Dr Bonnie Cheuk IDC Future of Work Keynote: Workforce Transformation Human Machine Collaboration
•
0 likes•364 views
Dr Bonnie Cheuk, AstraZeneca Digital Transformation & Global Capability Leader (Learning Culture and Learning Agility), delivered a keynote at IDC Future of Work Conference on 3 Mar 2020. She provoked the audience to go beyond the hype, and think deeper on how human and AI and data-driven Machine collaborate together. These 3 questions were discussed: 1. How should human and machine collaborate? What skills are required? 2. Will machines replace (most) jobs? 3. Will there be new jobs to enable human-machine collaboration? Drawing on Dervin's Sense-Making Methodology, Bonnie reminded us that human beings are not robotic machines. Human beings have feelings, experience, we are both scientists and artists, we are analytics and we are emotional. Bonnie asked the audience how would you like to build a high performance team? Who do you want to put in the team? Do you want everyone to have the same strength, same skills? Or would you pick a team making up of players who can complement one another, and can bring out the best of one another. So in order to propose how human and machine should collaborate in the future of work, it is useful to first ask: what is the strength of human beings? What is the strength of the machine? We need to understanding how AI-driven machines learn vs how human beings learned, and play to one another's strength. And what is the strength of human? It is being human. Let the machine handle the deductive reasoning, the data-driven predictions, repetitive tasks. Let the humans do what we do well, adapting, navigating the unknown, use our human skills, promote collective sense making to make judgement, decisions. And free up the time to allow us to learn, create and innovate. Bonnie highlighted that there are many unknowns as to how AI will be further developed, and there are ethical issues and risks that have to be addressed, and there are no precedents to follow. Collective human sense making is critical to bring out multiple perspectives from different stakeholders, to co-create AI-driven machines that human beings can trust, and to collectively address tricky ethical issues early on. Dervin’s Sense-Making Metaphor is introduced to facilitate two-way dialogue, to address power issues, and to explore common and divergent views to build common understanding of potential challenges, and co-create solutions to address them.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Dr Bonnie Cheuk IDC Future of Work Keynote: Workforce Transformation Human Machine Collaboration
Similar to Dr Bonnie Cheuk IDC Future of Work Keynote: Workforce Transformation Human Machine Collaboration (20)
Recently uploaded
Recently uploaded (18)
Dr Bonnie Cheuk IDC Future of Work Keynote: Workforce Transformation Human Machine Collaboration
- 1. Workforce Transformation with Human and Machine Collaboration 03 March 2020 London, IDC Future of Work Transformation Dr Bonnie Cheuk Business & Digital Transformation Leader Global Capability Leader - Learning Agility
- 2. 2018 March R&D Digital Transformation Leader 2018 Sept R&D Digital Transformation Leader Culture & Change Management Hello…. My name is Bonnie Cheuk SOCIAL SCIENTIST RESEARCHER PhD in Human Sense-Making Author, social strategy in action: driving digital transformation Knowledge Management 2020 Jan AZ Global Capability Leader Learning Agility & Learning Culture DIGITAL TRANSFORMATION Knowledge Management, Enterprise2.0, Digital Marketing & Comms, Innovation, Customer Experience WORK ACROSS INDUSTRIES Consulting, Government, Academia, Banking, Pharmaceutical /bonniecheuk
- 3. 3 Automation • Explicit rules/processes • Do better/faster • Accurate output • Digitalise the process Data-Driven AI Machine Learning • No need to understand how things work • Give computer a set of data (as input) • Give computer a set of results (as output) • Ask the computer to figure out what goes on in between • Can be a software, a robot, an algorithm
- 4. 4 $21.1b n South San Francisco Boston Osaka Shanghai Gaithersburg Cambridge Gothenburg 65k Employees 26 Operations sites in 17 countries $22.1bn Total Revenue AstraZeneca: Three strategic R&D sites Focus on Three main therapy areas Oncology Respiratory Cardiovascular, Renal & Metabolism
- 5. 5 Transforming everything we do for a digital future. Digital technology is… …harnessing data to develop better medicines, faster, for the right patients …accelerating clinical trials and making them more convenient for patients …delivering medicines more efficiently …empowering patients in a new world of integrated health
- 6. Machines can read more radiology scans in one day than a radiologist can see in a life time
- 7. Human Machine Collaboration 1 2 3 How should human and machine collaborate? What skills are required? Will machines replace (most) jobs? Will there be new jobs to enable human- machine collaboration?
- 8. The starting point to think about human-machine collaboration is to start with ____________ ______ ________ .
- 10. We should appreciate human intellect in our ability to learn and reason, and to adapt to a complex world
- 11. Which situation are you facing? 1 2 3 4 5 6 7 8 9 10 11 12 Sense-Making Methodology Institute http://www.sense-making.org Copyright @Brenda Dervin @bonniecheuk
- 12. Voice of our Employees Speaker Kit 12 22 March 2020Speaker Kit Voice of our Employees
- 13. Human Machine Collaboration 1 2 3 How should human and machine collaborate? What skills are required? Will machines replace (most) jobs? Will there be new jobs to enable human- machine collaboration?
- 14. Building a high performing Team
- 15. 15 Machine Learning Feed a million pictures of bicycle to a computer “Teach” the computer: this is a bicycle, this is not a bicycle After a while it “learns” Show the computer any picture, and it can tell whether it is a bicycle or not 4-year old Learning Show 2 or 3 bicycle pictures to a child “Find all the other bicycles in this pile of pictures” Found unicycles, tricycles… The child can generalise much broadly than would seem possible
- 16. 16 Machine LearningHuman Learning Something that is very hard for machine, it is simple for human. Something human find very hard, it is simple for machine.
- 17. 17 Machine LearningHuman Learning Something that is very hard for machine, it is simple for human. Something human find very hard, it is simple for machine.
- 18. Intelligence Reasoning Inductive (observe > generalise) Deductive (data > infer > predict) Make Sense of the World Artist Scientist Handle Complexity & Ambiguity Flexible and Adaptive Repetitive What Human Beings can do…
- 19. Intelligence Reasoning Inductive (observe > generalise) Deductive (data > infer conclusion > prediction) Make Sense of the World Human skills Empathy – Engagement – Creativity – Perception – Hunches – Emotions – Relationship – Experience Data Driven Crunch data – complex calculations for prediction & reasoning – use some measure of probability to validate an argument Handle Complexity & Ambiguity Flexible and Adaptive Handle non-routine situation – apply learning from one situation into a totally different context – navigate unstructured physical space Repeat in Predictable environment Assembly line work – Clerical and keyboard tasks – Navigate predictable physical space (e.g. train run on track) What Human Beings can do…
- 20. Intelligence Reasoning Inductive (observe > generalise) Deductive (data > infer conclusion > prediction) Make Sense of the World Human skills Empathy – Engagement – Creativity – Perception – Hunches – Emotions – Relationship – Experience Data Driven Crunch data – complex calculations for prediction & reasoning – use some measure of probability to validate an argument Handle Complexity & Ambiguity Flexible and Adaptive Handle non-routine situation – apply learning from one situation into a totally different context – navigate unstructured physical space Repeat in Predictable environment Assembly line work – Clerical and keyboard tasks – Navigate predictable physical space (e.g. train run on tracks) What Machine can do well…
- 21. “The future’s most valuable skills will be those that are complementary to prediction — in other words, those related to judgment.” “Develop management processes that build the most effective teams of judgement-focused humans AND prediction-focused AI agents” Source: What to expect from AI. MIT Sloan Management Review, July 2017
- 22. 22 • Human sense making – Reflecting, adapting, navigating complexity & ambiguity • Human judgement – Responsible decision making (based on values, ethics, risk) • Human (non-robotic) skills • Perception, reasoning, creativity, innovation • Communication, deep dialogue • Empathy, emotional Intelligence • Engage with customers, stakeholders, employees • Read in between the lines • Identify opportunities with limited data • Human well-being, mental sharpness Human skills provide added value in a mechanised world
- 23. Human Machine Collaboration 1 2 3 How should human and machine collaborate? What skills are required? Will machines replace (most) jobs? Will there be new jobs to enable human- machine collaboration?
- 24. “Oxford researchers have forecast that machines might be able to do away half of all U.S. jobs within two decades.” The Atlanta: World without work (2015) https://www.theatlantic.com/magazine/archive/2015/07/world-without-work/395294/
- 25. Less Automatable Activities → Highly Automatable Activities It depends what tasks you perform in your job… Source: Where machines can replace humans – and where they can’t yet (McKinsey Digital Insights)
- 26. 26 Break down your job into specific tasks Today I spend my time piecing together information & reworking the data I create bespoke spreadsheets for every ask Tomorrow Machine Machine I share insight from excellent financial analysis & decision making I am keen to connect with my colleagues to help understand the end to end process Human Human The data in the system is never right for us My colleagues respect me because I’m the one who knows the numbers I am valued because I share the trends & insights with my colleagues Finance will succeed if we all work better together – my role is just a part of that AI can support some tasks (e.g. data entry/processing, prediction, repetitive cognitive tasks, physical work in predictable environment), but not all (social interaction, apply expertise, do physical work in unpredictable environment) DoesThinks
- 27. 7 ❑ Machine cannot eliminate jobs (but will replace tasks) ❑ Mid-level jobs will decrease, may downgrade to lower-paid jobs (lower- paid jobs involve a lot of physical activities is harder to automate). ❑ Income gap between high and low-skills workers will increase And… what do you do with the additional time? Will Machines replace (most) jobs?
- 28. Human Machine Collaboration 1 2 3 How should human and machine collaborate? What skills are required? Will machines replace (most) jobs? Will there be new jobs to enable human- machine collaboration?
- 29. 9 AI ethics and risks: Jobs to Monitor? Machine can do ❑ Filtering news ❑ Putting us in echo chambers ❑ Unfair matching of jobs ❑ Unfair matching of partners We are worried ❑ What the machine might do? We need a “pilot in the cockpit” Human performing oversight ❑ Be part of the system operations ❑ Monitor it in case it misbehaviours ❑ Intervene and take actions
- 30. AI ethics and risks: Jobs to Regulate? Regulate drugs or consumer products ❑ Safety standards ❑ Clear expectation on the function of the product ❑ Who is liable, who is at fault if something goes wrong Regulate AI-driven Machines ❑ Not passive tools - some kind of autonomy/Intentionality ❑ Adapt and learn - unable to pre-certify machines as being ethical Regulate AI is more like regulating human behaviours (not products) We cannot ❑ Open people’s brain to certify they are good people or will commit crime We can ❑ Set clear expectation ❑ Observe behaviours in real world ❑ Hold them accountable if they violate laws, social norms, company policies We cannot easily regulate AI-driven Machines ❑ Machines have no experience, so we cannot give them punishment or rewards ❑ Machines gain more autonomy as they learn but do not gain more experience We cannot ❑ Attribute liability to the programmer - because the machine learn from the data
- 31. 1 ❑ If the outcome of using AI have an impact on other areas ❑ e.g. one stakeholder cares about safety, another cares about speed, costs, efficiency ❑ Can human beings make up our minds and agree what the machines should do for us, before we know how human beings should monitor it? We need jobs to facilitate collective Sense-Making (e.g. on moral dilemmas) Jobs to facilitate collective Sense-Making
- 32. Jobs to facilitate collective Sense-Making ❑ Understand norms and expectations of different stakeholders on AI ❑ Discuss how they perceive the expected behaviours and anticipate surprises ❑ Be mindful of potential biases ❑ Anticipate the reaction when AI might go wrong Need to bring stakeholders together to have dialogue
- 33. Sense-Making Methodology Institute http://www.sense-making.org Sense-Making Toolkit: Learning/Unlearning Moment Q1: Where do I/we want to achieve [with AI]? Q2: What situation am I/are we facing? What led up to this point? Q3: What challenges, questions, muddles do I/we have [with AI]? How do I/we feel? Q4: What is helping me/us? What gets in the way? Q7. What are the (small) steps I/we can take to develop [trust in AI]? • • • Q6. If I have a magic wand, I would like …. Collective Sense-Making to create self-awareness and trust [with AI] Q5. Are there power issues or constraints which prevent me/us from speaking up?
- 35. Jobs to explain the inner workings of complex algorithm to non-tech staff Source: The Jobs that Artificial Intelligence Will Create, MIT Sloan Management Review, 2017
- 37. 37 How would human-machine collaborate when the future of AI is unknown? ❑ We know: More things are feasible using AI ❑ We don’t know: … how fast… how soon… how feasible?
- 38. 8 Human-Machine Collaboration Machine • Deductive reasoning • Data-driven predictions • Repetitive tasks in predictive environments Human Focus on the strategy & big picture Human judgment focussed Collective Sense Making Human Skills Lifelong Learning & Learning Agility Work as a high performing team – play to our strength – Be Human!
- 39. 39 Learning Agility & Lifelong Learning Culture Global Capability Leader, AstraZeneca Credit to Dr Brenda Dervin for her ongoing inspiration and guidance @bonniecheuk/bonniecheuk http://www.sense-maker.org Thank you. Let’s learn together!