Massive Open Online Courses MOOC (Noun)

Total Page:16

File Type:pdf, Size:1020Kb

Massive Open Online Courses MOOC (Noun) MOOCs Massive Open Online Courses MOOC (noun) Massive Open Online Course, a term used to describe web technologies that have enabled educators to create virtual classrooms of thousands of students. Typical MOOCs involve a series of 10-20 minute lectures with built-in quizzes, weekly auto-graded assignments, and TA/professor moderated discussion forums. Notable companies include Coursera, edX, and Udacity. 1 THE HISTORY OF DISTANCE LEARNING 1 THE HISTORY OF DISTANCE LEARNING 2000s 1960s 1920s 1840s ONLINE TV RADIO MAIL As technology has evolved, so has distance learning. It began with mailing books and syllabi to students, then radio lectures, then tv courses, and now online courses. 2 WHY ARE MOOCs DIFFERENT? 2 WHY ARE MOOCs DIFFERENT? Beginning with the first correspondence courses in the 1890s from Columbia University, distance learning has been an important means of making higher education available to the masses. As technology has evolved, so has distance learning; and in just the last 5 years a new form of education has arisen, Massive Open Online Courses (MOOCs). MOOCs are becoming increasingly popular all over the world and the means by which learning is measured, evaluated, and accredited has become topic of controversy in higher education. 2 WHY ARE MOOCs DIFFERENT? Short (10-20 minute) lectures recorded specifically for online Quizzes that are usually integrated into lectures 2 WHY ARE MOOCs DIFFERENT? TA / Professor moderated discussion forums Letters, badges, or certificate of completion 2 WHY ARE MOOCs DIFFERENT? Graded assignments with set due dates (graded by computer) Large class sizes (often tens of thousands of students) 2 WHY ARE MOOCs DIFFERENT? Automated grading Final exams and grades 3 COMPANIES AND UNIVERSITIES SERVE MOOCs TO THE MASSES 3 COMPANIES AND UNIVERSITIES SERVE MOOCs TO THE MASSES The modern MOOC began with an open Computer Science course at Stanford, Introduction to Artificial Intelligence, taught by Professor Sebastian Thrun in 2011. The wildly successful course, with 160,000 students in attendance, led Thrun (along with his colleagues David Stavens and Mike Sokolsky) to create Udacity in 2012, kicking o MOOC mania. 3 COMPANIES AND UNIVERSITIES SERVE MOOCs TO THE MASSES DATE FOUNDED 2012 April 2012 2011 FOUNDED Andrew Ng Anant Agarwal Sebastian Thrun BY (Stanford) MIT and Harvard Mike Sokolsky Daphne Koller President (MIT) David Stavens (Stanford) (Stanford) WHY Enable the best Bring education to the Expanded after huge FOUNDED professors to teach masses & research popularity of initial tens or hundreds of how students learn experimental AI course. thousands of and how technology students. To serve can transform students who were learning. not enrolled on a traditional campus. 3 COMPANIES AND UNIVERSITIES SERVE MOOCs TO THE MASSES REVENUE Revenue through Non-profit. Non-profit. MODEL Amazon aliate program. Revenue through Revenue through retail retail partners like partners like textbook “Signature Track”: textbook suppliers. suppliers. $30-100 for course credit.# Udacity Career Placement Program $60-90 proctored exams. Coursera Career Services 3 COMPANIES AND UNIVERSITIES SERVE MOOCs TO THE MASSES PROFIT Partner universities University Produced: Courses produced SHARING get 6-15% of gross edX collects first $50k in-house independent revenue, plus 20% of generated by course, of universities. profits generated by $10k for recurring “aggregate set of courses. University courses provided by gets 50% of all further the university”. revenue. EdX Produced: Costs $250k for each new course, $50k for additional terms. University gets 70% of revenue. 3 COMPANIES AND UNIVERSITIES SERVE MOOCs TO THE MASSES CREDIT Identity verified, Universities accept MODEL “Signature Track” credit after completion courses oer accredited of certificate & final. completion certificate. REACH 33 Colleges and 6 Universities 400,000 Users Universities 600,000 Users 22 active courses 1.9 Million Registered Users 24 Classes 4 CONTROVERSY 4 CONTROVERSY As MOOCs become increasingly popular all over the world, the means by which learning is measured, evaluated, and credited is a topic of controversy in higher education. Some courses have already been accredited and universities are beginning to accept transfer credit for completing MOOCs. These companies have quickly grown in size and hype, and their rapid growth has led to many questions around how MOOCs may shape the future of higher education. Coursera, Udacity, and edX were not originally meant to grant credit, and the recent push from administrators to enable students to earn credit for the successful completion of a MOOC raises many questions. 5 DISCUSSIONS TODAY 5 5 DISCUSSIONS TODAY What are people saying? "MOOCs are just the tip of the iceberg," said John Mitchell, professor of computer science and Stanford's first vice provost for online learning. "One of the great things about online technology is we can produce one kind of material – a video, an interactive session, an experimental laboratory that is online – and use it in multiple dierent ways. We're evolving our way of presenting educational material." 5 DISCUSSIONS TODAY Professors Credit: 72% of professors say students should NOT earn units for MOOCs. Cons: 55% say teaching a MOOC diverts their attention away from their existing responsibilities on campus. Pros: MOOCs have the potential to greatly further the spread of higher knowledge and help individual professors gain larger recognition for their work. Some professors report having higher engagement with their students, and believe MOOCs will produce a larger number of solutions for projects and assignments, as many more students will be participating. 5 5 DISCUSSIONS TODAY Presidents Presidents remain unpersuaded by, if not skeptical of, MOOC mania. Only 14 percent of presidents strongly agree, and another 28 percent agree, that massive open online courses have “great potential to make a positive impact” on higher education; 31 percent disagree or strongly disagree, and the rest are neutral. 5 5 DISCUSSIONS TODAY Registrars The biggest concern remains how to keep the integrity of the student record. If a student is attempting to receive credit for completing a MOOC course, how does a university verify the student’s identity and that that student completed the assignments and passed the exams? Needs: Keeping constantly informed about the issues surrounding MOOCs will help Registrars fully support the needs of their faculty and students. 5 5 DISCUSSIONS TODAY Legislators Legislators are primarily concerned with remedying the problems of accessibility and aordability in public higher education. Many public institutions struggle with over-enrollment in core classes necessary for graduation and MOOCs have the potential to help students complete their degrees on time. By passing legislation to permit the teaching of core classes using MOOCs, legislators and universities stand to gain huge cost savings. 5 5 DISCUSSIONS TODAY Librarians The biggest challenge will be in supporting the resource needs of their institution’s courses. The open nature of a MOOCs course necessitates using content with open copyrights. 5 5 DISCUSSIONS TODAY Employers MOOCs will provide new opportunities to help employers find and evaluate candidates. In the future, employers will be able to purchase access to student names and accomplishments and students can leverage their new skills to land better jobs. 5 5 DISCUSSIONS TODAY Students MOOC courses have been met with resistance from tuition-paying students who want distinct experiences for the amount of money they pay. 6 MOVING FORWARD, HOW WILL UNIVERSITIES CHANGE? 56 MOVING FORWARD, HOW WILL UNIVERSITIES CHANGE? In the future we may see major changes, driven by the rise of MOOCs, in the way higher education institutions measure achievement, oer courses, and earn revenue. Universities hit hard by budget cuts may ooad the economic burden of lower-level courses like introductory mathematics to MOOC providers to focus eorts on upper-division courses. 56 MOVING FORWARD, HOW WILL UNIVERSITIES CHANGE? The student transcript may shift from measuring achievement in Carnegie credit hours to instead recording competency-based accomplishments. The university structure itself could dramatically shift; lower level universities might become facilitators for online courses, hiring instructors skilled in education facilitation rather than research. 7 WHAT'S HAPPENING TODAY? WHAT CAN YOU DO? 5 7 WHAT'S HAPPENING TODAY? WHAT CAN YOU DO? Universities Research must be done to evaluate the eectiveness and future of MOOCs. Universities are running pilot programs with MOOC providers with select classes to test their feasibility, such as San Jose State University’s Udacity math classes. SJSU is currently oering 3 classes for credit, open to anyone. Beginning June 1, Edx will be available as an open source learning platform. Stanford will integrate features of its existing Class2Go open source online learning platform into the edX platform. 5 7 WHAT'S HAPPENING TODAY? MOOC Providers The companies themselves are collecting data on every interaction they have with students. The researchers behind each provider hope to use that data to support the argument in favor of the expansion of MOOCs. Coursera is using the data collected from the thousands of students in its 30+ classes to study the most eective teaching methods. 5 7 WHAT'S HAPPENING TODAY? Government The California State Senate is currently considering a bill (SB520) that, if passed, would force state universities
Recommended publications
  • Stanley: the Robot That Won the DARPA Grand Challenge
    STANLEY Winning the DARPA Grand Challenge with an AI Robot_ Michael Montemerlo, Sebastian Thrun, Hendrik Dahlkamp, David Stavens Stanford AI Lab, Stanford University 353 Serra Mall Stanford, CA 94305-9010 fmmde,thrun,dahlkamp,[email protected] Sven Strohband Volkswagen of America, Inc. Electronics Research Laboratory 4009 Miranda Avenue, Suite 150 Palo Alto, California 94304 [email protected] http://www.cs.stanford.edu/people/dstavens/aaai06/montemerlo_etal_aaai06.pdf Stanley: The Robot that Won the DARPA Grand Challenge Sebastian Thrun, Mike Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, Kenny Lau, Celia Oakley, Mark Palatucci, Vaughan Pratt, and Pascal Stang Stanford Artificial Intelligence Laboratory Stanford University Stanford, California 94305 http://robots.stanford.edu/papers/thrun.stanley05.pdf DARPA Grand Challenge: Final Part 1 Stanley from Stanford 10.54 https://www.youtube.com/watch?v=M2AcMnfzpNg Sebastian Thrun helped build Google's amazing driverless car, powered by a very personal quest to save lives and reduce traffic accidents. 4 minutes https://www.ted.com/talks/sebastian_thrun_google_s_driverless_car THE GREAT ROBOT RACE – documentary Published on Jan 21, 2016 DARPA Grand Challenge—a raucous race for robotic, driverless vehicles sponsored by the Pentagon, which awards a $2 million purse to the winning team. Armed with artificial intelligence, laser-guided vision, GPS navigation, and 3-D mapping systems, the contenders are some of the world's most advanced robots. Yet even their formidable technology and mechanical prowess may not be enough to overcome the grueling 130-mile course through Nevada's desert terrain. From concept to construction to the final competition, "The Great Robot Race" delivers the absorbing inside story of clever engineers and their unyielding drive to create a champion, capturing the only aerial footage that exists of the Grand Challenge.
    [Show full text]
  • Hierarchical Off-Road Path Planning and Its Validation Using a Scaled Autonomous Car' Angshuman Goswami Clemson University
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Clemson University: TigerPrints Clemson University TigerPrints All Theses Theses 12-2017 Hierarchical Off-Road Path Planning and Its Validation Using a Scaled Autonomous Car' Angshuman Goswami Clemson University Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Recommended Citation Goswami, Angshuman, "Hierarchical Off-Road Path Planning and Its Validation Using a Scaled Autonomous Car'" (2017). All Theses. 2793. https://tigerprints.clemson.edu/all_theses/2793 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. HIERARCHICAL OFF-ROAD PATH PLANNING AND ITS VALIDATION USING ASCALED AUTONOMOUS CAR A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Master of Science Mechanical Engineering by Angshuman Goswami December 2017 Accepted by: Dr. Ardalan Vahidi, Committee Chair Dr. John R. Wagner Dr. Phanindra Tallapragada Abstract In the last few years. while a lot of research efforthas been spent on autonomous vehicle navigation, primarily focused on on-road vehicles, off-road path planning still presents new challenges. Path planning for an autonomous ground vehicle over a large horizon in an unstructured environment when high-resolution a-priori information is available, is still very much an open problem due to the computations involved. Local- ization and control of an autonomous vehicle and how the control algorithms interact with the path planner is a complex task.
    [Show full text]
  • Passenger Response to Driving Style in an Autonomous Vehicle
    Passenger Response to Driving Style in an Autonomous Vehicle by Nicole Belinda Dillen A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Mathematics in Computer Science Waterloo, Ontario, Canada, 2019 c Nicole Dillen 2019 I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Despite rapid advancements in automated driving systems (ADS), current HMI research tends to focus more on the safety driver in lower level vehicles. That said, the future of automated driving lies in higher level systems that do not always require a safety driver to be present. However, passengers might not fully trust the capability of the ADS in the absence of a safety driver. Furthermore, while an ADS might have a specific set of parameters for its driving profile, passengers might have different driving preferences, some more defensive than others. Taking these preferences into consideration is, therefore, an important issue which can only be accomplished by understanding what makes a passenger uncomfortable or anxious. In order to tackle this issue, we ran a human study in a real-world autonomous vehicle. Various driving profile parameters were manipulated and tested in a scenario consisting of four different events. Physiological measurements were also collected along with self- report scores, and the combined data was analyzed using Linear Mixed-Effects Models. The magnitude of a response was found to be situation dependent: the presence and proximity of a lead vehicle significantly moderated the effect of other parameters.
    [Show full text]
  • A Self-Supervised Terrain Roughness Estimator for Off-Road Autonomous
    A Self-Supervised Terrain Roughness Estimator for O®-Road Autonomous Driving David Stavens and Sebastian Thrun Stanford Arti¯cial Intelligence Laboratory Computer Science Department Stanford, CA 94305-9010 fstavens,[email protected] Abstract 1 INTRODUCTION Accurate perception is a principal challenge In robotic autonomous o®-road driving, the primary of autonomous o®-road driving. Percep- perceptual problem is terrain assessment in front of tive technologies generally focus on obsta- the robot. For example, in the 2005 DARPA Grand cle avoidance. However, at high speed, ter- Challenge (DARPA, 2004), a robot competition orga- rain roughness is also important to control nized by the U.S. Government, robots had to identify shock the vehicle experiences. The accuracy drivable surface while avoiding a myriad of obstacles required to detect rough terrain is signi¯- { cli®s, berms, rocks, fence posts. To perform ter- cantly greater than that necessary for obsta- rain assessment, it is common practice to endow ve- cle avoidance. hicles with forward-pointed range sensors. Terrain is We present a self-supervised machine learn- then analyzed for potential obstacles. The result is ing approach for estimating terrain rough- used to adjust the direction of vehicle motion (Kelly ness from laser range data. Our approach & Stentz, 1998a; Kelly & Stentz, 1998b; Langer et al., compares sets of nearby surface points ac- 1994; Urmson et al., 2004). quired with a laser. This comparison is chal- lenging due to uncertainty. For example, at When driving at high speed { as in the DARPA Grand range, laser readings may be so sparse that Challenge { terrain roughness must also dictate vehicle signi¯cant information about the surface is behavior because rough terrain induces shock propor- missing.
    [Show full text]
  • Stanley: the Robot That Won the DARPA Grand Challenge
    Stanley: The Robot that Won the DARPA Grand Challenge ••••••••••••••••• •••••••••••••• Sebastian Thrun, Mike Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, Kenny Lau, Celia Oakley, Mark Palatucci, Vaughan Pratt, and Pascal Stang Stanford Artificial Intelligence Laboratory Stanford University Stanford, California 94305 Sven Strohband, Cedric Dupont, Lars-Erik Jendrossek, Christian Koelen, Charles Markey, Carlo Rummel, Joe van Niekerk, Eric Jensen, and Philippe Alessandrini Volkswagen of America, Inc. Electronics Research Laboratory 4009 Miranda Avenue, Suite 100 Palo Alto, California 94304 Gary Bradski, Bob Davies, Scott Ettinger, Adrian Kaehler, and Ara Nefian Intel Research 2200 Mission College Boulevard Santa Clara, California 95052 Pamela Mahoney Mohr Davidow Ventures 3000 Sand Hill Road, Bldg. 3, Suite 290 Menlo Park, California 94025 Received 13 April 2006; accepted 27 June 2006 Journal of Field Robotics 23(9), 661–692 (2006) © 2006 Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com). • DOI: 10.1002/rob.20147 662 • Journal of Field Robotics—2006 This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without manual intervention. The robot’s software system relied predominately on state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results of the Grand Chal- lenge race. © 2006 Wiley Periodicals, Inc. 1. INTRODUCTION sult of an intense development effort led by Stanford University, and involving experts from Volkswagen The Grand Challenge was launched by the Defense of America, Mohr Davidow Ventures, Intel Research, ͑ ͒ Advanced Research Projects Agency DARPA in and a number of other entities.
    [Show full text]
  • Autonomous Vehicle Technology: a Guide for Policymakers
    Autonomous Vehicle Technology A Guide for Policymakers James M. Anderson, Nidhi Kalra, Karlyn D. Stanley, Paul Sorensen, Constantine Samaras, Oluwatobi A. Oluwatola C O R P O R A T I O N For more information on this publication, visit www.rand.org/t/rr443-2 This revised edition incorporates minor editorial changes. Library of Congress Cataloging-in-Publication Data is available for this publication. ISBN: 978-0-8330-8398-2 Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2016 RAND Corporation R® is a registered trademark. Cover image: Advertisement from 1957 for “America’s Independent Electric Light and Power Companies” (art by H. Miller). Text with original: “ELECTRICITY MAY BE THE DRIVER. One day your car may speed along an electric super-highway, its speed and steering automatically controlled by electronic devices embedded in the road. Highways will be made safe—by electricity! No traffic jams…no collisions…no driver fatigue.” Limited Print and Electronic Distribution Rights This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.html. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous.
    [Show full text]
  • Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection
    Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection John Lambert1,2 and James Hays1,2 1Argo AI 2Georgia Institute of Technology 1 2 Match Match Match Mismatch Mismatch Mismatch Figure 1: Scenes from an example log from our TbV dataset where a real-world change causes an HD map to become stale. Corresponding sensor data (top), map data (middle), and a blended combination of the two (bottom) are shown at 6 timestamps in chronological order from left to right. Within each column, all images are captured or rendered from identical viewpoints. We use red to denote implicit lane boundaries. Faint grey lines in row 1, columns 4-6 show where the previous paint was stripped 3 away. Abstract 4 High-definition (HD) map change detection is the task of determining when sensor 5 data and map data are no longer in agreement with one another due to real-world 6 changes. We collect the first dataset for the task, which we entitle the Trust, but 1 7 Verify (TbV) dataset, by mining thousands of hours of data from over 9 months 8 of autonomous vehicle fleet operations. We present learning-based formulations 9 for solving the problem in the bird’s eye view and ego-view. Because real map 10 changes are infrequent and vector maps are easy to synthetically manipulate, we 11 lean on simulated data to train our model. Perhaps surprisingly, we show that such 12 models can generalize to real world distributions. The dataset, consisting of maps 13 and logs collected in six North American cities, is one of the largest AV datasets to 14 date with more than 7.9 million images and will be made available to the public, 2 15 along with code and models under the the CC BY-NC-SA 4.0 license.
    [Show full text]
  • Online and Hybrid Learning Janani Ramanathan
    World Academy of Art & Science Eruditio, Volume 2 – Issue 4, July 2018 Online and Hybrid Learning Janani Ramanathan ERUDITIO, Volume 2, Issue 4, July 2018, 171-173 Online and Hybrid Learning Janani Ramanathan Senior Research Analyst, The Mother’s Service Society; Associate Fellow, World Academy of Art & Science “This has caught all of us by surprise,” said David Stavens, co-founder of the educational organization Udacity, when he saw the overwhelming response to one of the first Massive Open Online Courses in 2011. Since then, MOOCs have continued to surprise, excite, empower, disrupt and unsettle the education paradigm. Inspired by The Khan Academy, Stanford University Professor Sebastian Thrun decided to make his course on Artificial Intelligence available online, expecting a few thousand people to show interest. 160,000 students signed up. They came from 190 countries, their ages ranging from 10 to 70! This triggered a revolution in global online higher education. Thrun founded Udacity, and this was followed by the founding of a number of other MOOC providers, and 2012 came to be called by The New York Times as the Year of the MOOC. NY Times columnist Thomas Friedman declared of the MOOC that “nothing has more potential to lift more people out of poverty.” Distance education is centuries old. The Boston Gazette carried an advertisement for short hand course through lessons mailed weekly by post in 1728. The University of London offered its first distance learning degree in 1858. The 20th century saw the founding of Open Universities worldwide, the largest being the Indira Gandhi National Open University in India, with 4 million students.
    [Show full text]
  • Off-Policy Reinforcement Learning for Autonomous Driving
    Off-Policy Reinforcement Learning for Autonomous Driving Hitesh Arora CMU-RI-TR-20-34 July 30, 2020 The Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA Thesis Committee: Jeff Schneider, Chair David Held Ben Eysenbach Submitted in partial fulfillment of the requirements for the degree of Master of Science in Robotics. Copyright c 2020 Hitesh Arora. All rights reserved. Keywords: Reinforcement Learning, Autonomous Driving, Q-learning, Reinforcement Learning with Expert Demonstrations To my friends, family and teachers. iv Abstract Modern autonomous driving systems continue to face the challenges of handling complex and variable multi-agent real-world scenarios. Some subsystems, such as perception, use deep learning-based approaches to leverage large amounts of data to generalize to novel scenes. Other subsystems, such as planning and control, still follow the classic cost-based trajectory optimization approaches, and require high efforts to handle the long tail of rare events. Deep Reinforcement Learning (RL) has shown encouraging evidence in learning complex decision-making tasks, spanning from strategic games to challenging robotics tasks. Further, the dense reward structure and modest time horizons make autonomous driving a favorable prospect for applying RL. As there are practical challenges in running RL online on vehicles and most self-driving companies have millions of miles of collected data, it motivates the use of off-policy RL algorithms to learn policies that can eventually work in the real world. We explore the use of an off-policy RL algorithm, Deep Q-Learning, to learn goal-directed navigation in a simulated urban driving environment. Since Deep Q-Learning methods are susceptible to instability and sub-optimal convergence, we investigate different strategies to sample experiences from the replay buffer to mitigate these issues.
    [Show full text]
  • The Year of the MOOC
    Ma1s1s/2iv9e/1 O2pen Online Courses Are Multiply ing at a Rapid Pace - NYTimes.com November 2, 2012 The Year of the MOOC By LAURA PAPPANO IN late September, as workers applied joint compound to new office walls, hoodie-clad colleagues who had just met were working together on deadline. Film editors, code-writing interns and “edX fellows” — grad students and postdocs versed in online education — were translating videotaped lectures into MOOCs, or massive open online courses. As if anyone needed reminding, a row of aqua Post-its gave the dates the courses would “go live.” The paint is barely dry, yet edX, the nonprofit start-up from Harvard and the Massachusetts Institute of Technology, has 370,000 students this fall in its first official courses. That’s nothing. Coursera, founded just last January, has reached more than 1.7 million — growing “faster than Facebook,” boasts Andrew Ng, on leave from Stanford to run his for-profit MOOC provider. “This has caught all of us by surprise,” says David Stavens, who formed a company called Udacity with Sebastian Thrun and Michael Sokolsky after more than 150,000 signed up for Dr. Thrun’s “Introduction to Artificial Intelligence” last fall, starting the revolution that has higher education gasping. A year ago, he marvels, “we were three guys in Sebastian’s living room and now we have 40 employees full time.” “I like to call this the year of disruption,” says Anant Agarwal, president of edX, “and the year is not over yet.” MOOCs have been around for a few years as collaborative techie learning events, but this is the year everyone wants in.
    [Show full text]
  • Self-Supervised Monocular Road Detection in Desert Terrain
    Self-supervised Monocular Road Detection in Desert Terrain Hendrik Dahlkamp∗, Adrian Kaehler†, David Stavens∗, Sebastian Thrun∗, and Gary Bradski† ∗Stanford University, Stanford, CA 94305 †Intel Corporation, Santa Clara, CA 95052 Abstract— We present a method for identifying drivable sur- faces in difficult unpaved and offroad terrain conditions as encountered in the DARPA Grand Challenge robot race. Instead of relying on a static, pre-computed road appearance model, this method adjusts its model to changing environments. It achieves robustness by combining sensor information from a laser range finder, a pose estimation system and a color camera. Using the first two modalities, the system first identifies a nearby patch of drivable surface. Computer Vision then takes this patch and uses it to construct appearance models to find drivable surface outward into the far range. This information is put into a drivability map for the vehicle path planner. In addition to evaluating the method’s performance using a scoring framework run on real-world data, the system was entered, and won, the Fig. 1. Image of our vehicle while driving in the 2005 DARPA Grand 2005 DARPA Grand Challenge. Post-race log-file analysis proved Challenge. that without the Computer Vision algorithm, the vehicle would not have driven fast enough to win. I. INTRODUCTION For the DARPA Grand Challenge, however, their system[5] This paper describes a Computer Vision algorithm devel- is not directly applicable: Their road finder operates on grey, oped for our entry to the 2005 DARPA Grand Challenge. Our not color images. The desert terrain relevant for the Grand vehicle (shown in Fig.
    [Show full text]
  • A Personal Account on the Development of Stanley, the Robot
    AI Magazine Volume 27 Number 4 (2006) (© AAAI) Articles A Personal Account of the Development of Stanley, the Robot That Won the DARPA Grand Challenge Sebastian Thrun ■ This article is my personal account on the work at as Stanley used some state of the art AI methods in Stanford on Stanley, the winning robot in the areas such as probabilistic inference, machine DARPA Grand Challenge. Between July 2004 and learning, and computer vision. Of course, it is also October 2005, my then-postdoc Michael Monte- the story of a step towards a technology that, one merlo and I led a team of students, engineers, and day, might fundamentally change our lives. professionals with the single vision of claiming one of the most prestigious trophies in the field of robotics: the DARPA Grand Challenge (DARPA 2004).1 The Grand Challenge, organized by the Thinking about It U.S. government, was unprecedented in the My story begins in March 2004. Both Michael nation’s history. It was the first time that the U.S. Congress had appropriated a cash price for Montemerlo and I attended the first Grand advancing technological innovation. My team Challenge Qualification Event at the Fontana won this prize, competing with some 194 other Speedway, and Mike stayed on to watch the teams. Stanley was the fastest of five robotic vehi- race. The race was short: within the first seven cles that, on October 8, 2005, successfully navigat- miles, all robotic vehicles had become stuck. ed a 131.6-mile-long course through California’s The best-performing robot, a modified Humvee Mojave Desert.
    [Show full text]