Overview of AI
The world has gone mad for AI. Constantly, I see overhype and poor messaging leading to a misunderstanding of potential.
AI is not new; the ability to use it for commercial gain is new at the scale we now have. AI excels at helping us identify patterns, gather information, and is primarily a decision support system (DSS).
AI is not intelligent, but rather good at making complex decisions, and it has biases that we teach it.
This means AI is useful for specialisation, not a generalisation of "smartness", now that ChatGPT et al. are wide-ranging, people are assuming it is a general-purpose tool. Actually, ChatGPT is a specialist in breaking down and grouping language based on a data source. For those in the technology industry, we pretty much know that ChatGPT is a good Google (search engine).
So, what is AI going to be successful at? Well, this is my prediction:
AI will have a massive impact on many industries:
1. Healthcare - guess what? More surgeons and people will be needed, not fewer. Here, I focus on Healthcare examples. People need to interact with others; avatars are a joke. I can talk to Alexa already. There is very little to nothing in this space except for snake oil salesmen. Please prove me wrong! More skilled people are needed.
2. Software Development/IT - This is a significant one. Programmers' roles will change significantly; those with a good understanding and knowledge will thrive, while those with superficial knowledge and a lack of ability to truly understand and work through challenges will likely disappear. Technologists will focus on challenging problems and introduce significant improvements to all business processes. The amount will continue to grow. There is not a lot of agentic, "smart AI" in the space, and we are 50 years away from this, imo.
3. Manufacturing - it won't make the impact that the media says it will. We are good at manufacturing. The sub-functions that will benefit include machine maintenance, sensor usage, and performance/behaviour will change. This will allow you to improve Machine Maintenance (MM) and scheduling. Think of railway lines; they need to be shut down, and it costs millions to trim hedges. Imagine now that you know the level crossing "lifty uppy-doowny" box/bar is showing signs of fatigue. Shift the fix left and save the unscheduled breakdown; the train line and knock-on effects shall result in massive improvement. We are already proficient in manufacturing and, to some extent, automation. If the AI is not significantly better, it is not worthwhile. More skilled people are needed.
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| Machine Maintenance in Manufacturing. AI is needed to mature MM. |
Techniques such as defect detection are already well-established using Visual AI at the micron level. Rubbish detection. Using AI will be beneficial - sure, it will become cheaper and easier to acquire this system capability for less, but AI is merely an enabler, and it has been available for well over a decade. More skilled people are needed.
4. Service Industry - Robots serving people, please, it's mad, except at MacyD's (McDonald's) and honestly, minimum wage workers are pretty efficient there, and it will be too sterile. Pushing out patties, well, if you need AI for this, you don't know what AI tries to do. AI & automation are already in the processing and packaging processes. The big stuff with AI will be in social media and advertising (and don't get me started there, automated advertising will absolutely fail. We need to invent a missile to destroy non-human posts). More people will be required in these new and changed services.
Analogy:
1. Old technology: Hand weaving material was a big profitable business in Britain; along came looms; these workers got upset and broke the looms and ended up in prison or broken; these were the Luddites (who refused to embrace technology). The Luddites ended up broke, and all this could have been avoided by embracing technology, as they were the most knowledgeable about materials and production. They are the natural experts.
2. Trend jumpers on: Too many companies wanted to build looms, and a handful of players did brilliantly and still exist today. Think Microsoft and AWS; they are transitioning from being programming technology companies to AI technology. They still solve the same problem of process improvement. The weavers who decided to go into building and repairing looms did exceptionally well, but ultimately ran out of demand, and their prices were driven down as there was an excess of supply. Still a good change. Many people also got hurt here. Be careful inventing new technology in processes; you get it right, you are a hero; get it wrong, go find a new job. Lots of sales silver bullets are being produced. There are tons of "AI experts", but mostly this is absolute rubbish. With rare exceptions, you are not an AI expert unless AI was in your job description more than 5 years ago. Beware the snake oil salesmen, nowadays they come in many forms, sizes and shapes :)
3. Embrace change: Normal, common-sense (smart) people realised they actually had 4 options:
- Learn how to use a loom. Use the technology available and use it to build garments faster;
- Build looms and support the loom business;
- Do nothing, continue to offer hand-weaving labour to the market. So take your pension and hope like hell you win the lottery (I'm still backing this option for myself); or
- Expert hand craftsmen or women :) Become the best hand weaver in the world, and people pay you for your expertise; these people's descendants/business still exist. But big surprise: it's hard, it takes a long time, it's unlikely to make you rich.,, So, sure, go do this if you are a genius in your field and love it, but don't die of surprise when you go broke or don't get the return you deserve for all that hard work.
Summary: Embrace technology and AI, it is only a decision support system. More skilled people are needed, as you have the background, being professional and embracing change means you are more in demand. Sitting on your backside waiting for the lottery means you are like 90% of people, and you'll get 2 jet skis and a new husband! yipee.
Healthcare
Good Use Case: Diagnostic medicine
Diagnostic medicine has become the centre of healthcare, and the ability to use AI, which is better at detecting abnormalities than the best radiologist using a single trained model, yields results in near real-time. This means that consultant radiologists and specialists can receive reports in seconds that are of unparalleled quality. GPs have the best guess within seconds, rather than well... we all know this.
AI also provides probability, so it's easy to prioritise any reporting that's life-threatening to a specialist, allowing them to focus on the most challenging work and receive the in-depth information provided by the AI.
This is possible because we are dealing with a relatively narrow field of data that we have taught AI to handle. Think of X-rays; the results are far superior to an expensive resource (a Radiologist) that takes at least 12 years to train. And more to get brilliant.
Should we stop training Radiologists and diagnosticians and allocate our resources to AI? Absolutely not!!
Radiologists should be using the AI reports, validating, using the info and extrapolating, when an issue is detected, this must be added back into the learning model resulting in improving the AI. AI should not act. It must only be used to support. Acting should be restricted to notifying relying parties such as GPs.
