While many expect that the use of advanced learning technologies like intelligent tutoring systems (ITSs) will substitute for human teaching and thus reduce the influence of teachers on student outcomes, studies consistently show that outcomes vary substantially across teachers and schools (Pane et al. 2010; Pane et al. 2014; Ritter et al. 2007a; Koedinger et al. 1997; Koedinger and Sueker 2014). Despite these findings, there have been few efforts (e.g., Schofield 1995) to understand the mechanisms by which teacher practices influence student learning on such systems. We present analyses of Carnegie Learning’s Cognitive Tutor ITS data from a large school district in the southeastern United States, which present a variety of usage and implementation profiles that illuminate disparities in deployments in practical, day-to-day educational settings. We focus on differential effectiveness of teachers’ implementations and how implementations may drive learner efficiency in ITS usage, affecting long term learning outcomes. These results are consistent with previous studies of predictors and causes of learning outcomes for students using Cognitive Tutor. We provide recommendations for practitioners seeking to deploy intelligent learning technologies in real world settings.

Hector Levesque has a strong critical position regarding the place of the Turing Test in Artificial Intelligence. A key argument concerns the test’s use of, or even, reliance on deception for subjectively demonstrating intelligence, and counters with a test of his own based on Winograd Schemas that he suggests is more objective. We argue that the subjectivity of the test is a strength, and that evaluating the outcome of Levesque’s objective test introduces other problems.

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.

"Dry cleaning is really inconvenient," Norton said. "As we looked at this space we recognized the entire industry is outdated. We figured there had to be a better way to do it." So his team built an app and they created Press, what they call "the Uber of laundry and dry cleaning." Press is one of 15 startups that pitched Thursday night at Longhorn Startup Lab Demo Day at the Lady Bird Johnson Auditorium at UT.

We are a company of individuals with hopes, plans and passions, all using and developing our talents for good, at work and in life. Employees can be a force for good only when they are working at the top of their ability, learning new skills and challenging themselves with new responsibilities. Allstate's Enterprise Talent Market was developed with that in mind, to help you reach your full potential. Allstate is looking to hire researchers to join our Innovation team at our downtown Chicago and Menlo Park(SF Bay area) locations. Our team works on a diverse set of data and systems including GPS probe, Accident data, LiDAR and high-resolution imagery to create driver safety solutions and helps build next-generation road risk assessing platforms for ADAS, and self- driving vehicles.

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Write on topics related to machine learning Learn from the contributions by others. The app brings 15 subjects, 90 units, 1200 topics on Machine learning, computer science and related courses. The the app includes subjects related to machine learning such as Artificial Intelligence, Algorithms, Mathematics, Automata, Graph theory and more.

Hello, I've been following the machine learning videos on coursera with Andrew ng as the instructor. I don't know any math beyond a high school level so this is a bit tricky. I didn't understand how he was graphing this and what the H theta (x) meant when it came to graphing. I've searched on the internet a lot and couldn't find a video explaining what this means at all. If anyone would like to point me in the right direction that would be greatly appreciated.

Since January, "Jill," as she was known to the artificial-intelligence class, had been helping graduate students design programs that allow computers to solve certain problems, like choosing an image to complete a logical sequence.

Online translators are getting better, but there's still room for improvement. Researchers are now contributing new artificial intelligence techniques that could help accurately build full sentences. Algorithms developed by researchers at the University of Liverpool give computers a human-like touch while translating words and languages. They believe their methods are key to improving accuracy. Using the algorithms, a computer will be able to translate a word from an unknown language, and then provide context to it.