A 20,000 year old fossil uncovered in a tarantula-filled cave has paleontologists stunned. Breakthroughs, discoveries, and DIY tips sent every weekday. Bees are frequently associated with large queen-serving colonies featuring hundreds if not thousands of insects . They lay their eggs in small cavities, and they leave pollen for the larvae to eat," explained paleontologist Lazasro Viñola López . "Some bee species burrow holes in wood or in the ground, or use empty structures for nests." Viñola López, a researcher at Chicago's Field Museum, added that some European and African species even construct nests inside vacant snail shells. That said, a beehive inside a bone is a new one even for seasoned researchers. Estimated to be around 20,000 years old, this newly discovered specimen is also the first known example of such a home, past or present. The findings are detailed in a study published on December 16 in the journal . Researchers located the unique find while exploring the many limestone caves that dot the southern Dominican Republic. Sinkholes are common across the Caribbean island of Hispaniola, and are often so well sheltered from the elements that they function like underground time capsules. These windows into the past are largely thanks to the work of the island's owls . The predatory birds often make their nests inside these caves, where they regularly cough up owl pellets filled with the undigested bones of their prey. Over thousands of years, these layers of bones fossilize atop one another across carbonate layers created from rainy periods. "The initial descent into the cave isn't too deep-we would tie a rope to the side and then rappel down," Viñola López said. "If you go in at night, you see the eyes of the tarantulas that live inside." After proceeding past the large spiders through about 33 feet of underground tunnel, the paleontologists began finding various fossils. Many belonged to rodents, but there were also bones from birds, reptiles, and even sloths for a total of over 50 different animal species. "We think that this was a cave where owls lived for many generations, maybe for hundreds or thousands of years," said Viñola López. "The owls would go out and hunt, and then come back to the cave and throw up pellets.

Soft prompts have been popularized as a cheap and easy way to improve task-specific LLM performance beyond few-shot prompts. Despite their origin as an automated prompting method, however, soft prompts and other trainable prompts remain a black-box method with no immediately interpretable connections to prompting. We create a novel theoretical framework for evaluating the interpretability of trainable prompts based on two desiderata: faithfulness and scrutability. We find that existing methods do not naturally satisfy our proposed interpretability criterion. Instead, our framework inspires a new direction of trainable prompting methods that explicitly optimizes for interpretability. To this end, we formulate and test new interpretability-oriented objective functions for two state-of-the-art prompt tuners: Hard Prompts Made Easy (PEZ) and RLPrompt. Our experiments with GPT-2 demonstrate a fundamental trade-off between interpretability and the task-performance of the trainable prompt, explicating the hardness of the soft prompt interpretability problem and revealing odd behavior that arises when one optimizes for an interpretability proxy.

Aitana López seems to have come out of nowhere and taken the modeling industry by storm. The pink-haired, toned 25-year-old from Barcelona has reportedly secured advertising deals worth more than $1,000 per Instagram post, and has more than 100,000 online followers. Her Instagram feed shows her posing in outfits from Guess, Brandy Melville, and Victoria's Secret, and tagging haircare brand Olaplex to give them apparent credit for her vibrant locks. In fact, Aitana López was created using artificial intelligence. The caption, translated from Spanish, reads, 'No matter the occasion, the'little black dress' never fails!

Marcos López de Prado, whom we have featured in previous Math Scholar articles (see Article A, Article B and Article C), has been invited to present a keynote presentation at the ACM Conference on Artificial Intelligence in Finance, to be conducted virtually October 14-16, 2020. López de Prado is a faculty member of Cornell University and also CEO of True Positive Technologies, LP, a private firm that provides machine learning techniques techniques for finance applications. He is also the author of two books in the field: Advances in Financial Machine Learning, published by Wiley (2018) and Machine Learning for Asset Managers, published by Cambridge University Press (2020). López de Prado has graciously provided the viewgraph file for the talk he is scheduled to present at the ACM Conference on AI in Finance: Viewgraph file. For full details, see López de Prado's viewgraphs at the above link.

Following a brief overview discussing why we prefer listening to expressive music instead of lifeless synthesized music, we examine a representative selection of well-known approaches to expressive computer music performance with an emphasis on AI-related approaches. In the main part of the paper we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on TempoExpress, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting on complementing audio information with information of the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This paper is based on the "2011 Robert S. Engelmore Memorial Lecture" given by the first author at AAAI/IAAI 2011.

Watchdogs ask EC to delay repo haircut floors. It should come as no surprise that credit card companies supplement their revenues by selling real-time access to consumer transaction data – albeit aggregated and anonymised – and even less of a surprise that enterprising hedge funds have found a way to monetise it. This week, Risk.net reported how scrutinising data from millions of credit card transactions allowed a quant team to infer whether a company's sales are on the up or trending lower – without the need to wait for quarterly sales reports to be published. The analysis was delivered through a machine learning implementation of the random forest technique in which multitudes of decision trees combine to produce predictions. In this case, the algorithm enabled the quant shop to get an early warning on the health of companies whose options it held.

In his new book Advances in Financial Machine Learning, noted financial scholar Marcos López de Prado strikes a well-aimed karate chop at the naive and often statistically overfit techniques that are so prevalent in the financial world today. He points out that not only are business-as-usual approaches largely impotent in today's high-tech finance, but in many cases they are actually prone to lose money. But López de Prado does more than just expose the mathematical and statistical sins of the finance world. Instead, he offers a technically sound roadmap for finance professionals to join the wave of machine learning. What is particularly refreshing is the author's empirical approach -- his focus is on real-world data analysis, not on purely theoretical methods that may look pretty on paper but which in many cases are largely ineffective in practice. The book is geared to finance professionals who are already familiar with statistical data analysis techniques, but it is well worth the effort for those who want to do real state-of-the-art work in the field.

A data analytics program can be an engine that fuels digital transformation projects and operations, helps you better engage with customers, and uncovers insights that lead to that next revenue stream. These programs have become of strategic value to organizations and essential components of digital transformation efforts. Whether you are just starting with a business intelligence program or you are looking to add advanced technologies such as machine learning and artificial intelligence to your existing program, it's important to do some research before you invest time and money. The best place to get real world insights is from people who have been there and done that. One place you can find those people is at the Data & Analytics Track at Interop ITX in 2018.

Nearly half a century has passed between the release of the films 2001: A Space Odyssey (1968) and Transcendence (2014), in which a quirky scientist's consciousness is uploaded into a computer. Despite being 50 years apart, their plots, however, are broadly similar. Science fiction stories continue to imagine the arrival of human-like machines that rebel against their creators and gain the upper hand in battle. In the field of artificial intelligence (AI) research, over the last 30 years, progress has been similarly slower than expected. While AI is increasingly part of our everyday lives - in our phones or cars - and computers process large amounts of data, they still lack human-level capacity to make deductions from the information they're given.