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Careers in an AI World

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Artificial Intelligence (AI) in everyday use

Publicado en: Tecnología
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Careers in an AI World

  1. 1. Careers in an AI world
  2. 2. Artificial Intelligence (AI) in everyday use • In the past, humans would recognise patterns in data, hand design and write the steps (rules) in to a computer programme • Now the AI looks for patterns, and from those helps create the rules • Yes, you have used Ai this week: • Google home, Amazon Alexa, Siri • Google search, maps, traffic • Credit card fraud detection, Superannuation stock market trading • Recommendations on Netflix, shopping sites, Facebook advertisements 2
  3. 3. What is AI? • Use of computer-based algorithms to perform tasks that would require intelligence if performed by a human • Includes predictive analytics, voice recognition, language processing, image recognition, robotics • And different learning techniques: • supervised learning • unsupervised learning • reinforcement learning 3
  4. 4. Competitiveness in AI
  5. 5. Digital Disruption 4th industrial revolution* • Machine learning / AI • Big data analytics • Internet of things + • Desire for improved health outcomes • Increasing health delivery costs • Limited public funds Healthcare Delivery Transformation • Role of health care providers • Augmenting decision-making processes • Transforming hospital delivery
  6. 6. Global Outlook 6 40% CAGR 2017 - 2024
  7. 7. Industry AI Advantage Source: ARTIFICIAL INTELLIGENCE: THE NEXT DIGITAL FRONTIER?, McKinsey Global Institute, June 2017, er%20real%20value%20to%20companies/mgi-artificial-intelligence-discussion-paper.ashx
  8. 8. Will this happen to me?
  9. 9. • The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment.
  10. 10. Checkout Insurance broker Travel agent Taxi driver Paralegal Bank Teller Journalist Tax agent Stockbroker Financial advisor Warehouse inventory Telemarketers
  11. 11. Checkout Insurance broker Travel agent Taxi driver Paralegal Bank Teller Journalist Tax agent Stockbroker Financial advisor Warehouse inventory Telemarketers
  12. 12. Will Robots and AI take my job?
  13. 13. Will Robots and AI take my job?
  14. 14. Will Robots and AI take my job?
  15. 15. Will Robots and AI take my job?
  16. 16. The Roboapocalypse
  17. 17.
  18. 18. Will we really replace entire Jobs? • Most jobs are a series of related tasks • In time individual tasks can be replaced, rather than jobs • Many such tasks are monotonous and error prone • So we will move from a series of repetitive simple tasks to more variable, complex, cognitive tasks
  19. 19. • An aging workforce is changing job expectations
  20. 20. Cognitive Input per patient is and will be limited, Care gap emerges without extra help. 2020 2025 2030 2035 2040 2045 2050 Total Care requirements Care addition from clinicians Care from pathways, DSS, AI Growing care gap 24
  21. 21. Clinical decision support needs to grow to fill the gap. 2020 2025 2030 2035 2040 2045 2050 Total Care requirements Care addition from clinicians Care from pathways, DSS, AI Need to grow clinical decision support 25
  22. 22. Diabetic retinopathy (2017) • 54 opthalmologists on panel, 3-7 per image • 128,000 classified images fed in to tensor flow • Results equivalent to human • F-score 0.95 vs human 0.91 • Deploying in to India • So provides resources where they don’t exist • Shows levels of human fallibility 26 CNN, Inception –v3 architecture Optimization by distributed stochastic descent (Dean)
  23. 23. TREWScore • Targeted Real-time Early Warning Score to predict septic shock • MIMIC=II database, 13,014 patients (1836 septic) • Supervised machine learning technique • AUC 0.83, sensitivity 0.85 at specificity 0.67 • Mean detection 28 hrs before onset of septic shock • 2/3rds identified before onset of sepsis-related organ dysfunction 27
  24. 24. C-Path - Stanford • Machine learning to diagnose breast cancer • Iterative process using known data set • Better than pathologist eventually looking at cancer cells • AND • Found patterns in the stroma, which combined with the cell data was a better predictor. Sci Transl Med 9 November 2011: Vol. 3, Issue 108, p. 108-113 Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival 28 6642 features considered L1 regularised logistic regression 31 features tested 8 fold cross-validation Multivariate cox proportional hazard
  25. 25. Breast Cancer metastases (lymph nodes) • 400 Gigapixel images, tensorflow CNNs • AUC 0.925 (human = 0.966) • Combined = 0.995 • Augmented Intelligence, combining human and AI, is the key to success 29 Used GoogLeNet, AlexNet, VGG16, FaceNet Won Camelyon16 both slide and lesion
  26. 26. The serial job hopper – a new norm • The job marketplace is changing, and will continue to change • Within a single jobs, roles will constantly change • Regularly changing jobs will not have associated stigma • Continuous learning, broader view, regular new challenges • AI itself is the fastest area of change
  27. 27. Find good oil Drill and retrieve oil Store oil Refine oil Turn to power Create cars, electricity Use cars, electricity Sources of unbiased data Readable structured data In useable databases Create data views Turn data to knowledge Turn knowledge to products Engage users and customers Domain experts Engineers Cloud architects Database administrators AI programmers User interface design Change management
  28. 28. INTELLI a partnership between GCUH, Universities and Industry to transform healthcare through the practical application of next generation technologies. 1. Improve Health Outcomes 2. Create local opportunities for skills, jobs and venture development 3. Attract Entrepreneurs & Investment 4. Achieve Global Recognition for Innovation & Commercialisation
  29. 29. 4 Floors Research Industry Startups Academy With access to Customers and Capital
  30. 30. GCUH Customer Innovation Partner Delivery Model Universities • Local: Griffith, Bond • Regional: QUT, UQ, SCU • National: Curtin, … • International: NUS, MIT, ?UEF … VenturesVenturesVenturesVenturesVentures Advisory Board Investment Fund Investment Fund Investment Fund Commercialisation Pathway Professional Associations • College of Intensivists, RACS, … Heath Technology Assessment Facility Precision Medicine Data Platform Other • Data 61, AEHRC • Health & Knowledge Precinct Government • Qld Govt, Advance Queensland • Federal Government Industry Partners • Platform: AWS • Devices: GE, Philips. … • EMR: IMDSoft, Cerner… • SMEs: TechConnect, KJR, Veriluma • Other: Verily, CIMIT Selection Board Research & Education Health Economics Advisory Global Innovation Hubs • Startup Health, TMCx, CIMIT, NIA, … Mentoring / Advisory
  31. 31. What is your view of our future?
  32. 32. 43