AI is all the rage, but do businesses truly understand it? That is the question. As popular culture reduces this sophisticated and complex set of technologies to the human form, in all its Hollywood glory, it does businesses no favours with its warped perception. When these references mix in with business potential they generate both fear and excitement. Observers ponder the hype – is it real, or is it not? Meanwhile, the Channel supports customers’ demands. They want AI and they want it now. But to truly help customers transform their businesses with valuable AI applications, we need to do a little myth busting first.
At the heart of the AI buzz sit some rather juicy figures. The global market value is forecast to hit $169.41 billion by 2025 – that’s up from $4.06 billion in 2016. The interest and desire for business innovation through AI applications is real. With the number of enterprises implementing some form of AI technology increasing by 270 per cent between 2015 and 2019, it’s fair to say that awareness is strong. But the term ‘implementing AI technology’ is quite general and could mean a number of things. To understand the true scale of adoption we need to go back to the basics, first.
Getting real with the AI basics
As we know, Artificial Intelligence refers to a vein of computer science that deals with algorithms and is inspired by natural intelligence. It encompasses a range of tasks that might normally require natural or human intelligence, for example problem solving, translation, speech recognition and visual perception. We are already seeing the impact of AI across industries whether it’s sourcing a new antibiotic, providing business insights or assisting us in our daily routines through our mobile phones. It does not take the human form and is far from reaching the level of complexity needed to out-pace human intelligence – contrary to popular beliefs lifted from sci-fi classics.
AI can be separated into two categories. The first is most recognisable from sci-fi legends: Artificial General Intelligence (AGI), which is the hypothetical intelligence of a machine that has the capacity to understand or learn any intellectual task that a human being can – cognitive systems. Then, there is Artificial Narrow Intelligence (ANI) which refers to specific aspects of human intelligence and perception like recognition of faces or voices. This is the type of AI we are seeing in action today. Lift the hood on AI and we will find the mechanics that give it meaning.
Machine Learning, on the other hand, is a form of AI that uses algorithms to learn from data. Rather than being explicitly programmed, these algorithms build a model based on input and in turn uses the resulting insights to make decisions or predictions. This is the kind of mechanism is used to recommend your next Netflix series, detect spam or credit card fraud. These machine learning models can be developed quickly and relatively effortlessly. However, it takes time to convey precise results while ‘learning’ – but if the data sets change, it will re-train. Machine learning models are only as good as the defining properties applied.
Finally, to refresh the concept, Deep Learning is a type of machine learning that uses layers of neural networks to allow algorithms more freedom – there are no rules. Mapping inputs to classifications more accurately via layers of abstraction is reminiscent of how the human brain functions. Deep Learning defines its own criteria – it does not lean on predefined features or characteristics like machine learning – and learns if it was right or wrong based on its own exploration. Progress in this arena is fuelling the leaps and bounds made in the development of computer vision and speech recognition – but it requires an incredible amount of data and compute power to sustain.
Supporting channel businesses on their AI journey
Today, these technologies can be found in motion all around us through the automation of everything from targeted advertising to smart home devices. Deep learning is creating better futures in healthcare and machine learning is providing invaluable business insights. AI is not any one thing. With the exponential growth in data the business opportunity is ripe, to garner valuable insights and create new, innovative products and services. But one size does not fit all, not all data deserves this treatment – and most importantly of all, AI is only as good as the data it’s fed. So, businesses must get their data in order first and then get to grips with the AI tools available to them.
Technological innovation means now is the time for AI to start realising its potential, with the promise of 5G to truly turbocharge its development. But cultural attitudes towards AI, along with a skills gap mean that businesses need help in first understanding its value – and when it is truly a business need.
The Channel has a role to play, in supporting customers in their journey towards automation. In order to grab the AI opportunity, Channel partners need to develop their expertise around AI in so they can lead the way. Honing this understanding and really embracing the complexities of AI, ML and DL is the first step towards becoming a trusted advisor for customers. Supporting customers in developing an AI strategy that is tailored to their business needs is crucial to realising the full benefits of AI technologies – and stepping beyond they hype, to real-world, integrated applications.
Helping businesses understand how to capture the value of their data, applying AI mechanisms where there is genuine need will reap the highest rewards. But to understand how to get the most of out of it, Channel partners must first understand AI.