For me there are a few main takeaways on how AI _could_ supersede the average ER doctor.
The first is that a technical solution can be trained on _ALL_ medical data and have access to it all in the moment. It is difficult to assume a doctor could also achieve this.
The second is that for medical cases understanding the sum of all symptoms and the patients vitals would lead to an accurate diagnosis a majority of the time. AI/ML is entirely about pattern recognition, when you combine this with point one, you end up with a system that can quickly diagnose a large portion of patients in extremely short timeframes.
On a different note, I think we can leave the ad-hominem attacks at home please.
As someone that started using Co-work, I feel like I am going insane with the frequency that I have to keep telling it to stay on task.
If you ask it to do something laborious like review a bunch of websites for specific content it will constantly give up, providing you information on how you can continue the process yourself to save time. Its maddening.
I would be interested to see how people would apply this working as a coding assistant. For me, its application in solutioning seem very strong, particularly vibe coding, and potentially agentic coding. One of my main gripes with LLM-assisted coding is that for me to get the output which catches all scenarios I envision takes multiple attempts in refining my prompt requiring regeneration of the output. Iterations are slow and often painful.
With the speed this can generate its solutions, you could have it loop through attempting the solution, feeding itself the output (including any errors found), and going again until it builds the "correct" solution.
It has been interesting what both groups of 'yes' and 'no' chime in here. Personally I am on the side of 'no' but for a rather simple reason. I ask myself the following question:
Why spend time being good at something you don't care about being good at any more?
It is purely a personality thing however for me I would like to continue moving up the career ladder and you rarely see CTOs, VpEng rolling up their sleeves and sifting through CloudWatch logs. I want my focus to be on working the skills associated with those roles.
As a people manager that works with many incredibly capable engineers that are aspiring to be managers, I share with them this advice, 'excellent engineers compound their value by making other engineers excellent. It's far more difficult to do that when you are writing code.'
I have posited a similar idea with some of the people I work with. The issue of having complex, multi-step tasks be completed successfully has already been solved. You don't heavily invest in having one single expert for your business to solve all your problems. You build a team. Multiple specialized experts working in unison to achieve a shared outcome. Some people work on the task simultaneously, others sequentially. All with a specific purpose associated with the goal.
These assets are horizontally and vertically scalable based off skills, quality, or performance required. An efficiently designed AI architecture I believe could do the same. Its not mixture-of-experts as you aren't necessarily asking each model simultaneously but designing and/or having the system intelligently decide when it has completed its task and where the output should travel next.
Think of a platform where you had 'visual design' models, 'coding' models, 'requirements' models, 'testing' models, all wired together. The coding models you incorporate are trained specifically for the languages you use, testing the same. All interchangeable / modularized as your business evolves.
You feed in your required outcome at the front of your 'team' and it funnels through each 'member' before being spit out the other end.
I have yet to see anyone openly discussing this architecture pattern so if anyone could point me in that direction I would thoroughly appreciate it.
As a reasonably experienced programmer that has watched Andrej's videos the one thing I would recommend is that they not be used as a starting point to learn neural networks but as a reinforcement or enhancement method once you know the fundamentals.
I was ignorant enough to try and jump straight in to his videos and despite him recommending I watch his preceeding videos I incorrectly assumed I could figure it out as I went. There is verbiage in there that you simply must know to get the most out of it. After giving up, going away and filling in the gaps though some other learnings, I went back and his videos become (understandably) massively more valueable for me.
I would strongly recommend anyone else wanting to learn neural networks that they learn from my mistake.
I think it's fair to say when you hit the hundreds of millions of dollars mark the diminishing returns for making things happen faster have well and truly kicked in.
Perhaps the only benefit would be extra computational power yet I would struggle to understand the benefit of jumping from 500 million to 5 billion with such short timeframes.
The first is that a technical solution can be trained on _ALL_ medical data and have access to it all in the moment. It is difficult to assume a doctor could also achieve this.
The second is that for medical cases understanding the sum of all symptoms and the patients vitals would lead to an accurate diagnosis a majority of the time. AI/ML is entirely about pattern recognition, when you combine this with point one, you end up with a system that can quickly diagnose a large portion of patients in extremely short timeframes.
On a different note, I think we can leave the ad-hominem attacks at home please.