In the past couple of months, there have been a lot of discussions about artificial intelligence (AI), owing to ChatGPT’s ability to provide a human interface to this technology. People want to know if it’s as great as it’s hyped up to be by AI researchers, influencers and academia, and as threatening as the media makes it out to be.
Not all AI is created equal, so it’s not fair to say all AI is bad, scary, threatening or whatever other adjective is being used to stoke fear among the public and spur politicians to regulate the technology. Yes, some of the latest generative AI solutions have problems that must be ironed out, such as those that can throw up results that are inaccurate and/or biased.
And there’s real concern that some generative AI tools can infringe on copyright. But, like any type of technology, these issues aren’t universal. So, while AI researchers and other technologists work with government leaders to address these very specific concerns about the behavior of certain AI, I think it’s important to emphasise that not all AI is created equal. AI, like people, can be taught to do the right thing.
In fact, AI is doing a lot of good in the world today. Manufacturers and retailers use adaptive AI to forecast demand and plan inventory, especially consumer packaged goods (CPG) companies. AI-powered machine vision assists with detailed quality inspections of everything from medications to automotive and electronic parts to 2-liter soda bottles and frozen dinners coming off the manufacturing line.
Autonomous mobile robots (AMRs) equipped with machine learning work in support of people in factories, warehouses and distribution centres, helping ensure they have the raw materials, products and packages they need to get products made, packaged and loaded onto trailers on schedule.
Front-line workers responsible for delivering services and experiences have to keep people in the store and online happy at the same time. They are essentially serving at least two customers at once. Well, the only reason many essential workers are able to serve so many of us at once is because there’s trustworthy adaptive AI working alongside them somewhere. It may be working within an app on a wearable or handheld mobile computer, in a business’s inventory, logistics and/or e-commerce information system, or even in the cloud tied to an industrial automation system.
But wherever it lives, it’s well-trained to “listen” to what everyone wants, “see” what is happening in the world (the nuanced changes or trends that a human might miss) and assess all angles of every situation before ultimately telling front-line and back-end workers what they should do now and next to make everyone happy.
In other words, the AI you would use to make your business run better – the AI you need to hit your business targets this year – is not the generative AI that’s drawing ire or fueling speculative fear among government officials, tech industry leaders, academic communities and the general public.
The AI you need to pay attention to as a business or team leader is the one that can take all the data generated by physical Internet of Things (IoT) objects – think barcode labels/scanners, handheld mobile computers, tablets, RFID tags/readers wearables, machine vision cameras, robots, and environmental sensors – and make it make sense to you and your team. And there are a lot of responsible AI-powered technology tools that fit this bill, ranging from workforce management and task management software to visual inspection systems, loss prevention systems, and forecasting/planning platforms, with more being ethically designed and trained every day.
Plus, as the number of smart devices – IoT physical objects – at the edge of networks continues to grow, we know responsible and ethical AI is going to become as necessary to business as the smartphone is to your personal livelihood. AI is going to be called upon more and more to help us make sure all the data your business is collecting is worth something.
That said, we know the concerns around generative AI extend to all AI, and that we must take care to consider the implications of new AI use cases that are brought forth. Every decision made related to AI use should also be driven by critical factors such as data privacy needs, the latency of responses, cost to compute and network bandwidth availability. Continuous evaluation and refinement of responsible AI methodology in terms of ethics, development and deployment is needed, supported by evolved processes, principles, tools and training while ensuring consistency and compliance.
