What is Artificial Intelligence and Why it Matters
This article provides a general overview on what Artificial Intelligence (AI) is and its subcategories: Machine Learning and Deep Learning. It also discusses why AI matters and where it is going.
By Dr Andrea Olguin
What is AI
Artificial intelligence (AI) is disrupting industries across the globe. In the 1955, Marvin Minsky (mathematician and cognitive scientist) and John McCarthy (computer scientist and cognitive scientist) described AI as “any task performed by a programme or machine that, if humans carried out the same activity, we would say that humans had to apply intelligence to complete the task.” In other words, artificial intelligence covers everything that allows computers to act like humans. The emergence of AI has played an important role in the Fourth Industrial Revolution. AI is a set of concepts and technologies that means different things, depending on who you are talking to.
Machine Learning is a subset of AI and it deals with extracting patterns from data sets. The theory behind it is that computers are able to learn without being programmed to carry out specific tasks. Machine learning is iterative, meaning that as models are exposed to new data or information, they are capable of independently adapting and learn to produce reliable results. Even though several machine learning algorithms have been around for quite a while, their ability to adapt to big data is a new development.
Deep Learning is a specific class of machine learning algorithms that use complex neural networks. The building block of the brain is the neuron, while the basic building block of an artificial neural network is a perceptron that accomplishes signal processing. Perceptrons are then connected into a large mesh network. The neural network is taught how to perform a task by having it process and analyze examples, which have been previously labeled. For example, in an object recognition task, the neural network is presented with a large number of objects of a certain type (i.e. a dog, a car). The neural network learns to categorize new images by having been “trained” on recurring patterns. This approach combines advances in computing power and neural networks to learn complex patterns in large amounts of data.
Types of learning:
- Supervised – feeding machine labelled data
- Unsupervised – Learning with no training data or examples i.e. clustering and dimensionality reduction
- Reinforcement – mimics the process of training animals through punishments and rewards
Why it matters
Companies that don’t adop machine learning and AI technologies are destined to be left behind. Most industries are already being changed by the emergence of AI. Companies that adopt the “data-driven” mentality are growing at an average of more than 30% annually and are on track to earn $1.8 trillion by 2021 (Forrester Report, 2018). Most industries work with vasts amounts of data and are on their way to incorporating machine learning technology in order to work more efficiently and gain an advantage over their competitors. Some of the industries that are using such methods include Financial Services, Health Care, Retail, Oil and Gas, and Transportation, to name a few.
“Humans can typically create one or two good models a week; machine learning can create thousands of models a week.”
Thomas H. Davenport, Analytics thought leader – excerpt from The Wall Street Journal
Where it’s going
2019 has shown a growing confidence in AI and its predictive technology. However, for it to achieve its full potential, AI needs to be trusted by enterprises. It is important for them to understand what it is doing with their data and how decisions are made. What makes AI particularly one of a kind is its capability to see patterns and make inferences which are not obvious and are even counter-intuitive to the human eye. It is clear that there will be an increase in businesses using their data to generate new revenue streams. More enterprises will adopt this data-driven strategy and will come to understand the value of the information they already own.
These are some of the industries that are bound to be revolutionised by AI:
Deep Learning and Intellegens
Intellegens was founded with a vision to accelerate innovation and help businesses reach their goals faster. Most industries are adopting a “data-driven” approach – however, this comes with some caveats. Experimental real-world data is not always clean or complete – enter Intellegens. We have developed a set of deep neural networks that specialise in sparse and noisy data. Our first commercial product Alchemite™ can see correlations between all available parameters, both inputs and outputs, in fragmented, unstructured, corrupt or even noisy datasets. The result ismodels that can predict missing values, find errors and optimise target properties. Capable of working with data that is as little as 0.05% complete, Alchemite™ can unravel data problems that are not accessible to traditional deep learning approaches. Suitable for deployment across any kind of numeric dataset, Alchemite™ is delivering ground breaking solutions in drug discovery, advanced materials, patient analytics and predictive maintenance – enabling organisations to break through data analysis bottlenecks, reduce the amount of time and money spent on research, and support better, faster decision-making.