Shapes of cognition is a new conceptual paradigm for the computational cognitive modeling of Language - Endowed Intelligent Agents (LEIAs) . S hapes are remembered constellations of sensory, linguistic, conceptual, episodic, and procedural knowledge that allow agents to cut through the complexity of real life the same way as people do: by expecting things to be typical, recognizing patterns, acting by habit, reasoning by analogy, satisficing, and generally minimizing cognitive load to the degree situations permit . Atypical outcomes are treated using shapes - based recovery method s, such as learning on the fly, asking a human partner for help, or seeking an actionable, even if imperfect, situational understanding . Although shapes is an umbrella term, it is not vague: shapes - based modeling involves particular objectives, hypotheses, modeling strategies, knowledge bases, and actual models of wide - ranging phenomena, all implemented within a particular cognitive architecture . Such s pecificity is needed both to vet the our hypotheses and to achieve our practical aims of building useful agent systems that are explainable, extensible, and worthy of our trust, even in critical domains . However, a lthough the LEIA example of shapes - based modeling is specific, the principles can be applied more broadly, giving new life to knowledge - based and hybrid AI .

If there's one thing that turns people off from adopting new tech, it's being forced to look silly and feel uncomfortable for extended lengths of time. It was always the Achilles' heel for 3D in the past, and it remains the primary hurdle for VR headsets and goofy-looking smart glasses. Laptops, tablets, and even computer monitors have started embracing a new form of 3D technology that solves this problem entirely, without giving up just how compelling 3D can look. I've used the latest iteration of the technology and spoke with the creators--this might finally be the version of 3D that sticks. I was skeptical when I first saw this next generation of 3D technology. Interest in 3D comes in waves.

For AI agents to emulate human behavior, they must be able to perceive, meaningfully interpret, store, and use large amounts of information about the world, themselves, and other agents. Metacognition is a necessary component of all of these processes. In this paper, we briefly a) introduce content-centric computational cognitive (C4) modeling for next-generation AI agents; b) review the long history of developing C4 agents at RPI's LEIA (Language-Endowed Intelligent Agents) Lab; c) discuss our current work on extending LEIAs' cognitive capabilities to cognitive robotic applications developed using a neuro symbolic processing model; and d) sketch plans for future developments in this paradigm that aim to overcome underappreciated limitations of currently popular, LLM-driven methods in AI.

Explanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems -- which account for almost all current AI -- can't explain because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining "explanation". The human-centered explainable AI (HCXAI) movement identifies the explanation-oriented needs of users but can't fulfill them because of its commitment to machine learning. In order to achieve the kinds of explanations needed by real people operating in critical domains, we must rethink how to approach AI. We describe a hybrid approach to developing cognitive agents that uses a knowledge-based infrastructure supplemented by data obtained through machine learning when applicable. These agents will serve as assistants to humans who will bear ultimate responsibility for the decisions and actions of the human-robot team. We illustrate the explanatory potential of such agents using the under-the-hood panels of a demonstration system in which a team of simulated robots collaborate on a search task assigned by a human.

Adapting English-based large language models (LLMs) to other languages has become increasingly popular due to the efficiency and potential of cross-lingual transfer. However, existing language adaptation methods often overlook the benefits of cross-lingual supervision. In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages. This method involves augmenting the target language corpus with English entity names and training the model using left-to-right language modeling. We assess LEIA on diverse question answering datasets using 7B-parameter LLMs, demonstrating significant performance gains across various non-English languages. The source code is available at https://github.com/studio-ousia/leia.

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. All hell broke loose in the AI world after The Washington Post reported last week that a Google engineer thought that LaMDA, one of the company's large language models (LLM), was sentient. The news was followed by a frenzy of articles, videos and social media debates over whether current AI systems understand the world as we do, whether AI systems can be conscious, what are the requirements for consciousness, etc. We are currently in a state where our large language models have become good enough to convince many people -- including engineers -- that they are on par with natural intelligence. At the same time, they are still bad enough to make dumb mistakes, as these experiments by computer scientist Ernest Davis show.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. One of the dominant trends of artificial intelligence in the past decade has been to solve problems by creating ever-larger deep learning models. And nowhere is this trend more evident than in natural language processing, one of the most challenging areas of AI. In recent years, researchers have shown that adding parameters to neural networks improves their performance on language tasks. However, the fundamental problem of understanding language--the iceberg lying under words and sentences--remains unsolved.

All the sessions from Transform 2021 are available on-demand now. One of the dominant trends of artificial intelligence in the past decade has been to solve problems by creating ever-larger deep learning models. And nowhere is this trend more evident than in natural language processing, one of the most challenging areas of AI. In recent years, researchers have shown that adding parameters to neural networks improves their performance on language tasks. However, the fundamental problem of understanding language--the iceberg lying under words and sentences--remains unsolved.

Fox News Flash top entertainment and celebrity headlines for Dec. 19 are here. Check out what's clicking today in entertainment. The first "Star Wars" film, "A New Hope" was released 42 years ago in 1977. Since then, countless films, video games, television spin-offs and books have been produced to fill in every corner of the galaxy far, far away. What started out as a campy, low-budget sci-fi flick that was expected to flop, quickly grew into a juggernaut of a film franchise with plenty of content for everyone.