Understanding what AI can do using the AI framework
By Andrew Burgess, Visiting Senior Fellow in AI and RPA, Loughborough University and author of 'The executive guide to artificial intelligence'
My Artificial Intelligence Framework was developed over the past few years through a need to be able to make sense of the plethora of information, misinformation and marketing-speak that is written and talked about in AI. I am not a computer coder or an AI developer, so I needed to put the world of AI into a language that business people like myself could understand. I was continually frustrated by the laziness in the use of quite specific terminology in articles that were actually meant to help explain AI, and which only made people more confused than they were before. Terms like Artificial Intelligence, Cognitive Automation and Machine Learning were being used interchangeably, despite them being quite different things.
Through my work as a management consultant creating automation strategies for businesses, through reading many articles on the subject, and speaking to other practitioners and experts, I managed to boil all the available information down into eight core capabilities for AI: Image Recognition, Speech Recognition, Search, Clustering, Natural Language Understanding (NLU), Optimisation, Prediction and Understanding. In theory, any AI application can be associated with one or more of these capabilities.
The first four of these are all to do with capturing information - getting structured data out of unstructured, or big, data. These Capture categories are the most mature today. There are many examples of each of these in use today: we encounter Speech Recognition when we call up automated response lines; we have Image Recognition automatically categorising our photographs; we have a Search capability read and categorise the emails we send complaining about our train being late; and we are categorised into like-minded groups every time we buy something from an online retailer. AI efficiently captures all this unstructured and big data that we give it and turns it into something useful (or intrusive, depending on your point of view, but that's a different topic altogether).
The second group of NLU, Optimisation and Prediction are all trying to work out, usually using that useful information that has just been captured, what is happening. They are slightly less mature but all still have applications in our daily lives. NLU turns that speech recognition data into something useful - i.e. what do all those individual words actually mean when they are put together in a sentence? The Optimisation capability (which includes problem solving and planning as core elements) covers a wide range of uses, including working out what the best route is between your home and the shops. And then the Prediction capability tries to work out what will happen next - if we bought that book on early Japanese cinema then we are likely to want to buy this book on Akira Kurosawa.
Once we get to Understanding, it's a different picture all together. Understanding why something is happening really requires cognition; it requires many inputs, the ability to draw on many experiences, and to conceptualise these into models that can be applied to different scenarios and uses, which is something that the human brain is extremely good at, but AI, to date, simply can't do. All of the previous examples of AI capabilities have been very specific (these are usually termed Narrow AI) but Understanding requires general artificial intelligence, and this simply doesn't exist yet outside of our brains. Artificial General Intelligence, as it is known, is the holy grail of AI researchers but it is still very theoretical at this stage.
You will already be starting to realise from some of the examples I have given already that when AI is used in business it is usually implemented as a combination of these individual capabilities strung together. Once the individual capabilities are understood, they can be combined to create meaningful solutions to business problems and challenges. For example, I could ring up a bank to ask for a loan: I could end up speaking to a machine rather than a human, in which case AI will first be turning my voice into individual words (Speech Recognition), working out what it is I want (NLU), deciding whether I can get the loan (Optimisation), and then asking me whether I wanted to know more about car insurance because people like me tend to need loans to buy cars (Clustering and Prediction). That's a fairly involved process that draws on key AI capabilities, and one that doesn't have to involve a human being at all. The customer gets great service (the service is available day and night, the phone is answered straight away and they get an immediate response to their query), the process is efficient and effective for the business (operating costs are low, the decision making is consistent) and revenue is potentially increased (cross-selling additional products). So, the combining of the individual capabilities will be key to extracting the maximum value from AI.
The AI Framework therefore gives us a foundation to help understand what AI can do (and to cut through that marketing hype), but also to help us apply it to real business challenges. With this knowledge, we will be able to answer questions such as: How will AI help me enhance customer service? How will it make my business processes more efficient? And, how will it help me make better decisions? All of these are valid questions that AI can help answer, and ones that I explore in detail in the course of my book.
About the Author: Andrew has worked as an advisor to C-level executives in Technology and Sourcing for the past 25 years. He is considered a thought-leader and practitioner in Artificial Intelligence and Robotic Process Automation, and is regularly invited to speak at conferences on the subject. He is a strategic advisor to a number of ambitious companies in the field of disruptive technologies. He has written two books; ‘The Executive Guide to Artificial Intelligence (Palgrave MacMillan, 2018) and, with the London School of Economics, ‘The Rise of Legal Services Outsourcing’ (Bloomsbury, 2014). He is Visiting Senior Fellow in AI and RPA at Loughborough University and was recently awarded ‘Automation Champion of the Year’ by the Global Sourcing Association. He is a prolific writer on the ‘future of work’ both in his popular weekly newsletter and in industry magazines and blogs.
You can find Andrew on Twitter @A_J_Burgess, or visit his website.