Machine Networks – Competitive Strength In Numbers

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Personal experience informs everyday decisions. And the wiser heads among us combat any individual biases that might have influenced their thinking by seasoning judgement with other, more diverse opinions to enable faster, more accurate solutions.

The interesting thing is that this kind of collective intelligence is not an exclusively human trait. Machines do it, too.

The collective computer brain

It hardly needs saying but individual algorithms have strengths and weaknesses. Some are better at dealing with sparse data sets, some handle only numeric inputs, and others consume text like nobody’s business – each attribute colouring the quality of the algorithmic prediction. In the same way, the data source and wrangling method can give one algorithm a clear advantage over others. Not surprisingly then, applying multiple algorithms in concert (aka Ensemble Modelling) can enhance performance considerably.

In fact, more advanced artificial intelligence (AI) algorithms such as neural networks make use of collective intelligence (little networked machines working together towards a common goal).

One hand washes the other

Okay, we know that collective intelligence works with humans and that it can be leveraged between multiple algorithms. But should application be kept within one population or broadened out to include human and machine together? Humans and machines working together can create unique value. For example, when it comes to detecting cancer, medical-imaging analytics have proven to be more accurate than the deductive powers of human pathologists. But a pathologist’s input to image analytic algorithms can help to assess how advanced the cancer is.

So, machine and human decision making are on a par. However, the machine’s ability to automate allows businesses to make millions of decisions that would otherwise be impossible. Speed of execution is a huge benefit and a key differentiator for machine learning techniques.

The power of automation

KPMG predicts that part-automating the insurance-claims journey could cut processing times from months to minutes. Similarly, a SkyFuture drone operator and engineer in the oil industry can complete a rig inspection in five days instead of the eight weeks it usually takes.

Automation allows tens of thousands of decisions to run in parallel. And each business decision has a massive effect on the environment, markets, customer opinion, etc. Making sure a proposed decision is the best possible option requires the execution and observation of multiple decisions in parallel – a challenger methodology.

One vision; multiple viewpoints

The assessment of multiple decisions also benefits machine-learning algorithms. They learn from the positive and negative effects of decisions, altering predictions to mitigate or enhance particular outcomes.

In the field of sports science, analytics companies provide coaches with recommendations to improve the conditioning and performance of individual players. Following a single strategy would become predictable, so athletes are taught different techniques and approaches as part of a programme of continuous improvement.

Intelligent machines 2.0

Machine learning within business is in its infancy, e.g. we still need to manually create and feed algorithms to ensure precision. But before too long, AI will develop two-way interaction. Machines will help us challenge our biases by asking questions that require additional (or more precise) data. Currently, machines are limited by having to learn from the data we decide is relevant. The next wave of supercharged machine learning will be able to navigate its own learning programme. This human : machine partnership will benefit the c-suite considerably by freeing leaders from bias, automating run-of-the-mill management, and allowing them time to develop creative and insightful actions.

Through technological advances such as the cloud, computing power, and the application of data and analytics at scale, machine learning is now available to all. The real challenge for executives will be changing corporate and operational cultures to maximise the benefits of data-driven decision making.

Because human : machine collaboration is the key that’s going to unlock business intelligence for the foreseeable future.


Yasmeen AhmadYasmeen 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.

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