Evaluation is a critical activity associated with any theory. Yet this has proven to be a n exceptionally challenging activity for theories based on cognitive architectures. For an overlapping set of reasons, evaluation can also be challenging for theories based on generative neural architectures. T h is dual challenge is approached here by leveraging a broad perspective on theory evaluation to yield a wide - ranging, albeit qualitative, comparison of whole - mind - orie n ted cognitive and generative architectures an d the full systems th a t are based on these architectures .

One goal of AI (and AGI) is to identify and understand specific mechanisms and representations sufficient for general intelligence. Often, this work manifests in research focused on architectures and many cognitive architectures have been explored in AI/AGI. However, different research groups and even different research traditions have somewhat independently identified similar/common patterns of processes and representations or "cognitive design patterns" that are manifest in existing architectures. Today, AI systems exploiting large language models (LLMs) offer a relatively new combination of mechanisms and representations available for exploring the possibilities of general intelligence. This paper outlines a few recurring cognitive design patterns that have appeared in various pre-transformer AI architectures. We then explore how these patterns are evident in systems using LLMs, especially for reasoning and interactive ("agentic") use cases. Examining and applying these recurring patterns enables predictions of gaps or deficiencies in today's Agentic LLM Systems and identification of subjects of future research towards general intelligence using generative foundation models.

A beginning is made at mapping four neural theories of consciousness onto the Common Model of Cognition. This highlights how the four jointly depend on recurrent local modules plus a cognitive cycle operating on a global working memory with complex states, and reveals how an existing integrative view of consciousness from a neural perspective aligns with the Com-mon Model.

The Common Model of Cognition (CMC) provides an abstract characterization of the structure and processing required by a cognitive architecture for human-like minds. We propose a unified approach to integrating metacognition within the CMC. We propose that metacog-nition involves reasoning over explicit representations of an agent's cognitive capabilities and processes in working memory. Our proposal exploits the existing cognitive capabilities of the CMC, making minimal extensions in the structure and information available within working memory. We provide examples of metacognition within our proposal.

We present a system for interpretable, symbolic, interactive task learning from dialog using a GPT model as a conversational front-end. The learned tasks are represented as hierarchical decompositions of predicate-argument structures with scoped variable arguments. By using a GPT model to convert interactive dialog into a semantic representation, and then recursively asking for definitions of unknown steps, we show that hierarchical task knowledge can be acquired and re-used in a natural and unrestrained conversational environment. We compare our system to a similar architecture using a more conventional parser and show that our system tolerates a much wider variety of linguistic variance.

Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. In this paper, we explore how computational models of analogical processing can be brought into these architectures to enable concept acquisition from examples obtained interactively. We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We frame the problem of concept learning as embedded within the larger context of interactive task learning (ITL) and embodied language processing (ELP). We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts that are useful not only in recognition of a concept in the environment but also in action selection. Our approach has been instantiated in an implemented cognitive system AILEEN and evaluated on a simulated robotic domain.

John Laird, the John L. Tishman Professor of Engineering, has been awarded the 2018 Herbert A. Simon Prize for Advances in Cognitive Systems along with his collaborator Prof. Paul Rosenbloom of the University of Southern California. This award recognizes the pair's research on cognitive architectures, especially their Soar project, their applications to knowledge-based systems and models of human cognition, and their contributions to theories of representation, reasoning, problem solving, and learning. The recipients, the awarding committee writes, have been "energetic contributors to AI and cognitive science" for over 30 years. Laird's and Rosenbloom's interdisciplinary and integrative research, both jointly and individually, has addressed many facets of high-level cognition, and their contributions to Soar have helped create one of the industry's most successful tools for developing intelligent systems. Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior.

This paper proposes that the "right" abstraction for representing general intelligence depends on the timescale of behavior under study (Newell 1990) and overall goals of the research – is it to faithfully model the brain, the mind, or to achieve the same functionality? I briefly describe my approach, which focuses on functionality and time scales above .1 seconds. My strategy is to draw inspiration from neuroscience and cognitive psychology to achieve general intelligence through the study and development of the Soar symbolic cognitive architecture.

At a nondescript building near downtown Chicago, Marc Gyongyosi along with also the small but growing team of IFM/Onetrack.AI have a single principle that rules them: think easy. The words have been written in easy ribbon on a simple sheet of paper that is stuck into a back upstairs wall of the industrial two-story workspace. Sitting at his desk, situated near an oft-used ping-pong table along with prototypes of drones out of his school days suspended overhead, Gyongyosi throws some keys on a notebook to pull grainy video footage of a forklift driver running his car in a warehouse. It had been seized from overhead courtesy of a Onetrack. Artificial intellect is impacting the potential of virtually every business and every individual being. Artificial intelligence has acted as the primary catalyst of emerging technologies such as large statistics, robotics and IoT, and it'll continue to function as a technological innovator for the near future.

June 8, 2019 Updated: April 20, 2020 "[AI] is going to change the world more than anything in the history of mankind. AI oracle and venture capitalist Dr. Kai-Fu Lee, 2018 In a nondescript building close to downtown Chicago, Marc Gyongyosi and the small but growing crew of IFM / Onetrack.AI have one rule that rules them all: think simple. The words are written in simple font on a simple sheet of paper that's stuck to a rear upstairs wall of their industrial two-story workspace. Sitting at his cluttered desk, located near an oft-used ping-pong table and prototypes of drones from his college days suspended overhead, Gyongyosi punches some keys on a laptop to pull up grainy video footage of a forklift driver operating his vehicle in a warehouse. It was captured from overhead courtesy of a Onetrack.AI "forklift vision system." The Future of Artificial Intelligence Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. Employing machine learning and computer vision for detection and classification of various "safety events," the shoebox-sized device doesn't see all, but it sees plenty. Like which way the driver is looking as he operates the vehicle, how fast he's driving, where he's driving, locations of the people around him and how other forklift operators are maneuvering their vehicles. IFM's software automatically detects safety violations (for example, cell phone use) and notifies warehouse managers so they can take immediate action. The main goals are to prevent accidents and increase efficiency. The mere knowledge that one of IFM's devices is watching, Gyongyosi claims, has had "a huge effect." Marc Gyongyosi Photo Credit: IFM/OneTrack.AI The lower level of IFM was designed to mimic a warehouse environment so products can be effectively tested on site. Photo Credit: IFM/OneTrack.AI "If you think about a camera, it really is the richest sensor available to us today at a very interesting price point," he says. "Because of smartphones, camera and image sensors have become incredibly inexpensive, yet we capture a lot of information.