For many years we’ve been helping clients with the application of Artificial Intelligence and Machine Learning. While it can appear complex and intimidating, it’s not so bad once you understand a few key concepts.
1. Getting the big picture — what is Artificial Intelligence?
Artificial Intelligence (AI) is a sector characterised by buzzwords and hype — so how do you cut through the noise? Think of AI as the superset — and everything else being a subset of it. Put another way, AI is the universe and things like Machine Learning, Neural Networks and Deep Learning are the solar systems that it’s made up of. Broken-down in more detail, AI and its main components stack up like the image to the right.
Another common misconception has to do with the difference between AI and automation - click here to read another article that clarifies the difference.
2. If AI is a hammer – not every business problem is a nail
Business media is abuzz with all things AI, and for many decision makers there’s a growing fear that if they’re not implementing some sort of AI solution they’re missing out. There’s no need to panic. From a business perspective AI solutions are just tools like any other — sometimes they’re the right one for the job and other times they’re not — and the fundamentals still apply. In order for an AI tool to be useful or a Machine Learning or Deep Learning project to be successful the following are required:
3: It’s much much faster than it used to be
What is new (and deserving of the hype) is that advances in technology mean that the speed at which AI solutions can be created, deployed, and start delivering value is increasing. When Datamine first started working in the AI space twenty years ago we could have spent days processing calculations in a computer model — something we can now do in milliseconds. Ultimately this means practical use of AI solutions for a wider range of problems and lower processing costs. But beware; speed isn’t better if you’re headed in the wrong direction. The key to successful deployment of AI is having people in your organisation that can set the business parameters.
The speed at which an application can be developed will be impacted by the toolset you use and how proprietary it is. Google’s TPU chip, for instance, has been designed specifically to work with Google’s Neural Network software TensorFlow (we’re fans), and Microsoft have also begun manufacturing their own chips for its virtual reality solutions. So whether internal, external, or a hybrid of both, choose an AI project team that chooses its tools wisely.
4. Know when to pull AI out of the bag
Some tasks are predisposed to an AI solution — recommendation engines, fraud detection, chat bots, and customer behaviour & prediction analysis — the list is wide ranging. What all of these use cases have in common is that they’re ‘messy’ problems. Their variables will change over time and new patterns will emerge. Fraud is a particularly good example, because you can count on criminals to come up with new and inventive ways to beat the system — an AI solution can detect these changing patterns long before a human investigator.
Another common use for AI (Deep Learning is very good at this) is ‘feature detection’ — where a computer learns to identify features — and can then recognise those features when it sees them again. That could be the spending
5. This ain’t DIY — experts will be required
If your business problem is right for AI you’re going to need a team with a diverse skill set. Yes AI projects are all about data, data science, and analytics — so your shortlist should include people with proven expertise in those fields, but don’t just leave them to it. You’ll also need people with the ability to set the business goals, translate ‘geek speak’ so that other stakeholders in the business can understand it, and most importantly, keep the team focused on how the information being generated can be applied to solving business problems.
AI solutions are proven and are being applied to an ever expanding universe of business problems. At the same time, things like Machine Learning and Deep Learning are still a long way from being off-the-shelf plug & play options — and getting expert advice from someone in the know is recommended.
Want to know more about applying AI in your business? Contact us today, or download the Datamine Guide to Predictive and AI Modelling below.