As I write this, our planet apparently faces a dilemma, looking forward either to a new golden age of machine-assisted prosperity - or being mere months away from a machine-enabled apocalypse, as super-intelligent machines carelessly wipe their unworthy human creators from the face of the Earth.I refer of course to the technology subject currently on everyone's lips - not to mention countless, breathless press releases - Artificial Intelligence (AI). I should probably say something right up front. I'm an old AI hacker going right back to the 80s (pretty much the last time AI was popular), and designing and building intelligent systems was both my first ever job, and has been my first and enduring technology love affair - and yes, I wish there was a way to write that sounds slightly less disturbing.
On a slightly more serious note, a long association with AI does provide a sense of perspective - I think we're finally arriving at a point where AI can deliver some serious value to media businesses, amongst others. I want to talk about where some of this value is going to come from, and how we get there. But first - there are a ton of assumptions about what people understand by AI , so I'd like to go back to basics a little, and provide a view on what Artificial Intelligence is and how it works and as part of that, think briefly about some of the organisation and technical challenges that need to be thought through to make exploiting AI a success.
What is AI? Despite the hype surrounding AI as the new hotness, AI is actually one of the oldest disciplines in computing - scientists and researchers have been fascinated by the potential of machines that can think , right back to the days of Alan Turing. Slightly formally, Artificial Intelligence is a collective name for computer systems that simulate human-level cognitive processes i.e. they mimic the human brain by reasoning, learning, judging, predicting, inferring and initiating action.
More usually, we talk about AI by giving examples of systems that seem intelligent in some way. These can be very complex tasks such as recognising speech or driving cars. They can also be simpler seeming or more mundane tasks such as automatically clipping up the important bits of a football match - or recommending video clips that you might be interested in watching. This list starts to suggest the breadth of possible areas in which AI can be called upon to support businesses - whilst at the same time giving us some food for thought - one of the reasons that we use examples so readily is because we find it so difficult to capture and define what intelligence is. If we find it so hard to define, how can we build technology to emulate it?
In common with many areas of study and technology, AI comes in lots of shapes and sizes. You'll hear about strong and weak AI, symbolic and connectionist approaches, many different varieties of deep learning - there are thousands of books, Medium articles and blogs covering AI, and we can only think about what is effectively the view from orbit in this piece. Nonetheless, I'd like to plant the seed of a couple of important approaches.
Firstly, let's consider what is probably considered a traditional approach to AI, where we build systems (knowledge-based systems) that try and emulate the way that humans think and reason. This usually involves quite a complicated process of human analysts (once upon a time referred to as Knowledge Engineers) working with experts to try and understand how they go about solving particular problems in quite constrained domains, and then codifying that knowledge into a general set of rules that can be applied to get to a solution when you start from a given set of facts. Simple example of this:
IF UK_Broadcast_Customer THEN RequiredDeliveryPackage = DPP IF US_Studio_Customer THEN RequiredDeliveryPackage = IMF So, given we assert that UKTV is a UK_Broadcast_Customer - if we then ask the system what RequiredDeliveryPackage we require for UKTV - the answer comes back as DPP , and we can start to follow the rules that deal with actions for DPP packages. It's a ridiculously trivial example, but it shows simply that we can build systems that can reason, generate new knowledge and take specific actions, based on general rules. That's a powerful concept, especially when you think about the amount of time spent across the industry coding up workflows to do repetitive tasks with similar outcomes. Obvious problems with this approach is that it can be very time consuming to build and maintain knowledge-bases, and they can only infer and reason about stuff they know about - intuition and leaps of deduction that humans make all the time are completely out of reach. But it's a proven approach and still a valid one.
Our second glance is at a different class of techniques, involving constructs called neural networks, which again have been around in theory since about 1968. These are often referred to as machine learning techniques (beware there are lots more ML techniques than just neural nets), or commonly these days deep learning techniques. This style of AI is essentially based on trying to emulate some of the physical processes in the human brain. A neural network consists of a set of inputs, a collection of internal (hidden layer) nodes connected together (with each connection having different weighting), and a set of outputs, as shown in the example diagram below.
Depending on the weighting of the connections in the hidden layer, each combination of inputs will result in a defined output. Sounds very simple - but the beauty of the neural network is that you don't need to set the weights either manually or through some kind of programming - instead the net can be set up to learn the correct weightings through example. Putting the net
