Testing with quiz questions has proven to be an effective way to assess and improve the educational process. However, manually creating quizzes is tedious and time-consuming. To address this challenge, we present Leaf, a system for generating multiple-choice questions from factual text. In addition to being very well suited for the classroom, Leaf could also be used in an industrial setting, e.g., to facilitate onboarding and knowledge sharing, or as a component of chatbots, question answering systems, or Massive Open Online Courses (MOOCs). The code and the demo are available on GitHub.

This blog is part of the Torch Thursdays series. The plan is to share some tidbits on PyTorch usage through this. Today's blog particularly is to share some quick notes based on PyTorch's video tutorial on tensors. This blog assumes familiarity with PyTorch framework and numpy. PyTorch provides torch.Tensor to represent a multi-dimensional array containing elements of a single data type.

The graph represents a network of 1,107 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 21 January 2022 at 11:47 UTC. The requested start date was Friday, 21 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 8-hour, 23-minute period from Tuesday, 18 January 2022 at 16:30 UTC to Friday, 21 January 2022 at 00:53 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

Never mind having robots deliver pizza -- if MIT and QCRI researchers have their way, the automatons will make your pizza as well. They've developed a neural network, PizzaGAN (Generative Adversarial Network), that learns how to make pizza using pictures. After training on thousands of synthetic and real pizza pictures, the AI knows not only how to identify individual toppings, but how to distinguish their layers and the order in which they need to appear. From there, the system can create step-by-step guides for making pizza using only one example photo as the starting point. The result is a system that isn't perfect (it's better at ordering synethetic pizza images than real ones), but it's still reasonably accurate. The scientists found that PizzaGAN could determine the right order 88 percent of the time, albeit using pizzas with just two toppings.

Modern Natural Language Processing course is designed for anyone who wants to grow or start a new career and gain a strong background in NLP. Nowadays, the industry is becoming more and more in need of NLP solutions. Chatbots and online automation, language modeling, event extraction, fraud detection on huge contracts are only a few examples of what is demanded today. Learning NLP is key to bring real solutions to the present and future needs. Throughout this course, we will leverage the huge amount of speech and text data available online, and we will explore the main 3 and most powerful NLP applications, that will give you the power to successfully approach any real-world challenge.

It highlighted the Sultanate's infrastructure readiness for Artificial Intelligence (AI) as an embodiment of Oman's Vision 2040, and the digital transformation strategy announced by the Ministry of Transport, Communications and Information Technology, which aim at promoting and accelerating the digital transformation of the government sectors and services in Oman in line with the global technical development. Above 30 ministries, authorities and establishments participated in the workshop. The workshop targeting all government entities came within the framework of ODP's national efforts that aim at contributing to accelerating the Sultanate digital transformation, through utilising the Artificial Intelligence applications (AI) and the cost-effective and efficient support available through Oman Data Park's Nebula AI infrastructure which is powered by Nvidia and hosted in Oman. The delivered content of the workshop included an AI transformation roadmap for government entities, their cyber security solutions, a framework for creating an AI project within the public sector and highlighted the action of the national programme for adopting Artificial Intelligence and Advanced Technologies in the Sultanate. The holding of the workshop comes in light of the Sultanate's interest in accelerating the adoption of Artificial Intelligence, while the Sultanate has designated a National Programme for Artificial Intelligence and Advanced Technologies in the year 2020, within the Ministry of Transport, Communications, and Information Technology (MTCIT).

One of the co-founders, Martin Nisser, a PhD student from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), explains the digital literacy and self-efficacy focused objectives: "Some of the women haven't had the opportunity to work with a computer for 25 years, and aren't yet accustomed to using the internet. We're working with them to build their capabilities with these modern tools in order to prepare them for life outside," says Nisser. Even for the students who became incarcerated more recently, it can be difficult to keep up with the fast pace of technological advances, since technical programs in correctional facilities are few and far-between. This scarcity of preparatory programs undoubtedly contributes to high and rising recidivism rates: More often than not, those who are released from prison eventually return. While working at TEJI, Nisser had a fortuitous meeting with his two co-founders, Marisa Gaetz (a PhD student from MIT's Department of Mathematics) and Emily Harburg (co-founder of Brave Initiatives, a nonprofit that develops coding bootcamps for young women).

This is Supervised machine learning full course. It covers all basic concepts from Python, Pandas, Django, Ajax and Scikit Learn. The course start on Jupyter notebook where different operations will performed on data. The end goal of this course is to teach how to deploy machine learning model on Django Python web framework. Actually, that is the purpose of machine learning.

The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems so that they are capable of learning (and improving) continuously, leveraging data on one task to improve performance on another, and doing so in a computationally sustainable way. Performers on this program developed systems capable of performing a diverse range of functions, including autonomous driving, real-time strategy, and drone simulation. These systems featured a diverse range of characteristics (e.g., task structure, lifetime duration), and an immediate challenge faced by the program's testing and evaluation team was measuring system performance across these different settings. This document, developed in close collaboration with DARPA and the program performers, outlines a formalism for constructing and characterizing the performance of agents performing lifelong learning scenarios. In Section 2, we introduce the general form of a lifelong learning scenario.