Deep learning vs. machine learning: What’s the difference?
Soon enough, your digital assistant will be assigned with more than just playing your favourite song.
Artificial intelligence (AI) is increasingly finding ways to enhance simple, everyday tasks such as helping you pick a show on Netflix or suggesting a new outfit on Amazon.
This ability to discover what you enjoy or dislike from your interactions is due in no small part to the sophisticated technology behind it: machine learning and one of its approaches, deep learning.
The two sub-disciplines are allowing software engineers to adapt the platform for an evolving array of needs that extend far beyond what many thought was possible just a decade ago. In the process, consumers and companies alike are set to benefit greatly over the coming years.
Deep learning and machine learning push AI to new heights
Before diving into the potential that AI has to offer, it’s important to understand the basics of how they work. AI originated from the concept that software can mimic human behaviour and complete jobs with better efficiency than highly skilled personnel.
This manifests itself in two distinct ways: applied and generalised AI. The former represents systems that were designed for a specific function, like share trading or fraud detection in the financial industry. These are straightforward programmes that take in data and generate customised responses.
Generalised AI is much more broad and in turn, far less common. In theory, it can handle any task it’s given. Rather than teach the software the information it needs, why not give it access to the internet and allow it to learn on its own?
Machine learning is one approach to achieving generalised artificial intelligence. It has excelled primarily in image detection over recent years with the help of algorithms like reinforcement learning, XGBoost, Bayesian networks, linear regression and Support Vector Machines (SVMs). In the future, it’s expected to be a key asset in developing predictive technologies and platforms.
It’s best deployed in tandem with small data sets as classic machine learning algorithms can work without a comprehensive repository of information, but they often need to be fed with hand-designed features. Feature extraction is far from effortless, and the algorithms are also difficult to maintain and scale.
Deep learning is one method of implementing machine learning. It uses artificial neural networks that have a seemingly infinite number of layers to support complex algorithms and extremely large data sets.
It works by feeding massive amounts of information to the neural network, allowing it to assess the accuracy of the data relative to the task that’s being performed. Over time it ‘learns’ the correct answers, which are more or less based on a probability derived from the result of previous actions.
One of the historic drawbacks to deep learning was the sheer computing power it needed to run. With the recent advancement in GPUs, it has become a more viable form given the correct investments being made into the hardware that’s running it.
AI’s uncapped potential
Deep learning has the potential to lift machine learning to new heights. Its latest achievements – made possible through its neural networks – are often billed as a ‘superhuman performance’.
Take Google’s PlaNet, which is a deep learning machine that leverages long short-term memory (LSTM) architecture and convolutional neural networks to gain unparalleled accuracy in geolocation detection through images, according to Tobias Weyand and his team of computer vision specialists.
The platform was fed over 126 million images as it learned to identify the photograph’s location based on the pixel. Whereas humans can look at landmarks, structures and food to try and form an estimated guess as to where the snapshot came from, PlaNet uses pixels to classify the pictures based on a geographical grid it has developed.
PlaNet yielded a 50 percent improvement in accuracy over single-image models, the research team at Google found. When tested against people who have travelled all over the world, the neural network produced a median localisation error of 1131.7 kilometres, opposed to its counterpart’s 2320.75 kilometres.
PlaNet is just one example of how deep learning can help refine modern AI as it evolves. In the meantime, leaders are quickly trying to prepare for AI’s widespread acceptance. Roughly 95 percent of respondents to a Deloitte survey of EMEA corporations believe it will alter at least one component of their value chains. Around 37 percent expect that the whole industry they work in will see radical changes because of AI.
The financial sector is one that could find a higher degree of accuracy through the help of automation. Robo-advisers are already growing in popularity, though they have their flaws. Applying deep learning to the software can enable these robots/chatbots to take on a greater role in real-time market sentiment.
The moral of the story? AI is learning – the only question is ‘to what extent?’
The future is around the corner
As exciting as the potential for AI is, don’t overlook the fact that machine learning is already in use today. Companies all over the world use Big Data for eye-opening analysis and hidden insights.
Interested in learning more about the power of AI and Big Data? Our expert data scientists are able to demonstrate solutions fully customised to suit your business’ unique needs.