This is an article I had originally written as part of a stream of work that has now been put on hold indefinitely. I thought it a shame for it to languish in OneNote.
What’s with all this attention to Artificial Intelligence then?
Well that is a very good question. To be perfectly frank, not that much has changed of late in the world of Artificial Intelligence (AI) as a whole that should justify all the current excitement. That’s not to say that there isn’t cool stuff going on; there really is great progress being made… in the world of Machine Learning. And if we are to begin the process of ‘Demystifying AI’ then this is a very good place to start.
AI is a very broad area of technology encompassing research from robotics to computing emotions (affective computing) and everything in between, including Machine Learning or ‘ML’. As alluded to just a moment ago it is within ML specifically that we are seeing the greatest progress. Think of a modern ‘AI’ technology that is gaining a lot of attention and you can place a safe bet on it specifically using ML techniques: Natural Language Processing? That’s ML. Image classification? That’s ML. Sentiment Analysis? Also ML. The recent news of Go players being defeated by a computer? You guessed it… ML.
What is ML?
ML is an approach to analysing data that is based on training statistical models to predict outcomes. You may well have come across Statistical (or Linear) Regression back in your school days; well this is possibly the best known example from a range of techniques that make up the world of ML. To put it simply, an ML model learns from past data to make better decisions in the future.
Now it’s time to introduce what is arguably the beating heart of the AI frenzy: Deep Learning. While there are no trendy acronyms for Deep Learning it is fair to say that Deep Learning has become a bit of a buzz-word itself. Deep Learning takes its name from the concept of Deep Neural Networks (or DNNs, there’s your acronym!). The useful details of what DNNs are and how they function cannot be easily summarized, suffice it to say that DNNs are an ML technology that borrows heavily from the structure of the brain, hence the ‘neural’ part of the name. [N.B. These details are already planned for a follow-up piece.] To re-cap: Deep Learning is a subset of ML, which is in-turn a subset of AI and it is Deep Learning that drives the current hype.
What is Deep Learning?
Say you wanted to build some software to identify objects in an image; your usual non-Deep-Learning approach to this would include manually writing rules into the software to recognise the details you’re looking for. If you wanted to identify if a picture was of a bird or a cat, you would manually write rules to identify features such as whiskers or ears or wings and so on. This is complicated, time-consuming and error prone. Deep Learning takes a different approach. Instead you would create a Deep Learning model then supply it with a bunch of pictures. For each picture you supply, you would tell the model if it was of a bird or a cat. As you supply each image and it’s corresponding label, the model learns. Once enough data has been supplied you can then supply an image without a label and the model will give an accurate indication of whether it is a bird or a cat.
So what is the ‘explosion’ all about?
Continuing the bird/cat model example, the more example labelled pictures you supply to the model, the better the results will be. This seems simple and even somewhat obvious but it strikes at the heart of the current ‘AI boom’. Deep Learning has been around for a while now, evolving over a period of 30 years more or less, and one of the key reasons that it has never been so commercially successful as now is that there just hasn’t been enough readily available data to make it so accessible. To give you some idea of why this has been an issue, if you want to get to a high level of accuracy for classifying complex pictures then you’re going to need thousands or even millions of examples depending on the complexity. Well we now have data, lots and lots and lots of it and it has never been more easy to get our hands on it. Do a quick Google image search for ‘Cat’; there is a rough cut of half your ‘training’ set (*ahem* copyright issues aside) and I’m sure you can figure out how to get the other half.
So we have data, but that isn’t all we need. The other side of the current explosion is raw computing power. Building a statistical model that can accurately identify cats and birds in pictures is very heavy work for a computer but thankfully with the advent of cloud-scale computing resources, available computing power is now big enough and cheap enough to make running this sort of model both practical and cost-effective. It’s cheap enough that Google can even give this stuff away as an educational toy (https://teachablemachine.withgoogle.com/).
So its all about pictures of cats and birds?
Beyond the abundance of data and computing power, probably the most significant factor in the commercial success of Deep Learning is its versatility. This is especially true when considering the success of Deep Learning against other ML techniques which have not gained the same level of attention. If you have enough data, regardless of its form, Deep Learning can be trained to extract knowledge from it. This has sent businesses, scientists and engineers into a global flurry of R&D to find all the amazing ways in which this technology can enhance our lives.
For years now the financial services industry has been at the forefront of applying ML techniques to everything from fraud prevention and risk management to investments and savings predictions; there are few – if any – areas of the industry that have yet to see the benefits of AI.
Manufacturing is seeing growing uptake in the application of ML to improve efficiency through waste reduction and better predictive analysis of production demands and infrastructure maintenance.
More recently utilities are beginning to get into the ML game with the UK National Grid striking up discussions with Google to investigate applying the infamous DeepMind AI to maximise National Grid’s use of renewables and to more efficiently balance supply and demand across its nationwide infrastructure.
Across all sectors business now find themselves in a position to use ML to better understand and engage with their customers. From utilities gaining greater knowledge of their customer’s consumption habits through to retailers and service providers more effectively capturing sales conversion opportunities, the possibilities are as varied as your data.
Would you like some knowledge with that?
So that concludes this effort to clear away some of the fog and hyperbole from the current AI phenomenon (ahem! It’s all ML, remember!?). In a nutshell, if you have a ton of data and you need to get knowledge from it then Deep Learning could well be your go-to tool.
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