Generations in Dialogue: Bridging Perspectives in AI is a podcast from AAAI featuring thought-provoking discussions between AI experts, practitioners, and enthusiasts from different age groups and backgrounds. Each episode delves into how generational experiences shape views on AI, exploring the challenges, opportunities, and ethical considerations that come with the advancement of this transformative technology. In the fourth episode of this new series from AAAI, host Ella Lan chats to Professor Marynel Vázquez about what inspired her research direction, how her perspective on human-robot interactions has changed over time, robots navigating the social world, potential for using robots in education, modeling interactions as graphs, addressing misunderstandings with regards to robots in society, getting input from target users, the challenge of recognising when errors happen, making robots that adapt, and more. Marynel Vázquez is a computer scientist and roboticist whose research focuses on Human-Robot Interaction (HRI), particularly in multi-party settings. She studies social group dynamics--such as spatial behavior and social influence--in HRI, and develops perception and decision-making algorithms that enable autonomous, socially aware robot behavior.

The multilingual dictionary of noun valency Portlex is considered to be the trigger for the creation of the automatic language generators Xera and Combinatoria, whose development and use is presented in this paper. Both prototypes are used for the automatic generation of nominal phrases with their mono- and bi-argumental valence slots, which could be used, among others, as dictionary examples or as integrated components of future autonomous E-Learning-Tools. As samples for new types of automatic valency dictionaries including user interaction, we consider the language generators as we know them today. In the specific methodological procedure for the development of the language generators, the syntactic-semantic description of the noun slots turns out to be the main focus from a syntagmatic and paradigmatic point of view. Along with factors such as representativeness, grammatical correctness, semantic coherence, frequency and the variety of lexical candidates, as well as semantic classes and argument structures, which are fixed components of both resources, a concept of a multi-sided prototype stands out. The combined application of this prototype concept as well as of word embeddings together with techniques from the field of automatic natural language processing and generation (NLP and NLG) opens up a new way for the future development of automatically generated plurilingual valency dictionaries. All things considered, the paper depicts the language generators both from the point of view of their development as well as from that of the users. The focus lies on the role of the prototype concept within the development of the resources.

This paper describes how the field of Combinatorial Semantics has contributed to the design of three prototypes for the automatic generation of argument patterns in nominal phrases in Spanish, French and German (Xera, Combinatoria and CombiContext). It also shows the importance of knowing about the argument syntactic-semantic interface in a production situation in the context of foreign languages. After a descriptive section on the design, typologie and information levels of the resources, there follows an explanation of the central role of the combinatorial meaning (roles and ontological features). The study deals with different semantic f ilters applied in the selection, organization and expansion of the lexicon, being these key pieces for the generation of grammatically correct and semantically acceptable mono- and biargumental nominal phrases.

Deep learning on graphs is taking more importance by the day. If you search for machine learning online you'll find around 2,050,000,000 results. It's not easy to find that description or definition that fits every use or case, but there are amazing ones. Here I'll propose a different definition of machine learning, focusing on a new paradigm, the data fabric. If we can construct a data fabric that supports all the data in the company, then a business insight inside of it can be thought as a dent in it.

If you are new to the word ontology don't worry, I'm going to give a primer on what it is, and then why it matters for the data world. I'll be explicit in the difference between philosophical ontology and the ontology related to information and data in computer science. In simple words, one can say that ontology is the study of what there is. But there is another part to that definition that will help us in the following sections, and that is ontology is usually also taken to encompass problems about the most general features and relations of the entities which do exist. Ontology open new doors for what there is too.

Machine Learning (ML) is not easy, but creating a good workflow which you can reproduce, revisit and deploy to production is even harder. There has been many advances towards creating a good platform or managing solution for ML. Note that this is not the Data Science (DS) Lifecycle, which is more complex and has many parts. The ML lifecycle exists inside the DS lifecycle. These packages are great, but not so easy to follow.

Deep Learning made easy with Deep Cognition This past month I had the luck to meet the founders of DeepCognition.ai. Deep Cognition breaks the significant barrier…becominghuman.ai Dataset for this problem has been collected by researcher at Case Western Reserve University in Cleveland, Ohio. Original dataset is available here (Edit: the original link is not working anymore, download from Kaggle). This dataset is preprocessed by nice people at Kaggle that was used as starting point in our work.

My journey into Deep Learning In this post I'll share how I've been studying Deep Learning and using it to solve data science problems.

Robots following coded instructions to complete a task? Robots learning to do things by watching how humans do it? Stanford's Animesh Garg and Marynel Vázquez shared their research in a talk on "Generalizable Autonomy for Robotic Mobility and Manipulation" at the GPU Technology Conference last week. In lay terms, generalizable autonomy is the idea that a robot can observe human behavior, and learn to imitate it in a way that's applicable to a variety of tasks and situations. Learning to cook by watching YouTube videos, for one.

I come from physics and computer engineering. I studied both in Venezuela, and then I did a Master in Physics in Mexico. But I consider myself a Data Scientist. So even though I have a good and extensive background in math, calculus and statistics, it was not easy to get started with machine learning and then deep learning. This subjects are not new, but the way we study them, how we build software and solutions that use them, and also the way we program or interact with them has changed dramatically.