AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What are the different state of AI Systems (AIS)? Neural Symbolic AI Systems, The current state of #AI is one of "reunification," rather than #deeplearning and symbolic learning. To develop genuinely robust and reliable AI systems the next level of AI will require an integration of the highly successful data-driven paradigm with a knowledge-driven approach coupled with human feedback for human-aligned intelligent systems. Data-Centric AIS, DCAI represents the recent transition from focusing on modeling to the underlying data used to train and evaluate models. Increasingly, common model architectures have begun to dominate a wide range of tasks, and predictable scaling rules have emerged.

Modern artificial intelligence is capable of wonders. It can produce breathtaking original content: poetry, prose, images, music, human faces. Last year it produced a solution to the "protein folding problem," a grand challenge in biology that has stumped researchers for half a century. Yet today's AI still has fundamental limitations. Relative to what we would expect from a truly intelligent agent--relative to that original inspiration and benchmark for artificial intelligence, human cognition--AI has a long way to go. Critics like to point to these shortcomings as evidence that the pursuit of artificial intelligence is misguided or has failed. The better way to view them, though, is as inspiration: as an inventory of the challenges that will be important to address in order to advance the state of the art in AI.

Abductive reasoning, frequently misidentified as deductive reasoning, is the process of making a plausible prediction when faced with incomplete information. For example, given a photo showing a toppled truck and a police cruiser on a snowy freeway, abductive reasoning may lead someone to infer that dangerous road conditions caused an accident. Humans can quickly consider this sort of context to arrive at a hypothesis. But AI struggles, despite recent technical advances. Motivated to explore the challenge, researchers at the Allen Institute for Artificial Intelligence, the University of California, Berkeley, and the MIT-IBM Watson AI lab created a dataset called Sherlock, a collection of over 100,000 images of scenes paired with clues a viewer could use to answer questions about the scenes.

In the last year, new neural architectures and multilingual pre-trained models have been released for Russian, which led to performance evaluation problems across a range of language understanding tasks. This paper presents Russian SuperGLUE 1.1, an updated benchmark styled after GLUE for Russian NLP models. The new version includes a number of technical, user experience and methodological improvements, including fixes of the benchmark vulnerabilities unresolved in the previous version: novel and improved tests for understanding the meaning of a word in context (RUSSE) along with reading comprehension and common sense reasoning (DaNetQA, RuCoS, MuSeRC). Together with the release of the updated datasets, we improve the benchmark toolkit based on \texttt{jiant} framework for consistent training and evaluation of NLP-models of various architectures which now supports the most recent models for Russian. Finally, we provide the integration of Russian SuperGLUE with a framework for industrial evaluation of the open-source models, MOROCCO (MOdel ResOurCe COmparison), in which the models are evaluated according to the weighted average metric over all tasks, the inference speed, and the occupied amount of RAM. Russian SuperGLUE is publicly available at https://russiansuperglue.com/.

In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. A Winograd schema is a pair of sentences that differ only in one or two words and that contain a referential ambiguity that is resolved in opposite directions in the two sentences. We have compiled a collection of Winograd schemas, designed so that the correct answer is obvious to the human reader, but cannot easily be found using selectional restrictions or statistical techniques over text corpora. A contestant in the Winograd Schema Challenge is presented with a collection of one sentence from each pair, and required to achieve human-level accuracy in choosing the correct disambiguation.

We study disambiguating of pronoun references in Winograd Schemas, which are part of the Winograd Schema Challenge, a proposed replacement for the Turing test. In particular we consider sentences where the pronoun can be resolved to both antecedents without semantic violations in world knowledge, that means for both readings of the sentence there is a possible consistent world. Nevertheless humans will strongly prefer one answer, which can be explained by pragmatic effects described in Relevance Theory. We state formal optimization criteria based on principles of Relevance Theory in a simplification of Roger Schank's graph framework for natural language understanding. We perform experiments using Answer Set Programming and report the usefulness of our criteria for disambiguation and their sensitivity to parameter variations.

We present an approach for cyberbullying detection based on state-of-the-art text classification and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors.

Concerned about the Turing test's ability to correctly evaluate if a system exhibits human-like intelligence, the Winograd Schema Challenge (WSC) has been proposed as an alternative. A Winograd Schema consists of a sentence and a question. The answers to the questions are intuitive for humans but are designed to be difficult for machines, as they require various forms of commonsense knowledge about the sentence. In this paper we demonstrate our progress towards addressing the WSC. We present an approach that identifies the knowledge needed to answer a challenge question, hunts down that knowledge from text repositories, and then reasons with them to come up with the answer. In the process we develop a semantic parser (www.kparser.org). We show that our approach works well with respect to a subset of Winograd schemas.

In this article we present DUAL-PECCS, an integrated Knowledge Representation system aimed at extending artificial capabilities in tasks such as conceptual categorization. It relies on two different sorts of cognitively inspired common-sense reasoning: prototypical reasoning and exemplars-based reasoning. Furthermore, it is grounded on the theoretical tenets coming from the dual process theory of the mind, and on the hypothesis of heterogeneous proxytypes, developed in the area of the biologically inspired cognitive architectures (BICA). The system has been integrated into the ACT-R cognitive architecture, and experimentally assessed in a conceptual categorization task, where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies. Compared to human-level categorization, the obtained results suggest that our proposal can be helpful in extending the representational and reasoning conceptual capabilities of standard cognitive artificial systems.

General videogame playing has come a long way in a short period of time, but remains at the level of solving relatively short games made up of distinct and isolated episodes. Even simple console role-playing games (RPGs) are far beyond the reach of current techniques, requiring the synthesis of cultural knowledge with compositional reasoning over several interconnected sub-games. We explore how the challenges of playing these games could spark new advances in compositional analysis of games and common-sense reasoning. General RPG playing can leverage advances in episodic general game playing and in areas like text understanding, image classification, and automated game design learning. It has direct applications in design support and AI-based game design, and the techniques used to enable it could generalize to other families of games such as adventure, open-world, and simulation games. In this paper, we describe the motivation behind general RPG playing in a sub-domain of Nintendo Entertainment System (NES) RPGs, some promising approaches to some of its fundamental issues, and immediate next steps; we conclude by describing a few concrete benchmark problems on the path towards automated play of these complex games.