Assaf Marron

Tao Xie, David Harel, Dezhi Ran et al.

Sustainable AI is a subfield of AI for concerning developing and using AI systems in ways of aiming to reduce environmental impact and achieve sustainability. Sustainable AI is increasingly important given that training of and inference with AI models such as large langrage models are consuming a large amount of computing power. In this article, we discuss current issues, opportunities and example solutions for addressing these issues, and future challenges to tackle, from the data and system perspectives, related to data acquisition, data processing, and AI model training and inference.

Assaf Marron, Smadar Szekely, Irun Cohen et al.

An autoencoder (AE) is a neural network that, using self-supervised training, learns a succinct parameterized representation, and a corresponding encoding and decoding process, for all instances in a given class. Here, we introduce the concept of a meta-autoencoder (MAE): an AE for a collection of autoencoders. Given a family of classes that differ from each other by the values of some parameters, and a trained AE for each class, an MAE for the family is a neural net that has learned a compact representation and associated encoder and decoder for the class-specific AEs. One application of this general concept is in research and modeling of natural evolution -- capturing the defining and the distinguishing properties across multiple species that are dynamically evolving from each other and from common ancestors. In this interim report we provide a constructive definition of MAEs, initial examples, and the motivating research directions in machine learning and biology.

David Harel, Assaf Marron

In his seminal paper ``Computing Machinery and Intelligence'', Alan Turing introduced the ``imitation game'' as part of exploring the concept of machine intelligence. The Turing Test has since been the subject of much analysis, debate, refinement and extension. Here we sidestep the question of whether a particular machine can be labeled intelligent, or can be said to match human capabilities in a given context. Instead, we first draw attention to the seemingly simpler question a person may ask themselves in an everyday interaction: ``Am I interacting with a human or with a machine?''. We then shift the focus from seeking a method for eliciting the answer, and, rather, reflect upon the importance and significance of this Human-or-Machine question and the use one may make of a reliable answer thereto. Whereas Turing's original test is widely considered to be more of a thought experiment, the Human-or-Machine matter as discussed here has obvious practical relevance. While it is still unclear if and when machines will be able to mimic human behavior with high fidelity in everyday contexts, we argue that near-term exploration of the issues raised here can contribute to refinement of methods for developing computerized systems, and may also lead to new insights into fundamental characteristics of human behavior.

Raz Yerushalmi, Guy Amir, Achiya Elyasaf et al.

Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints. Moreover, our proposed approach allows formulating these constraints using advanced model engineering techniques, such as scenario-based modeling. This mix of black-box learning-based tools with classical modeling approaches could produce systems that are effective and efficient, but are also more transparent and maintainable. We evaluated our technique using a case-study from the domain of internet congestion control, obtaining promising results.

Assaf Marron, Lior Limonad, Sarah Pollack et al.

When developing autonomous systems, engineers and other stakeholders make great effort to prepare the system for all foreseeable events and conditions. However, these systems are still bound to encounter events and conditions that were not considered at design time. For reasons like safety, cost, or ethics, it is often highly desired that these new situations be handled correctly upon first encounter. In this paper we first justify our position that there will always exist unpredicted events and conditions, driven among others by: new inventions in the real world; the diversity of world-wide system deployments and uses; and, the non-negligible probability that multiple seemingly unlikely events, which may be neglected at design time, will not only occur, but occur together. We then argue that despite this unpredictability property, handling these events and conditions is indeed possible. Hence, we offer and exemplify design principles that when applied in advance, can enable systems to deal, in the future, with unpredicted circumstances. We conclude with a discussion of how this work and a broader theoretical study of the unexpected can contribute toward a foundation of engineering principles for developing trustworthy next-generation autonomous systems.

David Harel, Rami Marelly, Assaf Marron et al.

In all software development projects, engineers face the challenge of translating the requirements layer into a design layer, then into an implementation-code layer, and then validating the correctness of the result. Many methodologies, languages and tools exist for facilitating the process, including multiple back-and-forth `refinement trips' across the requirements, design and implementation layers, by focusing on formalizing the artifacts involved and on automating a variety of tasks throughout. In this paper, we introduce a novel and unique development environment, which integrates scenario-based programming (SBP) via the LSC language and the object-oriented, visual Statecharts formalism, for the development of reactive systems. LSC targets creation of models and systems directly from requirement specifications, and Statecharts is used mainly for specifying final component behavior. Our integration enables semantically-rich joint execution, with the sharing and interfacing of objects and events, and can be used for creating and then gradually enhancing testable models from early in requirements elicitation through detailed design. In some cases, it can be used for generating final system code. We describe the technical details of the integration and its semantics and discuss its significance for future development methodologies.

David Harel, Assaf Marron, Joseph Sifakis

The potential benefits of autonomous systems have been driving intensive development of such systems, and of supporting tools and methodologies. However, there are still major issues to be dealt with before such development becomes commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next generation autonomous systems is a must. We discuss what is needed for such a foundation, identify a central aspect thereof, namely, decision-making, and focus on three main challenges: (i) how to specify autonomous system behavior and the associated decisions in the face of unpredictability of future events and conditions and the inadequacy of current languages for describing these; (ii) how to carry out faithful simulation and analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems,; and (iii) how to engineer systems that combine executable model-driven techniques and data-driven machine learning techniques. We argue that autonomics, i.e., the study of unique challenges presented by next generation autonomous systems, and research towards resolving them, can introduce substantial contributions and innovations in system engineering and computer science.

Guy Katz, Assaf Marron, Aviran Sadon et al.

Scenario-Based Programming is a methodology for modeling and constructing complex reactive systems from simple, stand-alone building blocks, called scenarios. These scenarios are designed to model different traits of the system, and can be interwoven together and executed to produce cohesive system behavior. Existing execution frameworks for scenario-based programs allow scenarios to specify their view of what the system must, may, or must not do only through very strict interfaces. This limits the methodology's expressive power and often prevents users from modeling certain complex requirements. Here, we propose to extend Scenario-Based Programming's execution mechanism to allow scenarios to specify how the system should behave using rich logical constraints. We then leverage modern constraint solvers (such as SAT or SMT solvers) to resolve these constraints at every step of running the system, towards yielding the desired overall system behavior. We provide an implementation of our approach and demonstrate its applicability to various systems that could not be easily modeled in an executable manner by existing Scenario-Based approaches.

David Harel, Guy Katz, Rami Marelly et al.

Encouraged by significant advances in algorithms and tools for verification and analysis, high level modeling and programming techniques, natural language programming, etc., we feel it is time for a major change in the way complex software and systems are developed. We present a vision that will shift the power balance between human engineers and the development and runtime environments. The idea is to endow the computer with human-like wisdom - not general wisdom, and not AI in the standard sense of the term - but wisdom geared towards classical system-building, which will be manifested, throughout development, in creativity and proactivity, and deep insights into the system's own structure and behavior, its overarching goals and rationale. Ideally, the computer will join the development team as an equal partner - knowledgeable, concerned, and responsibly active. We present a running demo of our initial efforts on the topic, illustrating on a small example what we feel is the feasibility of the ideas.