In 2002, artificial intelligence was still in winter. Despite decades of effort, dreams of bestowing computers with human-like cognition and real-world understanding had not materialized. To look for a way forward, a small group of scientists gathered for "The St. Thomas Common Sense Symposium." AI pioneer Marvin Minsky was the central presence, along with his protégé Pushpinder Singh. After the symposium, Minsky, Singh, and renowned philosopher Aaron Sloman published a paper on the group's ideas for how to reach human-like AI.

You are at the Princess's ball, and she is telling you a secret, but her orchestra of bears is making such a fearful lot of noise you cannot hear what she is saying. What do you say, dear? I'd lean in closer and say, "Could you repeat that? The bear-itone section is a bit too enthusiastic tonight!" In 1958, the year the illustrated children's book "What Do You Say, Dear?" appeared, the leaders of a field newly dubbed "artificial intelligence" spoke at a conference in Teddington, England, on "The Mechanisation of Thought Processes." Marvin Minsky, of M.I.T., talked about heuristic programming; Alan Turing gave a paper called "Learning Machines"; Grace Hopper assessed the state of computer languages; and scientists from Bell Labs débuted a computer that could synthesize human speech by having it sing "Daisy Bell" ("Daisy, Daisy, give me your answer, do . .

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.

This introductory chapter provides an overview of the evolution and impact of Artificial Intelligence (AI) technologies in today's society. Beginning with a historical context while exploring a few general definitions of AI, the author provides a timeline of the used technologies, highlighting its periods of stagnation, commonly referred to as "AI winters," and the subsequent resurgence fueled by relentless enthusiasm and investment. The narrative then transitions to focus on the transformative effects of AI on society at large, with a particular emphasis on educational applications. Through examples, the paper shows how AI technologies have moved from theoretical constructs to practical tools that are reshaping pedagogical approaches and student engagement. The essay concludes by discussing the prospects of AI in education, emphasizing the need for a balanced approach that considers both technological advancements and societal implications. Introduction We have learned from our mistakes throughout history to adapt to a hostile environment. For example, after inventing fire, which often got out of control, we went on to invent fire extinguishers, fire alarms, and develop fire services. Similarly, the invention of gunpowder and firearms led to the creation of bulletproof vests and armor-plated vehicles and the development of guard and protection services. The invention of cars was followed by the introduction of seat belts, airbags, and, more recently, self-driving automobiles. It is safe to say that technology is an expression of human will. Through technological advancements, we seek to extend our control over various aspects of our environment - be it distance, nature, or even interpersonal dynamics. Each of the tools we developed possesses the power to influence our perspectives and shape the future (Vrabie & Eduard, 2018; Vrabie, 2016). For example, farming tools have revolutionized agricultural practices, and lab instruments have opened new frontiers for scientists. Books, maps, and similar devices, often called "intellectual technologies" (Goody & Bell, 1975), have expanded our world understanding. These last ones, in particular, have had the most significant impact on society as we know it.

In the wake of large language models, there has been a resurgence of claims and questions about the Turing test and its value for AI, which are reminiscent of decades of practical "Turing" tests. If AI were quantum physics, by now several "Schr\"odinger's" cats could have been killed. Better late than never, it is time for a historical reconstruction of Turing's beautiful thought experiment. In this paper I present a wealth of evidence, including new archival sources, give original answers to several open questions about Turing's 1950 paper, and address the core question of the value of Turing's test.

Studying language models (LMs) in terms of well-understood formalisms allows us to precisely characterize their abilities and limitations. Previous work has investigated the representational capacity of recurrent neural network (RNN) LMs in terms of their capacity to recognize unweighted formal languages. However, LMs do not describe unweighted formal languages -- rather, they define \emph{probability distributions} over strings. In this work, we study what classes of such probability distributions RNN LMs can represent, which allows us to make more direct statements about their capabilities. We show that simple RNNs are equivalent to a subclass of probabilistic finite-state automata, and can thus model a strict subset of probability distributions expressible by finite-state models. Furthermore, we study the space complexity of representing finite-state LMs with RNNs. We show that, to represent an arbitrary deterministic finite-state LM with $N$ states over an alphabet $\alphabet$, an RNN requires $\Omega\left(N |\Sigma|\right)$ neurons. These results present a first step towards characterizing the classes of distributions RNN LMs can represent and thus help us understand their capabilities and limitations.

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.

When he was a student at the Massachusetts Institute of Technology (MIT), Ethernet inventor Bob Metcalfe briefly considered pursuing a career in tennis. He was captain of the 1968–1969 MIT tennis team, which had a record of 15 wins and 4 losses, and he was ranked sixth in New England in doubles, even while taking classes and holding a programming job at defense contractor Raytheon. That, unfortunately, was not enough to make a go of it. "There's playing pros and there's teaching pros," Metcalfe says. "I could easily be a teaching pro, but that just seemed boring. Metcalfe wrote his undergraduate thesis on a bus coming back from a tennis match and submitted it to Minsky at the last possible moment. The tennis world's loss was the computer world's gain, however, as Metcalfe went on to become an Internet pioneer, develop Ethernet, and help get it named a networking standard, actions that earned him the 2022 ACM A.M. Turing Award on the 50th anniversary of the invention of the technology.

Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge. However, these knowledge bases are highly incomplete. To solve this problem, we propose a web-based question answering system system with multimodal fusion of unstructured and structured information, to fill in missing information for knowledge bases. To utilize unstructured information from the Web for knowledge base completion, we design a web-based question answering system using multimodal features and question templates to extract missing facts, which can achieve good performance with very few questions. To help improve extraction quality, the question answering system employs structured information from knowledge bases, such as entity types and entity-to-entity relatedness.

Randomized neural networks are immortalized in this AI Koan: In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. Why is the net wired randomly?'' Sussman replied,I do not want it to have any preconceptions of how to play.'' Minsky then shut his eyes. Why do you close your eyes?''