ARTIFICIAL INTELLIGENCE: Cutting through the complexity of AI

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The 'AI' space is complex

Not only is it notoriously difficult to categorise (is AI Machine Learning? Deep Learning? Intelligent Automation? Orchestration?... All of the above?… none of the above? or some of the above?) the landscape of players claiming to do AI is growing at an exponential pace.

Analyst firm Venture Scanner publish the below infographic, presenting an overview of the 1,500+ AI Enterprise grade companies now in existence.


These enterprise grade providers are but the tip of the AI Iceberg.

Extending far deeper is the plethora of open-source, 'Garage' style developments happening all over the world.

According to MyBridge.Ai (A curator of public content) the topic of Machine Learning alone had more than 8,800 unique open source machine learning projects added to GitHub (the major code sharing platform) between January and December 2017. You can view the ranking and description of the top 30 Machine Learning projects here

These open-source projects range from text, face and object recognition, face recognition to speech and video manipulation. And is getting more complex each day.

The AI Index - which tracks the number of academic papers published, university/college enrolments, conferences, open source projects, AI Start-ups and related funding, job openings and robotic imports - has reported significant growth in all areas (up to 14x in some cases) over the past 12 months.

The number of Computer Science papers published and tagged with the keyword "Artificial Intelligence" in the Scopus database of academic papers.

The sheer pace at which 'AI' is developing presents a significant challenge for business leaders.

Disconnecting hype from practical reality

A significant number of technology providers will be (or are already) offering some form of AI as part of their toolset. 

However, without some form of framework to understand what AI is vs what is just good programming, there is potential to confuse the two and then quickly become disillusioned with the perceived value / impact of potentially game changing tools. 

A critical learning from our experience is to always start with clearly articulating the challenge you are trying to solve, rather than starting with the whizz bang shiny new toy. While detecting faces is both impressive and concerning, what business problem does it solve? How can the concept be applied in other forms? How will this solution scale from front to back office (and vice versa)? Why would we invest in solving this problem vs other problems in our area?

Appreciating that it is often difficult to see the forest for the trees when it comes to self-diagnosis, we have developed a simple, but effective framework for understanding the AI and Intelligent Automation landscape - and will be sharing elements from this framework over the coming months. 

We will also be presenting this framework at our AI Masterclass - find out more here


The AI Masterclass is run by myself and Andrew Burgess, Strategic Advisor at Symphony and Author of 'The Executive Guide to Artificial Intelligence' book.


If you'd like to follow along as we seek to cut through the complexity of AI:


About Symphony Labs

Symphony Labs aims to be the watchtower for our business and our clients, enabling both entities to identify interesting new solutions, ways of thinking and capabilities that will improve operations, enhance customer experience and drive real bottom-line impact.

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