properties such as fairness and the absence of backdoor. The goal is to construct a neural network that satisfies the property by (minimally) adjusting the given neural network's parameters (i.e., weights). Specifically, we propose CARE (\textbf{CA}usality-based \textbf{RE}pair), a causality-based neural network repair technique that 1) performs causality-based fault localization to identify the `guilty' neurons and 2) optimizes the parameters of the identified neurons to reduce the misbehavior. We have empirically evaluated CARE on various tasks such as backdoor removal, neural network repair for fairness and safety properties. Our experiment results show that CARE is able to repair all neural networks efficiently and effectively. For fairness repair tasks, CARE successfully improves fairness by $61.91\%$ on average. For backdoor removal tasks, CARE reduces the attack success rate from over $98\%$ to less than $1\%$. For safety property repair tasks, CARE reduces the property violation rate to less than $1\%$. Results also show that thanks to the causality-based fault localization, CARE's repair focuses on the misbehavior and preserves the accuracy of the neural networks.

Curriculum Learning is a powerful training method that allows for faster and better training in some settings. This method, however, requires having a notion of which examples are difficult and which are easy, which is not always trivial to provide. A recent metric called C-Score acts as a proxy for example difficulty by relating it to learning consistency. Unfortunately, this method is quite compute intensive which limits its applicability for alternative datasets. In this work, we train models through different methods to predict C-Score for CIFAR-100 and CIFAR-10. We find, however, that these models generalize poorly both within the same distribution as well as out of distribution. This suggests that C-Score is not defined by the individual characteristics of each sample but rather by other factors. We hypothesize that a sample's relation to its neighbours, in particular, how many of them share the same labels, can help in explaining C-Scores. We plan to explore this in future work.

The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still uncertain what are the most influential components leading to their performance improvement. This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a well-performing Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) designed by the irace package on nine constrained problems. We then contrast the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the hypervolume. Our results indicate that the most influential components were the restart and update strategies, with higher increments in performance and more distinct metric values. Also, their relative influence depends on the problem difficulty: not using the restart strategy was more influential in problems where MOEA/D performs better; while the update strategy was more influential in problems where MOEA/D performs the worst.

Mental health disorders may cause severe consequences on all the countries' economies and health. For example, the impacts of the COVID-19 pandemic, such as isolation and travel ban, can make us feel depressed. Identifying early signs of mental health disorders is vital. For example, depression may increase an individual's risk of suicide. The state-of-the-art research in identifying mental disorder patterns from textual data, uses hand-labelled training sets, especially when a domain expert's knowledge is required to analyse various symptoms. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyse the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base (KB) for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating the generating of training data from a domain-specific Knowledge Base (KB). We adopt a typical scenario for analysing social media to identify major depressive disorder symptoms from the textual content generated by social users. We use this scenario to evaluate how our knowledge-based approach significantly improves the quality of results.

Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in reinforcement learning and robotic scenarios, to better understand the robot decision-making process. Previous work, however, has been widely focused on providing technical explanations that can be better understood by AI practitioners than non-expert end-users. In this work, we make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action. These explanations are intended to be understood by people who have no or very little experience with artificial intelligence methods. This paper presents a user trial to study whether these explanations that focus on the probability an action has of succeeding in its goal constitute a suitable explanation for non-expert end-users. The results obtained show that non-expert participants rate robot explanations that focus on the probability of success higher and with less variance than technical explanations generated from Q-values, and also favor counterfactual explanations over standalone explanations.

Carlos Gaitan, the CEO and Co-founder of Benchmark Labs a leading provider of AI & IoT-driven weather forecasting solutions for the agriculture, energy, and insurance sectors joins Enterprise Radio. Dr. Gaitan is the Co-founder and CEO of Benchmark Labs. He did his doctoral studies at the University of British Columbia (Vancouver, Canada) working with William Hsieh in machine learning applications in the environmental sciences. He also holds a Bachelor degree in Civil Engineering and a Master degree in Hydrosystems from the Pontificia Universidad Javeriana (Bogota, Colombia). He is an elected member of the American Meteorological Society's (AMS) Artificial Intelligence Committee.

Having a rich multimodal inner language is an important component of human intelligence that enables several necessary core cognitive functions such as multimodal prediction, translation, and generation. Building upon the Conscious Turing Machine (CTM), a machine model for consciousness proposed by Blum and Blum (2021), we describe the desiderata of a multimodal language called Brainish, comprising words, images, audio, and sensations combined in representations that the CTM's processors use to communicate with each other. We define the syntax and semantics of Brainish before operationalizing this language through the lens of multimodal artificial intelligence, a vibrant research area studying the computational tools necessary for processing and relating information from heterogeneous signals. Our general framework for learning Brainish involves designing (1) unimodal encoders to segment and represent unimodal data, (2) a coordinated representation space that relates and composes unimodal features to derive holistic meaning across multimodal inputs, and (3) decoders to map multimodal representations into predictions (for fusion) or raw data (for translation or generation). Through discussing how Brainish is crucial for communication and coordination in order to achieve consciousness in the CTM, and by implementing a simple version of Brainish and evaluating its capability of demonstrating intelligence on multimodal prediction and retrieval tasks on several real-world image, text, and audio datasets, we argue that such an inner language will be important for advances in machine models of intelligence and consciousness.

As the number of smart devices that surround us increases, so do the opportunities to create smart socially-aware systems. In this context, mobile devices can be used to collect data about students and to better understand how their day-to-day routines can influence their academic performance. Moreover, the Covid-19 pandemic led to new challenges and difficulties, also for students, with considerable impact on their lifestyle. In this paper we present a dataset collected using a smartphone application (ISABELA), which include passive data (e.g., activity and location) as well as self-reported data from questionnaires. We present several tests with different machine learning models, in order to classify students' performance. These tests were carried out using different time windows, showing that weekly time windows lead to better prediction and classification results than monthly time windows. Furthermore, it is shown that the created models can predict student performance even with data collected from different contexts, namely before and during the Covid-19 pandemic. SVMs, XGBoost and AdaBoost-SAMME with Random Forest were found to be the best algorithms, showing an accuracy greater than 78%. Additionally, we propose a pipeline that uses a decision level median voting algorithm to further improve the models' performance, by using historic data from the students to further improve the prediction. Using this pipeline, it is possible to further increase the performance of the models, with some of them obtaining an accuracy greater than 90%.