Fig. S1: The proposed framework as a probabilistic graphical model. In this section we derive the variational lower-bound introduced in Sec.2.3 of the main article. W e firstly introduce Lemmas 1 and 2 as they appear in our derivations. As illustrated in Fig.S1, the ANN's input In Fig.S1 the lower boxes are the inducing points and other variables that determine the GPs' posterior. S1.1 Deriving the Lower-bound With Respect to the Kernel-mappings In the right-hand-side of Eq.S6 only the following terms are dependant on the kernel-mappings The first term is the expected log-likelihood of a Gaussian distribution (i.e. the conditional log-likelihood of Therefore, we can use Lemma.2 to simplify the first term: E According to Lemma.1 we have that Therefore, the KL-term of Eq.S8 is a constant with respect to the kernel mappings All in all, the lower-bound for optimizing the kernel-mappings is equal to the right-hand-side of Eq.S9 which was introduced and discussed in Sec.2.3. of the main article. S1.2 Deriving the Lower-bound With Respect to the ANN Parameters According to Eq.4 of the main article, in our formulation the ANN's parameters appear as some variational parameters. Therefore, the likelihood of all variables (Eq.S6) does not generally depend on the ANN's parameters. This likelihood turns out to be equivalent to commonly-used losses like the cross-entropy loss or the mean-squared loss. Here we elaborate upon how this happens. This conclusion was introduced and discussed in Eq.6 of the main article. W e can draw similar conclusions when the pipeline is for other tasks like regression, or even a combination of tasks.

This study evaluates the recently proposed Document Attention Network (DAN) for extracting key-value information from Uruguayan birth certificates, handwritten in Spanish. We investigate two annotation strategies for automatically transcribing handwritten documents, fine-tuning DAN with minimal training data and annotation effort. Experiments were conducted on two datasets containing the same images (201 scans of birth certificates written by more than 15 different writers) but with different annotation methods. Our findings indicate that normalized annotation is more effective for fields that can be standardized, such as dates and places of birth, whereas diplomatic annotation performs much better for fields containing names and surnames, which can not be standardized.

This week I took my son, Zac, to see the new Minecraft movie, which is hardly a remarkable statement in the highly video game-branded world of 21st-century cinema – except that what followed was not typical at all. As you may have seen from a number of bewildered news reports over the last few days, A Minecraft Movie has quickly engendered a community of, let's say, highly engaged and enthusiastic fans. Spurred on by TikTok meme posts, vast portions of the film's audience are now yelling out key lines of dialogue as they happen and singing along to the songs. In one key moment where a rare character from the game – the zombie chicken jockey – is introduced, they go absolutely crazy, throwing drinks and popcorn around, and in some US cinemas, getting escorted from the screening by police. The reaction was a little more muted in our tiny independent cinema in Frome, but still, there were rows of teenagers who had clearly seen all the TikTok posts telling them which lines to shout along to, and went to throw stuff, and they were extremely excited to be doing so, a few surreptitiously filming their mates' reactions so they could add to the social media carnage.

Jules White used to believe his 11-year-old son needed to know how to code to be successful. Now, though, the Vanderbilt computer science professor says it's more crucial for James to learn a new, more useful skill: how to prompt artificial intelligence (AI) chatbots. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. Since OpenAI released ChatGPT in 2022, White has been showing his son the ropes of generative AI.

Google will start testing a feature this year that uses machine learning to weed out children trying to access adult content on YouTube. The "machine learning-based age estimation model" will try to predict whether a user is under 18 and, if so, apply appropriate age filter settings to their account. The announcement came amid a flurry of Google child safety announcements as the US Senate considers a bill that would ban pre-teens from social media. YouTube CEO Neal Mohan first mentioned the ML age restriction feature on Tuesday in his letter about the platform's "bets" for the coming year. "We'll use machine learning in 2025 to help us estimate a user's age -- distinguishing between younger viewers and adults -- to help provide the best and most age-appropriate experiences and protections," he wrote.

The debate surrounding the world's population and the collective plan of action to tackle overpopulation has its proponents and critics. Some believe we're definitely headed towards an overpopulation crisis, while others think we'll be experiencing a catastrophic population decrease. Granted, this debate can be an entirely separate story in itself, but let's take a look at one side of the coin first. With regard to overpopulation, AI expert and renowned behavioral psychologist Catriona Campbell believes that'Tamagotchi children' could pose a very real future for parents 50 years from now, outlined in her recently published book – AI by Design: A Plan for Living with Artificial Intelligence. A survey involving a slew of Nobel Laureates cites population rise and its link to environmental degradation as the biggest threat to humankind.

Kotaku writes that the drawing of what some parents described as the face of Satan is actually a mask worn by the character Xiao in "Genshin Impact," a popular action role-playing game. In the game, Xiao is an immortal guardian of the Liyue region tasked with defeating the land's evil spirits. Donning the mask pictured in the mural triggers Xiao's ability Bane of All Evil, and its design is inspired by those worn in Nuo opera, a genre of Chinese opera developed from ancient religious ceremonies intended to drive away demons.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Can an AI model tell you which parenting style is the best for your children?

It'd be pretty fascinating to see the Democratic Party's latest internal polling on COVID restrictions. We haven't seen it, but it must have been pretty awful, apocalyptic, because something spooked them bad. Over the course of less than a week, the same people who have systematically turned America into a quarantine camp suddenly, out of nowhere, started calling in unison for medical freedom. Suddenly, they sounded like Bobby Kennedy Jr., pretty much all of them. Even the whiny hypochondriacs at The Atlantic Magazine, those neurotic cat owners who've turned COVID hysteria into a religion are now calling for a total abandonment of all Coronavirus restrictions. Believe it or not, that was the headline on The Atlantic's website today.

Aggregative deontic detachment is a new form of deontic detachment that keeps track of previously detached obligations. We argue that it handles iteration of successive detachments in a more principled manner than the traditional systems do. To study this new form of deontic detachment, we introduce a'minimal' logic for aggregative deontic detachment, and we discuss various properties of the logic.