AI as a user of AI: Towards responsible autonomy.

Recent advancements in Artificial Intelligence (AI), particularly in generative language models and algorithms, have led to significant impacts across diverse domains. AI capabilities to address prompts are growing beyond human capability but we expect AI to perform well also as a prompt engineer. Additionally, AI can serve as a guardian for ethical, security, and other predefined issues related to generated content. We postulate that enforcing dialogues among AI-as-prompt-engineer, AI-as-prompt-responder, and AI-as-Compliance-Guardian can lead to high-quality and responsible solutions. This paper introduces a novel AI collaboration paradigm emphasizing responsible autonomy, with implications for addressing real-world challenges. The paradigm of responsible AI-AI conversation establishes structured interaction patterns, guaranteeing decision-making autonomy. Key implications include enhanced understanding of AI dialogue flow, compliance with rules and regulations, and decision-making scenarios exemplifying responsible autonomy. Real-world applications envision AI systems autonomously addressing complex challenges. We have made preliminary testing of such a paradigm involving instances of ChatGPT autonomously playing various roles in a set of experimental AI-AI conversations and observed evident added value of such a framework.

Hyper-flexible Convolutional Neural Networks based on Generalized Lehmer and Power Means.

Convolutional Neural Network is one of the famous members of the deep learning family of neural network architectures, which is used for many purposes, including image classification. In spite of the wide adoption, such networks are known to be highly tuned to the training data (samples representing a particular problem), and they are poorly reusable to address new problems. One way to change this would be, in addition to trainable weights, to apply trainable parameters of the mathematical functions, which simulate various neural computations within such networks. In this way, we may distinguish between the narrowly focused task-specific parameters (weights) and more generic capability-specific parameters. In this paper, we suggest a couple of flexible mathematical functions (Generalized Lehmer Mean and Generalized Power Mean) with trainable parameters to replace some fixed operations (such as ordinary arithmetic mean or simple weighted aggregation), which are traditionally used within various components of a convolutional neural network architecture. We named the overall architecture with such an update as a hyper-flexible convolutional neural network. We provide mathematical justification of various components of such architecture and experimentally show that it performs better than the traditional one, including better robustness regarding the adversarial perturbations of testing data.

Neural Networks With Disabilities: An Introduction to Complementary Artificial Intelligence.

Machine learning is a good tool to simulate human cognitive skills as it is about mapping perceived information to various labels or action choices, aiming at optimal behavior policies for a human or an artificial agent operating in the environment. Regarding autonomous systems, objects and situations are perceived by some receptors as divided between sensors. Reactions to the input (e.g., actions) are distributed among the particular capability providers or actuators. Cognitive models can be trained as, for example, neural networks. We suggest training such models for cases of potential disabilities. Disability can be either the absence of one or more cognitive sensors or actuators at different levels of cognitive model. We adapt several neural network architectures to simulate various cognitive disabilities. The idea has been triggered by the "coolability" (enhanced capability) paradox, according to which a person with some disability can be more efficient in using other capabilities. Therefore, an autonomous system (human or artificial) pretrained with simulated disabilities will be more efficient when acting in adversarial conditions. We consider these coolabilities as complementary artificial intelligence and argue on the usefulness if this concept for various applications.