Things to Do in L.A. Hollywood director Rob Reiner engineered Proposition 10, a 1998 tobacco tax that created First 5 California, generating more than $11 billion for early childhood programs statewide. This is read by an automated voice. Please report any issues or inconsistencies here . After his tragic death Sunday, the world remembers Rob Reiner as a cinematic force -- and he was one, as an unforgettable presence on the ambitious 1970s sitcom "All in the Family" and later as the director of beloved films. I came to know him differently: as a restless thinker who transformed his own life story into bold public policy, reshaping how California understands and invests in its youngest children.

It was previously thought that children younger than 7 couldn't find efficient solutions to complex problems, but new research suggests that much earlier, children can happen upon known sorting algorithms used by computer scientists Complex problem-solving may arise earlier in a child's development than previously thought Children as young as 4 years old are capable of finding efficient solutions to complex problems, such as independently inventing sorting algorithms developed by computer scientists. The scientists behind the finding say these skills emerge far earlier than previously thought, and should force a rethink of developmental psychology. Take control of your brain's master switch to optimise how you think Experiments carried out by Swiss psychologist Jean Piaget and widely popularised in the 1960s asked children to physically sort a collection of sticks into length order, a task Piaget called seriation. His tests revealed until around age 7, children applied no structured strategies; they approached the problem in messy ways through trial and error. But new research by Huiwen Alex Yang and his colleagues at University of California, Berkeley, shows a minority of even 4-year-old children can develop algorithmic solutions to the same task, and by 5 years old more than a quarter are capable of the same thing.

Starting from the Boolean notion of logical proportion in Piaget's sense, which turns out to be equivalent to analogical proportion, this note proposes a definition of analogical proportion between numerical values based on triangular norms (and dual co-norms). Frank's family of triangular norms is particularly interesting from this perspective. The article concludes with a comparative discussion with another very recent proposal for defining analogical proportions between numerical values based on the family of generalized means.

Developmental psychologists have long-established the importance of socio-cognitive abilities in human intelligence. These abilities enable us to enter, participate and benefit from human culture. AI research on social interactive agents mostly concerns the emergence of culture in a multi-agent setting (often without a strong grounding in developmental psychology). We argue that AI research should be informed by psychology and study socio-cognitive abilities enabling to enter a culture too. We discuss the theories of Michael Tomasello and Jerome Bruner to introduce some of their concepts to AI and outline key concepts and socio-cognitive abilities. We present The SocialAI school - a tool including a customizable parameterized uite of procedurally generated environments, which simplifies conducting experiments regarding those concepts. We show examples of such experiments with RL agents and Large Language Models. The main motivation of this work is to engage the AI community around the problem of social intelligence informed by developmental psychology, and to provide a tool to simplify first steps in this direction. Refer to the project website for code and additional information: https://sites.google.com/view/socialai-school.

Developmental psychologists have spent decades devising experiments to test the intelligence and knowledge of infants and children, tracing the origin of crucial concepts and capacities. Moreover, experimental techniques in developmental psychology have been carefully designed to discriminate the cognitive capacities that underlie particular behaviors. We propose that using classical experiments from child development is a particularly effective way to probe the computational abilities of AI models, in general, and LLMs in particular. First, the methodological techniques of developmental psychology, such as the use of novel stimuli to control for past experience or control conditions to determine whether children are using simple associations, can be equally helpful for assessing the capacities of LLMs. In parallel, testing LLMs in this way can tell us whether the information that is encoded in text is sufficient to enable particular responses, or whether those responses depend on other kinds of information, such as information from exploration of the physical world. In this work we adapt classical developmental experiments to evaluate the capabilities of LaMDA, a large language model from Google. We propose a novel LLM Response Score (LRS) metric which can be used to evaluate other language models, such as GPT. We find that LaMDA generates appropriate responses that are similar to those of children in experiments involving social understanding, perhaps providing evidence that knowledge of these domains is discovered through language. On the other hand, LaMDA's responses in early object and action understanding, theory of mind, and especially causal reasoning tasks are very different from those of young children, perhaps showing that these domains require more real-world, self-initiated exploration and cannot simply be learned from patterns in language input.

Fox News Flash top entertainment and celebrity headlines are here. Check out what clicked this week in entertainment. Kate Middleton is making her mark as a future queen consort. On Thursday, the Duchess of Cambridge led her first roundtable with U.K. politicians to champion her cause on early childhood development. The mother of three urged the politicians present that there is "more we can all do" to prioritize the well-being of children.

A conceptual system with rich connotation is key to improving the performance of knowledge-based artificial intelligence systems. While a conceptual system, which has abundant concepts and rich semantic relationships, and is developable, evolvable, and adaptable to multi-task environments, its actual construction is not only one of the major challenges of knowledge engineering, but also the fundamental goal of research on knowledge and conceptualization. Finding a new method to represent concepts and construct a conceptual system will therefore greatly improve the performance of many intelligent systems. Fortunately the core of human cognition is a system with relatively complete concepts and a mechanism that ensures the establishment and development of the system. The human conceptual system can not be achieved immediately, but rather must develop gradually. Developmental psychology carefully observes the process of concept acquisition in humans at the behavioral level, and along with cognitive psychology has proposed some rough explanations of those observations. However, due to the lack of research in aspects such as representation, systematic models, algorithm details and realization, many of the results of developmental psychology have not been applied directly to the building of artificial conceptual systems. For example, Karmiloff-Smith's Representation Redescription (RR) supposition reflects a concept-acquisition process that re-describes a lower level representation of a concept to a higher one. This paper is inspired by this developmental psychology viewpoint. We use an object-oriented approach to re-explain and materialize RR supposition from the formal semantic perspective, because the OO paradigm is a natural way to describe the outside world, and it also has strict grammar regulations.

Playing with dolls uses a brain region that helps children develop empathy for other people and social processing skills, a study has discovered. Researchers from Cardiff found that doll play activated the use of the so-called posterior superior temporal sulcus (pSTS) more than other creative activities. In addition, the social benefits of the dolls were observed even when children played alone -- rather than with others -- and were equal among girls and boys. The findings support the pioneering theories of the Swiss'father of developmental psychology' Jean Piaget, who argued in 1945 that pretend play was inherently social. 'We use this area of the brain when we think about other people, especially when we think about another person's thoughts or feelings,' said paper author and developmental researcher Sarah Gerson of Cardiff University.

Humans have no trouble recognizing objects and reasoning about their behaviors -- it's at the core of their cognitive development. Even as children, they group segments into objects based on motion and use concepts of object permanence, solidity, and continuity to explain what has happened and imagine what would happen in other scenarios. Inspired by this, a team of researchers hailing from the MIT-IBM Watson AI Lab, MIT's Computer Science and Artificial Intelligence Laboratory, Alphabet's DeepMind, and Harvard University sought to simplify the problem of visual recognition by introducing a benchmark -- CoLlision Events for Video REpresentation and Reasoning (CLEVRER) -- that draws on inspirations from developmental psychology. CLEVRER contains over 20,000 5-second videos of colliding objects (three shapes of two materials and eight colors) generated by a physics engine and more than 300,000 questions and answers, all focusing on four elements of logical reasoning: descriptive (e.g., "what color"), explanatory ("what's responsible for"), predictive ("what will happen next"), and counterfactual ("what if"). It comes with ground-truth motion traces and event histories for each object in the videos, and with functional programs representing underlying logic that pair with each question.

Despite the recent advancement in the social robotic field, important limitations restrain its progress and delay the application of robots in everyday scenarios. In the present paper, we propose to develop computational models inspired by our knowledge of human infants' social adaptive abilities. We believe this may provide solutions at an architectural level to overcome the limits of current systems. Specifically, we present the functional advantages that adaptive Theory of Mind (ToM) systems would support in robotics (i.e., mentalizing for belief understanding, proactivity and preparation, active perception and learning) and contextualize them in practical applications. We review current computational models mainly based on the simulation and teleological theories, and robotic implementations to identify the limitations of ToM functions in current robotic architectures and suggest a possible future developmental pathway. Finally, we propose future studies to create innovative computational models integrating the properties of the simulation and teleological approaches for an improved adaptive ToM ability in robots with the aim of enhancing human-robot interactions and permitting the application of robots in unexplored environments, such as disasters and construction sites. To achieve this goal, we suggest directing future research towards the modern cross-talk between the fields of robotics and developmental psychology.