Artificial Intelligence promises systems that can reason , learn, and solve problems much like humans do, or even better. From diagnosing diseases to driving cars and managing complex logistics, the potential applications are vast. However, a fundamental challenge has persistently hampered the development and deployment of truly intelligent systems , particularly those requiring deep domain expertise: the knowledge acquisition bottleneck . This bottleneck refers to the inhere

For years, Artificial Intelligence has largely resided in the cloud. Powerful data centers, packed with high-performance processors,...

1. Introduction: The Quest for Automated Scientific Discovery: Significance and Context The scientific method, a cornerstone of human...

1. Introduction The pursuit of artificial intelligence capable of conducting scientific research autonomousl y represents a significant...

I. Introduction: Purpose, Core Claim and Significance The SakanaAI AI Scientist project represents a significant initiative within the...

I. Introduction to Neuro-Symbolic AI (NeSy) A. Defining Neuro-Symbolic AI: The Convergence of Learning and Reasoning Neuro-Symbolic Artificial Intelligence (NeSy AI) , also referred to using terms like neural-symbolic or neurosymbolic AI , represents a significant and evolving paradigm within the broader field of AI. Fundamentally, NeSy AI is characterized by the integration of connectionist systems, primarily deep learning models based on artificial neural networks (ANNs), w

Neuromorphic computing represents a fundamental departure from traditional computing paradigms, drawing inspiration directly from the structure and function of the biological brain to engineer novel hardware and software systems. This article provides a comprehensive analysis of neuromorphic computing, detailing its core concepts, foundational principles such as Spiking Neural Networks (SNNs) and event-based processing, and its stark contrast with the prevailing von Neumann

I. Introduction The Artificial Intelligence of Things (AIoT) represents a pivotal technological convergence, merging the data-gathering capabilities of the Internet of Things (IoT) infrastructure with the analytical and decision-making power of Artificial Intelligence (AI). This integration transforms connected devices from simple data conduits into intelligent systems capable of autonomous analysis, learning, and action. AIoT achieves more efficient IoT operations, enhanc

1. Introduction The rapid advancement and pervasive potential of Artificial Intelligence have propelled it to the forefront of national strategic agendas worldwide. In response to the dominance of a few global technology giants and the profound implications of AI for security, economy, and society, a distinct trend has emerged: Sovereign AI . This article defines Sovereign AI as a nation's strategic endeavor to develop, deploy, manage, and control AI capabilities—encompassing

1. Introduction: The Landscape of Reasoning Reasoning , the capacity to draw inferences, make predictions, and generate explanations, stands as a cornerstone of intelligence , both human and artificial. It is the process that allows agents to move beyond immediate perception and stored memory, enabling them to navigate complexity, solve problems, and understand the world. This article looks into the multifaceted nature of reasoning, exploring its definition, forms, and mecha

I. Introduction: AI's Expanding Role in Understanding and Interacting with Our Planet The Earth's biosphere faces unprecedented challenges, driven by anthropogenic climate change, accelerating biodiversity loss, widespread pollution, and unsustainable resource depletion. The urgency to find innovative and effective solutions for environmental monitoring, conservation, and sustainable management has never been greater. In this context, Artificial Intelligence (AI), particularl

Artificial intelligence systems increasingly operate in complex, real-world environments where information is often incomplete, ambiguous, conflicting, or scarce. Traditional probability theory, which assigns a single, precise numerical value to the likelihood of an event (e.g., P(Rain) = 0.7), often struggles to faithfully represent these deeper forms of uncertainty . It can force an AI agent to express a level of confidence that isn't justified by the available evidence,

In Artificial Intelligence, particularly in Machine Learning (ML) and Optimization, there's a constant search for the "best" algorithm –...

1. Introduction: Redefining Computation in Robotics The history of robotics and artificial intelligence has largely been dominated by a paradigm centered on sophisticated computation performed by centralized control units. In this traditional view, robots are typically constructed from rigid components, driven by precise actuators like high-torque servo motors, and governed by complex algorithms executed within a distinct 'brain'. The primary goal of the controller is often

Embodied Intelligence (EI) represents a paradigm shift in Artificial Intelligence , focusing on systems integrated into physical or simulated bodies capable of sensing, acting, and learning through direct interaction with their environment. This approach contrasts sharply with traditional disembodied AI, such as Large Language Models (LLMs) , which operate primarily on abstract data without direct physical grounding . EI emphasizes the critical role of the physical body ,

Hugo Mercier and Dan Sperber's 2017 book, " The Enigma of Reason, " presents a provocative and influential challenge to traditional views...

Artificial Intelligence constantly grapples with uncertainty . Real-world data is often incomplete, imprecise, vague, or even...

The prospect of Artificial Superintelligence (ASI)—an intellect vastly surpassing human cognitive performance across nearly all domains—presents both immense potential and profound risks. Among the most serious concerns is the possibility of existential risk (x-risk), defined as threats that could lead to human extinction or the permanent, drastic curtailment of humanity's future potential. While discussions often gravitate towards dramatic scenarios, a more insidious challen

1. Introduction to Evolutionary Intelligence / Computation Evolutionary Computation (EC) stands as a significant and dynamic subfield within the broader domain of Artificial Intelligence. It is often categorized under the umbrella of Computational Intelligence (CI), alongside other biologically or naturally inspired paradigms such as Artificial Neural Networks (ANNs) and Fuzzy Systems. While the term "Evolutionary Intelligence" (EI) is sometimes encountered, particularly in

Large Language Models (LLMs) like ChatGPT and image generators like Midjourney showcase capabilities that often feel like genuine comprehension. However, beneath this impressive facade often lies a significant limitation: shallow understanding . AI, particularly in its current dominant form (deep learning), often operates on sophisticated pattern matching rather than deep, human-like comprehension of the world . This article looks into what shallow understanding in AI means