This paper is an invited layperson summary for The Academic of the paper referenced on the last page. We summarize how the formal framework of autocatalytic networks offers a means of modeling the origins of self-organizing, self-sustaining structures that are sufficiently complex to reproduce and evolve, be they organisms undergoing biological evolution, novelty-generating minds driving cultural evolution, or artificial intelligence networks such as large language models. The approach can be used to analyze and detect phase transitions in vastly complex networks that have proven intractable with other approaches, and suggests a promising avenue to building an autonomous, agentic AI self. It seems reasonable to expect that such an autocatalytic AI would possess creative agency akin to that of humans, and undergo psychologically healing -- i.e., therapeutic -- internal transformation through engagement in creative tasks. Moreover, creative tasks would be expected to help such an AI solidify its self-identity.

The field of artificial intelligence has never lacked for hype. Back in 1965, AI pioneer Herb Simon declared, "Machines will be capable, within 20 years, of doing any work a man can do." That hasn't happened -- but there certainly have been noteworthy advances, especially with the rise of "deep learning" systems, in which programs plow through massive data sets looking for patterns, and then try to make predictions. Perhaps most famously, AIs that use deep learning can now beat the best human Go players (some years after computers bested humans at chess and Jeopardy). Mastering language has proven tougher, but a program called GPT-3, developed by OpenAI, can produce human-like text, including poetry and prose, in response to prompts.

There has been a rise in the number of studies relating to the role of artificial intelligence (AI) in healthcare. Its potential in Emergency Medicine (EM) has been explored in recent years with operational, predictive, diagnostic and prognostic emergency department (ED) implementations being developed. For EM researchers building models de novo, collaborative working with data scientists is invaluable throughout the process. Synergism and understanding between domain (EM) and data experts increases the likelihood of realising a successful real-world model. Our linked manuscript provided a conceptual framework (including a glossary of AI terms) to support clinicians in interpreting AI research. The aim of this paper is to supplement that framework by exploring the key issues for clinicians and researchers to consider in the process of developing an AI model.

As a teenager, I discovered a worn copy of the book Gödel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter on a bookshelf at home. It still had a computer punch card in it that my Mom had used as a bookmark, back when she briefly worked as a programmer in the early 1980s. Reading that book was like falling into another world. I found myself thinking about the mind and computers in brand new ways. I learned about Alan Turing's work for the first time.

Every company worth its weight is set on achieving practical and scalable artificial intelligence and machine learning. However, it's all much easier said than done -- to which AI leaders within some of the most information-intensive enterprises can attest. For more perspective on the challenges of building an AI-driven organization, we caught up with Jing Huang, senior director of engineering and machine learning at Momentive (formerly SurveyMonkey), who shares the lessons being learned as AI and ML are rolled out. Q: AI and machine learning initiatives have been underway for several years now. What lessons have enterprises been learning in terms of most productive adoption and deployment?

Dr. Thomas Berger has landed a NASA grant to research space weather with machine learning. Berger, the executive director of the University of Colorado Boulder Space Weather Technology, Research and Education Center, is leading a team that has received a two-year, $496,000 grant to design a better forecasting system for solar magnetic eruptions on the sun. These events lead to solar flares and coronal mass ejections that can wreak havoc on radio communications, endanger satellites in low Earth orbit, and even destabilize the electric power grid here on Earth. "Up to very recently, there have only been subjective tools, human forecasters who view images of sunspots and use historical data tables to say, 'The probability of this sunspot flaring in the next 24 hours is X%'," Berger said. A 24-hour range for solar eruption forecasts is about the best a human forecaster can do with current technology.

This paper is the preprint of an invited commentary on Lake et al's Behavioral and Brain Sciences article titled "Building machines that learn and think like people". Lake et al's paper offers a timely critique on the recent accomplishments in artificial intelligence from the vantage point of human intelligence, and provides insightful suggestions about research directions for building more human-like intelligence. Since we agree with most of the points raised in that paper, we will offer a few points that are complementary.

"Artificial intelligence is among the most consequential issues facing humanity, yet much of today's commentary has been less than intelligent: awe-struck, credulous, apocalyptic, uncomprehending. Gary Marcus and Ernest Davis, experts in human and machine intelligence, lucidly explain what today's AI can and cannot do, and point the way to systems that are less A and more I." --Steven Pinker, Johnstone Professor of Psychology, Harvard University, and the author of How the Mind Works and The Stuff of Thought "Finally, a book that tells us what AI is, what AI is not, and what AI could become if only we are ambitious and creative enough. No matter how smart and useful our intelligent machines are today, they don't know what really matters. Rebooting AI dares to imagine machine minds that goes far beyond the closed systems of games and movie recommendations to become real partners in every aspect of our lives." Every CEO should read it, and everyone else at the company, too.

In May 2017, researchers at Google Brain declared the formation of AutoML, an artificial intelligence (AI) that is equipped for producing its own AIs. All the more as of late, they chose to give AutoML its greatest challenge to date, and the AI that can construct AI made a "child" that beat the majority of its human-made partners. With it, Google may soon figure out how to make AI innovation that can incompletely remove the people from building the AI frameworks that many accept are the future of the innovation business. The venture is a piece of a lot bigger exertion to bring the best in class AI techniques to a more extensive collection of organizations and software developers. The Google analysts automated the structure of ML models utilizing a methodology called reinforcement learning.

Many real-life databases contain missing values. Yet many popular algorithms and statistical models do not accept data rows containing missing values. Some libraries drop these data rows with little warning. Without those data rows, a model is likely to make biased predictions. For example, a majority of the rows in the Lending Club data have never had a negative credit action and therefore contain a missing value.