If you work in information technology the chances are you have noticed regular articles in the media about artificial intelligence (AI), machine learning and deep learning.
Some commentators make no distinction between these terms and they often use them interchangeably. But to attribute the same meaning to these names is an oversimplification which is unhelpful to those looking for new ways to add value to their businesses. While AI, machine learning and deep learning are often intertwined, they hinge upon different technologies and have their own unique attributes.
Conversations about artificial intelligence sound futuristic and or even fanciful. This is because the topic has been surfacing periodically in the media for more than 60 years. Until recently though, we have been unable to provide the resources required to run sophisticated AI algorithms effectively. But thanks to advances in technology we are now able to process data at a faster – and more cost efficient – rate, sufficient to support AI programmes on a large scale. These can carry out specific tasks just as well as, or if not better than, humans can. Common examples of software that uses AI include programmes which can automatically index emails or sort your search for flights on a travel agent’s website.
The activities conducted by AI programmes described above are limited as they rely on lines of pre-determined code, meaning the same task can be repeated an infinite number of times, but can never be improved upon. While also relying on algorithms and codes, the premise behind machine learning is to crunch data, learn from it and make a decision. Instead of using one simple instruction, machine learning technologies gather data from a wide set of sources to learn and make a forecast based on that information. While the technology is exciting, its real-world use has been constrained because it relies on a series of predetermined flowchart-like algorithms, known as decision trees. Therefore, AI technology is not dynamic enough to process more than a few variables.
Deep learning is where things get interesting. It is a branch of machine learning whereby each successive layer of data captured uses the output from the previous layer as input, so that there is a continual ‘learning’ process. The potential is not just to gain greater insight into trends in areas such as consumer behaviour, but to truly transform business models by significantly reducing staff training time, removing the need for humans to work on mundane tasks, and accelerating the analysis of information, all while controlling costs.
Deep learning relies on big data analytics and digs through vast volumes of information from enterprise databases, files and emails as well as social media and consumer purchases, to recognise small and larger hidden trends, giving your business the power to exploit opportunities and create a competitive advantage. These and other related strategies save organisations millions, increase revenues and improve services and ultimately even the overall quality of life of consumers.
Deep learning incorporates the artificial intelligence and neural network movements from the past to yield systems that can harness information, multi-layered algorithms, and software to actually mimic human learning. These systems can teach themselves to, for example, understand spoken commands, sort through photos, recognise objects and faces, discover potential new drugs or a carry out a host of other breakthrough functions on their own, automatically.
In the past, the ability to run deep learning programmes was reserved for a select few companies who could afford it. Thanks to developments in technology and falling prices, today deep learning programmes are more available to enterprises but many have been slow to seize on the opportunity presented to them. Deep learning requires a high-performance IT infrastructure that is purpose-built to analyse, and – crucially – learn from, large data sets in real-time. In turn, the underlying storage for deep learning must have the agility to accommodate and process a variety of data types at speed. This is one of the reasons why software defined storage, thanks to the high degree of flexibility it offers, has jumped to the top of the wish list for most enterprises.
Today, countless organisations rely on deep learning to make decisions which ultimately underpin their business success. Thanks to recent innovations in IT, you can significantly accelerate the time-to-value of deep learning. Consequently, your business can gain a competitive advantage as it can operate more efficiently and at less expense.
Deep learning algorithms are exploding in the industry as organisations are under competitive pressure to support the increasing sophistication of data modelling and simulation to develop data as a basis for managerial or technical decision making. The rise of deep learning means that infrastructure administrators will carefully need plan to cope with the massive storage required to deliver effective deep learning. It requires content intensive storage which can store massive amounts of data very efficiently and can scale out horizontally as business demands.
In the future, larger organisations are likely to succeed only if they have adequately prepared themselves so that they can exploit the opportunities that deep learning can unlock in order to adapt their business models and practices to the ever-evolving market dynamics