Automation or Augmentation?
After that first step, I've built software systems automating machine tools, robots, assembly systems…and finally whole factories. In the industrial evolution: static-->mechanized-->electrified-->computerized-->cognitized-->connected, I've started in the early stage of computerization.
Automated factories produced distinct items faster, cheaper, preciser...with much less people, but automators acquired new skills to leverage technologies. In the mid 80s I've managed about 100 mathematicians, computer scientists, manufacturing experts…
Our work has displaced workers, but also augmented the demand for skilled labor at the user as well the builder side.
Today, as AIs encroach on knowledge work, it's hard to see how humans will remain employed in large numbers.
Although this is a political issue, it's my strong belief that avoiding an antisocial polarity, future work needs to become more hybrid, life-long education needs to be emancipated from schools, teachers and curricula, weekly working time needs to be reduced and work life stretched...by reframing the use of intelligent systems as augmentation.
What kind of positions can they take to empower, enrich...jobs of knowledge workers? Demanding but not killing?
Super-Brain or Collaborative AIs?
25 years ago, I've conducted my first AI project and I know, the progress in AI is incredibly fast.
Since then I've managed machine learning projects for metal forming, ski production, paper making, printing, medicine…we've even made our own multi method machine learning framework…with crisp and fuzzy decision techniques, ANNs, kernel technologies…represented in a symbolic programming language.
Companies as Google, Amazon…want to create a channel between people and algorithms...we just need tot rebuild our systems by massive inherent parallelism, massive data and better algorithms they say…AI that is driven in large nets will cognitize things…our thinking will be extended with some extra intelligence?
Collaborating with smart machines, we can learn how to manage difficulties and even learn more about intelligence.
Agreed, but I talk about AI as a set of techniques, from mathematics, engineering, science…not just a ubiquitous post-human species.
I believe in the intelligent combination of modeling, adaptive calibration, simulation…with an intelligent identification of features. The storm of parallelism, bigger data and deeper ANNs alone will not be able to replicate complex real behavior.
We need to continue making knowledge computational and behavior quantifiable, but even more important, find new interaction paradigms between intelligent "Silicon" and us.
Innovations Supporting Career Steps
I've developed The Innovation Mesh as a diagnosis scheme assessing whether an innovation will work and sell. It adds emotional factors. I've introduced The Quant Innovation Spreadsheet and The Quant Innovation Mesh for the assessment of systems that require quantitative treatment and filled them in for UnRisk Capital Manager...with references to UnRisk Quant to explain the differences…especially through the lens of the actors. What makes the innovation thrilling is the dominant question.
But, with an augmentation mindset actors may come to see innovations as (sparring) partners in doing the difficult work and promoting their career.
In which possible steps?
Four UnRisk Ways to Augment Actor's Strength
Let me construct an "UnRisk" developed from the UnRisk Technology Stack: the UnRisk Financial Language, implemented in the Valuation-, VaR- and Exposure/xVA Engines, the UnRisk Data Framework, Interface Builders and Deployment Services. it's built as solution and development system in one, covering a wide range of deal types, portfolio and scenario instances…all practical details mapped…for usage from the front office to accounting.
What possible career steps can an actor, a financial expert, consider…driven by the values they can most likely add?
Climb - a strategy of, say, a front office expert...heading higher management grounds, avoiding the plumbing, to become capable of more big-picture thinking striving for better risk-informed deal decisions. For her, UnRisk will play the role of a digital risk quant and manager.
Dive In - a strategy of, say, a financial engineer structuring new deal types, funds…exploiting UnRisk's symbolic Financial Language and inherently parallel and platform-agnostic engines building new valuation systems and tools swiftly. For her, UnRisk acts as a library quant providing foundations that are comprehensive and bank proof.
Surf - a strategy of, say, a financial advisor…helping non experts to understand complex financial concepts. They may even head a position where they help their financial institution to find new offers for new clients and define competitive game rules…For them, UnRisk will act as a deal composer and simulator supporting smart user interfaces enabling simplicity but explanatory depth.
Leap Forward - a strategy of innovators, who want to build the next generation of quant finance products for their own markets. They will exploit UnRisk's Financial Language, Engines and Services, stretch and expand them in co-evolution with their product life cycles. They could even build UnRisk atop UnRisk.
A lot of brain work cannot be coded (machine emotions and machine thinking will be different)…actors need to decide in which direction they want to enhance smart systems.
If I once walk through the city with my digital aura attached…an information may pop up: "Herbert, hi, I'm your smart portfolio…I've engineered an instrument mix that fits perfectly to you risk appetite, the probability to win is…your log loss expectation is…my adaptive Kelly calculator recommends a revolving investment of x% of your wealth…with 97% confidence...".
It'll be still realized by preprogramming machines…when will machines learn to program themselves?
And we better don't think like machines to enable machines to think like us.
However, even if the path beyond automation goes to augmentation, there are big challenges waiting for the politics.
