Embarking on the journey of the fourth industrial revolution, we find ourselves at the precipice of a new era shaped by Artificial Intelligence. This primer serves as your compass, navigating the complex landscape of AI, its implications, and transformative potential.
What is AI? Why is it important? And why now?
While there is growing interest in AI, the field is understood mainly by specialists.
- This guide cuts through the hype and explains why – whether you’re a consumer or executive, entrepreneur or investor – this emerging trend will be important for us all.
Deep Learning: Offloading feature specification
Deep learning avoids the programmer having to undertake the tasks of feature specification and optimization
- The breakthrough in deep learning is to model the brain, not the world
- Artificial, software-based calculators that approximate the function of neurons in a brain are connected together
- They form a ‘neural network’ which receives an input; analyses it; makes a determination about it and is informed if its determination is correct
Why is AI important?
Since the 1950s, AI research has focused on five fields of enquiry: Reasoning, Knowledge, Planning, Communication, Perception, AI is valuable because in many contexts, progress in these capabilities offers revolutionary, rather than evolutionary, capabilities
- In the coming years, machine learning capabilities will be employed in almost all sectors in a wide variety of processes
Extensive data
Today, as we enter the ‘third wave’ of data, humanity produces 2.2 exabytes (2,300 million gigabytes) of data every day; 90% of all the world’s data has been created in the last 24 months
- Beyond increases in the availability of general data, specialist data resources have catalysed progress in machine learning
Artificial intelligence: The science of intelligent programs
Artificial Intelligence (AI) is a general term that refers to hardware or software that exhibits behavior which appears intelligent
- Basic AI has existed for decades via rules-based programs that deliver rudimentary displays of intelligence in specific contexts
- Progress has been limited because algorithms to tackle many real-world problems are too complex for people to program by hand
Cloud services
The development of machine learning is being catalysed by the provision of cloud-based machine learning infrastructure and services from the industry’s leading cloud providers
- Google, Amazon, Microsoft, and IBM all offer Cloud-based infrastructure to reduce the cost and difficulty of developing machine learning capabilities
Machine Learning: Offloading Optimization
The goal of machine learning is to develop a prediction engine for a particular use case
- Algorithms learn through training
- Quality of predictions improve with experience
- There are more than 15 approaches to machine learning
- Some of the most effective machine learning algorithms beyond deep learning include: ‘random forests’ that create multitudes of decision trees to optimize a prediction
- Bayesian networks
- Support vector machines that are fed categorised examples and create models to assign new inputs to one of the categories
Specialised hardware
Graphical Processing Units (GPUs) are specialised electronic circuits that are slashing the time required to train the neural networks used for deep learning
- Modern GPUs were originally developed in the late 1990s to accelerate 3D gaming and 3D development applications
- A simple GPU can offer a 5x improvement in training time for a neural network, while gains of 10x or much greater are possible on larger problems
Interest and entrepreneurship
The public’s interest in AI has increased six-fold in the last five years.
What happens next?
The benefits of machine learning will be numerous and significant.
- We expect a period of disillusionment regarding AI at some point in the future, to be followed by a longer and lasting recognition of its value as machine learning is used to improve and then reimagine existing systems.
How does deep learning work?
Deep learning involves using an artificial ‘neural network’ – a collection of ‘neurons’ (software-based calculators) connected together
- An artificial neuron has one or more inputs.
- It performs a mathematical calculation based on these to deliver an output
- The output will depend on both the ‘weights’ of each input and the configuration of ‘input-output function’ in the neuron
- A neural network is created when neurons are connected to one another; the output of one neuron becomes an input for another
Why is AI coming of age today?
The effectiveness of AI has been transformed in recent years due to the development of new algorithms, greater availability of data to inform them, better hardware to train them, and cloud-based services to catalyse their adoption among developers.
Improved Algorithms
Our ability to recognize objects within images has been transformed by the development of convolutional neural networks (CNNs).
- In 2015, Microsoft’s CNN-based computer vision system identified objects in pictures more accurately than humans.
- Broadly, applications of CNNs include video and speech recognition.
