Z Advanced Computing, Inc. (ZAC) of Potomac, MD announced on August 27 that it is funded by the US Air Force, to use ZAC's detailed 3D image recognition technology, based on Explainable-AI, for drones (unmanned aerial vehicle or UAV) for aerial image/object recognition. ZAC is the first to demonstrate Explainable-AI, where various attributes and details of 3D (three dimensional) objects can be recognized from any view or angle. "With our superior approach, complex 3D objects can be recognized from any direction, using only a small number of training samples," said Dr. Saied Tadayon, CTO of ZAC. "For complex tasks, such as drone vision, you need ZAC's superior technology to handle detailed 3D image recognition." "You cannot do this with the other techniques, such as Deep Convolutional Neural Networks, even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," continued Dr. Bijan Tadayon, CEO of ZAC.

Software star-up, Z Advanced Computing, Inc. (ZAC), has received funding from the U.S. Air Force to incorporate the company's 3D image recognition technology into unmanned aerial vehicles (UAVs) and drones for aerial image and object recognition. ZAC's in-house image recognition software is based on Explainable-AI (XAI), where computer-generated image results can be understood by human experts. ZAC – based in Potomac, Maryland – is the first to demonstrate XAI, where various attributes and details of 3D objects can be recognized from any view or angle. "With our superior approach, complex 3D objects can be recognized from any direction, using only a small number of training samples," says Dr. Saied Tadayon, CTO of ZAC. "You cannot do this with the other techniques, such as deep Convolutional Neural Networks (CNNs), even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," adds Dr. Bijan Tadayon, CEO of ZAC.

The agent also uses everyday language that non-experts can understand. The explanations, or "rationales" as the researchers call them, are designed to be relatable and inspire trust in those who might be in the workplace with AI machines or interact with them in social situations. "If the power of AI is to be democratized, it needs to be accessible to anyone regardless of their technical abilities," said Upol Ehsan, Ph.D. student in the School of Interactive Computing at Georgia Tech and lead researcher. "As AI pervades all aspects of our lives, there is a distinct need for human-centered AI design that makes black-boxed AI systems explainable to everyday users. Our work takes a formative step toward understanding the role of language-based explanations and how humans perceive them."

Much of what we see in AI today is working to reproduce the way natural intelligence works, with the hopes that we'll get to human-level decisions that are faster and more circumspect. For example, in the world of accounting, an application of AI would be to identify spending variance (i.e., transactions that break from normal practices of the company or industry). Human beings can identify spending trends and variance in a group of a few hundred transactions, most of which get identified as false positives after further investigation. In the same or less time, AI can identify spending trends and variance across billions of transactions and perform further investigation to eliminate false flags within milliseconds. The concern in this scenario is that the "further investigation" is some nebulous black box that we are expected to trust.

European Law now requires AI to be explainable in the context of adverse decisions affecting European Union (EU) citizens. At the same time, it is expected that there will be increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally-inspired framework called decision stacks that can provide for a way forward in research aimed at developing explainable AI. Leveraging findings from memory systems in biological brains, the decision stack framework operationalizes the definition of explainability and then proposes a test that can potentially reveal how a given AI decision came to its conclusion.

SAN FRANCISCO – Applications of Artificial Intelligence, machine learning, and deep learning are relatively useless without a lucid understanding of how the outputs of their predictive models are derived. Explainable AI hinges on explainability--a clear verbalizing of how the various weights and measures of machine learning models generate their outputs. Those explanations, in turn, are determined by interpretability: the statistical or mathematical understanding of the numerical outputs of decisions made by predictive models. Interpretability is foundational to unraveling some of the more consistent issues plaguing AI today. Facilitating interpretability--and using it as the impetus for refining machine learning models and the data on which they're trained--is indispensable for overcoming the threat of biased models once and for all.

Artificial intelligence (AI) is becoming big business, with all kinds of fascinating opportunities. Growth has been extraordinary: in 2015, global AI revenues were $126 billion, and last year revenues were $482 billion. The prediction for 2024 is that revenues will top $3.061 trillion. Advances in AI are making it possible for computers to take on more tasks that were formally done by humans. While this trend is creating greater efficiencies, it is also increasing the degree to which people feel that they are talking to a wall.

In abstract argumentation theory, many argumentation semantics have been proposed for evaluating argumentation frameworks. This paper is based on the following research question: Which semantics corresponds well to what humans consider a rational judgment on the acceptability of arguments? There are two systematic ways to approach this research question: A normative perspective is provided by the principle-based approach, in which semantics are evaluated based on their satisfaction of various normatively desirable principles. A descriptive perspective is provided by the empirical approach, in which cognitive studies are conducted to determine which semantics best predicts human judgments about arguments. In this paper, we combine both approaches to motivate a new argumentation semantics called SCF2. For this purpose, we introduce and motivate two new principles and show that no semantics from the literature satisfies both of them. We define SCF2 and prove that it satisfies both new principles. Furthermore, we discuss findings of a recent empirical cognitive study that provide additional support to SCF2.

A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision is b ecoming important to ensure the secure and safe use of AI techniques. Thus, we, the Special Interest Group on Semantic Web and Ontology of the Japanese Society for AI, organized a challenge calling for techniques that reason and/or estimate which character s are criminals while providing a reasonable explanation based on an open knowledge graph of a well - known Sherlock Holmes mystery story . This paper presents a summary report of the first challenge held in 2018, including the knowledge graph construction, t he techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results. The first prize went to an approach that formalized the problem as a constraint satisfaction problem and solved it using a lightweight formal method; the secon d prize went to an approach that used SPARQL and rules; the best resource prize went to a submission that constructed word embedding of characters from all sentences of Sherlock Holmes novels; and the best idea prize went to a discussion multi - agents model . We conclude this paper with the plans and issues for the next challenge in 2019.

ConArg is a suite of tools that offers a wide series of applications for dealing with argumentation problems. In this work, we present the advances we made in implementing a ranking-based semantics, based on computational choice power indexes, within ConArg. Such kind of semantics represents a method for sorting the arguments of an abstract argumentation framework, according to some preference relation. The ranking-based semantics we implement relies on Shapley, Banzhaf, Deegan-Packel and Johnston power index, transferring well know properties from computational social choice to argumentation framework ranking-based semantics.