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.

The first major wave of AI was based on the premise that knowledge could be "represented" as a set of rules that computers could process with logic. If you could add enough rules, you could eventually produce commonsense knowledge of the world and general intelligence. In its day, it generated great excitement and funding. But its focus was on a process to produce knowledge (logic), not on knowledge itself. The assumption that knowledge consists merely as a set of assertions that could be represented in symbols was flawed. It did not scale; knowledge was never achieved.

BY RAFFAELLA AGHEMO 2022 APRIL Joint Research Centre study for a correct taxonomy of Artificial Intelligence WRITTEN BY Raffaella Aghemo, Lawyer In the context of AI Watch, the knowledge service of the European Commission to monitor the development, adoption and impact of Artificial Intelligence (AI) for Europe, launched in December 2018, is the recent study carried out by the Joint Research Centre (JRC), entitled "AI Watch - Defining Artificial Intelligence 2.0 - Towards an operational definition and taxonomy for the AI landscape". Furthermore, in April 2021, the European Commission proposed a set of actions to promote excellence in AI and rules to ensure that the technology is trusted. The taxonomy of AI is a classification of the technology itself with concepts originating mainly in mathematics, logic, philosophy and information theory, which has Russel and Norvig's taxonomy as its starting point, and is found in several studies from different perspectives. The classification in question first divides AI into weak or narrow AI (Weak or Narrow AI - ANI) and strong or General AI (AGI). ANI is the type of AI that exists today.

D. Silver et al. (2016) Mastering the game of Go with deep neural networks and tree search.

This means that the test favors "program synthesis," the subfield of AI that involves generating programs that satisfy high-level specifications. This approach is in contrast with current trends in AI, which are inclined toward creating programs that are optimized for a limited set of tasks (e.g., playing a single game). In his experiments with ARC, Chollet has found that humans can fully solve ARC tests.

This means that the test favors "program synthesis," the subfield of AI that involves generating programs that satisfy high-level specifications. This approach is in contrast with current trends in AI, which are inclined toward creating programs that are optimized for a limited set of tasks (e.g., playing a single game). In his experiments with ARC, Chollet has found that humans can fully solve ARC tests.

In this paper we present a set of key demarcations, particularly important when discussing ethical and societal issues of current AI research and applications. Properly distinguishing issues and concerns related to Artificial General Intelligence and weak AI, between symbolic and connectionist AI, AI methods, data and applications are prerequisites for an informed debate. Such demarcations would not only facilitate much-needed discussions on ethics on current AI technologies and research. In addition sufficiently establishing such demarcations would also enhance knowledge-sharing and support rigor in interdisciplinary research between technical and social sciences.

The Library of Babel --- Jorge Luis Borges 10. to google: transitive verb that means using the Google search engine to obtain information from the Web. Nominal Forms Infinitive: to google Participle: googled Gerund: googling Indicative Present I google you google he googles we google you google they google Perfect I have googled you have googled he has googled we have googled you have googled they have googled Past I googled you googled he googled we googled you googled they googled Pluperfect I had googled you had googled he had googled we had googled you had googled they had googled Future I will google you will google he will google we will google you will google they will google Future perfect I will have googled you will have googled he will have googled we will have googled you will have googled they will have googled Subjunctive Present I google you google he google we google you google they google Perfect I have googled you have googled he have googled we have googled you have ...

Google shows 44m hits on AI and 9m on Cognitive Computing and the figure below from Google Trends clearly shows that the search term "Artificial Intelligence" is more popular than "Cognitive Computing", however, I'm sure we'll start to see that gap close in 2017. In our white paper "Surviving in the AI hype", we explained some of the fundamental concepts behind AI, as well as touching on Cognitive Science and Computing but in this post we want to focus in more detail on the relationship between AI and Cognitive Computing specifically. To start off, what do Intelligence and Cognition mean if we search for a definition online? Intelligence: "the ability to learn or understand or to deal with new or trying situations: reason; also: the skilled use of reason (2): the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (as tests)." Cognition: "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses."