Although it has been argued that under-threes should not have any screen time at all, research has found that digital tech can offer "rich opportunities" for young children's development. A two-year study, Toddlers, Tech and Talk, funded by the Economic and Social Research Council and led by researchers from Manchester Metropolitan University (MMU), working with Lancaster, Queen's Belfast, Strathclyde and Swansea universities, looked at children's interactions with everything from Amazon Alexa to Ring doorbells, in diverse communities across the UK, to find out how tech was influencing 0- to three-year-olds' early talk and literacy. It examined how children use technology with parents or by themselves, whether taking photos and videos, using learning apps and playing games, listening and singing to songs, talking about favourite characters, or chatting on video calls. The researchers found that children were not only interacting with smart devices and appliances when very young, but also that digital tech could have benefits for language development and other skills. "The evidence generated through this study suggests that young children's digital activity often involves sensory exploration through touch, vision, hearing, movement and embodied cognition," the report said.

The rapid dissemination of misinformation on the internet complicates the decision-making process for individuals seeking reliable information, particularly parents researching child development topics. This misinformation can lead to adverse consequences, such as inappropriate treatment of children based on myths. While previous research has utilized text-mining techniques to predict child abuse cases, there has been a gap in the analysis of child development myths and facts. This study addresses this gap by applying text mining techniques and classification models to distinguish between myths and facts about child development, leveraging newly gathered data from publicly available websites. The research methodology involved several stages. First, text mining techniques were employed to pre-process the data, ensuring enhanced accuracy. Subsequently, the structured data was analysed using six robust Machine Learning (ML) classifiers and one Deep Learning (DL) model, with two feature extraction techniques applied to assess their performance across three different training-testing splits. To ensure the reliability of the results, cross-validation was performed using both k-fold and leave-one-out methods. Among the classification models tested, Logistic Regression (LR) demonstrated the highest accuracy, achieving a 90% accuracy with the Bag-of-Words (BoW) feature extraction technique. LR stands out for its exceptional speed and efficiency, maintaining low testing time per statement (0.97 microseconds). These findings suggest that LR, when combined with BoW, is effective in accurately classifying child development information, thus providing a valuable tool for combating misinformation and assisting parents in making informed decisions.

Adopting contextually appropriate, audience-tailored linguistic styles is critical to the success of user-centric language generation systems (e.g., chatbots, computer-aided writing, dialog systems). While existing approaches demonstrate textual style transfer with large volumes of parallel or non-parallel data, we argue that grounding style on audience-independent external factors is innately limiting for two reasons. First, it is difficult to collect large volumes of audience-specific stylistic data. Second, some stylistic objectives (e.g., persuasiveness, memorability, empathy) are hard to define without audience feedback. In this paper, we propose the novel task of style infusion - infusing the stylistic preferences of audiences in pretrained language generation models. Since humans are better at pairwise comparisons than direct scoring - i.e., is Sample-A more persuasive/polite/empathic than Sample-B - we leverage limited pairwise human judgments to bootstrap a style analysis model and augment our seed set of judgments. We then infuse the learned textual style in a GPT-2 based text generator while balancing fluency and style adoption. With quantitative and qualitative assessments, we show that our infusion approach can generate compelling stylized examples with generic text prompts. The code and data are accessible at https://github.com/CrowdDynamicsLab/StyleInfusion.

How should we raise our children? Research has shown that the amount of parental time invested is not the only crucial element for children's skill development (Del Boca et al. 2014, Attanasio et al. 2016); parenting style also matters (Fiorini and Keane 2014). Parenting style is a strategic choice linked to incentives (Doepke and Zilibotti 2014). In order to study the relationship between parenting style and child development, researchers rely on ad hoc perceptions or previous research in order to restrict the complexities of parenting to certain key actions. For instance, reading to children has been found to be highly predictive of children's skill development (Kalb and Jan van Ours 2013).

Washington – Two years into the pandemic, concerns around the effect of masks on the linguistic, emotional and social development of children are taking center stage. In the United States, calls to lift mask mandates at school have multiplied in recent weeks, including within the scientific community, at a time when new cases of COVID-19 are plunging. Scientific studies have shown that masks do indeed impact children's ability to recognize faces and emotions. As with adults, masks can also interfere with verbal communication. But experts are divided on the long-term effects on children's development.

Two years into the pandemic, concerns around the effect of masks on the linguistic, emotional and social development of children are taking center stage. In the United States, calls to lift mask mandates at school have multiplied in recent weeks, including within the scientific community, at a time when new cases of Covid-19 are plunging. Scientific studies have shown that masks do indeed impact children's ability to recognize faces and emotions. As with adults, masks can also interfere with verbal communication. But experts are divided on the long-term effects on children's development.

After a surge in popularity of supervised Deep Learning, the desire to reduce the dependence on curated, labelled data sets and to leverage the vast quantities of unlabelled data available recently triggered renewed interest in unsupervised learning algorithms. Despite a significantly improved performance due to approaches such as the identification of disentangled latent representations, contrastive learning, and clustering optimisations, the performance of unsupervised machine learning still falls short of its hypothesised potential. Machine learning has previously taken inspiration from neuroscience and cognitive science with great success. However, this has mostly been based on adult learners with access to labels and a vast amount of prior knowledge. In order to push unsupervised machine learning forward, we argue that developmental science of infant cognition might hold the key to unlocking the next generation of unsupervised learning approaches. Conceptually, human infant learning is the closest biological parallel to artificial unsupervised learning, as infants too must learn useful representations from unlabelled data. In contrast to machine learning, these new representations are learned rapidly and from relatively few examples. Moreover, infants learn robust representations that can be used flexibly and efficiently in a number of different tasks and contexts. We identify five crucial factors enabling infants' quality and speed of learning, assess the extent to which these have already been exploited in machine learning, and propose how further adoption of these factors can give rise to previously unseen performance levels in unsupervised learning.

Paolo Pirjanian is an Armenia born in Iran and fled to Denmark as a teen. From the time he was young, he was fascinated by computers and started coding in his bedroom. After getting his PhD in robotics, Paolo became an early leader in the field of consumer robotics who has 16 years of experience developing and commercializing cutting-edge home robots. He worked at NASA JPL and led world-class teams and companies at iRobot, Evolution Robotics, and others. In 2016, Paolo founded Embodied, Inc. with the vision to build socially and emotionally intelligent digital companions that improve care and wellness and support people in living better lives every day.

A new Canadian study has linked increased screen time with delayed development in children, adding new fuel to the debate over how long is too long for kids to spend in front of their electronic devices. Researchers from the University of Waterloo, University of Calgary and Alberta Children's Hospital Research Institute said toddlers who spent more time watching a screen at 2 years old did worse on developmental markers than those who spent less time watching a screen. "What is new in this study is that we are studying really young children, so aged 2-5, when brain development is really rapidly progressing and also child development is unfolding so rapidly," Dr. Sheri Madigan told the Guardian. "We are getting at these lasting effects." The authors of the study, who tested 2,400 children, say parents should be cautious about how long they allow their kids to spend on their devices.

In the late 1930's Frank had been a senior executive with the Macy Foundation, where he was a friend and mentor to Fremont-Smith. Frank's longtime interests included child development, and he is often considered to be the godfather of the American child development field. At the time the cybernetics group coalesced, he was what we'd now call a'free-floating consultant'. Frank was no stranger to cybernetics' prehistory. He'd been intrigued by Walter Cannon's 1929 writings on'homeostasis' and how this concept might pertain to child development.