We’ve got to the stage where we take intelligent machines for granted. We trust them to carry out complex tasks requiring extreme precision, speed, and accuracy – from pacemakers to auto-pilots.
Machine learning algorithms are evolving in every industry – translating text, identifying faces in photographs, recognising handwriting, piloting drones, driving cars, and so on. And now, machine learning is moving into social, education, and medicine – diverse areas like predicting which participants will drop out of drug-trials and improving selection processes in recruitment.
In fact, machine learning supports every single aspect of decision making whether that’s strategic, tactical, financial, or another facet of our day-to-day lives.
Focusing on the facts
And speaking of improving the recruitment process, the National Bureau of Economic Research wanted to find out if experienced recruiters and hiring managers could be outperformed by machines. A study of 15 companies concluded that – for low-skilled service jobs – the algorithm’s selection of employees resulted in a retention rate that was 15 percent higher than the human recruiters’ rate.
Where there are clear success criteria such as employee tenure, utilisation, and performance, the case for using machine learning is proven. The problem with recruitment professionals is that they select candidates who are similar to themselves; people they feel they’d get along with socially. The machine has no such bias and focuses exclusively on the hard targets and objectives.
A fresh look at industry
In addition to helping with traditional problems like recruitment, machine learning is enabling new business models for old industries – manufacturing cars, for example. Sensor technology now provides ultimate control over every aspect of the vehicle and its environment, collecting millions of sensor readings that are digested continuously. This is more than any human could ever do. And as vehicles become more sophisticated (navigating complex environments, predicting maintenance issues, etc.) completely autonomous driving moves a step nearer.
It’s a prime example of machines learning from traffic patterns, sensor readings, and road conditions, to get smarter, faster.
So the question is, augment or replace?
With machines in play, we might reasonably conclude that human blind spots, our biases and subjective judgements, will soon be eliminated from the decision-making process. In reality, big data contains the self-same blind spots and biases that impair human judgement because we introduce our own biased data into the algorithms we design.
So, it’s less a case of man versus machine. It’s more like a partnership; man and machine. As we get a better handle on the world around us (understanding more about the business domain), we have a better chance of correcting any inadvertent biases.
The importance of the partnership is demonstrated by the fact that machine learning centres on its ability to learn from human inputs (to train models, predict outcomes and make decisions). Crucially, machines can’t ask the most important question: “Why?”. But, as interfaces to the machine, we can.
And it’s our responsibility to determine what data best serves the machine – how to cleanse and prepare that data, ensuring it represents the past while remaining relevant to the future. Consequently, machines will not eliminate human intervention in decision making. Machines will simply change the nature and timing of our intervention.
Arguably, a machine self-learns so that the algorithms can predict accurately. However, final decisions have to be taken by living, breathing, empathetic entities. On their own, analytics cannot deliver the fine-tuned decisions we crave. Human context – the ability to weigh-up the full implications of a decision – is an integral part of the process. Clearly, man and machine have to work together.
Action and reaction
In the future (with increasing dependence on machine learning), employees having adequate training to become machine interfaces will be our greatest business challenge. Without this kind of interface, machine intelligence will be lost. So, in spite of the fact that machines will be creating the intelligence, humans will still be responsible for actions taken and outcomes delivered.
We will be the orchestrators. Machines will do the heavy lifting – processing vast amounts of data to help us make intuitive and well-informed decisions, while allowing us the time and space to be more creative; more innovative.
It’s the perfect environment for developing new business models, products, and services – activities better suited to the human mind.
Yasmeen Ahmad – Business Analytics Leader and Partner Practice at Teradata
Over the years, I have worked with many businesses to identify challenges and understand business context, providing an analytical perspective for achieving solutions. This has meant using new, or untapped, sources of data coupled with innovative analytics to enhance competitiveness. Techniques have included event-based analytics, predictive modelling, natural language processing, time-series analysis, and attribution strategy development. My work takes me to all parts of the globe, covering a wide range of industries including finance, retail, utilities, and telecommunications. I speak regularly at international conferences and events. After an undergraduate in computing, I took a tangent into the world of Life Sciences, completing my PhD in Data Management, Mining, and Visualisation, at the Wellcome Trust Centre for Gene Regulation and Expression. Following this, I worked as a data scientist, building analytical pipelines for complex, multi-dimensional data types. Along the way, I’ve provided leadership, training, and guidance, for many analytical teams, creating actionable insights and business outcomes through the development of analytical use cases. I have also lectured on Masters programmes for BI and Data Science. I grew up in Scotland, and love the great outdoors. Consequently, I’m an avid hiker (the Scottish Munros are perfect) and sea kayaker. Oh yes, and a keen traveller, too.