Prifina is thrilled to welcome Dr. Peter Cotton as our special guest in the fifth episode of the "Liberty. Peter currently serves as the Senior Vice President and Chief Data Scientist at Intech Investment Management LLC. D. degree in Mathematics from Stanford, he held leadership roles at major U.S. financial institutions. Peter has led data science projects at Morgan Stanley, J.P. Morgan Chase, and several major hedge funds, where he built solutions solving complex data problems. He has extensive experience with crowdsourcing models and helped build one of the first in the world privacy-preserving computation mechanisms at J.P. Morgan Chase. In this podcast we talk about algorithms and innovation, with a focus on financial data. How does a hedge fund normalize messy financial data to build bespoke predictive models? What are the current trends and challenges related to machine learning in the financial services industry? From the innovation point of view, what happens when we cut down the cost of building algorithms to minimum functionality? Is it possible to build personal AI systems for small and mid-cap companies as well as individuals? We also delve into geeky topics, such as how to build a financial probability model for the pricing of vanilla bonds. You can find this podcast on Spotify, Apple Podcasts, Google Podcasts, and SoundCloud. He noted that one of the main areas of focus in his career was to level the playing field in the machine learning (ML) space. He notes that one of the first things to do before building financial prediction, ML, and AI models is create a system that helps clean and normalize data. "It is the most interesting mathematical problem of all because the cleaning of data implies that you understand the market itself.

There is certainly an important role AI and Machine Learning technology plays in addressing the most sophisticated online fraud as well as improving …

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In the series, we considered both the teaching of AI and machine learning itself, and the use of AI techniques to improve education in general. You can also find out more about conferences and events, and other interesting research at the intersection of AI and education. There are a number of conferences and workshops that focus on the education side of AI. In our focus series we heard from the co-chairs of the Symposium on Educational Advances in Artificial Intelligence (EAAI), which was held in February this year. This event is held as an independent symposium within the AAAI conference, and provides the opportunity for researchers, educators, and students to share educational experiences involving AI.

During the progress of any data science project, most data scientists tend to utilize tools and gadgets that would help them reach their goals faster and more efficiently. They use these tools to speed up routine tasks to save their energy and brain-power to find solutions for the current problem they are trying to solve. Because of this desire to speed up a project's workflow, there are so many data science tools out there that you can choose from, whichever suits the task at hand. And believe me, when I say this, there are hundreds of tools you can choose to finish your project; at the end of the project, you will discover that you used multiple of these tools to finish one project. Since any data science project consists of different steps, from gathering and collecting data to clean it, analyzing, and visualizing it, there are tools designed and developed for each of these steps. Tools to automatically collect data for you from all over the web, or tools to visualize your data and help you tell the story hidden within, or tools to help you clean your data and use the most relevant part of it in your analysis.

Excel is perhaps the most well known data analysis tool out there. It's used to store and organize data such as sales numbers, profit rates, expenditures or revenues. Some businesses even use it to store text data. However, Excel is unable to organize text data without the help of machine learning. Machine learning algorithms can automatically analyze hundreds and thousands of rows of text data in a fast, consistent and scalable way. In other words, machine learning algorithms are able to quantify words and phrases in Excel, by assigning topics, keywords, entities, and even sentiment to each row of text.

INTRODUCTION With increasing awareness of the societal risks of algorithmic bias and encroaching automation, issues of fairness, accountability, and transparency in data-driven AI systems have received growing academic attention in multiple high-stakes contexts, including healthcare, loan-granting, and hiring (e.g., Barocas & Selbst, 2016; Holstein, Wortman Vaughan, Daumé III, Dudik, & Wallach, 2019; Veale, Van Kleek, & Binns, 2018). Given these noble intentions, why might AIEd systems have inequitable impacts? In this chapter, we ask whether AIEd systems will ultimately serve to A mplify I nequities in Ed ucation, or alternatively, whether they will help to A lleviate existing inequities. We discuss four lenses that can be used to examine how and why AIEd systems risk amplifying existing inequities: (1) factors inherent to the overall socio-technical system design; (2) the use of datasets that reflect historical inequities; (3) factors inherent to the underlying algorithms used to drive machine learning and automated decision-making, and (4) factors that emerge through a complex interplay between automated and human decision-making. Building from these lenses, we then outline possible paths towards more equitable futures for AIEd, while highlighting debates surrounding each proposal. In doing so, we hope to provoke new conversations around the design of equitable AIEd, and to push ongoing conversations in the field forward. PATHWAYS TOWARD INEQUITY IN AIED We begin by presenting four lenses to understand how AIEd systems might amplify existing inequities or even create new ones (cf. While each lens provides a different way of examining pathways towards inequity in AIEd, all are pointed at the same underlying socio-technical system. Figure 1 provides a coarse-grained overview of the broader social-technical systems in which AIEd systems are embedded, and some of the components we will refer to in the four lenses. The accumulated, collective decisions of designers, researchers, policy-makers, and other stakeholders shape these systems' designs. In addition to using or being affected by AIEd systems, on-the-ground stakeholders such as students, teachers, or school administrators may also play a role in shaping their designs; whether directly, through participatory design processes, or indirectly through the passive generation of training data while interacting with an AIEd interface. In turn, decisions regarding what data is used to shape an AIEd system's design (e.g., when used as training data for use with machine learning methods) can shape an AIEd system's algorithmic behavior (e.g., instructional policies learned from data).

Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. However, due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support. Learners may post their feelings of confusion and struggle in the respective MOOC forums, but with the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention. This problem has been studied as a Natural Language Processing (NLP) problem recently, and is known to be challenging, due to the imbalance of the data and the complex nature of the task. In this paper, we explore for the first time Bayesian deep learning on learner-based text posts with two methods: Monte Carlo Dropout and Variational Inference, as a new solution to assessing the need of instructor interventions for a learner's post. We compare models based on our proposed methods with probabilistic modelling to its baseline non-Bayesian models under similar circumstances, for different cases of applying prediction. The results suggest that Bayesian deep learning offers a critical uncertainty measure that is not supplied by traditional neural networks. This adds more explainability, trust and robustness to AI, which is crucial in education-based applications. Additionally, it can achieve similar or better performance compared to non-probabilistic neural networks, as well as grant lower variance.

In Visual Semantics we study how humans build mental representations, i.e., concepts , of what they visually perceive. We call such concepts, substance concepts. In this paper we provide a theory and an algorithm which learns substance concepts which correspond to the concepts, that we call classification concepts , that in Lexical Semantics are used to encode word meanings. The theory and algorithm are based on three main contributions: (i) substance concepts are modeled as visual objects , namely sequences of similar frames, as perceived in multiple encounters ; (ii) substance concepts are organized into a visual subsumption hierarchy based on the notions of Genus and Differentia that resemble the notions that, in Lexical Semantics, allow to construct hierarchies of classification concepts; (iii) the human feedback is exploited not to name objects, as it has been the case so far, but, rather, to align the hierarchy of substance concepts with that of classification concepts. The learning algorithm is implemented for the base case of a hierarchy of depth two. The experiments, though preliminary, show that the algorithm manages to acquire the notions of Genus and Differentia with reasonable accuracy, this despite seeing a small number of examples and receiving supervision on a fraction of them.