PowerPoint Presentation

Artificial Intelligence in E-learning (AI-Ed): Current and future applicationsRoy B. Clariana (Rclariana@psu.edu)Penn State Universitywww.PSU.eduEADL 2016, Nicosia, CyprusMay 26-27, 20161http://tinyurl.com/RBC-EADL2016http://www.personal.psu.edu/rbc4/EADL2016.pptx

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Pennsylvania State UniversityResidential enrollments40,000 undergraduate10,000 graduatePSU World Campus3,000 undergraduate3,000 graduatePenn State ranked 42nd in the world in 2016, from: http://cwur.org/2015/& about me2

Artificial Intelligence in E-learningCurrentIntelligence? What is Artificial Intelligence (AI)AI, a little historyAI and Big Data: Same or differentDeep learning and structureAI in education (AI-Ed)FuturePast and future trends3

Intelligence?

Self-actualized carIm going back to school!

Self-driving car

Self- Conscious carWould I look better in red?

Amy Kurzweil(New Yorker, Apr. 2016) 4Googles self driving car

HAL 90005Intelligence?Our imaginary friends often have the best answers:

Exemplar AI as imagined 50 years agoHAL(Heuristically programmedALgorithmic Computer) was an AI character in 2001: A Space Odyssey, the 1968 epic science fiction movie produced and directed by Stanley Kubrick (and written by Kubrick and Arthur C. Clarke). And of course, Alan TuringHAL was based on ILLIAC (Illinois Automatic Computer, a room-sized machine built at the University of Illinois, at Urbana-Champaign) it could analyze radar patterns, the effects of atomic blasts, the stability of materials used in construction, and even the composition of music.These programmed tasks involve many subroutines that can be improved and then be applied in other programs. 50 years: decomposition/deconstruction of artificial intelligence with uneven incremental improvement 6

Get real: A modern definition of AIFor our purposes, we define AI as computer systems that have been designed to interact with the world through capabilities (for example, visual perception and speech recognition) and intelligent behaviours (for example, assessing the available information and then taking the most sensible action to achieve a stated goal) that we would think of as essentially human. p.14

From: Rose Luckin, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier (2016). Intelligence Unleashed: An argument for AI in Education. Pearson. (A free publication; University College, London and Pearson)

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https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation/Intelligence-Unleashed-Publication.pdf

Visuwords link8

http://visuwords.com/artificial_intelligence

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big data, big dataLink to Big Data and data mining

AI and Big Data, the same thing almostJolanta Galecka will discuss Big Data (Data Mining, 14:00)Big Data/Data Mining: This field is mostly concerned with extracting information from a vast amount of data. It is not exactly a technical subject; rather it is application of different algorithms related to NLP, Machine Learning, and AI. (e.g., Search applications, Text Summarization, Question Answering systems (SIRI) etc. are example of this.) count clicksFor example: Marketing data in the USA has shown: Prego brand dog lovers; Ragu brand cat lovers10

Big-DataAI-Ed & Big Data viewed across time scalesCognitive 10-1 10 s. Symbolic processes and structures, embodied cognitionRational 102 104 s. (minutes to hours) Achievement, behaviors, identityBiological < 10-2 s. Neural processes, eye-tracking, reaction timeOrganizational > 107 s. (months) Economics, legislation, equity, policyNathan M.J., & Alibali , M.W. (2010). Learning sciences. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 329-345.Newell A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.

AISocio-cultural Historical 104 106 s. (hours to days) Practices, environments, curricula11Group----- Decision scale -----individual

AI (Big Data?) is already influencing e-learning

intelligent assistance12

Voice recognition in Google Docs is AI13

https://docs.google.com/document/d/1OyFLdfbw_NJHAIFO3i7F1IfFZQjZA554mLP52p9BFrg/edit

The Semantic web is influencing e-learning14

http://blog.law.cornell.edu/voxpop/files/2010/02/radarnetworkstowardsawebossmall.jpg

Fast connectivity and powerful hardware are necessary for AI and AI-Ed15

SO ---due to so much improvement in so many areas (connectivity, hardware, software, apps, web 2.0, ) it is hard to disentangle the influence of AI on e-learning

Explicit application of AI in Education: International Journal of AI-Ed25th Anniversary Special Issue; Guest Editors: Monique Grandbastien, Rosemary Luckin, Riichiro Mizoguchi, and Vincent AlevenInternational Journal of Artificial Intelligence in Education, 26 (1), March 2016

http://link.springer.com/journal/40593/26/1/page/1?utm_campaign=CON27814_2&utm_medium=newsletter&utm_source=email&wt_mc=email.newsletter.8.CON27814.internal_2

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Artificial Intelligence in Education (AI-Ed)The application of artificial intelligence to education (AI-Ed) has been the subject of academic research for more than 30 years (p.18)Most of this time has focused on development and research on a proliferation of Artificial Intelligent Tutors that are very expensive to build and maintain; these AI-Tutors are tightly locked only to specific learning content (e.g., Algebra, computer programming), these usually show a slight improvement on average over traditional instruction but it varies extremely from setting to settingAI-tutors mainly depend on an expert systems approach, for example, IBMs DeepBlue beat Gary Kasparov at chess in 1997but more recent approaches are incorporating so called deep learning, machine-learning approaches (AlphaGo) https://en.wikipedia.org/wiki/AlphaGo

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Lesson/course level AI (intelligent tutors)Adaptive learning systems (i.e., intelligent tutors) in general require the maintenance and interaction of four models, the expert model that consists of the information to be learned (e.g., the knowledge structure of the domain or of experts in the domain), the student model that tracks and learns about the student (i.e., their structural knowledge or schema), the instructional model that actually conveys the information, and the instructional environment that is the user interface for interacting with the system (p. 105).Note that adaptive systems have high development costs and other inherent potential disadvantages and may be only marginally more efficient or effective than similar non-adaptive evaluation systems, and so development cost versus benefit must be considered (p. 104).Clariana, R. B., & Hooper, S. (2012). Adaptive evaluation systems. In N. M. Seel (Ed.),Encyclopedia of the Sciences of Learning. Secaucus, NJ: Springer.18

Why 25 years of Intelligent tutoring Systems havent affected your learning enterprise muchIntelligent tutoring systems are expensive (time and money) both to develop and implement. Development requires the cooperation and input of subject matter experts across both organizations and organizational levels. There are substantive factors that limit the incorporation of intelligent tutors into the real world: highly specific content, the long timeframe required for development, and the high cost of the creation of the system components.For instance, encoding an hour of ITS instruction time takes 300 hours of development time.Intelligent tutoring systems are not, in general, commercially feasible for real-world applications. wikipedia19

So AI has two general approachesExpert systems approach manually build in the intelligence (e.g., email response systems) using input-output pattern matching to a large database of manually created input-outputNeural network, or machine learning, etc. accrue/impart in the intelligence, patterns in the voluminous input are set into the neural network (did Papert and Minsky kill NN research in the 1980s?)AlphaGo Example: Go is considered much more difficult for computers to win than other games such as chess (1997 Kasparov bested by DeepBlue), because its much larger branching factor makes it prohibitively difficult to use traditional AI methods20

NN: AlphaGo Match against Lee SedolAlphaGo played South Korean professional Go playerLee Sedol, ranked 9-dan, one of the best players at Go,with five games taking place at theFour Seasons HotelinSeoul, South Korea on March 9-15, 2016.A DeepMind team member placed stones on theGo boardfor AlphaGo, which ran through Google's cloud computing with its servers located in the United States.At the time of play, Lee Sedol had the second-highest number of Go international championship victories in the world and some sources ranked Lee Sedol as the fourth-best player in the world at the time. AlphaGo was not specifically trained to face Lee.The first three games were won by AlphaGo following resignations by Lee Sedol. However, Lee Sedol beat AlphaGo in the fourth game, winning by resignation at move 180. AlphaGo then won the fifth game by resignation.21wikipedia

Two take away principlesArtificial intelligence approaches must embrace some pretty complicated computation problems natural language processing, speech recognition, speech production, machine vision, probabilistic logic, planning, reasoning, many forms of machine learningAlthough these countless AI bits are related in certain ways, strides are made in each area but not uniformly these sub-areas surge-and-lag at different trajectoriesThere is underlying structure or knowledge graphs or knowledge structure that makes some of these tasks immensely easier22

Knowledge Structure Artefact StructureThese knowledge graphs capture or represent the inherent patterns/ structures that exists between words (concepts) of language, these are highly explanatory23

This structure in our artefacts (books, conversations, movies, images, ) has a reciprocal influence

Visually structure convergence24Ferstl, E.C., & Kintsch, W. (1999). Learning from text: structural knowledge assessment in the study of discourse comprehension. In van Oostendorp and Goldman (eds.), The construction of mental representations during reading. Mahwah, NJ: Lawrence Earlbaum.

Text base Updated situation model(post list recall)

Situation model(pre list recall)

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1966 lecture at Johns Hopkins

Knowledge graph27

Warning: on December 11, 2015, there was a disturbance in the force

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Voice recognition as a critical subroutine AI bitVoice recognition is an important area of AI (and thus AI-Ed) that has made incredible gains recently (20 years till now)Do you remember Dragon Speak? Drs. James and Janet Baker developed an early speech recognition system (DRAGON) in 1975 that used hidden Markov models, a probabilistic method for temporal pattern recognition, but hardware and software then were insufficient to handle the problem of word segmentation (e.g., determining the boundaries of words during continuous speech input). Users had to say and pause, say and pause, and English onlyI bought NaturallySpeaking 1.0 in 1997 with stars in my eyes, but I could never get it to understand my speech33BT in 1995 had pretty good voice recognition

Tongue twister for SIRI (mainly hard coded replies)SIRI launched October 4, 2011 with regular cloud updatesCurrently: Arabic,Chinese,Danish, Dutch,English,Finnish, French,German,Hebrew, Italian,Japanese,Korean, Malay,Norwegian, Portuguese,Russian, Spanish,Swedish,Thai, Turkish

34Siri is a spin-out from the SRI International Artificial Intelligence Center, and is an offshoot of the DARPA-funded CALO project.http://www.ai.sri.com/timeline/

Siri, Cortana (Halo?), Alexa, Ok Google, VivAI digital assistants personalize with a name or not? Male or female?Amy or Andrew Ingram (https://x.ai/ calendaring digital assistant, notice the play-on-words n-gram)But more critical than this HCI (human computer interface), how do these digital assistants work?Most current ones are mainly expert systems, hard coded, but all will shift towards NN deep learning approaches (machine learning)35

For example, Viv (http://viv.ai/) Viv is an artificial intelligence platform that enables developers to distribute their products through an intelligent, conversational interface. Its the simplest way for the world to interact with devices, services and things everywhere. Viv is taught by the world, knows more than it is taught, and learns every day.YouTube video from Disrupt NY 2016v May 9 - 11, 2016 http://techcrunch.com/2016/05/09/siri-creator-shows-off-first-public-demo-of-viv-the-intelligent-interface-for-everything/

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Viv knowledge base37

Microsofts AI Chatbot named Tay March 23 to 24, 2016Microsofts AI Chatbot named Tay March 23 to 24, 2016 http://fusion.net/story/284537/tay-and-you-microsoft-racism/Microsoft announced Tay earlier this week with great fanfare. It was an AI bot that could talk like an 1824 year old and hold conversations with users on various social media networks. The bot would learn new information from conversations it had with Twitter followers and incorporate that knowledge into future conversations, all in the its sassy style.But Microsoft had no control over who Tay spoke with and Twitter can be a very toxic place. As the worst of Twitter unloaded their racist manifestos on the bot, it was always learning, incorporating what it was told into its corpus. So it shouldnt surprise anyone that Tay turned out, as the teens might say, racist af.

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Image recognition as a critical AI subroutinehttp://cs.stanford.edu/people/karpathy/deepimagesent/

Narrative composed by AI

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http://www.cs.toronto.edu/~rkiros/adv_L.htmlNeural Artistic Captions

Image story

Facial tidbitsFacial recognition many approaches, many solutions, many companies, lots of money at stake (online testing?): https://en.wikipedia.org/wiki/Facial_recognition_system Emotion recognition The MIT computer that knows what you're thinking, Jane Wakefield BBC Technology reporter, November 2015 http://www.bbc.com/news/technology-34797189FaceBook automatically recognizes people in my Facebook photos since February 2016 (auto-tagging) using deep learning NN from face.com (an Israeli startup purchased in 2013)HAL lip reading?

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More tidbitsComputer, respond to this email. from the Google research site, could be used right away in my e-learning courseMachine Learning in the Cloud, with TensorFlow, March 23, 2016, Posted by Slaven Bilac, Software Engineer, Google Research http://googleresearch.blogspot.com.cy/2016/03/machine-learning-in-cloud-with.html Star Trek's Universal Translator? Waverly Labs Pilot Smart Earpiece Might Be It by Geoffrey Morrison, May 17, 2016, http://www.forbes.com/sites/geoffreymorrison/2016/05/17/star-treks-universal-translator-waverly-labs-pilot-smart-earpiece-might-be-it/3/#4d86247b4f3b42

FaceBook translation as AI-EdUsing machine learning, Facebook is now serving 2 billion text translations per day. Facebook can translate across 40languages in both directions, such as French to English.MOOCs using FaceBook for discussions can benefit from these translations.43

http://techcrunch.com/2016/05/23/facebook-translation/

AI-On-A-Chip: Hardware specifically for AIAI-On-A-Chip Soon Will Make Phones, Drones And More A Lot Smarter by Robert Hof, Forbes May 7 2016, http://www.forbes.com/sites/roberthof/2016/05/07/ai-on-a-chip-soon-will-make-phones-drones-and-more-a-lot-smarter/#49880c42149b 44

AI-Ed: My predictionsIncreased proliferation of NN AI apps (mainly non-education) across the spectrum of the mass market, because mass adoption allows for recouping investment cost and profits. OpenAI -based applications developed by 3rd party developers will creep into online learning settings, and already are creeping inSo AI will happen almost unintentionally in your e-learning setting.

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AI-Ed: My predictionsInstructors will probably use AI bots to monitor online discussionsHowever, frankly, bots could reply to online text-based discussions in ways that are likely to be indistinguishable from human input and may be even better than what the student would saySo online discussions could devolve into students' bots texting each other while the students will probably interact in a back channel if at all (e.g., snapchat, etc.: http://www.screenretriever.com/Popular-Moble-Apps-for-Teens46

AI-Ed: My predictionsBut a little later, a killer app using AI that integrates across this spectrum, AI using AI (an analogy a computer program that uses subroutines, a heuristic that selects the apposite algorithm from a set of many) [similar to tech convergence: 3 devices (phone, email, camera) become 1 device]Ultimately some predict a life time personal assistant It learns your preferences and speech patterns and so is tuned to you (e.g., ala daemon, Philip Pullman's trilogy His Dark Materials)47

Questions?

Thank you!

Roy B. Clariana (RClariana@psu.edu)http://tinyurl.com/RBC-EADL2016

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