Humans and machines: the partnership

This is a guest blogpost by Yasmeen Ahmad , Director of Think Big Analytics, Teradata

Will you be superseded by an intelligent algorithm? Despite the fear mongering of robots taking over the world (let alone stealing our jobs), it will be a significant length of time before machines are versatile enough to do the breadth of tasks that humans can. Automating the human mind is out of reach for now but we can leverage intelligent algorithms to support and automate certain levels of decision making. Our focus is thus on augmentation – machines and humans working collaboratively.

Here are three predictions for the ever-developing relationship of the human analyst and the algorithmic machine:

  1. The ongoing need for human expertise

In the future, there will be many jobs that exist alongside smart machines, either working directly with them or doing things they cannot. But which jobs are going to get displaced, and which enhanced?

Although algorithms and AI can automate more and more labour-intensive roles, they are not yet able to complete complicated tasks such as persuading or negotiating, and are unable to generate new ideas as efficiently as solving problems. As a result, jobs that require a certain level of creativity and emotional/social intelligence are not likely to be superseded by algorithms any time soon. It’s likely job titles such as entrepreneur, illustrator, leader and doctor will stay human for now.

In addition, acting upon the intelligence of machines will still require a human in many cases. The sophisticated algorithms may predict high risk of a cancer, but it is the doctor who will relay that information to a patient. Self-driving cars may move us from point A to point B, but it is the human that will be the ultimate navigational influence deciding the destination of the journey and changes along the way.

As these applications of intelligent machines develop, the most advanced technology companies have kept their human support teams. When there is an issue with automated processes, the fixing is often carried out by a human. The need for onsite human expertise dealing with smart machines is not being eliminated: the new systems require updates, corrections, ongoing maintenance and fixes. The more we rely on automation, the more we will need individuals with the relevant skills to deal with the complex code, systems and hardware. This creates a raft of new careers, disciplines and areas of expertise not existing today.

  1. Humans will adapt skillsets to sync with machines

The jobs of tomorrow do not exist in the job ads of today. We will need to change human skillsets, and become digital-industrial people. But what exactly does this mean?

In the future machines will automate a range of tasks and survival will belong to those who are most adaptable and learning agile. Digitalisation and algorithms are creating a new interface between the worker and end task. Humans are now faced with dashboards providing indicators of machine performance. Interpreting, understanding and acting upon the data in this dashboard becomes the task of the future. A new interface is emerging for the human.

This new interface drives a change in the skillsets required. In order to adapt to the possibilities that Artificial Intelligence creates, businesses globally will have to hire a multitude of individuals who are data and digital savvy, as well as understand how to interact with machine interfaces. We will see the continued rise of new teams with data and analytical expertise to create the intelligent algorithms of the future.

Not only has technology opened up new jobs and departments within businesses, but it’s also created the requirement for completely new organisations and business models. Siemens is an example of a traditional rail industry transforming from no longer selling trains to now providing an on-time transportation service. The need for data and analytical expertise is only likely to increase as analytical automation grows: autonomous vehicles will still need mechanics, as will the self-driving systems within the vehicle.

  1. Humans in the loop: a new role will be established for analysts and business users

As we embrace AI and deep learning algorithms that automate the detection of insights – we must not lose sight of the importance of the analyst who deploys the algorithm and the user who consumes insights.

Analysts explore data, generating new ideas, being creative and solving problems by using algorithms. Machine learning and AI models will be able to harness complex data and make more accurate predictions, but it is still the human analyst that will make the decisions on what type of data to feed the algorithm, which algorithms to deploy and how best to interpret the results.

As algorithms create more and more predictions, can we leave all decision making to automated algorithms? Is there a danger that this automation will become a crutch for business users – allowing human judgement to be overlooked? It is crucial that business users are equipped to understand the value of human judgement and how to manage algorithms making questionable decisions.

If CIOs want to take the lead in introducing AI to their organisations, they should begin to identify which business processes have cognitive bottlenecks, need fast and accurate decisions or involve too much data for humans to analyse. These are the areas that can be positively impacted by human analysts leveraging algorithmic machines.

When it comes to the next step for businesses globally, augmentation with smart humans alongside smart machines is the most likely future.