Overview of AI
The world has gone mad for AI. Constantly, I see overhype and poor messaging leading to misunderstanding of potential.
AI is not new, the ability to use it for commercial gain is new at the scale we now have. AI is great at helping us find patterns, get information and is primarily a decision support system (DSS).
AI is not smart, good at making complex decisions, and has bias.
This means AI is useful for specialization not generalization of "smartness", now as ChatGPT et al. is wide ranging, people are assuming it is a general area tool. Actually, ChatGPT is specialist in breaking up and grouping language on top of 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 predation:
AI will impact massively, in aspects of each industry:
1. Healthcare - guess what more surgeons and people will be needed not less. Here I focus on Healthcare examples. People need people to interact with avatars are a joke, I can talk to alexia already. There is very little to nothing in this space except for snake oil sales men. Please prove me wrong! More skilled people needed.
2. Software development/IT - here is a big one. Programmers roles will change significantly, people with good understanding and knowledge will thrive and people with superficial knowledge and lack of ability to truly understand and work thru challenges will disappear. Technologist will focus on difficult problems and add unbelievable improvements to all business processes. The amount will continue to grow. There is not a lot for agentic, "smart AI" int he space and we are 50 years away from this imo.
3. Manufacturing - it won't make the impact where the media says it will. We are good at manufacturing. The sub functions that will benefit will be things like machine maintenance, using sensors, performance/behavior will change. This will allows you to improve Machine Maintenance (MM) and scheduling. Think railway lines, they need to be shut down and it costs millions to trim hedges, imaging now you know the level crossing "lifty uppy-doowny" box/bar is showing signs of fatigue. Shift the fix left and save un scheduled breakdown, the train line and it's know on-effects result in a massive improvement. We are already good at manufacturing and lean and automation, the improvement, order of magnitude better is in machine maintenance not product improvement. More skilled people need. More skilled people needed.
Things like defect detection are already well established using Visual AI down the mm or less. Rubbish detection, using AI will be better - sure it will get cheaper and easier to buy these system but AI is merely the enabler and its been available for well over a decade. More skilled people needed.
4. Service Industry - Robots serving people, please, its mad, except at MacyD's (MacDonalds) and honestly, min 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. automation yes, but in processing, packaging it is already there. 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 needed in services.
Analogy:
1. Old technology: Hand weaving material was big profitable business in Britain, along came looms, these workers got upset and broke the looms, and ended up in prison or broke, these were the luddites (refused to embrace technology). The luddites, ended up broke and all could have been avoided by embracing technology as they know the most about material 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, AWS, they are transitioning from being programming technology companies to AI technology companies. They still solve the same problem of process improvement. The weavers that decided to go into building, repairing looms did exceptionally well but ultimately ran out of requirement and their price was driven down as there was enough supply. Still a good change. A lot of people also got smashed here, be careful inventing the technology in processes, you get it right, you are a hero, get it wring, go find a new job. Lots of sales silver bullets are being produced. Lot's of AI experts, absolute rubbish. With rare exception, you are not an AI expert unless AI was in you job description more than 5 years ago. Beware the snake oil sales men, nowadays they come in many forms, sizes and shapes :)
3. Embrace change: Normal common-sense (smart) people, realized 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 labor 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 int he 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 unlike 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 skilled 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 center of health care and the ability to use AI, which is better at detecting abnormalities than the best radiologist using a single trained model, results are in near time. This means consultant Radiologist and specialists can get reports in seconds that are of unbelievable quality. GP's have best guess within seconds rather than well... we all know this.
AI also give probability so it's easy to prioritize any reporting that is life threatening to a specialist so they are working om the hardest work and get the deep information provided by the AI.
This is possible because we are talking about a relatively narrow field of data we have taught AI to deal with. Think of x-rays, the results are far superior to an expensive resource that takes at least 12 years to train.
Should we stop training Radiologists and diagnosticians and spend our money on 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 GP's on.
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