Technology and the Future of the Professions E1

This is a live blog from the CIPD ACE conference 2016 so please excuse any typos!

Daniel Susskind
Daniel is going to introduce us to to futures, take us to the vanguard who are trying to solve problems in a different way, look at trends and evolution, technology and AI, and the impact on jobs.
He has co-authored a book with his father with the same title as the session.
In the book they look at 8 professions, conducted 100 interviews and took into account over 800 sources. The picture they got was one of radical change and the book is trying to make sense of it. They said there are two possible futures. One is the reassuring one – the existing models augmented by technology eg doctors consulting with patients by Skype. In the second model, increasingly capable machines gradually take on more of the tasks traditionally done by the professions. In the book they think the two versions will develop in parallel but eventually the second one will take over.
Why do we have the professions? No one can know everything. Professionals have the ability, knowledge and understanding. They are the solution of the print-based industrial society. But in the modern technological world, the professions are creaking. They are unaffordable, antiquated, opaque and they underperform. So as we move from print-based to Internet-based, can we solve the problem differently?
They went to the vanguard. Who is doing this differently? They have hundreds of case studies in the book and he gave quite a list. For example, more people signed up for Harvard’s online learning courses than have ever attended the university. Google DeepMind just beat a human at a complex game called Go. The Huffington Post has more monthly subscribers at 6 years old than the 160+ year old New York Times.
They identified 8 patterns and 30 trends. Professionals are being asked to do more with less. More competition is emerging – they say the competition that kills you doesn’t look like you. There is a move away from bespoke service, driven by technology. Professional work is being decomposed, broken down and disaggregated. So some of the work can be done by others. And also the “routinisation” of work.
There is a flow happening within professional firms:

Craft -> standardisation -> systematisation -> …externalisation then happens, offering three options: charge online, no charge online, commons
Commons eg Wikipedia. The commoditisation of professional work. Technology is driving this.
Technology is seen in their book through four lenses:

  • Exponential growth – see Moore’s Law – by 2020, the average desktop compute I’ll have as much processing power as the human brain – by 2050, more than all of humanity)
  • Increasingly capable – big data, increasingly developing problem solving, affective computing – they can distinguish between a face showing genuine pain and fake pain, robotics
  • Increasingly pervasive – there is no finishing line. The technology today is as bad as it’s ever going to be
  • Increasingly connected

Artificial Intelligence: originally the model was computer programmes that were based on a decision-tree. They were costly, there was no incentive to firms charging hourly rates and then most importantly, the web came along. The “AI winter” began. Then in 1997, Deep Blue beat Garry Kasparov. It was thought impossible because the way you built them was to sit with a human expert, understand how they did used expertise, then model them in a set of rules. It was thought that because it couldn’t be articulated, AI could never work. They didn’t foresee the exponential growth of processing power.
There are lots of ways of being smart that aren’t smart like us” – Patrick Winter
Many of us think what we do requires judgement. Can a machine exercise that judgement? That’s the wrong question. What is it that you do that requires judgement? Uncertainty. Can a machine deal with uncertainty better than a human? The answer is yes. They can process much more data. Can machines think? From a practical point of view it’s largely irrelevant.
IBM’s Watson doesn’t know it won at Jeopardy. It didn’t let out a whoop of joy or go to the pub to celebrate. Second wave AI is computers being able to do things in a different way to us.
When we think about the future of the professions in terms of jobs, it’s misleading. It encourages us to think that professionals do one indivisible thing. Technology doesn’t destroy jobs wholesale. Very different roles will still be required. If you’re thinking about work in the 2020s, you have two options in respect of machines: compete with them or build them.
How do we produce and distribute practical expertise? Traditional answer – through the professions. In an Internet society there are six possible models.

  • The networked experts model – workers on tap, not congregating in bricks and mortar buildings. Example being axiom in the legal world
  • The para-professional model – you still need the basics doing and machines do the more expert aspects. Example of a nurse using IBM Watson to diagnose rather than going to a doctor.
  • The knowledge engineering model – mine the jewels of expertise from professionals and put them into a system
  • Community experience model – communities solve each other’s problems
  • Embedded knowledge model – embed the expertise in the system, for example so when playing solitaire you can’t put a red 5 on a red 6. Or a driverless car which has its speed limit dictated by geo location

The machine-generated model – the assistance of machines that can produce and develop expertise independently
What shouldn’t we do?

  • Don’t hold out for retirement
  • Don’t try to protect the traditional model

What should we do?

  • Explore the new roles
  • Explore the new models
  • Start with a blank sheet of paper – don’t ask how tech can help me do what I already do. Ask how can tech solve my problem in an entirely different way.

What the future will look like doesn’t depend on what e say or think – it depends on the decisions that we make and those inside the professions make. Alan Kay – “The best way to predict the future is to invent it”.