The issue is people trying to use these AI tools to investigate complex data not the throwaway UI part.
I work as the non-software kind of engineer at an industrial plant there is starting to emerge a trend of people who just blindly trust the output of AI chat sessions without understanding what the chat bot is echoing at them which is wasteful of their time and in some cases my time.
This not not new in the past I have experienced engineers who use (abuse) statistics/regression tools etc. Without understanding what the output was telling them but it is getting worse now.
It is not uncommon to hear something like: "Oh I investigated that problem and this particular issue we experienced was because of reasons x, y and z."
Then when you push back because what they've said sounds highly unlikely it boils down to. "I don't know that is what the AI told me".
Then if they are sufficiently optimistic they'll go back and prompt it with "please supply evidence for your conclusion" or some similar prompt and it will supply paragraphs of plausible sounding text but when you dig into what it is saying there are inconsistencies or made up citations. I've seen it say things that were straight up incorrect and went against Laws of Thermodynamics for example.
It has become the new "I threw the kitchen sink into a multivariate regression and X emerged as significant - therefore we should address x"
I'm not a complete skeptic I think AI has some value, for example if you use it as a more powerful search engine by asking it something like "What are some suggested techniques for investigating x" or "What are the limitations of Method Y" etc. It can point you to the right place assist you with research, it might find papers from other fields or similar. But it is not something you should be relying on to do all of the research for you.
One big feature at the time was Firefox had a built in popup blocker, IE did not. Popup ads were rife towards the backend of the 90's and the internet felt borderline unusable without a blocker.
I'm an engineer I think there is definitely some pain points translating math to code.
I've written some nasty numerical integration code (in C using for loops) for example I'm not proud of it but it solved my issue. I remember at the time thinking surely there must be a better way for computers to solve integrals.
I struggled with this originally and it took years for it to click. But when it did I became both a much better programmer and mathematician for it.
I think what helps is to take the time to sit down and practice going back and forth. Remember, math and code are interchangeable. All the computer can do is math. Take some code and translate it to math, take some math and translate it to code. There's easy things to see like how variables are variables, but do you always see what the loops represent? Sums and products are easy, but there's also permutations and sometimes they're there due to lack of an operator. Like how loops can be matrix multiplication, dot products, or even integration.
I highly suggest working with a language like C or Fortran to begin with and code that's more obviously math. But then move into things that aren't so obvious. Databases are a great example. When you get somewhat comfortable try code that isn't obviously math.
The reason I wouldn't suggest a language like Python is because it abstracts too much. While it's my primary language now it's harder to make that translation because you have to understand what's happening underneath or be working with a diffident mathematical system and in my experience not many engineers (or many outside math majors) are familiar with abstract algebra and beyond so these formulations are more challenging at first.
For motivation, the benefits are that you can switch modes for when a problem is easier to solve in a different context. It happens much more than you'd think. So you end up speaking like Spanglish, or some other mixture of languages. I also find it beneficial that I can formulate ideas when out and about without a computer to type code. I also find that my code can often be cleaner and more flexible as it's clearer to me what I'm doing. So it helps a lot with debugging too
Side note: with computers don't forget about statistics and things like Monte Carlo integration. We have GPUs these days and that massive parallelism can often make slower algorithms faster :). When looking at lots of computational code it's not written for the modern massively parallel environment we have today. Just some food for thought. You might find some fun solutions but also be careful of rabbit holes lol
I work at an industrial plant, we use "edge" to refer to something inside the production network.
As an example the control system network is air-gapped so to use ML for instrument control or similar the model needs to run on some type of "edge" compute device inside the production network all of the inferencing would need to happen locally (i.e. not in the cloud).
>There are no "higher level details" in software development, those are in the domain of different jobs like project managers or analysts. Once AI can reliably translate fuzzy natural language into precise and accurate code, software development will simply die as a profession. Our jobs won't morph into something different - this is our job.
I'm the non-software type of Engineer. I've always kind of viewed code as a way to bridge mathematics and control logic.
When I was at university I was required to take a first year course called "Introduction to Programming and Algorithms". It essentially taught us how to think about problem solving from a computer programming perspective. One example I still remember from the course was learning how you can use a computer solve something like Newton's Method.
I don't really hear a lot of software people talk about Algorithms but for me that is where the real power of programming lives. I can see some idealized future where you write programs just by mix and matching algorithms and almost every problem becomes essentially a state machine. To move from state A to State B I apply these transformations which map to these well known algorithms. I could see an AI being capable of that sort of pattern matching.
the hard thing is to define what State A and State B means Also to prepare for State C and D, so that it doesn’t cost more to add to the mix. And to find that State E everyone is failing to mention,…
I suspect it is to do with the amount of pedestrian traffic passing through an area. When you have a high population density there is an increased amount of foot traffic in the area you can charge less per individual serving because you have a higher overall volume of traffic.
Where I live in Australia the cheapest food tends to be Kebabs which congregate around pubs. There is a high amount of students walking (stumbling) home after a night out etc so they can afford to be cheap since they get so much foot traffic coming through.
I'm an Australian in my 40's almost everyone in my immediate circle (family, friends, work-peers) has an Android, at least in my world iPhone is a minority.
I grew up with Nokia phones all I want out of my phone is something cheap and rugged with a decent battery life.
> I'm an Australian in my 40's almost everyone in my immediate circle (family, friends, work-peers) has an Android, at least in my world iPhone is a minority.
It's very particular to your group I think as I am in the same country, similar age, and yet it's the complete opposite for me.
But none of us care because it's not the US and nobody is using some phone exclusive messaging service enough to care about what phone anyone else is using.
My own field Materials Engineering has:
"Hardness", "Toughness", Resilience", etc. which all describe different properties.
"Ferromagnetic" or "Ferrimagnetic best believe those are different.