Collaborating Authors


Can you teach AI common sense?


All the sessions from Transform 2021 are available on-demand now. Even before they speak their first words, human babies develop mental models about objects and people. This is one of the key capabilities that allows us humans to learn to live socially and cooperate (or compete) with each other. But for artificial intelligence, even the most basic behavioral reasoning tasks remain a challenge. Advanced deep learning models can do complicated tasks such as detect people and objects in images, sometimes even better than humans.

Can AI learn to reason about the world like children?


This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Even before they speak their first words, human babies develop mental models about objects and people. This is one of the key capabilities that allows us humans to learn to live socially and cooperate (or compete) with each other. But for artificial intelligence, even the most basic behavioral reasoning tasks remain a challenge. Advanced deep learning models can do complicated tasks such as detect people and objects in images, sometimes even better than humans.

The World of Reality, Causality and Real Artificial Intelligence: Exposing the Great Unknown Unknowns


"All men by nature desire to know." - Aristotle "He who does not know what the world is does not know where he is." - Marcus Aurelius "If I have seen further, it is by standing on the shoulders of giants." "The universe is a giant causal machine. The world is "at the bottom" governed by causal algorithms. Our bodies are causal machines. Our brains and minds are causal AI computers". The 3 biggest unknown unknowns are described and analyzed in terms of human intelligence and machine intelligence. A deep understanding of reality and its causality is to revolutionize the world, its science and technology, AI machines including. The content is the intro of Real AI Project Confidential Report: How to Engineer Man-Machine Superintelligence 2025: AI for Everything and Everyone (AI4EE). It is all a power set of {known, unknown; known unknown}, known knowns, known unknowns, unknown knowns, and unknown unknowns, like as the material universe's material parts: about 4.6% of baryonic matter, about 26.8% of dark matter, and about 68.3% of dark energy. There are a big number of sciences, all sorts and kinds, hard sciences and soft sciences. But what we are still missing is the science of all sciences, the Science of the World as a Whole, thus making it the biggest unknown unknowns. It is what man/AI does not know what it does not know, neither understand, nor aware of its scope and scale, sense and extent. "the universe consists of objects having various qualities and standing in various relationships" (Whitehead, Russell), "the world is the totality of states of affairs" (D. "World of physical objects and events, including, in particular, biological beings; World of mental objects and events; World of objective contents of thought" (K. How the world is still an unknown unknown one could see from the most popular lexical ontology, WordNet,see supplement. The construct of the world is typically missing its essential meaning, "the world as a whole", the world of reality, the ultimate totality of all worlds, universes, and realities, beings, things, and entities, the unified totalities. The world or reality or being or existence is "all that is, has been and will be". Of which the physical universe and cosmos is a key part, as "the totality of space and times and matter and energy, with all causative fundamental interactions".

Unmanned Aerial Search Using AI, Deep Learning & Computer Vision


Sentient Vision Systems is an artificial intelligence (AI) company that uses advanced software to enhance the performance of sensors and mission systems. ViDAR (for Visual Detection and Ranging) can detect a target in the imagery feed, discriminate between possible alternatives, and draw the operator's eye to what he or she is looking for. The power of AI can differentiate, from a distance of five nautical miles, between an arctic ice floe, a breaking wave and an upturned boat. AI and mastery of traditional computer vision technology underpins everything that Sentient Vision Systems has done over the past 17 years, since it started working on target detection solutions over land and maritime environments. Sentient's ViDAR systems use the AI within its deep learning and computer vision algorithms to detect tiny targets that are almost invisible in the imagery feed from an EO/IR sensor, especially in very challenging conditions, and filter out irrelevant information.

Online Handbook of Argumentation for AI: Volume 2 Artificial Intelligence

This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

Dive into Deep Learning Artificial Intelligence

Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. When the youngest among us (the authors) entered the field, machine learning did not command headlines in daily newspapers. Our parents had no idea what machine learning was, let alone why we might prefer it to a career in medicine or law. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g., speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component. Neural networks then, the antecedents of the deep learning models that we focus on in this book, were regarded as outmoded tools. In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical modeling. With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically draft the most mundane emails, helping people dig out from oppressively large inboxes, and software agents that dominate the worldʼs best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences--from astrophysics to biology.

Hard hat wearing detection based on head keypoint localization Artificial Intelligence

In recent years, a lot of attention is paid to deep learning methods in the context of vision-based construction site safety systems, especially regarding personal protective equipment. However, despite all this attention, there is still no reliable way to establish the relationship between workers and their hard hats. To answer this problem a combination of deep learning, object detection and head keypoint localization, with simple rule-based reasoning is proposed in this article. In tests, this solution surpassed the previous methods based on the relative bounding box position of different instances, as well as direct detection of hard hat wearers and non-wearers. The results show that the conjunction of novel deep learning methods with humanly-interpretable rule-based systems can result in a solution that is both reliable and can successfully mimic manual, on-site supervision. This work is the next step in the development of fully autonomous construction site safety systems and shows that there is still room for improvement in this area.

CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1 Artificial Intelligence

Neural networks are increasingly used in environmental science applications. Furthermore, neural network models are trained by minimizing a loss function, and it is crucial to choose the loss function very carefully for environmental science applications, as it determines what exactly is being optimized. Standard loss functions do not cover all the needs of the environmental sciences, which makes it important for scientists to be able to develop their own custom loss functions so that they can implement many of the classic performance measures already developed in environmental science, including measures developed for spatial model verification. However, there are very few resources available that cover the basics of custom loss function development comprehensively, and to the best of our knowledge none that focus on the needs of environmental scientists. This document seeks to fill this gap by providing a guide on how to write custom loss functions targeted toward environmental science applications. Topics include the basics of writing custom loss functions, common pitfalls, functions to use in loss functions, examples such as fractions skill score as loss function, how to incorporate physical constraints, discrete and soft discretization, and concepts such as focal, robust, and adaptive loss. While examples are currently provided in this guide for Python with Keras and the TensorFlow backend, the basic concepts also apply to other environments, such as Python with PyTorch. Similarly, while the sample loss functions provided here are from meteorology, these are just examples of how to create custom loss functions. Other fields in the environmental sciences have very similar needs for custom loss functions, e.g., for evaluating spatial forecasts effectively, and the concepts discussed here can be applied there as well. All code samples are provided in a GitHub repository.

Physion: Evaluating Physical Prediction from Vision in Humans and Machines Artificial Intelligence

While machine learning algorithms excel at many challenging visual tasks, it is unclear that they can make predictions about commonplace real world physical events. Here, we present a visual and physical prediction benchmark that precisely measures this capability. In realistically simulating a wide variety of physical phenomena -- rigid and soft-body collisions, stable multi-object configurations, rolling and sliding, projectile motion -- our dataset presents a more comprehensive challenge than existing benchmarks. Moreover, we have collected human responses for our stimuli so that model predictions can be directly compared to human judgments. We compare an array of algorithms -- varying in their architecture, learning objective, input-output structure, and training data -- on their ability to make diverse physical predictions. We find that graph neural networks with access to the physical state best capture human behavior, whereas among models that receive only visual input, those with object-centric representations or pretraining do best but fall far short of human accuracy. This suggests that extracting physically meaningful representations of scenes is the main bottleneck to achieving human-like visual prediction. We thus demonstrate how our benchmark can identify areas for improvement and measure progress on this key aspect of physical understanding.

Reports of the Association for the Advancement of Artificial Intelligence's 2021 Spring Symposium Series

Interactive AI Magazine

The Association for the Advancement of Artificial Intelligence's 2021 Spring Symposium Series was held virtually from March 22-24, 2021. There were ten symposia in the program: Applied AI in Healthcare: Safety, Community, and the Environment, Artificial Intelligence for K-12 Education, Artificial Intelligence for Synthetic Biology, Challenges and Opportunities for Multi-Agent Reinforcement Learning, Combining Machine Learning and Knowledge Engineering, Combining Machine Learning with Physical Sciences, Implementing AI Ethics, Leveraging Systems Engineering to Realize Synergistic AI/Machine-Learning Capabilities, Machine Learning for Mobile Robot Navigation in the Wild, and Survival Prediction: Algorithms, Challenges and Applications. This report contains summaries of all the symposia. The two-day international virtual symposium included invited speakers, presenters of research papers, and breakout discussions from attendees around the world. Registrants were from different countries/cities including the US, Canada, Melbourne, Paris, Berlin, Lisbon, Beijing, Central America, Amsterdam, and Switzerland. We had active discussions about solving health-related, real-world issues in various emerging, ongoing, and underrepresented areas using innovative technologies including Artificial Intelligence and Robotics. We primarily focused on AI-assisted and robot-assisted healthcare, with specific focus on areas of improving safety, the community, and the environment through the latest technological advances in our respective fields. The day was kicked off by Raj Puri, Physician and Director of Strategic Health Initiatives & Innovation at Stanford University spoke about a novel, automated sentinel surveillance system his team built mitigating COVID and its integration into their public-facing dashboard of clinical data and metrics. Selected paper presentations during both days were wide ranging including talks from Oliver Bendel, a Professor from Switzerland and his Swiss colleague, Alina Gasser discussing co-robots in care and support, providing the latest information on technologies relating to human-robot interaction and communication. Yizheng Zhao, Associate Professor at Nanjing University and her colleagues from China discussed views of ontologies with applications to logical difference computation in the healthcare sector. Pooria Ghadiri from McGill University, Montreal, Canada discussed his research relating to AI enhancements in health-care delivery for adolescents with mental health problems in the primary care setting.