Trl jaist 20180304 v6
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Preparing for our future with open artificial intelligence
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Trl jaist 20180304 v6
- 1. Jim Spohrer (IBM) IBM Research - Tokyo, Friday March 2 2018 http://www.slideshare.net/spohrer/trl-jaist-20180304-v6 3/4/2018 1 Preparing for Our Future with Opentech AI
- 2. Today’s Presentation 3/4/2018 © IBM MAP COG2018 2 Dr. Jim Spohrer IBM (Jim) Prof. Youji Kohda JAIST (Kohda-Sensei) Md. Abul Kalam Siddike JAIST Doctoral Candidate (Siddike-San)
- 3. IBM: How many know IBM is very active in open source communities? 3/4/2018 © IBM MAP COG2018 3 OPEN CONTAINER PROJECT Fact: An award program exists for open source, as well as patents.
- 4. GitHub: How many have an account? 3/4/2018 © IBM MAP COG2018 4 Technical Eminence: Explore, Read, Pull Request, Contributor, Committer, Governance
- 5. Kaggle: How many know about leaderboards? 3/4/2018 © IBM MAP COG2018 5
- 6. Measuring AI Progress (MAP) AI Progress on Open Leaderboards - Benchmark Roadmap Perceive World Develop Cognition Build Relationships Fill Roles Pattern recognition Video understanding Memory Reasoning Social interactions Fluent conversation Assistant & Collaborator Coach & Mediator Speech Actions Declarative Deduction Scripts Speech Acts Tasks Institutions Chime Thumos SQuAD SAT ROC Story ConvAI Images Context Episodic Induction Plans Intentions Summarizatio n Values ImageNet VQA DSTC RALI General-AI Translation Narration Dynamic Abductive Goals Cultures Debate Negotiation WMT DeepVideo Alexa Prize ICCMA AT Learning from Labeled Training Data and Searching (Optimization) Learning by Watching and Reading (Education) Learning by Doing and being Responsible (Exploration) 2015 2018 2021 2024 2027 2030 2033 2036 3/4/2018 (c) IBM 2017, Cognitive Opentech Group 6 Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer? Approx. Year Human Level ->
- 8. Every 20 years, compute costs are down by 1000x • Cost of Digital Workers – Moore’s Law can be thought of as lowering costs by a factor of a… • Thousand times lower in 20 years • Million times lower in 40 years • Billion times lower in 60 years • Smarter Tools (Terascale) – Terascale (2017) = $3K – Terascale (2020) = ~$1K • Narrow Worker (Petascale) – Recognition (Fast) – Petascale (2040) = ~$1K • Broad Worker (Exascale) – Reasoning (Slow) – Exascale (2060) = ~$1K 83/4/2018 (c) IBM 2017, Cognitive Opentech Group 2080204020001960 $1K $1M $1B $1T 206020201980 +/- 10 years $1 Person Average Annual Salary (Living Income) Super Computer Cost Mainframe Cost Smartphone Cost T P E T P E AI Progress on Open Leaderboards Benchmark Roadmap to solve AI/IA
- 9. GDP/Employee 3/4/2018 (c) IBM 2017, Cognitive Opentech Group 9 (Source) Lower compute costs translate into increasing productivity and GDP/employees for nations Increasing productivity and GDP/employees should translate into wealthier citizens AI Progress on Open Leaderboards Benchmark Roadmap to solve AI/IA
- 10. 3/4/2018 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 10 Cognitive Mediators for all people in all roles
- 11. Occupations = Many Tasks 3/4/2018 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 11
- 12. Watson Discovery Advisor 3/4/2018 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 12 Simonite, T. 2014. Software Mines Science Papers to Make New Discoveries. MIT. November 25, 2014. URL: http://m.technologyreview.com/news/520461/software-mines-science-papers-to-make-new-discoveries/
- 13. AI Trends 3/4/2018 © IBM Cognitive Opentech Group (COG) 13 Dota 2 “Deep Learning” for “AI Pattern Recognition” depends on massive amounts of “labeled data” and computing power available since ~2012; Labeled data is simply input and output pairs, such as a sound and word, or image and word, or English sentence and French sentence, or road scene and car control settings – labeled data means having both input and output data in massive quantities. For example, 100K images of skin, half with skin cancer and half without to learn to recognize presence of skin cancer.
- 14. TED Arai Todai Robot 3/4/2018 (c) IBM 2017, Cognitive Opentech Group 14 … when will your smartphone be smart enough to pass a university entrance exam?
- 15. Build: 10 million minutes of experience 3/4/2018 Understanding Cognitive Systems 15
- 16. Build: 2 million minutes of experience 3/4/2018 Understanding Cognitive Systems 16
- 17. Types: Progression of models and capabilities 3/4/2018 Understanding Cognitive Systems 17 Task & World Model/ Planning & Decisions Self Model/ Capacity & Limits User Model/ Episodic Memory Institutions Model/ Trust & Social Acts Tool + - - - Assistant ++ + - - Collaborator +++ ++ + - Coach ++++ +++ ++ + Mediator +++++ ++++ +++ ++ Cognitive Tool Cognitive Assistant Cognitive Collaborator Cognitive Coach Cognitive Mediator
- 18. Other Technologies: Bigger impact? Yes. • Augmented Reality (AR)/ Virtual Reality (VR) – Game worlds grow-up • Blockchain/ Security Systems – Trust and security immutable • Advanced Materials/ Energy Systems – Manufacturing as cheap, local recycling service (utility fog, artificial leaf, etc.) 3/4/2018 (c) IBM 2017, Cognitive Opentech Group 18
- 19. Industries Transformed Digital Natives Transportation Water Manufacturing Energy Construction ICT Retail Finance Healthcare Education Government “The best way to predict the future is to inspire the next generation of students to build it better”
- 20. 3/4/2018 20 1955 1975 1995 2015 2035 2055 Better Building Blocks
- 21. In Sum: Prepare by… • Participating – Opentech AI (GitHub) – Leaderboard Challenges (Kaggle) • Making – Smartphone apps become low-cost digital workers (expertise economy) – Return of mini-local factories and farms (manufacturing and agricultural economy) • Learning – Service science and knowledge science study the evolution – As well as influence the evolution of industries and professions 3/4/2018 © IBM MAP COG2018 21
- 22. 3/4/2018 © IBM 2015, IBM Upward University Programs Worldwide accelerating regional development 22
- 23. The age of “SERVICE+AI” 2nd March, 2018 Youji Kohda kohda@jaist.ac.jp School of Knowledge Science Japan Advanced Institute of Science and Technology
- 24. The age of SERVICE+AI 24 “In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. Find something that can be made better by adding online smartness to it.” – Kevin Kelly, The inevitable, p.33
- 27. From Institutional Trust to Distributed Trust 27https://rachelbotsman.com/thinking/
- 29. Trust leap with AI 29 “Our trust is based purely on the technology’s functionality, how predictable to do. But a significant shift is under way; we are no longer trusting machines just to do something but to decide what to do and when to do it.” – Rachel Botsman, Who can you trust?, p.179
- 30. Trust leaps • Trust leaps are necessary when innovative technologies/services are emerged –Open source has made the trust leap in the 1990s –Service economy has made the trust leap in the early 2010s • When and how “SERVICE+AI” will make the trust leap? 30Copyright 2018, Youji Kohda
- 31. (My) Questions (micro level) • What jobs will remain for us? –→ “Academic jobs/scholarly work is an exception?” • AI and ethical issues – → “ When a cyber-pet (e.g., Sony AIBO) sees a domestic violence at home, what should it do?” • AI and trustworthy issues – → “ Will AI be trustworthy enough as a service31Copyright 2018, Youji Kohda
- 32. (My) Conjecture (micro level) • AI + books > an (average) scholar/researcher – “Questioning becomes more important than Answering” as Kevin Kelly says in his book, The inevitable • AI + legal precedents > an (average) citizen – TBD • AI + (written) institutions > an (average) CEO – TBD 32Copyright 2018, Youji Kohda
- 33. (My) Questions (macro level) • People suffer from bounded rationality and organizations are key to overcome the limitation • AI has its version of limitation? –At least, your AI will be different from mine if people want to keep their privacy Copyright 2018, Youji Kohda 33
- 34. (My) Conjecture (macro level) • In future, AIs will start to collaborate together, forming “society of AIs” –At the moment, AIs are competing each other, e.g., in stock market –All of the automatic driving cars will start to communicate each other to optimize traffic • “Society of AIs” > “Community of human (average) professionals” Copyright 2018, Youji Kohda 34
- 35. 35
- 36. Towards Trust Building with Cognitive Assistants: Trust Determinants in People’s Interactions SIDDIKE, Md. Abul Kalam School of Knowledge Science, JAIST 2018-03-01
- 37. Professional and Research Experiences • Distinguished visiting scholar, IBM Almaden Research Center, CA, USA • Researcher, Tokyo Tech, Tokyo Japan • Research assistant, FSKTM, University of Malaya, Malaysia • Lecturer, University of Dhaka, Bangladesh • Information officer, icddr’b, Dhaka, Bangladesh 37
- 38. What is Cognitive Assistants (CAs)? • CAs are new decision tools, able to augment human capabilities and expertise understanding the environment around us with a depth and clarity. (Spohrer, 2016; Spohrer et al., 2017; Spohrer, Siddike and Kohda, 2017) • CAs can provide people with high-quality recommendations and help them make better data driven decision. (Demirkan et al., 2015) • People problem solving capabilities significantly augmented by the interaction of people and CAs. (Spohrer and Banavar, 2015; Spohrer and Siddike, 2017) 38 Figure 1: Examples of low level CAs Figure 2: Examples of high level CAs
- 39. Components of Trust in Different Disciplines 39 Components of Trust Discipline Authors Willingness, confidence, predictability, dependability, faith and integrity, group norms, altruism, shared values, good will Trust in close relationships Deutsch, 1960; Rempel, Holmes, and Zanna, 1985; Rotter, 1980; Scanzoni, 1979 Ability (competencies), benevolence (loyalty, openness, receptivity, availability of caring) and integrity (consistency, discreetness, fairness, promise, reliability, value congruence) Organizational trust Butler, 1991; Gabarro, 1978; Jones, James, and Bruni, 1975; Mayer, Davis, and Schoorman, 1995; Schoorman, Mayer, and Davis, 2007 Accuracy of information, trust in information, trust in action Trust in economics Henry and Dietz, 2011; Ostrom, 2003; Ostrom and Walker, 2003 Reduced workload, reduced uncertainty, reduced risk reliability, robustness, familiarity, accuracy, task complexity, ability, predictability, dependability, benevolence, openness Trust in automation Jian, Bisantz, and Drury, 2000; Lee and See, 2004; Muir, 1994; Muir and Moray, 1996; Parasuraman and Riley, 1997; Xu, Le, Deitermann, and Montague, 2014 Attractiveness, enjoyment, performance, attributes Trust in robots Yuksel, Collison and Czerwinski, 2017 Reliability, attractiveness, emotional attachment, trustworthiness, relative advantages Trust in CAs Our framework Table 1: Trust components in different disciplines
- 40. Framework of Trust Determinants with CAs 40 Figure 8: Trust determinants with CAs Perceived reliability Perceived attractiveness Perceived attachments Perceived trustworthiness of users Intention to use CAs P1 P2 P3 Relative advantages of innovation P4 P5Jim Spohrer (IBM): “Many people are very attached to their smartphones; Today, apps are digital tools that will become cognitive assistants (CAs). As we solve AI, they will become low-cost digital workers, and IA for people.”
- 41. Reliability of Scales 41 Variables Cronbach’s alpha Perceived reliability (4 items) .937 Perceived attractiveness (3 items) .888 Perceived emotional attachments (items 4) .973 Trustworthiness (5 items) .946 Relative advantages of innovation (5 items) .915 Intention to use CAs (4 items) .912 4.5 Validation of trust determinants • Scale reliability was assessed by calculating Cronbach’s Alpha, a measure of internal consistency, for each measured scale. • Nunally (1978) and DeVillis (2003) suggest alpha value of >.7 to be good reliability for scale items. • The internal reliability of these measures was proven to be acceptable. Table 12: Scales’ reliability
- 42. Future Research Directions 42 Open coding Selective coding Sub-categories Core-categories CAs show responsibilities Responsibilities Rights and responsibilities CAs gain rights Rights Need rules and regulations for accessing private information Rules and regulations Policy formulation Permission is necessary to use my personal data Required permission Well-encrypted information so that no one can access it without my permission Well-encryption Security and privacySecurity and privacy of personal information Security of personal data and information Need permission to sell my data Required permission for selling data Worry about lick of my private information Lick of private information Ownership of data Ownership of data Data ownership Trust on vendors Trust on vendors and CAs Trust Trust on CAs Share some parts of my life Knowing me wrong Accuracy of informationConcerned about grand children Negative effects to society Concerned about old people Machine can be error Machine errors Accuracy of performance 4.2 Factors to be considered for future CAs Table 7: Future factors for transformation of CAs as actors
- 43. Contact • Email: ”md.abul kalam Siddike" kalam.siddike@gmail.com • Please contact me if you see opportunities to collaborate on the study of trust of cognitive assistants (CAs) from a service science perspective 43
Notes de l'éditeur
- Please reuse – contact spohrer@us.ibm.com Reference: Spohrer, J (2018) Preparing for Our Future with Opentech AI. Friday March 2, 2018 URL http://www.slideshare.net/spohrer/trl-jaist-20180304-v6 The Future of AI: Measuring Progress and Preparing An industry perspective and forecast of where technology is going, including the what and when for "solving" Artificial Intelligence (AI), is presented. Next, the benefits and challenges will be discussed, including impact on jobs, both near term via Intelligence Augmentation (IA) and longer term via automation. The impact on different sectors of the economy will be explored, and how best to prepare for the changes that are anticipated. Speaker Bio: Dr. James ("Jim") C. Spohrer is IBM Director, Cognitive Opentech Group. Previously, he was Director of IBM Global University Programs, co-founded IBM Research Service Research area, ISSIP Service Science community, and was CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist and Technologist, he developed next generation learning platforms. Earlier (1974-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and worked at Verbex, an Exxon company on speech recognition and machine learning. With over ninety publications and nine patents, he is a PICMET Fellow and a winner of the Gummesson Service Research award as well as the Vargo & Lusch Service-Dominant Logic award. More information here: Sample presentation: https://www.slideshare.net/spohrer/future-20171110-v14 Bio and CV: http://service-science.info/archives/2233 Optional Business, Marketing, and Technical Pre-reads: IBM Bluemine: Industry Predictions 2018: "2018 sees increased adoption of AI and digital transformation across all industries, with cloud and security also very prominent." Another predication to consider: ...vendor performance on open challenge, AI leaderboards will increase adoption of the vendor's AI offerings. See for example, Alibaba annoucement yesterday on Standford open Q&A leaderboard: http://money.cnn.com/2018/01/15/technology/reading-robot-alibaba-microsoft-stanford/index.html Also, see Tencent paper and Github code: ArXiv: https://arxiv.org/abs/1606.01549 Github: https://github.com/bdhingra/ga-reader IBM Research was #1 Jan 2017 on same Standford open Q&A leaderboard (SQuAD) referred to above: https://rajpurkar.github.io/SQuAD-explorer/ And to understand why solving AI is still very, very, very hard, in spite of all the hype: Ernie Davis (NYU) pointers: Real “reading” with background knowledge and comonesense reasoning is very, very, very hard.... see: https://arxiv.org/abs/1707.07328 in which programs that were achieving as high as 75% on this same database dropped to an accuracy of 36% if you add an automatically generated distractor sentence --- down to 7% if the distractor sentences are allowed to be ungrammatical sequences of words. The MSFT/Alibaba program has not been tested this way, of course, so there is no saying what would be the effect. Here are the slides about the “human-level performance claim” which is hyped of course: http://u.cs.biu.ac.il/~yogo/squad-vs-human.pdf Optional Pre-read for Societal Implications: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced. We are not quite at 2030, but I believe we have reached the “Keynes point,” where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality. The problem in this new phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs have been the main means of access for only 200 or 300 years. When things settle I’d expect new political parties that offer some version of a Scandinavian solution: capitalist-guided production and government-guided attention to who gets what. Europe will find this path easier because a loose socialism is part of its tradition. The United States will find it more difficult; it has never prized distribution over efficiency. This is the image I have created which will go with the talk. Can we prepone our 1:1 to tomorrow? Or Friday is best? Thanks, -Jim Jim Spohrer, PhD Director, Cognitive Opentech Group (COG) IBM Research - Almaden, 650 Harry Road San Jose, CA 95120 (o) 408-927-1928<spohrer@us.ibm.com> (m) 408-829-3112<spohrer@gmail.com> Innovation Champion: http://service-science.info/archives/2233 The Future of AI and Education: Measuring Progress and Preparing An industry perspective and forecast of where technology is going,including the what and when for "solving" Artificial Intelligence (AI),is presented. Next, the benefits and challenges will be discussed,including impact on jobs, both near term via IntelligenceAugmentation (IA) and longer term via automation. The impact ondifferent sectors of the economy will be explored, especially the impacton education, and how best to prepare students and others for thechanges that are anticipated. In conclusion, the importanceof T-shaped skills that integrate deep skills in problem-solving (STEM)and broad skills in communications (both business as well as arts&humanities) will be discussed. The rationale for teaching students, whoare empowered by advanced technologies, to competeto find better ways to rapidly rebuild society from scratch will beexplained. Speaker Bio: Dr. James ("Jim") C. Spohrer is IBM Director, Cognitive Opentech Group.Previously, he was Director of IBM Global University Programs,co-founded IBM Research Service Research area, ISSIP Service Sciencecommunity, and was CTO of IBM’s VC Group in Silicon Valley. At Apple Computer (1990’s), as a Distinguished Engineer Scientist andTechnologist, he developed next generation learning platforms. Earlier(1974-1989), he earned an MIT BS Physics, Yale PhD in CS/AI, and workedat Verbex, an Exxon company on speech recognitionand machine learning. With over ninety publications and nine patents,he is a PICMET Fellow and a winner of the Gummesson Service Researchaward as well as the Vargo&Lusch Service-Dominant Logic award. More information here: Sample presentation:https://www.slideshare.net/spohrer/future-20171110-v14 Bio and CV: http://service-science.info/archives/2233
- The majority of IBM’s offerings to customers include open technologies – and customers increasingly demand solutions based on open source code. Tens of thousands of IBMers have GitHub accounts Awards program information URLS: Htttp://developer.ibm.com/code
- URL: https://guides.github.com/activities/hello-world/
- Expert predictions on HMLI: URL https://arxiv.org/pdf/1705.08807.pdf 2015 Pattern Recognition Speech: URL: http://spandh.dcs.shef.ac.uk/chime_challenge/chime2016/results.html 2015 Pattern Recognition Images: URL: http://www.image-net.org/ 2015 Patten Recognition Translation: URL: http://www.statmt.org/wmt17/ 2018 Video Understanding Actions: URL: http://www.thumos.info/home.html > Also UCF101 http://crcv.ucf.edu/data/UCF101.php 2018 Video Understanding Context: URL: http://visualqa.org/challenge.html 2018 Video Understanding DeepVideo: URL: http://cs.stanford.edu/people/karpathy/deepvideo/ 2021 Memory Declarative: URL: https://rajpurkar.github.io/SQuAD-explorer/ Also Allen AI Kaggle Science Challenge https://www.kaggle.com/c/the-allen-ai-science-challenge 2024 Reasoning Deduction: URL: http://www.satcompetition.org/ 2027: Social Interaction Scripts: URL: https://competitions.codalab.org/competitions/15333 2030: Fluent Conversation Speech Acts: URL: http://convai.io/ 2030: Fluent Conversation Intentions: URL: http://workshop.colips.org/dstc6/ 2030: Fluent Conversation Alexa Prize: URL: https://developer.amazon.com/alexaprize 2033: Assistant & Collaborator Summarization: URL: http://rali.iro.umontreal.ca/rali/?q=en/Automatic%20summarization 2033: Assistant & Collaborator Debate: URL: http://argumentationcompetition.org/2015/ 2036: Coach & Mediator General AI: URL: https://www.general-ai-challenge.org/ 2036: Coach & Mediator Negotiation: URL: https://easychair.org/cfp/AT2017
- https://rajpurkar.github.io/SQuAD-explorer/
- What is beyond Exascale? Zetta (21), Yotta (24) Time dimension (x-axis) is plus or minus 10 years…. Daniel Pakkala (VTT) URL: https://aiimpacts.org/preliminary-prices-for-human-level-hardware/ Dan Gruhl: https://www.washingtonpost.com/archive/business/1983/11/06/in-pursuit-of-the-10-gigaflop-machine/012c995a-2b16-470b-96df-d823c245306e/?utm_term=.d4bde5652826 In 1983 10 GF was ~10 million. That's 24.55 million in today's dollars. or 2.4 billion for 1 TF in 1983 Today 1 TF is about $3k http://www.popsci.com/intel-teraflop-chip
- Source: http://service-science.info/archives/4741
- O*NET Online is the occupation network online, started by the US Dept of Labor in the 1990’s – it now represents one of the most comprehensive lists of occupations along with a great deal of information about each occupation, including skills, tasks, certifications, demand for these jobs, etc. O*NET lists about 1000 occupations from Accountants to Zoologists – and many job families in between. O*NET updates the descriptions of the occupations as well as adding new occupations over time. Source: http://www.onetonline.org/find/family?f=0
- 1950 Nathaniel Rochester (IBM) 701 first commercial computer that did super-human levels of numeric calculations routinely. He worked at MIT on arithmetic unit of WhirlWind I programmable computer. Dota 2 is most recent August 11, 2017 as a super-human game player in Valve Dota 2 competition – Elon Musk’s OpenAI result. Miles Bundage tracks gaming progress: http://www.milesbrundage.com/blog-posts/my-ai-forecasts-past-present-and-future-main-post DOTA2: https://blog.openai.com/more-on-dota-2/
- URL: https://www.ted.com/talks/noriko_arai_can_a_robot_pass_a_university_entrance_exam
- The nature of reality changes when there is more than one intelligent species, and we are not the smartest. The nature of reality also changes when the cost of exploring alternate experience pathways are made less risky – the notions of time and identity changes as a result. Mitigate risks and harvest benefits of existence, by learning to evermore efficiently and rapidly rebuild from scratch to higher states of value and capability of entities. The evolving ecology of service system entities their value co-creation and capability co-elevation mechanisms, as well as their capabilities, constraints, rights, and responsibilities at each stage in time. Human progress as well as the development of individuals, and the arc of institutions can be viewed in this way. Entities exist as individuals and populations. Generations of entities, generations of species (populations), generations of individuals (cohorts).
- By 2036, there will be an accumulation of knowledge as well as a distribution of knowledge in service systems globally. We need to ensure as there is knowledge accumulation that service systems at all scale become more resilient. Leading to the capability of rapid rebuilding of service systems across scales, by T-shaped people who understand how to rapidly rebuild – knowledge has been chunked, modularized, and put into networks that support rapid rebuilding.
- The weakest link is what needs to be improved – according to system scientists. Accessing help, service, experts is the weakest link in most systems. By 2035 the phone may have the power of one human brain – by 2055 the phone may have the power of all human brains. Before trying to answer the question about which types of sciences are more important – the ones that try to explain the external world or the ones that try to explain the internal world – consider this, slide that shows the different telephones that I have used in my life. I grew up in rural Maine, where we had a party line telephone because we were somewhat remote on our farm in Newburgh, Maine. However, over the years phones got much better…. So in 2035 or 2055, who are you going to call when you need help?
- Good morning. Welcome to my preliminary defense presentation. First of all, I would like to thanks my supervisor Prof. Youji Kohda for his all kind of supports and helps. Secondly, I would like to express my deepest gratitude to Jim for is great mentorship and giving me opportunity to work with him. Thirdly, I would like to express my sincere thanks to my second supervisor Shirahada-sensei for his love and encouragement. Finally, I would like to thanks Yoshida sensei and Nishimoto sensei of being committee members of my doctoral examination. Today, I would like to talk about “Towards………………………………………….”
- CAs are new decision tools. It is able to augment human capabilities and expertieses. It can understand the environment around us. Cas can provide high quality recommendations. It also helps for better data driven decision. For example, Apple Sir, IBM Watson, Google home and Amazon echo are considered as low level CAs. On the other hand, IBM Watson Ochology, and Driverless car are considered as high level CAs.