The emergence of Artificial Intelligence (AI) is there for everyone to see, people have differing opinions on what it means for the future but there are a lot of misconceptions and there is still a lot of education to be undertaken before we fully understand how to implement AI and how it can be leveraged for our benefit.
There are two key elements to AI and we are guessing that you've heard both these terms before – Machine learning and Deep learning. These terms are often banded around in ways that can make them seem like buzzwords, hence why it's important to understand the differences.
Examples of machine learning and deep learning are everywhere but you may not have recognised them. It's how Netflix knows which shows to recommend you watch and how Google knows what images to show you when you search for pictures of Dogs (Computer Vision).
“Algorithms that analyse data, learn from that data, and then apply what they've learned to make informed decisions”
An easy example of a machine learning algorithm is an on-demand music streaming service and for the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener's preferences with other listeners who have similar musical taste. Amazon utilises this particularly well to recommend additional purchases (customers also bought) and have been for years it's just now people are learning about the technology that powers that engine.
Machine learning fuels all sorts of automated tasks and spans across multiple industries, from data security firms hunting down malware to finance professionals looking out for the best trades at any given time. They're designed to work like virtual personal assistants, and they work quite well. People often talk about RPA (Robot Process Automation) for a similar function but we'll address that in a separate blog as the two methods are intrinsically different.
Machine learning is a lot of complex math and coding that serves a mechanical function the same way a camera, fridge, or a television does. When something is capable of “machine learning”, it means it's performing a function with the data provided to it, and it gets progressively better at that function the longer its training.
Now, the way machines can learn new tricks gets really interesting when we start talking about deep learning .
In practical terms, deep learning is just a subset of machine learning and it functions in a similar way, but its capabilities are different and depending on what you are trying to achieve you'll need to make a decision on whether Machine Learning or Deep Learning should be applied.
Basic machine learning models become progressively better at whatever their function is, but they still need some guidance. If an Machine Learning algorithm returns an inaccurate prediction, then a Data Scientist needs to step in and make adjustments. But with a deep learning model, the algorithms can determine on their own if a prediction is accurate or not, this is usually because Deep Learning is run on much larger data sets and therefore has more data to analyse and the way the algorithms are structured.
A deep learning model is designed to continually analyse data with a logical structure similar to how a human would draw conclusions. To achieve this, deep learning uses a layered structure of algorithms called an artificial neural network (ANN) . The design of an ANN is inspired by the biological neural network of the human brain. This makes for machine intelligence that's far more capable than that of standard machine learning models.
It's a tricky prospect to ensure that a deep learning model doesn't draw incorrect conclusions, but when it works as it's intended to, functional deep learning is a scientific marvel and the potential bedrock of true artificial intelligence.
A great example of deep learning is how NVIDIA used Deep learning used to predict disruptions in a tokamak fusion reactor, paving the way to clean energy. Princeton University were able to leverage NVIDIA's Deep Learning to predict and steer fusion reactions to avoid disruption and safely continue to produce power. Researchers at Princeton University have developed the advanced machine learning Fusion Recurrent Neural Network (FRNN) predictive code. FRNN uses deep learning methods and has successfully scaled to 200 NVIDIA® Pascal™ P100 GPUs to predict the onset of highly deleterious disruption events under reactor-relevant conditions in magnetically-confined fusion tokamak devices.
Hopefully we've been able to demystify the differences between Machine Learning & Deep Learning but just in you're still unsure, here's a quick recap:
Strategy is a word that is so often over used and when used it rarely has any real meaning.
Successful people do what unsuccessful people are not willing to do. Don’t wish it were easier; wish you were better.
Think about it. You are set in the Bahamas, with your smartphone. You hit 2 buttons, you now have a supercomputer within your phone, with fastest internet in the world. The possibilites are endless.