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Artificial intelligence (AI)-powered technologies are becoming an integral part of youth's environments, impacting how they socialize and learn. Children (12 years of age and younger) often interact with AI through conversational agents (e.g., Siri and Alexa) that they speak with to receive information about the world. Conversational agents can mimic human social interactions, and it is important to develop socially intelligent agents appropriate for younger populations. Yet it is often unclear what data are curated to power many of these systems. This article applies a sociocultural developmental approach to examine child-centric intelligent conversational agents, including an overview of how children's development influences their social learning in the world and how that relates to AI. Examples are presented that reflect potential data types available for training AI models to generate children's conversational agents' speech. The ethical implications for building different datasets and training models using them are discussed as well as future directions for the use of social AI-driven technology for children.

The first T3 Annual Meeting session celebrated the achievements of the network over the last two and a half years, included an interactive project showcase o...

David Meza, senior data scientist at NASA, and his team are building a talent mapping database to better identify talent for jobs.

Leaning In: Connecting Your Organization to the Internet of CareersPresented by Etan Bernstein, Head of Ecosystem, Velocity Network Foundation

David Meza, senior data scientist at NASA, and his team are building a talent mapping database to better identify talent for jobs.

This WORK. podcast features Dror Gurevich, the Chief Executive Officer of Velocity Career Labs and the Velocity Network Foundation. These organizations co-exist to reinvent how career records and credentials are shared across the labor market, empowering individuals, businesses and educational institutions through transformational blockchain technology.

Lifescope lets you search the internet of you using passive automatic life logging. Connect accounts for events, people, places, things, and digital content in one lifelogger. Built on BitScoop.

Learn how NASA uses Neo4j to develop a knowledge graph as part of its knowledge architecture to analyze lessons learned and save astronauts' lives.

Getting Started with the Open Badges 2.0 EcosystemConsidering implementing Open Badges in your platform and looking to get IMS certified? Get a quick orienta...

An introduction on how to build a knowledge graph using employee and occupation skillsets. This presentation was given at the Nordic People Analytics Confere...

It’s time to adapt traditional forms of education into something the modern learner can access at any point in their lifelong learning journey.

In recent years Knowledge Graphs have been used to solve one of the biggest problems not only in machine learning but also in computer…

Graph database startup Neo4j raised $320 million at an over $2 billion valuation, highlighting the value of graph databases.

# VC Spec Enhancement Proposal [![hackmd-github-sync-badge](https://hackmd.io/rnY5xiXESWqWBlyDPHD

Doing cool things with data!

I started a data blog on my quantified self journey. You can read the first post here. Let me know what you think, and if there’s anything in particular you’d be interested in reading in future posts. Thanks!

A place to store your digital educational credentials

Online skill tree maker to design detailed, stunning skill trees. Prebuilt templates, customization options, and powerful collaboration capabilites.

The Digital Credentials Consortium was founded by 12 universities to bridge the gap between traditional academic credentials and the future of digital credentials. Core to our approach are W3C Verifiable Credentials and related standards, which enable a new digital credential infrastructure for today’s learner – one informed by learner welfare, rights, and agency. Learn about our approach, the communities we work with, the challenges we are working on, and how to get involved.

Networks have emerged as a popular method for studying mental disorders. Psychopathology networks consist of aspects (e.g., symptoms) of mental disorders (nodes) and the connections between those aspects (edges). Unfortunately, the visual presentation of networks can occasionally be misleading. For instance, researchers may be tempted to conclude that nodes that appear close together are highly related, and that nodes that are far apart are less related. Yet this is not always the case. In networks plotted with force-directed algorithms, the most popular approach, the spatial arrangement of nodes is not easily interpretable. However, other plotting approaches can render node positioning interpretable. We provide a brief tutorial on several methods including multidimensional scaling, principal components plotting, and eigenmodel networks. We compare the strengths and weaknesses of each method, noting how to properly interpret each type of plotting approach.

Stanford scholars found that people’s interactions with a virtual person in augmented reality, or AR, influenced how they behaved in the physical world.

Engineers could use this breakthrough in graph theory to design wildly efficient quantum computer chips.

Mat Zucker shares eight components integral to creative professionals presenting and marketing themselves.

Analyzing arXiv articles with neo4j

Quantified self refers to the practice of using wearable devices and other modern technologies to collect data about one’s own life and health.