In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it. We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech. The general rebuttals we describe here (written in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics. This allows creating responses beyond the scope of topics for which specific arguments are available. All data collected during this work is freely available for research.

Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. Despite its inherent challenges, recent deep neural networks based knowledge tracing models have achieved great success, which is largely from models' ability to learn sequential dependencies of questions in student exercise data. However, in addition to sequential information, questions inherently exhibit side relations, which can enrich our understandings about student knowledge states and has great potentials to advance knowledge tracing. Thus, in this paper, we exploit side relations to improve knowledge tracing and design a novel framework DTKS. The experimental results on real education data validate the effectiveness of the proposed framework and demonstrate the importance of side information in knowledge tracing. 1 Introduction Knowledge tracing - where machine monitors students' knowledge states and their skill acquisition levels - is essential for personalized education and a fundamental part of intelligent tutoring systems [15,7,1,12]. However, tracing student knowledge states is inherently challenging because of the complexity of human learning process, which involves a variety of factors from diverse domains such as neural science [3,4], psychology [10], and education [8].

How do you prepare to solve these problems and be ready for the workforce? Learn these three skills, which all are foundational, and build on each other, from easiest, to hardest. The really key thing about all of these skills is that they are also fundamental and critical to software development outside of data science, meaning that, in case you can't find a data science job, you can transition quickly to software development, or devops. I consider this flexibility just as important as training for a specific data-related gig. From HN: "Is there some kind of small and easy JS and/or PHP program allowing some easy work on a database?"

Machine learning is a set of techniques that allow machines to learn from data and experience, rather than requiring humans to specify the desired behavior by hand. Over the past two decades, machine learning techniques have become increasingly central both in AI as an academic field, and in the technology industry. This course provides a broad introduction to some of the most commonly used ML algorithms. It also serves to introduce key algorithmic principles which will serve as a foundation for more advanced courses, such as CSC412/2506 (Probabilistic Learning and Reasoning) and CSC421/2516 (Neural Networks and Deep Learning).

SqwidNet, in partnership with Sigfox, has concluded the second round of its Internet of Things (IoT) SA University Challenge with ten university teams competing in the final pitch presentation day this week. The programme is designed to challenge students to develop and create innovative projects focused on building solutions that support the UN Sustainable Development Goals using SqwidNet / Sigfox technology. "We were astounded by the creative thinking displayed by the ten teams that presented their solutions to the judges this week," says Phathizwe Malinga, managing director of SqwidNet. "The solutions presented ranged from agricultural solutions for early pest detection to avoid crop losses, to generating electricity from plants by collecting electrons from roots in an anode and converting that into electricity. We also saw an IoT water monitoring solution, an early fire detection for rural communities and a two-way learning solution using artificial intelligence."

Why this is important Types of Natural Language Processing Classical vs. Deep Learning Models End to End Deep Learning Models Seq2Seq Architecture & Training Beam Search Decoding Requirements Just some high school mathematics level Basic Python programming knowledge We've talked about, speculated and often seen different applications for Artificial Intelligence - But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd? ChatBots are here, and they came change and shape-shift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement. If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you! Why this is important Types of Natural Language Processing Classical vs. Deep Learning Models End to End Deep Learning Models Seq2Seq Architecture & Training Beam Search Decoding

UPDATE: You will always find the latest Python script (with the linear regression class definition and methods) HERE. Use it to build further or experiment. Data scientists often come from a background which is quite far removed from traditional computer science/software engineering -- physics, biology, statistics, economics, electrical engineering, etc. But ultimately, they are expected to pick up a sufficient amount of programming/software engineering to be truly impactful for their organization and business. Being a Data Scientist does not make you a Software Engineer! And, what is at the heart of most modern programming languages and software engineering paradigms?

Paul Hildreth peered at a display of dozens of images from security cameras surveying his Atlanta school district and settled on one showing a woman in a bright yellow shirt walking a hallway. A mouse click instructed the artificial intelligence-equipped system to find other images of the woman, and it immediately stitched them into a video narrative of where she was currently, where she had been and where she was going. There was no threat, but Hildreth's demonstration showed what's possible with AI-powered cameras. If a gunman were in one of his schools, the cameras could quickly identify the shooter's location and movements, allowing police to end the threat as soon as possible, said Hildreth, emergency operations coordinator for the Fulton County School District. AI is transforming surveillance cameras from passive sentries into active observers that can identify people, suspicious behavior and guns, amassing large amounts of data that help them learn over time to recognize mannerisms, gait and dress.

Artificial intelligence salaries benefit from the perfect recipe for a sweet paycheck: a hot field and high demand for scarce talent. It's the ever-reliable law of supply and demand, and right now, anything artificial intelligence-related is in very high demand. According to Indeed.com, the average IT salary -- the keyword is "artificial intelligence engineer" -- in the San Francisco area ranges from approximately $134,135 per year for "software engineer" to $169,930 per year for "machine learning engineer." However, it can go much higher if you have the credentials firms need. One tenured professor was offered triple his $180,000 salary to join Google, which he declined for a different teaching position.

This course will teach you regression analysis for both statistical data analysis and machine learning in Python in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting & make business forecasting related decisions...All of this while exploring the wisdom of an Oxford and Cambridge educated researcher. Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling.