Eating boogers is funny no matter your species. A hilariously lucky moment I caught of these these three lions yawning at the same time. Breakthroughs, discoveries, and DIY tips sent every weekday. Nature can be captivating, awe-inspiring, and downright metal . It can also be hilarious.

Keep fake spider webs close to your house, and ditch the real pumpkins if you live near wildlife. Breakthroughs, discoveries, and DIY tips sent every weekday. With spooky season just on the horizon, Halloween decorations are beginning to pop up everywhere--tombstones, pumpkins, and of course, tons and tons of fake spiderwebs . Amidst all the autumnal celebrations, it's easy to forget those who not only can't join in on the celebration, but might even be threatened by the decorations: wildlife. While Jennifer Bloodgood, a Cornell University wildlife veterinarian, hasn't personally witnessed it before, she tells that she agrees with the dangers of some Halloween decorations. "Birds would definitely be the major concern," she says, referring specifically to fake spider webs.

Bird photos that really take flight. Breakthroughs, discoveries, and DIY tips sent every weekday. An estimated 50 billion wild birds populate our planet, according to a 2021 study . From garbage-eating urban pigeons to colorful parrots in tropical forests, the diversity of birds is impressive. For the past 120 years, the National Audubon Society has worked to helped Earth's birds through conservation and awareness campaigns.

Jamie Smart (UK) portrays a red deer stag as it gives a mighty bellow during the autumn rut in Bradgate Park, UK. Breakthroughs, discoveries, and DIY tips sent every weekday. Hunting is a crucial skill for young cheetahs . Photographer Marina Cano captured the intense moment before the siblings killed the prey in a stunning image (seen below) that that took Highly Commended honors in the Mammals: Behavior category at the Wildlife Photographer of the Year awards. The prestigious competition is now in its 61st year and is developed and produced by the Natural History Museum, London.

The quest to find extraterrestrial life is a critical scientific endeavor with civilization-level implications. Icy moons in our solar system are promising targets for exploration because their liquid oceans make them potential habitats for microscopic life. However, the lack of a precise definition of life poses a fundamental challenge to formulating detection strategies. To increase the chances of unambiguous detection, a suite of complementary instruments must sample multiple independent biosignatures (e.g., composition, motility/behavior, and visible structure). Such an instrument suite could generate 10,000x more raw data than is possible to transmit from distant ocean worlds like Enceladus or Europa. To address this bandwidth limitation, Onboard Science Instrument Autonomy (OSIA) is an emerging discipline of flight systems capable of evaluating, summarizing, and prioritizing observational instrument data to maximize science return. We describe two OSIA implementations developed as part of the Ocean Worlds Life Surveyor (OWLS) prototype instrument suite at the Jet Propulsion Laboratory. The first identifies life-like motion in digital holographic microscopy videos, and the second identifies cellular structure and composition via innate and dye-induced fluorescence. Flight-like requirements and computational constraints were used to lower barriers to infusion, similar to those available on the Mars helicopter, "Ingenuity." We evaluated the OSIA's performance using simulated and laboratory data and conducted a live field test at the hypersaline Mono Lake planetary analog site. Our study demonstrates the potential of OSIA for enabling biosignature detection and provides insights and lessons learned for future mission concepts aimed at exploring the outer solar system.

Machine learning is no longer about experiments. Most industry-leading enterprises have already seen dramatic successes from their investments in machine learning (ML), and there is near-universal agreement among business executives that building data science capabilities is vital to maintaining and extending their competitive advantage. The bullish outlook is evident in the U.S. Bureau of Labor Statistics' predictions regarding growth of the data science career field: Employment of data scientists is projected to grow 36% from 2021 to 2031, much faster than the average for all occupations. The aim now is to grow these initial successes beyond the specific parts of the business where they had initially emerged. Companies are looking to scale their data science capabilities to support their entire suite of business goals and embed ML-based processes and solutions everywhere the company does business.

Artificial intelligence (AI) adoption and its impact on organizations are at a critical juncture. As businesses see its significant benefits, AI is widely adopted every year. According to IBM Global AI Adoption Index 2022, the need to cut costs and automate crucial processes, increasing competitive pressure, and changing customer expectations are the top drivers influencing AI adoption. Without question, AI will significantly change how many industries operate. And for this reason, a lot of leaders are making rapid investments in AI.

Let's say your company's data science teams have documented business goals for areas where analytics and machine learning models can deliver business impacts. Now they are ready to start. They've tagged data sets, selected machine learning technologies, and established a process for developing machine learning models. They have access to scalable cloud infrastructure. Is that sufficient to give the team the green light to develop machine learning models and deploy the successful ones to production?

Regulation marched along in the EU, Asia, and the U.S., and we saw companies like Zillow hobbled by AI model quality snafus. Based on my recent discussions with dozens of Fortune 500 data science teams, I expect to see a continued spotlight on AI model quality in 2023. Here are six areas I'm keeping an eye on. Similar to software development 20 years ago, it wasn't until testing and monitoring became common that enterprise software use really took off. AI is at a similar inflection point.

It's been two years since Mckinsey invented the term analytics translator, called it the'new must-have role' and predicted we'd need around 5 million of them. For the past ten years, we've struggled with the ambiguous title'data scientist', then'citizen data scientist'. Although I've seen many'data scientists' change their Linkedin titles to'analytics translator', the problem remains that no one knows what'analytics translator' really means. Mckinsey seems to have slipped this term into a Harvard Business Review article, and it has somehow taken root. What's more, people seem truly excited by the term.