Creative processes are typically divided into three types: combinatorial, exploratory, and transformational. Here, we provide a graphical theory of transformational scientific creativity, synthesizing Boden's insight that trans-formational creativity arises from changes in the "enabling constraints" of a conceptual space (Boden 1992) and Kuhn's structure of scientific revolutions as resulting from paradigm shifts (Kuhn 1962). We prove that modifications made to axioms of our graphical model have the most transformative potential and then illustrate how several historical instances of transforma-tional creativity can be captured by our framework.

Formalizing creativity-related concepts has been a long-term goal of Computational Creativity. To the same end, we explore Formal Learning Theory in the context of creativity. We provide an introduction to the main concepts of this framework and a re-interpretation of terms commonly found in creativity discussions, proposing formal definitions for novelty and transformational creativity. This formalisation marks the beginning of a research branch we call Formal Creativity Theory, exploring how learning can be included as preparation for exploratory behaviour and how learning is a key part of transformational creative behaviour. By employing these definitions, we argue that, while novelty is neither necessary nor sufficient for transformational creativity in general, when using an inspiring set, rather than a sequence of experiences, an agent actually requires novelty for transformational creativity to occur.

The word creativity originally described a concept from human psychology, but in the realm of computational creativity (CC), it has become much more. The question of what creativity means when it is part of a computational system might be considered core to CC. Pinning down the meaning of creativity, and concepts like it, becomes salient when researchers port concepts from human psychology to computation, a widespread practice extending beyond CC into artificial intelligence (AI). Yet, the human processes shaping human-inspired computational systems have been little investigated. In this paper, we question which human literatures (social sciences, psychology, neuroscience) enter AI scholarship and how they are translated at the port of entry. This study is based on 22 in-depth, semi-structured interviews, primarily with human-inspired AI researchers, half of whom focus on creativity as a major research area. This paper focuses on findings most relevant to CC. We suggest that which human literature enters AI bears greater scrutiny because ideas may become disconnected from context in their home discipline. Accordingly, we recommend that CC researchers document the decisions and context of their practices, particularly those practices formalizing human concepts for machines. Publishing reflexive commentary on human elements in CC and AI would provide a useful record and permit greater dialogue with other disciplines.

Editor's note: This is part of a KSL.com series looking at the rise of artificial intelligence technology tools such as ChatGPT, the opportunities and risks they pose and what impacts they could have on various aspects of our daily lives. In the 1992 movie "Wayne's World," the character Garth is working on a robotic arm when Benjamin comes to ask him about making a change to his show. "We fear change," Garth says. He then looks down at the mechanical hand and begins to repeatedly smash it with a hammer. Many Americans have a similar reaction to change and technology, especially when it comes to using artificial intelligence in health care.

A recently developed data science pathway for fourth-year radiology residents will help prepare the next generation of radiologists to lead the way into the era of artificial intelligence and machine learning (AI-ML), according to a special report published in Radiology: Artificial Intelligence. AI-ML has the potential to transform medicine by delivering better and more efficient healthcare. Applications in radiology are already arriving at a staggering rate. Yet organized AI-ML curricula are limited to a few institutions and formal training opportunities are lacking. Three senior radiology residents at Brigham and Women's Hospital (BWH) in Boston recently helped devise a data science pathway to provide a well-rounded introductory experience in AI-ML for fourth-year residents.

A recently developed data science pathway for fourth-year radiology residents will help prepare the next generation of radiologists to lead the way into the era of artificial intelligence and machine learning (AI-ML), according to a special report published in Radiology: Artificial Intelligence. AI-ML has the potential to transform medicine by delivering better and more efficient healthcare. Applications in radiology are already arriving at a staggering rate. Yet organized AI-ML curricula are limited to a few institutions and formal training opportunities are lacking. Three senior radiology residents at Brigham and Women's Hospital (BWH) in Boston recently helped devise a data science pathway to provide a well-rounded introductory experience in AI-ML for fourth-year residents.

This is a timely event which will examine the unprecedented disruption and transformation of industries, business and the jobs market due to advancements in Artificial Intelligence and Blockchain Technologies. Codex will bring together leaders from industry who are uniquely positioned to showcase the latest advances in AI and Blockchain and how they might interface with one another, whilst also providing commercial applications and actionable insights for these disruptive technologies. The format will be a series of Codex Talks. A Codex Talk is a concise presentation, lasting 15 minutes (or less), in which the speaker addresses a challenging question faced by their technology or industry today and ends with a bold prediction for the future. Codex Talks are engaging, entertaining and elucidating.

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. He covered examples of how his team addressed business problems with descriptive, predictive, and prescriptive ML solutions. This post provides distilled highlights, a transcript, and a video of the session. In the Rev session, "Data Science at The New York Times", Chris Wiggins provided insights into how the Data Science group at The New York Times helped the newsroom and business be economically strong by developing and deploying ML solutions. Wiggins advised that data scientists ingest business problems, re-frame them as ML tasks, execute on the ML tasks, and then clearly and concisely communicate the results back to the organization. He advocated that an impactful ML solution does not end with Google Slides but becomes "a working API that is hosted or a GUI or some piece of working code that people can put to work". Wiggins also dove into examples of applying unsupervised, supervised, and reinforcement learning to address business problems. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. The data science group, in particular, includes people from a "wide variety of intellectual trainings" including cognitive science, physics, finance, applied math, and more. Wiggins closed the session with indicating how he looks forward to hiring from even more diverse job applications. For more insights from this session, watch the video or read through the transcript. I have about 30 minutes with you. I'm going to try to tell you all about data science at the New York Times, and in case I run out of time my email address and my Twitter are here. If you don't remember anything else, just remember we're hiring.

In this Oct. 31, 2018, photo, a screen displays a computer-generated image of a Watrix employee walking during a demonstration of their firm's gait recognition software at their company's offices in Beijing. A Chinese technology startup hopes to begin selling software that recognizes people by their body shape and how they walk, enabling identification when faces are hidden from cameras. Already used by police on the streets of Beijing and Shanghai, "gait recognition" is part of a major push to develop artificial-intelligence and data-driven surveillance across China, raising concern about how far the technology will go. As many businesses prepare for the coming year, one of the key priorities is determining best use case and strategic implementation of artificial intelligence as it applies to the core competencies of the company. This is a fairly challenging area on a variety of levels. But as this work occurs, one of the most important narratives in the arena is also further coming to light.

As publishers grapple with how to best make use of the troves of audience data at their disposal, a growing number are handing brands the keys to in-house data and artificial intelligence tools that could change the way ads and sponsored content are sold. The New York Times, Group Nine Media and the Washington Post are among the media companies that have taken advantage of data science projects built for editorial purposes to give advertisers a clearer picture of who's consuming their content and how to best speak to them. Publishers hope programs like these might help them gain back ground from tech giants like Facebook and Google that dominate the ads industry through targeting precision. The Times debuted a unit earlier this year called nytDEMO that encompasses two new data-crunching tools. One, called "Project Feels," is meant to gauge and analyze readers' emotional reaction to articles and videos through a crowdsourced survey tool.