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Art of artificial intelligence and automation

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Art of artificial intelligence and automation

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AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?

AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.

AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?

AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.

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Art of artificial intelligence and automation

  1. 1. Art of AI and Automation: primer The history of AI technologies
  2. 2. Table of Contents 1. General History of AI and Automation 2. History of Natural Language Processing 3. History of Robotic Vision 4. Panel Discussion
  3. 3. Andrew Liew Cool!Let’s rock! 10,000++ 2,000++ Name: Andrew Liew Weida Title: Analytics + Innovation + Technology Role: Subject matter expert Brief Profile: Andrew is a experienced technologist with over 10 years of experience, having cofounded 4 startups and being the early employee executives of 2 startups, having work over 9 international cities.. He specialized in Technology industry, particularly within the HR, SaaS, Analytics and On Demand Marketplace segment. Andrew has worked extensively across 8 countries and assisted numerous clients in advising how technology can scale their operations, how to reduce risk in implementing technology and the potential impact of tech to companies top line. • Startup experience – over 5 years of CXO work for tech startups & tech enabled companies in areas of financial management, HR management, digital product management • Financial Analytics- creating data sourcing map to build data driven simulations for MNCs in the banking, Private and Public service sectors for increasing revenue , reducing cost and reducing risk management. • Reward Design –working with Remuneration committee of MNCs to ensure financial feasibility of compensation design to ensure market competitiveness and internal harmony. Education: • PhD candidate [Econometrics]@ ANU • Master (Economics) (Admission with scholarship)@ ANU • Master (Finance) (1/400, Valedictorian)@ Usyd • BSc (2.5/4 years, fastest graduating student in faculty history) @NUS • AB leadership@ Korea University • UIUX HBF • Externalities assessment • Analytics • Benchmarking • Reward management • Tech based Cash flow Modelling • Fundraising • Digital Product Management • Salesforce Effectiveness Key Competencies: Relevant Work experience: Cofounder About the moderator
  4. 4. We will be superhuman within the next 10-20 years
  5. 5. 7 Key Schools of Thoughts in AI
  6. 6. General History 1
  7. 7. History of Planning and Scheduling Joseph-Louis (Giuseppe Luigi) comte de Lagrange Isaac Newton Iterative Method George Bernard Dantzig Linear Programming
  8. 8. History of Predictive Analytics John Atanasoff Statistical computing hardware
  9. 9. Five of the attendees of the 1956 Dartmouth Summer Research Project on Artificial Intelligence reunited at the July AI@50 conference. From left: Trenchard More, John McCarthy, Marvin Minsky, Oliver Selfridge, and Ray Solomonoff. (Photo by Joseph Mehling '69)
  10. 10. Reasoning
  11. 11. Knowledge Representation
  12. 12. Planning and Navigation
  13. 13. Natural Language Processing
  14. 14. Perception
  15. 15. Generalised Intelligence
  16. 16. History of VC/Govt investing in AI
  17. 17. 1st AI Winter: 1960s
  18. 18. 2nd AI Winter: 1970s
  19. 19. 3rd AI winter: 1980s
  20. 20. End of AI winter?: DL / AML Able to recognise objects without “directly teaching machines”
  21. 21. History of Deep Learning
  22. 22. Why is this possible?
  23. 23. A sense of Deep Learning Link: Google Tensorflow
  24. 24. What’s the caveat? Black Box Algorithmic model Unknown DGP Obtain predictive accuracy Prediction White Box Econometric model Known Mechanics Validate hypothesis + interpretability Causality Suitable for HR applications: reward, training, recruiting, talent management Suitable for HR applications: benefits, employee records, 1st degree of pre- defined screening for mature jobs
  25. 25. You need multiple disciplines to make AI useful.
  26. 26. History of NLP 2
  27. 27. Dr. Vaisagh (VT) Education • PhD in Computer Science - NTU, Singapore • B Eng in Computer Engineering - NTU, Singapore Relevant Experience • Built the impress.ai platform which is being used by top banks, telecom and consulting companies • Published several papers in top tier international journals and conferences on AI and complex systems (bit.ly/vt-publications) • Lead a research team working on city-scale traffic simulations at NRF-funded institute, TUM CREATE while supervising 5 PhD students and leading multi-entity collaborations with A-Star and Continental Automotive Pte Ltd. • Completed his PhD on developing agent based models for understanding human crowds in 2014 from Nanyang Technological University Co-founder and CTO About me Superpowers for recruiters Key Competencies: • Product Development • Scientific Research • Artificial Intelligence • Software Architecture • Simulation and Modelling • Complex Systems What does impress.ai do? • Intelligent productivity enhancement tool for recruiting • Bots screen, interview and shortlist talent in real time, at scale • Using Artificial Intelligence, Machine Learning and NLP to augment HR
  28. 28. Natural language processing (NLP) is concerned with the interactions between computers and human (natural) languages NLP – What is it?
  29. 29. • NLP deals with a lot of core issues of what we define as machine intelligence • Some applications – • Natural Language Understanding, Natural Language Generation, Sentiment Analysis, Translation, Text Classification, etc. etc. The Turing test NLP – What is it used for?
  30. 30. 1954 - The Georgetown-IBM Experiment Statistical machine translation Technically simple. Helped get funds into computational linguistics. 1950s - Descriptive Linguistics Movement NLP through the years 1950 1950s 1954 1957 1970s 1980s 1990s 1957 - Chomsky’s Syntactic Structures Lead to big focus on developing Universal grammars Away from statistical approaches 1950 - Turing test Defined what we expect from machine intelligence 1970s - Conceptual Ontologies and the Semantic Web Limited data, rules and inference systems 1980s - the rise of statistical linguistics Machine learning based approaches gain traction Focus on probabilistic analysis over rules 1990s onwards The internet, big data, personal computingStructural Linguistics (1916 ) – prepare corpora of nouns, verbs, phonemes etc. Shannon Probabilistic theory of computation (1947)
  31. 31. 1950s There was limited data available A lot of effort started to be put into digitization of records NLP through the years – Data perspective 1950 1950s 1954 1957 1970s 1980s 1990s 1960s and 70s NLP models developed worked with limited data 1980s onwards Availability of corpora for statistical ML models to thrive 2000s onwards Human computation and automatic data annotation Facebook, Twitter, etc. Captcha, Amazon Mechanical Turk 2000s
  32. 32. NLP through the years – The computational perspective 1950 1960 1970 1980 1990 1980s onwards Moore’s law Personal Computers 1990s onwards The internet 2000 Late 2000s onwards GP-GPUs to speed up neural network based processing Today Cloud Computing Specialized chips for AI
  33. 33. Natural Language Annotation – The food for NLP Data preparation amounts to 80% of the time spent on a typical data analysis project
  34. 34. Natural Language Annotation – The food for NLP • Generating corpora for NLP research is hard work • Till the 2000s: Graduate student man hours helped generate lots of corpora in universities • Human Computation – Captcha, Amazon Mechanical Turk • Data-centered design – Facebook, Twitter, impress.ai ☺ Captcha – An example of human computation Hashtags – An example of data-centered design
  35. 35. NLP through the years – Tools – a personal perspective Tools From using academic libraries like NLTK, numpy and scipy to using Tensorflow, spacy and keras. Writing complex neural networks and parsers in fewer than 10 lines of code. Techniques Bag of words and expert-based feature extraction to word2vec Single layered neural networks to Recurrent Neural Networks and LSTMs Example based approaches Cloud computing Difficulty of getting lab hours on a powerful enough PC to spinning up Cloud instances with GP-GPUs in an instant Deployment techniques and tools Containerization, CI/CD tools, Stack Overflow, Github Makes it super easy to get started and deploy code
  36. 36. Where we are today? Translation • Arms race between Technology giants to create the best translation engines • Excellent Wired Article last year on the same Voice assistants and the bot revolution • Siri, Google Assistant, Alexa, Customer Service bots, Interviewing bots
  37. 37. Concluding thoughts • Biggest development – Availability of tools, computing and data to the masses • A fundamental breakthrough in NLP along the lines of what has happened in computer vision in recent years is still missing • The answer to this may lie in going back to the roots of NLP and exploring paths that were not viable earlier
  38. 38. History of Robotic Vision 3
  39. 39. Cool!Let’s rock! Name: Abhishek Gupta Role: CEO Brief Profile: . • Startup experience – over 3 years of CEO work for Robotics Startups- Edgebotix and Movel AI • Hardware experience - Designed , prototyped and tested ARM MCU based educational robots Education: • Masters in Embedded Systems, NTU • Bachelors in Electronics and Instrumentation, VIT • Embedded System • Project Management • Hardware Design • Software Development • Artificial Intelligence • Fundraising Key Competencies: Relevant Work experience: Founder About the speaker 2 Abhishek is a serial entrepreneur. Abhishek co-founded MOVEL AI. Movel AI is the next generation robot navigation software platform. The AI software uses computer vision, deep learning, and sensor fusion for robot navigation. The technology makes robots work in many places that is not possible before: crowded places like a hospital with a lot of human traffic, or very large space like an airport where even human can easily get lost. He was a leading researcher at SUTD, where he worked on self-driving bicycles and solar-powered robots. Before starting Movel AI, he founded EdgeBotix, a hardware robotics company, where he designed and sold hundreds of educational robots to a Singapore University.
  40. 40. What is Robotic Vision
  41. 41. What is the difference….
  42. 42. History ●1950’s – Two dimensional imaging for statistical pattern recognition developed ●1960’s – Roberts begins studying 3D machine vision ●1970’s – MIT’s Artificial Intelligence Lab opens a “Machine Vision” course – Researchers begin tackling “real world” objects and “low-level” vision tasks (i.e. edge detection and segmentation: ●1980’s – Machine vision starts to take off in the world of research, with new theories and concepts emerging ●1990’s – Machine vision starts becoming more common in manufacturing environments leading to creation of machine vision industry
  43. 43. Future of Robotic Vision ●Letting robot decide based on Robotic Vision ●Collaborative learning ●Working together with Humans
  44. 44. Panel Discussion Andrew Liew Vaisagh (VT) Co-founder / CTO Co-founder / CEO Abhishek Gupta Co-founder / Analytics

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