In the midst of the growing integration of Artificial Intelligence (AI) into various aspects of our lives, agents are experiencing a resurgence. These autonomous programs that act on behalf of humans are neither new nor exclusive to the mainstream AI movement. By exploring past incarnations of agents, we can understand what has been done previously, what worked, and more importantly, what did not pan out and why. This understanding lets us to examine what distinguishes the current focus on agents. While generative AI is appealing, this technology alone is insufficient to make new generations of agents more successful. To make the current wave of agents effective and sustainable, we envision an ecosystem that includes not only agents but also Sims, which represent user preferences and behaviors, as well as Assistants, which directly interact with the user and coordinate the execution of user tasks with the help of the agents.

While Large Language Models (LLMs) perform exceptionally well in generating accurate outputs, their "black box" nature poses significant challenges to transparency and trust. To address this, the paper proposes the TranspNet pipeline, which integrates symbolic AI with LLMs. By leveraging domain expert knowledge, retrieval-augmented generation (RAG), and formal reasoning frameworks like Answer Set Programming (ASP), TranspNet enhances LLM outputs with structured reasoning and verification.This approach strives to help AI systems deliver results that are as accurate, explainable, and trustworthy as possible, aligning with regulatory expectations for transparency and accountability. TranspNet provides a solution for developing AI systems that are reliable and interpretable, making it suitable for real-world applications where trust is critical.

This article explores the convergence of connectionist and symbolic artificial intelligence (AI), from historical debates to contemporary advancements. Traditionally considered distinct paradigms, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs), exemplified by ChatGPT and GPT-4, highlight the potential of connectionist architectures in handling human language as a form of symbols. The study argues that LLM-empowered Autonomous Agents (LAAs) embody this paradigm convergence. By utilizing LLMs for text-based knowledge modeling and representation, LAAs integrate neuro-symbolic AI principles, showcasing enhanced reasoning and decision-making capabilities. Comparing LAAs with Knowledge Graphs within the neuro-symbolic AI theme highlights the unique strengths of LAAs in mimicking human-like reasoning processes, scaling effectively with large datasets, and leveraging in-context samples without explicit re-training. The research underscores promising avenues in neuro-vector-symbolic integration, instructional encoding, and implicit reasoning, aimed at further enhancing LAA capabilities. By exploring the progression of neuro-symbolic AI and proposing future research trajectories, this work advances the understanding and development of AI technologies.

Artificial intelligence based on neural networks has made significant progress. However, there are concerns about the reliability and security of this approach due to its lack of transparency. This is the black box problem of AI. Here we show how this problem can be solved using symbolic AI, which has a transparent white box nature. The widespread use of symbolic AI is hindered by the opacity of mathematical models and natural language terms, the lack of a unified ontology, and the combinatorial explosion of search options. To solve the AI black box problem and to implement general-purpose symbolic AI, we propose to use deterministic logic cellular automata with rules based on first principles of the general theory of the relevant domain. In this case, the general theory of the relevant domain plays the role of a knowledge base for the cellular automaton inference. A cellular automaton implements automatic parallel logical inference at three levels of organization of a complex system. Our verification of several ecological hypotheses provides a successful precedent for the implementation of white-box AI. Finally, we discuss a program for creating a general-purpose symbolic AI capable of processing knowledge and ensuring the reliability and safety of automated decisions.

This paper provides an overview of the intricate relationship between social dynamics, technological advancements, and pioneering figures in the fields of cybernetics and artificial intelligence. It explores the impact of collaboration and interpersonal relationships among key scientists, such as McCulloch, Wiener, Pitts, and Rosenblatt, on the development of cybernetics and neural networks. It also discusses the contested attribution of credit for important innovations like the backpropagation algorithm and the potential consequences of unresolved debates within emerging scientific domains. It emphasizes how interpretive flexibility, public perception, and the influence of prominent figures can shape the trajectory of a new field. It highlights the role of funding, media attention, and alliances in determining the success and recognition of various research approaches. Additionally, it points out the missed opportunities for collaboration and integration between symbolic AI and neural network researchers, suggesting that a more unified approach may be possible in today's era without the historical baggage of past debates.

MICHAEL WOOLDRIDGE: AI is not about trying to create life, right? But it's kind of, very much feels like that. I mean, if we ever achieved the ultimate dream of AI, which I call the "Hollywood dream of AI," the kind of thing that we see in Hollywood movies, then we will have created machines that are conscious, potentially, in the same way that human beings are. So it's very like that kind of dream of creating life- and that, in itself, is a very old dream. It goes back to the ancient Greeks: The Greeks had myths about the blacksmiths to the gods who could create life from metal creatures.

Barely a day goes by without some new story about AI, or artificial intelligence. The excitement about it is palpable – the possibilities, some say, are endless. Fears about it are spreading fast, too. There can be much assumed knowledge and understanding about AI, which can be bewildering for people who have not followed every twist and turn of the debate. So, the Guardian's technology editors, Dan Milmo and Alex Hern, are going back to basics – answering the questions that millions of readers may have been too afraid to ask.

Despite recent breakthroughs in the field of artificial intelligence (AI) - or more specifically machine learning (ML) algorithms for object recognition and natural language processing - it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states) and allows for understanding (e.g., taking insight into causal relationships instead of 'blindly' relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to 'true' agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on 'bits of meaning' instead of information in the sense of data, quite unlike both the 'good old fashioned', symbolic AI of the past, as well as the current wave of deep neural network based, 'sub-symbolic' AI, which both share the idea of thinking as (only) information processing. In the following I propose an alternative view of NI as information processing plus 'bundle pushing', discuss an example which illustrates how bundle pushing can cut information processing short, and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Even as many enterprises are just starting to dip their toes into the AI pool with rudimentary machine learning (ML) and deep learning (DL) models, a new form of the technology known as symbolic AI is emerging from the lab that has the potential to upend both the way AI functions and how it relates to its human overseers. Symbolic AI's adherents say it more closely follows the logic of biological intelligence because it analyzes symbols, not just data, to arrive at more intuitive, knowledge-based conclusions. It's most commonly used in linguistics models such as natural language processing (NLP) and natural language understanding (NLU), but it is quickly finding its way into ML and other types of AI where it can bring much-needed visibility into algorithmic processes. The technology actually dates back to the 1950s, says expert.ai's

The most accurate language understanding systems rely on enterprise knowledge to solve business problems of any complexity. Applying such knowledge is foundational to the symbolic AI approach that excels at horizontal use cases such as text analytics, cognitive processing automation (CPA) and smart customer interactions. Typically stored in a knowledge graph, this knowledge takes the form of vocabularies, taxonomies and rules. Such elements provide consistent definitions of terms so their meaning is clear, while rules supply a means of reasoning through this knowledge so that systems actually understand the text they encounter. The application of explicit knowledge consistently provides the most accurate results for language understanding systems.