One explanation for how people can plan efficiently despite limited cognitive resources is that we possess a set of adaptive planning strategies and know when and how to use them. But how are these strategies acquired? While previous research has studied how individuals learn to choose among existing strategies, little is known about the process of forming new planning strategies. In this work, we propose that new planning strategies are discovered through metacognitive reinforcement learning. To test this, we designed a novel experiment to investigate the discovery of new planning strategies. We then present metacognitive reinforcement learning models and demonstrate their capability for strategy discovery as well as show that they provide a better explanation of human strategy discovery than alternative learning mechanisms. However, when fitted to human data, these models exhibit a slower discovery rate than humans, leaving room for improvement.

How is it that humans can solve complex planning tasks so efficiently despite limited cognitive resources? One reason is its ability to know how to use its limited computational resources to make clever choices. We postulate that people learn this ability from trial and error (metacognitive reinforcement learning). Here, we systematize models of the underlying learning mechanisms and enhance them with more sophisticated additional mechanisms. We fit the resulting 86 models to human data collected in previous experiments where different phenomena of metacognitive learning were demonstrated and performed Bayesian model selection. Our results suggest that a gradient ascent through the space of cognitive strategies can explain most of the observed qualitative phenomena, and is therefore a promising candidate for explaining the mechanism underlying metacognitive learning.

AI solutions invariably flourish in huge, globe-spanning industries with healthy profit margins and enormous ecosystems. An accurate description of the global golf industry, which also explains why big-name brands and investors are so confident it's on course for a wealth of new business opportunities. US-based sports equipment manufacturer Callaway already had a reputation for innovation even before the advent of AI technology. The company's original Big Bertha driver was launched in 1991 and considered a radical departure from its predecessors. In 2019, Callaway introduced Flash Face, designed by an AI-powered US$8mn supercomputer which could generate 15,000 face design iterations and over 100 impact simulations.

Jason Callaway is an esteemed and seasoned expert in the field of Computer Science and Machine Learning. He is particularly passionate about data analytics with an affinity for using the python language among other core competencies which further characterize him as a refined strategist and intuitive analyst. Jason works as a university professor and is often contracted as a consultant and researcher for important I.T. firms. Teaching has always been Jason's second passion even as he prides himself on being one of the most appreciated teachers. He always takes the time to learn more about themes and topics which he is interested in and endeavors to interpret it into actionable and understandable information that he then imparts to his audience.

When someone thinks of artificial intelligence technology, often the mind goes to some of the world's biggest tech companies like Google, Amazon or even Tesla. But one company that creates products for a game that's existed since the mid-18th century is also getting involved in that realm. Senior Director of Brand and Product Management for Callaway Golf Dave Neville sat down with Kyle Porter of CBS Sports HQ at the PGA Merchandise Show to discuss the new MAVRIK driver. The club's name comes from a common compliment given to founder Ely Callaway Jr. for his knowledge of product, marketing and sales. Though the name is old-fashioned, the development of the driver is anything but, according to Neville.

When nine Callaway Mavrik drivers showed up on the USGA conforming list in mid-December, there was the expected here we go again refrain mixed in with a couple of sideways glances and some descriptions which were anything but parliamentary. That said, as noted in MyGolfSpy's 2019 Editor's Choice awards, the AI (Artificial Intelligence) component of Callaway's signature Flash Face technology is a new club technology that's likely to impact club design throughout the industry – and with the Mavrik irons, it's clear Callaway is dedicated to extending its use throughout its hardgoods lineup. So not to bury the lede, Callaway's Mavrik irons (3 models) incorporate for the first time, AI face design…in every iron. Yes, each individual iron will have a different face thanks to AI capabilities though as sets progress toward shorter irons (8-iron, 9-iron, PW) the designs are more similar than different due to the role loft plays in performance. Specifically, clubs with more loft result in less blunt impact conditions and therefore the face technology (materials, design, etc) has a reduced impact.

At Callaway, the high-end golf-equipment stalwart, the process of making clubs has always been quite labor-intensive--from grinding and polishing clubheads to crafting wood-and-steel-shafted irons and wedges. The company has also long combined such artisanal handwork with technological innovation, even partnering with aerospace titan Boeing recently to codesign several aerodynamic clubs. So when the company set out about four years ago to make its latest club line, called Epic Flash, it took the next evolutionary technological step, turning to artificial intelligence and machine learning for help. A typical club-design process might involve five to seven physical prototypes; for Epic Flash, Callaway created 15,000 virtual ones. From those, an algorithm determined the best design, selecting for peak performance--i.e., ball speed--while also conforming to the rules set forth by the U.S. Golf Association.