Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is the user perspective. In particular, there remain many open questions regarding how best to address the diverse information needs of users, incorporating varying facets and levels of difficulty. This paper introduces a taxonomy of user information needs, which guides us to study the pros, cons and possible synergies of Large Language Models, Knowledge Graphs and Search Engines. From this study, we derive a roadmap for future research.

Reasoning, the ability to logically draw conclusions from existing knowledge, is a hallmark of human. Together with perception, they constitute the two major themes of artificial intelligence. While deep learning has pushed the limit of perception beyond human-level performance, the progress in reasoning domains is way behind. One fundamental reason is that reasoning problems usually have flexible structures for both knowledge and queries, and many existing models only perform well on structures seen during training. Here we aim to push the boundary of reasoning models by devising algorithms that generalize across knowledge and query structures, as well as systems that accelerate development on structured data. This thesis consists of three parts. In Part I, we study models that can inductively generalize to unseen knowledge graphs with new entity and relation vocabularies. For new entities, we propose a framework that learns neural operators in a dynamic programming algorithm computing path representations. For relations, we construct a relation graph to capture the interactions between relations, thereby converting new relations into new entities. In Part II, we propose two solutions for generalizing across multi-step queries on knowledge graphs and text respectively. For knowledge graphs, we show that multi-step queries can be solved by multiple calls of graph neural networks and fuzzy logic operations. For text, we devise an algorithm to learn explicit knowledge as textual rules to improve large language models on multi-step queries. In Part III, we propose two systems to facilitate machine learning development on structured data. Our library treats structured data as first-class citizens and removes the barrier for developing algorithms on structured data. Our node embedding system solves the GPU memory bottleneck of embedding matrices and scales to graphs with billion nodes.

Artificial Intelligence is now recognized as a general-purpose technology with ample impact on human life. This work aims at understanding the evolution of AI and, in particular Machine learning, from the perspective of researchers' contributions to the field. In order to do so, we present several measures allowing the analyses of AI and machine learning researchers' impact, influence, and leadership over the last decades. This work also contributes, to a certain extent, to shed new light on the history and evolution of AI by exploring the dynamics involved in the field's evolution by looking at papers published at the flagship AI and machine learning conferences since the first International Joint Conference on Artificial Intelligence (IJCAI) held in 1969. AI development and evolution have led to increasing research output, reflected in the number of articles published over the last sixty years. We construct comprehensive citation collaboration and paper-author datasets and compute corresponding centrality measures to carry out our analyses. These analyses allow a better understanding of how AI has reached its current state of affairs in research. Throughout the process, we correlate these datasets with the work of the ACM Turing Award winners and the so-called two AI winters the field has gone through. We also look at self-citation trends and new authors' behaviors. Finally, we present a novel way to infer the country of affiliation of a paper from its organization. Therefore, this work provides a deep analysis of Artificial Intelligence history from information gathered and analysed from large technical venues datasets and suggests novel insights that can contribute to understanding and measuring AI's evolution.

The potential power of artificial intelligence (AI) has been touted for more than 60 years though a generally accepted definition is elusive. AI has often been defined in terms of human-like capabilities. In 1960, for example, AI pioneer Herbert Simon, an economics Nobel laureate and Turing Award winner, predicted that "machines will be capable, within twenty years, of doing any work a man can do." In 1970 Marvin Minsky, also a Turing Award winner, said that, "In from three to eight years we will have a machine with the general intelligence of an average human being." More recently, in 2015, Mark Zuckerberg said that, "One of our goals for the next five to 10 years is to basically get better than human level at all of the primary human senses: vision, hearing, language, general cognition."

The Turing Award has recognized some of the biggest names in AI and computing over the years, and the latest winners are particularly heavy hitters. The three prize recipients for 2018 are Google VP Geoffrey Hinton, Facebook's Yann LeCun (above) and Yoshua Bengio, the Scientific Director of the giant AI research center Mila. All three helped "develop conceptual foundations" for deep neural networks, according to the Association for Computing Machinery, and created breakthroughs that showed he "practical advantages" of the technology. Hinton, for instance, proved that a then-rare backpropagation algorithm could help neural networks solve problems that were previously unfeasible. LeCun was instrumental to developing technologies behind modern computer vision, while Bengio helped foster generative adversarial networks (that is, pitting a creative network against another that serves as a kind of quality control) that can create original images.

In 2017, we celebrated 50 years of the ACM A.M. Turing Award, known simply as the Turing Award. Justifiably, the Turing Award is often accompanied by the tagline "The Nobel Prize in Computing." How did this prestigious award come to be? The early history of the Turing Award is somewhat murky. The minutes of meetings of ACM Council from the mid-1960s shed some, but not complete light on this history.