Event forecasting is a challenging, yet consequential task, as humans seek to constantly plan for the future. Existing automated forecasting approaches rely mostly on structured data, such as time-series or event-based knowledge graphs, to help predict future events. In this work, we formulate the forecasting problem as a restricted-domain, multiple-choice, question-answering (QA) task that simulates the forecasting scenario. To showcase the usefulness of this task formulation, we introduce a dataset ForecastQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. We also present our experiments on ForecastQA using BERT-based models and find that our best model achieves 61.0\% accuracy on the dataset, which is still far behind human performance by about 18%. We hope ForecastQA will support future research efforts in bridging this gap.

We have done a lot of work to make sure that we can enable the student to work at their own pace … as they need.

WASHINGTON--Representatives Paul D. Tonko (D-NY) and Guy Reschenthaler (R-PA) have just announced the introduction of their Artificial Intelligence Education Act today, bipartisan legislation that would establish grant support within the National Science Foundation (NSF) to fund the creation of easily-accessible K-12 lesson plans for schools and educators to provide students with the tools, skills and social understanding of artificial intelligence (AI) technologies in 21st-Century Society. "The development of Artificial Intelligence has fundamentally changed the way we live and work, bringing untold potential in the fields of medical science, research and development, engineering, manufacturing and so much more," Congressman Tonko said. "By providing the resources for our children to learn about AI, we ensure that the next generation of our American workforce has the skill necessary to succeed in this rapidly growing field, thereby helping to drive innovation and economic opportunity. Our legislation will deliver the tools needed to teach AI to students across the nation. I thank Congressman Reschenthaler for his partnership as a co-lead of this bill and urge the support of my colleagues in Congress to help secure a bright future for both our students and our economy."

The ORCA Hub is led by the Edinburgh Centre for Robotics, a partnership between Heriot-Watt University and the University of Edinburgh. The consortium also includes Imperial College London, Oxford and Liverpool universities.

Machine learning could help predict and prevent lead poisoning in children (5:26 -- 13:54).

After decades in research labs, machine learning is now getting enormous attention for real-world applications that harness the technology's formidable power to discern patterns in huge quantities and types of data at high speed: fraud detection, customer 360, facial recognition, workflow management, shopping personalization and much more. The payback of such initiatives can be big. But even greater opportunities lie in creating advanced analytic systems that use machine learning's unmatched ability to see, organize and leverage insights from ever-growing mounds of data to unlock the deep, transformative potential of Big Data and the Internet of Things. To get to the next level of machine learning, companies must develop a sound business case; implement machine learning algorithms for speed at scale; use systems equipped with processors with multiple integrated cores, faster memory subsystems, and develop architectures that can handle massive amounts data in real time. For many organizations, it is an ideal time to build on or begin machine-learning experience, deepen knowledge, and reap the benefits and competitive advantages this sophisticated data analytics technology can provide.

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Facebook's artificial intelligence researchers have a plan to make algorithms smarter by exposing them to human cunning. They want your help to supply the trickery. Thursday, Facebook's AI lab launched a project called Dynabench that creates a kind of gladiatorial arena in which humans try to trip up AI systems. Challenges include crafting sentences that cause a sentiment-scoring system to misfire, reading a comment as negative when it is actually positive, for example. Another involves tricking a hate speech filter--a potential draw for teens and trolls.

You must have heard/read people telling you that if you want to be a rockstar Data Science or Machine Learning Engineer then have a good grip on mathematical concepts like Linear Algebra, Calculus, Probability, etc. Mathematics is necessary to build products or conduct academic research in machine learning. Maths and Programming are highly intertwined in machine learning workflows. Often, the code is built directly from mathematical intuition and shares the syntax of mathematical notation. In this article, I'm going to cover the two best courses from the top university in the world, that you can take to build a solid foundation in calculus. Single Variable Calculus is a first-year, first-semester course at MIT.

NLP - Natural Language Processing with Python Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing Bestseller What you'll learn Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.