Breakthroughs, discoveries, and DIY tips sent every weekday. Just as birth rates decline in many wealthy and developed nations, dog parenting is remaining steady and even gaining in popularity. Up to half of households in Europe and 66 percent of homes in the United States have at least one dog and these pets are often regarded as a family member or "fur baby." To dig into what this shift says about our society, researchers from Eötvös Loránd University in Budapest, Hungary conducted a literature review to analyze the data. They propose that while dogs do not replace children, they can offer a chance to fulfill an innate nurturing drive similar to parenting, but with fewer demands than raising biological children.

Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the last few decades. Despite this, the physical processes behind the phenomena, and particularly their importance, remain far from known, as correlations have primarily been established rather than the underlying causality. We address this challenge by applying the causal inference framework. Specifically, we tackle the fundamental open question of whether galaxy formation and evolution depends more on nature (i.e., internal processes) or nurture (i.e., external processes), by estimating the causal effect of environment on star-formation rate in the IllustrisTNG simulations. To do so, we develop a comprehensive causal model and employ cutting-edge techniques from epidemiology to overcome the long-standing problem of disentangling nature and nurture. We find that the causal effect is negative and substantial, with environment suppressing the SFR by a maximal factor of $\sim100$. While the overall effect at $z=0$ is negative, in the early universe, environment is discovered to have a positive impact, boosting star formation by a factor of $\sim10$ at $z\sim1$ and by even greater amounts at higher redshifts. Furthermore, we show that: (i) nature also plays an important role, as ignoring it underestimates the causal effect in intermediate-density environments by a factor of $\sim2$, (ii) controlling for the stellar mass at a snapshot in time, as is common in the literature, is not only insufficient to disentangle nature and nurture but actually has an adverse effect, though (iii) stellar mass is an adequate proxy of the effects of nature. Finally, this work may prove a useful blueprint for extracting causal insights in other fields that deal with dynamical systems with closed feedback loops, such as the Earth's climate.

Zane looks out to the calm ocean outside his window. This is his float time, when he lets his thoughts run wild. They generate a kaleidoscope, and he simply watches them, waiting to latch onto one that can be analyzed, deconstructed, and transformed into the next communication frequency. Zane exists in a world where humanity and artificial intelligence exist in a perfectly balanced symbiotic relationship. Human DNA is now intertwined with AI--a Triple Helix (TH)--embedded so deeply that ancestors from just a hundred years ago seem like mythical creatures.

Saudi Arabia has made no secret of its passion for gaming and e-sports, so there was no shortage of young Saudis to take in a museum of video game history stretching from the original Pac-Man to PlayStation 5. It is part of Gamers8, an eight-week festival of e-sports tournaments in the capital, Riyadh, with a $45m prize pool – a project to inspire young people to create their own blockbuster titles. The passion is believed to come from the very top, with Crown Prince Mohammed bin Salman (MBS) said to be an avid Call of Duty player. Last year, the 38-year-old de facto ruler announced a $38bn investment strategy for the Savvy Games Group, owned by the Public Investment Fund. As it gathers momentum, the national gaming and e-sports strategy emphasises local game production, promising to turn the kingdom into "an Eden for game developers" that can produce new titles "promoting Saudi and Arabic culture".

What is the most challenging aspect of marketing? Identifying your potential customer, predicting their interest, engaging, and nurturing them for an ultimate buyout is not that easy. This is where Artificial Intelligence can help you analyze the customer pool, identify and segregate lead. Further, you can even nurture them using AI-based algorithms, which helps in delivering high value. Take an example of the famous inbound marketing giant Hubspot.

Animals are equipped with a rich innate repertoire of sensory, behavioral and motor skills, which allows them to interact with the world immediately after birth. At the same time, many behaviors are highly adaptive and can be tailored to specific environments by means of learning. In this work, we use mathematical analysis and the framework of meta-learning (or'learning to learn') to answer when it is beneficial to learn such an adaptive strategy and when to hard-code a heuristic behavior. We find that the interplay of ecological uncertainty, task complexity and the agents' lifetime has crucial effects on the meta-learned amortized Bayesian inference performed by an agent. There exist two regimes: One in which metalearning yields a learning algorithm that implements task-dependent informationintegration and a second regime in which meta-learning imprints a heuristic or'hard-coded' behavior. Further analysis reveals that nonadaptive behaviors are not only optimal for aspects of the environment that are stable across individuals, but also in situations where an adaptation to the environment would in fact be highly beneficial, but could not be done quickly enough to be exploited within the remaining lifetime. Hard-coded behaviors should hence not only be those that always work, but also those that are too complex to be learned within a reasonable time frame. The'nature versus nurture' debate (e.g., Mutti et al., 1996; Tabery, 2014) - the question which aspects of behavior are'hard-coded' by evolution, and which are learned from experience - is one of the oldest and most controversial debates in biology.

To date, it might well be the most effective and useful algorithm -- or family of algorithms -- that humanity ever invented. Ever-improving methods for erecting models of how the world works and then testing those models against evidence make it possible to distinguish good ideas from bad. Step-by-step, humanity's understanding of the universe, the world, and itself, has grown. The Artificial Intelligence revolution, however, could well overturn how good ideas are sifted from bad and subvert science's ultimate goal of understanding. The claim that AI could undermine scientific understanding, or even make it obsolete, is far from new.

Is today's schooling preparing your child to be a creator? Fluid Intelligence is the ability to solve problems one has never faced before. We believe this is the single most important ability that will make a huge difference in the life of any child. The most important thinking skills such as decision making, problem solving and logical reasoning is what helps build fluid intelligence. We are specialists in working with children from a very young age to develop their fluid intelligence for a life long and ever lasting impact.

Legend has a prominent tale about a man who caught a large fish and kept it in a big tank filled with a lot of tiny fish. Over a few days, he observed that it was very easy for the big fish to catch the smaller ones and eat them. In due course of time, he partitioned the tank with a glass, in such a way that the big fish remains on one side of the glass, and the smaller ones on the other side. He noticed that the big fish tried hard over the first few days to catch its prey but failed to do so due to a glass partition. The man eventually removed the glass partition after a week and noticed that the big fish had stopped trying to hunt its prey.

Architecture and weights are two essential considerations for artificial neural networks. Architecture is akin to the innate human brain, and contains the neural network's initial settings such as hyperparameters, layers, node connections (or wiring), etc. Weights meanwhile are the relative strength of the different connections between nodes after model training, which can be likened to a human brain that has learned for example how to multiply numbers or speak French. As with the age-old "nature versus nurture" debate, AI researchers want to know whether architecture or weights play the main role in the performance of neural networks. In a blow to the "nurture" side, Google researchers have now demonstrated that a neural network which has not learned weights through training can still achieve satisfactory results in machine learning tasks. Google Brain researchers Adam Gaier and David Ha said their idea was inspired by precocial behaviors that have evolved in nature, explaining in a blog post: "In biology, precocial species are those whose young already possess certain abilities from the moment of birth. There is evidence to show that lizard and snake hatchlings already possess behaviors to escape from predators. Shortly after hatching, ducks are able to swim and eat on their own, and turkeys can visually recognize predators."