Re-thinking Enterprise business processes using Augmented Intelligence

 

In the 1990s, there was a popular book called Re-engineering the Corporation. Looking back now, Re-engineering certainly has had a mixed success – but it did have an impact over the last two decades. ERP deployments led by SAP and others were a direct result of the Business Process re-engineering phenomenon.

So, now, with the rise of AI: Could we think of a new form of Re-engineering the Corporation – using Artificial Intelligence? The current group of Robotic process automation companies focus on the UI layer. We could extend this far deeper into the Enterprise. Leaving aside the discussion of  the impact of AI on jobs, this could lead to augmented intelligence at the process level for employees (and hence an opportunity for people to transition their careers in the age of AI).

Here are some initial thoughts. I am exploring these ideas in more detail. This work is also a part of an AI lab we are launching in London and Berlin in partnership with UPM and Nvidia both for Enterprises and Cities

Re-thinking Enterprise business processes using Augmented Intelligence

How would you rethink Enterprise business processes using Augmented Intelligence?

To put the basics into perspective: we consider a very ‘grassroots’ meaning of AI. AI is based on Deep Learning. Deep Learning involves automatic feature detection using the data.  You could model a range of Data types (or combination thereof) using AI:

a)      Images and sound – Convolutional neural networks

b)      Transactional – ex Loan approval

c)       Sequences: including handwriting recognition via LSTMs and recurrent neural networks

d)      Text processing – ex natural language detection

e)      Behaviour understanding – via Reinforcement learning

To extend this idea to Process engineering for Enterprises and Cities, we need to

a)      Understand existing business processes

b)      Break the process down into its components

c)       Model the process using Data and Algorithms (both Deep Learning and Machine Learning)

d)      Improve the efficiency of the process by complementing the human activity with AI(Augmented intelligence)

But this just the first step: You would have to consider the wider impact of AI itself

So, here is my list / ‘stack’:

  • New processes due to disruption at the industry level (ex Uber)
  • Change of behaviour due to new processes( ex: employees collaborating with Robots as peers)
  • Improvements in current Business Processes for Enterprises: Customer services, Supply chain, Finance, Human resources, Project management, Corporate reporting, Sales and Logistics, Management
  • The GPU enabled enterprise  ex Nvidia Grid but more broadly GPUs Will Democratize Delivery of Modern Apps, More Efficient Hybridization of Workflows, Unify Compute and Graphics
  • The availability of bodies of labelled data
  • New forms of Communications: Text analytics, Natural language processing, Speech recognition, chatbots

I am exploring these ideas in more as part of my work on the Enterprise AI lab we are launching in London and Berlin in partnership with UPM and Nvidia both for Enterprises and Cities. Welcome your comments at ajit.jaokar at futuretext.com or @ajitjaokar

 

 

Young Data Scientist – more about forthcoming book/kickstarter

The role of a data scientist is one of the hottest jobs in the industry today – But how do we inspire the next generation of data scientists?

I have been working on the idea of Young Data Scientist for a years now – with various iterations and pivots

Its now ready to launch the next version as a book / kickstarter

The easiest way to inspire the next generation of Data Scientists is to go back to the basics – i.e. the Maths because Maths is the Universal language that underpins progress and innovation

This also aligns closely with my day job and my teaching – Data Science for Internet of Things at Oxford University

It allows me also to create the book /kickstarter using personal insights from my years of teaching

So, if you consider the maths foundations needed to learn Data Science, you could divide them into four key areas

Linear Algebra

Probability Theory and Statistics

Multidimensional Calculus

Optimization

All of these are taught(in some shape or form) in high schools(13 to 17 years)

So, the book aims to build upon these foundations for a high school audience to inspire them to take up Data Science

The challenge here is to simplify and co-relate to existing maths knowledge considering the audience(13 to 17 year olds)

and most importantly to inspire!

It also would take them on a path to be Artificial Intelligence(AI) aware

Young Data Scientist will be a book, a kickstarter, a community

It will have Open source foundations

Young Data Scientist community will also work with teachers

And finally, the Young Data Scientist community will draw upon interesting examples in Space exploration, Genomics, Ecology etc

Coding will be in Python (including numpy and tensorflow sometimes)

Please email me at ajit.jaokar at futuretext.com