Gone are the days of old science-fiction novels where AI referred to sentient robots and little else. These days, artificial intelligence is incredibly sophisticated and is implemented seamlessly into a vast number of sectors the world over. Put simply, without AI, the world today wouldn’t function the way it does. 

Data, too, is similarly important and globally omnipresent, with over 2.5 quintillion bytes of data created every single day in 2022. To put that into context, one quintillion equates to a billion billions. That’s a truly staggering quantity of information. 

With both AI and data clearly such important parts of the modern world and ever-evolving, it’s worth exploring what trends we expect to see in 2023. Where will advances be made? And what changes can we expect to see? 

The democratisation of data 

For the past decade or so now, data analysts and consultants have been leveraging data to glean useful insights that can then be used to improve a company in some way or another. Increasingly, however, there’s a call for the practical use of data to be made available to everyone throughout a workplace structure, rather than just those in the boardroom making the decisions. In other words – democratising data. 

What that means, in practice, is that it isn’t just the ultra-IT-savvy who can mine “Big Data” - anybody in the workplace can. For instance, the use of hand terminals by retail assistants on the shop floor, wherein recommendations are brought up in real-time based on a customer’s purchase history, is just one way in which the leveraging of data can be made more equitable within a business. 

The thing is, this more even distribution of data accessibility isn’t just a fairer means of operating, generally, it can lead to greater economic successes, too. A report by McKinsey, for example, suggested that, making data available throughout the workforce, would positively impact a business's overall revenue. 

Ethical AI 

AI and ethics have been talked about in the same vein for as long as the concept of the former has been around. Increasingly, though, companies are looking to implement practices, principles and procedures that ensure any and every implementation of AI within the organisation’s framework is done so in an ethical manner. 

The key ethical considerations for AI hinge on several core principles, including bias, transparency, accountability, privacy, safety, security and sustainability. But how exactly are companies introducing more scrupulous ethical standards into their structures? 

AI company QuantumBlack, for instance, has developed an approach called Tech Trust Teams (3T). These teams work directly with developers in a consulting capacity to ensure that those principles mentioned above are being properly considered and addressed. 

We explore ethical AI in more detail in our Ethical AI for Good Business microcredential.

AI and sustainability 

More and more, it’s being thought that AI will have a big role to play in helping us achieve a more sustainable planet, moving forward. Research from accounting giant PwC, for example, found that leveraging artificial intelligence could reduce worldwide greenhouse gas emissions by as much as 4% by 2030. 

How would this work, exactly? In agriculture, AI can help with monitoring and managing environmental conditions and crop yield. In the transport sector, AI can help provide better real-time traffic coverage and journey planning for more resource-efficient transport. 

The energy sector can massively benefit from utilising AI, too. Localised energy grids can make use of AI, for instance, by automating operations and improving operational efficiency. AI technologies help to enable predictive maintenance throughout the energy infrastructure, so that it’s better prepared for events such as hurricanes, for instance. 

Big data in health analytics 

The use of Big Data and AI in the healthcare sector is a growing trend, and we’d wager few would argue there was a more worthy sector for that growth. Some practical examples of the data analytics and AI used within the healthcare industry include: 

  • Risk and disease management 
  • Improved medical imaging 
  • Real-time alerting 
  • Cure development and implementation 
  • A reduction in human error 
  • Greater patient engagement 

One of the examples there that might have most drawn your eye is that of cure development and implementation. For instance, using machine learning algorithms (a form of AI), scientists at the Icahn School of Medicine have been able to create a computer program which personalises drug treatments for patients using their genetic information. 

In the same vein, algorithms have been developed to analyse huge data sets from cancer patients in order to understand (and therefore better treat) various types of cancer. AI hasn’t found the cure for cancer just yet, but there’s a good chance that if (or hopefully when) that cure does come around, AI algorithms and machine learning will have played a big part in discovering it. 

Want to learn more about data and AI? 

Whether you like it or not, ours is now a data-dependent world, and that’s not going to change anytime soon. From more thorough ethical examinations to the use of data in the quest for sustainability, AI and data will continue to have a pivotal role to play as the years go by.  

If you’re interested in all things data-related, consider taking a data-based short course with UTS Open. We offer a wide range of courses, including our “Ethical AI: from Principles to Practice” short course, our “Advanced Data Visualisation” microcredential, and our “Applied Data Science for Innovation” microcredential.