Artificial intelligence (AI) is starting to invade a lot of applications we use regularly from Apple’s Siri to Amazons Alexa and a few others. The algorithms are consistently improving and some interesting use cases in learning are appearing too. IBM’s Watson is probably one of the better know tools that power artificial intelligence.
Artificial Intelligence by definition is simply computers that do more than process information. By learning human behaviours, machines can act and respond in a humanlike manner. All those examples given previously in a typical morning routine, are already active processes using Artificial Intelligence that we see in our day to day lives.
We hear these words; Machine Learning, algorithms, Artificial Intelligence and Deep Learning, but what do they really mean? Algorithms are the reason Artificial Intelligence works. Algorithms can be easily defined by looking at them as a set of rules for solving a problem by following a sequence of actions in order. Think about those tired mornings where you’re getting ready for the office, and you try and put your shoes on before your work trousers (we’ve all been there). It just doesn’t work. This is the way that algorithms operate, and it is thanks to these that Artificial Intelligence exists.
If algorithms make up Artificial Intelligence, then Machine Listening is the next stage in the evolution process.
Machine Learning is the type of Artificial Intelligence that operates through the process of looking at patterns using algorithms. Machines can learn behaviour without the need for manual programming in this way. The easiest example to demonstrate Machine Learning in its purest form is by looking at your email inbox. Most email programmes now will automatically move specific emails into specific social, junk or spam folders without the need of being told.
Further progression and technology breakthroughs saw the growth of Deep Learning. Where Machine Learning predicts behaviour, Deep Learning, using advanced patterns and artificial neuron-like behaviour whilst learning the user’s patterns, can predict the future.
These are the processes behind interfaces such as Amazon, Netflix and YouTube and many other platforms that decide what you want to watch, buy or listen to next.
The easiest way to think of the relationship between Artificial Intelligence (AI), Machine Listening and Deep Learning is to visualise AI as the catch all term for algorithms mimicking human behaviour — the first and largest idea, then Machine Learning — developed later works mainly on data analysis and pattern recognition to make predictions, and finally Deep Learning — which is the driving factor behind today’s AI popularity in business, which creates a learning algorithm to attempt human decision making through data, patterns and their links.
In learning we have seen only a few initiatives, powered by artificial intelligence, yet a lot of people are thinking about this and working on it. The experiment carried out by artificial intelligence teacher Ashok Goel of Georgia Tech, wondered initially if students would detect if a teaching assistant was powered by artificial intelligence or not. In the Ted Talk about “Jill Watson”, which is the name given to their artificial teaching assistant, Ashok explains that the initial implementation had challenges, he also shares how they solved them.
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In my view artificial intelligence will allow for learning to become more personal. At the moment, we see most artificial intelligence applications similar to chat bots who provide answers, here and there people are looking at their application in recommendations for learning and some for analysis. My thinking is that we will see further expansion in the use cases and excitingly giving us the opportunity to have learning our way with our preferences taken into account from the outset. In games, we have had robots to play against for years and a lot of games have adaptive algorithms built in to let you have an enjoyable experience based on your skills. It is only a matter of time before we see this applied extensively in gamification for learning and learning technology.