In this piece, we’ll be unpacking myths surrounding the area of artificial intelligence, and begin to unpack what an AI-driven world will look like in the near future as well as the benefits it may have in contributing to increased productivity in the workplace. To start, it’s worth noting that artificial intelligence – in one form or another – will continue to reshape the business landscape at a rapid pace.
It’s estimated that the AI industry will be worth more than $247 billion by 2027. This staggering growth rate is due to the fact that businesses around the globe have increased their deployment of artificial intelligence by 270% in the past four years alone, and that 90% of the world’s leading businesses have made sizeable investments in AI.
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The result for us, as humans, employees and managers are that the speed and complexity of tasks we can tackle in a day is set to increase exponentially. 50% of businesses deploying AI are already reporting added productivity, and 44% have said they’re saving costs thanks to AI.
As technology continues to evolve, adapt and innovate, these numbers – and the benefits – for businesses will explode. But before we get to that point, it’s working unpacking 6 of the myths surrounding artificial intelligence so we can paint a more accurate picture of how AI will continue to revolutionise business-as-usual.
AI is considered by many to be an essential element of the future of work. It is predicted that tech companies will heavily adopt artificial intelligence in the workplace, helping with tasks such as human resources and customer service to provide a better employee experience. One of the most obvious ways that AI can help in a workplace is by automating repetitive tasks. This can include tasks such as big data entry, scheduling, and even monitoring and responding to emails. By automating these tasks, AI can free up employees’ time to focus on more important, strategic work. Additionally, because AI is able to work around the clock and make fewer mistakes than humans, it can often complete these tasks more quickly and accurately.
Artificial Intelligence Will Make Human Labour Obsolete
Last year, we reported on a release from the World Economic Forum stating that as many as 50% of work tasks completed by humans would be replaced by robots by 2025. The flip side of the equation, though, is that in the process of replacing 85 million jobs, they will actually create 97 million jobs, providing a surplus of 12 million positions.
Rather than make human labour obsolete, AI looks as though it will shift human labour away from certain industries – where simple, repeated tasks can be performed by AI and robots – all the while creating new industries, and making existing industries so productive that they can, in theory, hire more staff.
AI Systems are Inherently Unfair
One of the major criticisms of AI and decision-making drawn from machine learning is that the systems are unfair, and fail to take into account the impact on human lives. At the core, there are issues surrounding the biased decision-making that many believe AI will deploy if they are in charge of important calls. According to a post from Google’s AI department, human decision-making often results in unfair outcomes for vulnerable groups, and if AI Is trained in the same way, it could replicate these biases.
In order to tackle the potential for unfair decision-making, Google says that “well-designed, thoroughly vetted AI systems can limit unfair bias, and may even help us to identify and combat bias in human decision-making.” Organisations that are looking to implement AI can look forward to the benefit of having tasks completed in real-time. This can make tasks such as posting information on social media much easier and less time-consuming.
AI, Machine Learning and Deep Learning Are the Same Thing
In spite of the fact that artificial intelligence is the most widely used term by the public, there is actually no agreed-upon definition for artificial intelligence, considering how deep the field of study is. Instead, researchers say it’s helpful to think of artificial intelligence as the process, or science of making things ‘smart’, which is achieved through things like a machine and deep learning techniques.
Machine learning refers primarily to a series of computers learning to identify patterns from a predetermined dataset, without being programmed with specific rules. Deep learning, on the other hand, is a form of machine learning, that is “based on neural network technology, an algorithm whose architecture is inspired by the human brain and can learn to recognise pretty complex patterns, such as what ‘hugs’ are or what a ‘party’ looks like,” according to Google.
Artificial Intelligence is Approaching Human Intelligence
Artificial intelligence is no doubt an intellectual force that demands respect, however, researchers say that it is extremely limited when it comes to certain aspects of human creativity. While AI has approached, and in a number of cases, surpassed human intelligence at certain tasks like a game of chess, “they remain narrow and brittle, and lack true agency or creativity.” While AI might not necessarily replace humans and human jobs in the immediate future, AI-powered workplaces and digital workplaces are becoming increasingly common.
Artificial Intelligence Systems Are Only As Good As the Data
Researchers say that while data is no doubt the key ingredient that allows AI to learn and develop, they have had to adapt to “imperfect datasets” that provide low-quality, unbalanced or scarce data. Through careful problem formulation, targeted sampling, factoring-in constraints or using synthetic data, they have managed to find a way to continue AI innovation and development in the absence of good data.
AI Systems are Less Explainable Than Non-AI Techniques
In the same way that traditional business processes in the real world can be both incredibly simple and complex to explain, AI systems work in the same way, according to researchers. Explainability is something that AI developers say changes with the context and complexity of the AI system, and as they work through this problem, it can actually shed insight as to why the AI system makes certain decisions.
Google’s researchers say that “explanations for human decisions, for example, may not accurately reflect their influencing factors or unconscious biases. In fact, even if every individual decision made by some AI systems cannot be fully explained, we may be able to understand how they make decisions, in general, better than we understand how humans make similar decisions.”
