The 4th edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since January 2021. This report aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the ever-changing developments in the field. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, with a particular focus on four key themes: Ethical AI, Fairness & Justice, Humans & Tech, and Privacy. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Opening the report is a long-form piece by Edward Higgs (Professor of History, University of Essex) titled "AI and the Face: A Historian's View." In it, Higgs examines the unscientific history of facial analysis and how AI might be repeating some of those mistakes at scale. The report also features chapter introductions by Alexa Hagerty (Anthropologist, University of Cambridge), Marianna Ganapini (Faculty Director, Montreal AI Ethics Institute), Deborah G. Johnson (Emeritus Professor, Engineering and Society, University of Virginia), and Soraj Hongladarom (Professor of Philosophy and Director, Center for Science, Technology and Society, Chulalongkorn University in Bangkok). This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

In this paper, we revisit math word problems~(MWPs) from the cross-lingual and multilingual perspective. We construct our MWP solvers over pretrained multilingual language models using sequence-to-sequence model with copy mechanism. We compare how the MWP solvers perform in cross-lingual and multilingual scenarios. To facilitate the comparison of cross-lingual performance, we first adapt the large-scale English dataset MathQA as a counterpart of the Chinese dataset Math23K. Then we extend several English datasets to bilingual datasets through machine translation plus human annotation. Our experiments show that the MWP solvers may not be transferred to a different language even if the target expressions have the same operator set and constants. But for both cross-lingual and multilingual cases, it can be better generalized if problem types exist on both source language and target language.

As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.

Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects.

In the race to harness the power of cloud computing, and further develop artificial intelligence, academics have a new concern: falling behind a fast-moving tech industry. In the US, 22 higher education institutions, including Stanford and Carnegie Mellon, have signed up to a National Research Cloud initiative seeking access to the computational power they need to keep up. It is one of several cloud projects being called for by academics globally, and is being explored by the US Congress, given the potential of the technology to deliver breakthroughs in healthcare and climate change. Under the US proposal, authored by Fei-Fei Li and John Etchemendy from the Stanford Institute for Human-Centered Artificial Intelligence, a national cloud platform would enable more academic and industry researchers to work at the leading edge of AI, and help train a new generation of experts. Li and Etchemendy's NRC proposal cautions about declining government funding for basic and foundational research and highlights the US's history of federally funding research into innovations -- from gene sequencing to the internet itself.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. IBM's AI research division has released a 14-million-sample dataset to develop machine learning models that can help in programming tasks. Called Project CodeNet, the dataset takes its name after ImageNet, the famous repository of labeled photos that triggered a revolution in computer vision and deep learning. While there's a scant chance that machine learning models built on the CodeNet dataset will make human programmers redundant, there's reason to be hopeful that they will make developers more productive. In the early 2010s, impressive advances in machine learning triggered excitement (and fear) about artificial intelligence soon automating many tasks, including programming.

A new cross-disciplinary research initiative at MIT aims to promote the understanding and use of AI across all segments of society. The effort, called Responsible AI for Social Empowerment and Education (RAISE), will develop new teaching approaches and tools to engage learners in settings from preK-12 to the workforce. "People are using AI every day in our workplaces and our private lives. Being digitally literate is no longer enough. People need to be AI-literate to understand the responsible use of AI and create things with it at individual, community, and societal levels," says RAISE Director Cynthia Breazeal, a professor of media arts and sciences at MIT. "But right now, if you want to learn about AI to make AI-powered applications, you pretty much need to have a college degree in computer science or related topic," Breazeal adds.

This episode is about learning the options you have to get some money to start your startup and what is expected you achieve with that money. In this podcast series of episodes we are going to explain how to create a robotics startup step by step. We are going to learn how to select your co-founders, your team, how to look for investors, how to test your ideas, how to get customers, how to reach your market, how to build your product… Starting from zero, how to build a successful robotics startup. I'm Ricardo Tellez, CEO and co-founder of The Construct startup, a robotics startup at which we deliver the best learning experience to become a ROS Developer, that is, to learn how to program robots with ROS. Our company is already 5 years long, we are a team of 10 people working around the world.

With the use of Machine Learning (ML) on the rise, it is more important than ever to take a look at the leading five ML libraries being used today. But before we get into that, let’s look at what is an ML library? A Machine Learning library, or a Machine Learning framework, is a set of routines and functions that are written in a given programming language. Essentially, they are interfaces, libraries or tools helping developers to easily and quickly build machine learning models, going past the specific basic details of the underlying algorithms. So they basically help developers carry out complex tasks without having to rewrite many lines of code. Now let us look at the five best ML libraries out there for developers today: 1. TensorFlow Created by the Google Brain team, TensorFlow is a free and open-source software library used for research and production. Allowing easy and effective implementation of machine learning algorithms, it is an efficient math library and is also used for machine learning applications such as neural networks. The emergence of high-level APIs (Application Programming Interfaces) like Keras and Theano has made TensorFlow more effective in improving the capability of computers to predict solutions with a greater degree of accuracy. Bear in mind that TensorFlow offers stable APIs for Python and C. Providing parallel processing, it is easily trainable on CPU as well as GPU (Graphics Processing Unit) for distributed computing and enjoys a large community support with additional advantages including better computational graph visualizations, quick updates, frequent new releases with new features, good debugging methods and scalability. 2. Keras Several back-end neural network computation engines are supported by Keras, an open-source neural network library written in Python. It can run on top of frameworks such as TensorFlow, Microsoft Cognitive Toolkit, Theano. Keras has many impressive features. First is modularity, where a model can be understood as a sequence or a graph alone, next is minimalism so that the library shares just enough to get an outcome and there’s also the element of maximizing readability and extensibility which allows researchers to do more trials. Its advantages include its support for a wide range of production deployment options and integration with back-end engines/frameworks; it also helps that everything in Keras is native Python. Kids and teens interested in learning TensorFlow and Keras can join the YoungWonks afterschool coding program. Here, they will first get to learn the basics of Python and work their way up to learn about the two ML libraries in live online classroom sessions focusing on project-based and self-paced learning. 3. Scikit-learn Scikit-learn is a free machine learning library for Python built on SciPy. An effective tool for data mining and data analysis, it is used today for model selection, clustering, preprocessing, and more. Its popularity can be traced to the fact that it boasts a clean API, is easy to use, fast, comprehensive and enjoys good documentation and the support of an active developer community. It also scores well on the simplicity and accessibility front. 4. Theano Also a Python ML library, Theano is an open-source project developed by Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal. It allows developers to define, optimize and evaluate mathematical expressions that include multi-dimensional arrays. It provides features such as good integration with NumPy, transparent use of a GPU, extensive unit-testing, and self-verification. 5. PyTorch Developed by Facebook’s AI Research lab (FAIR), PyTorch is used for applications like computer vision and natural language processing. Also a free and open-source software, it has a polished Python interface along with a C++ interface. PyTorch offers Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU) and today many deep learning softwares have been/ are being built on top of PyTorch.

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