The whole AI/ML stuff has become so hyped up that its probably time to find another topic of interest in software engineering for me. Its a weird melange nowadays where frameworks and "academic credentials" are fused together by major tech companies and leaves me - who has deployed a dozen of classical ML models into production that are still running after couple of years - wondering what this is all about.
Overall, working with people with different backgrounds, a ML-related PhD is usually not correlated nor anti-correlated to these people having a good understanding of the relationship between models and and their applications.
I wish we could leave the framework and name-dropping behind and talk more about what it takes to evaluate predictions, how to cope with biases, etc.
Really familiar territory. I think the hype has poisoned the minds of many and at this state "AI/ML" has turned into a simple buzzword. Much like "blockchain" 2 years ago. And while I'm still as fascinated about ml as I was 5 years ago, like many others, I've decided to stay in the shadows and do my own thing just for the fun of it. Especially since marketing and ego started playing a big role around those communities. It genuinely makes me sad but I think I always knew in the back of my mind that this would likely turn out to be a nail in the coffin of AI/ML, not robots taking over the world.
The way I see it, ML/AI is nothing more than a marketing campaign for much of the industry and few people realize that it's often a small component and rarely a major selling point for anything. Like "ml-powered kitchen blender" or whatever. As you said, few people discuss evaluating predictions, tackling biases. I suspect because most people are a lot more interested in snatching a piece of the cake.
It's different. With the ML stuff there's a bunch of actually useful applications and interesting problems at the core with a lot of fluff and marketing piled on top of it. That's the reason why you're seeing the ML/AI hype last so much longer than blockchain (which was basically a quick cash grab with no substance).
Don't get me wrong, AI/ML is immensely more valuable than blockchain ever was. Not a single doubt in my mind. But in terms of exploitation for marketing purposes - it's a very similar story. That's what I'm referring to.
> It's different. With the ML stuff there's a bunch of actually useful applications and interesting problems at the core with a lot of fluff and marketing piled on top of it. That's the reason why you're seeing the ML/AI hype last so much longer than blockchain (which was basically a quick cash grab with no substance).
LOL...I thought supermarkets were using blockchain to track the provenance of their cabbages, coffee beans, beef joints, etc LOL
I think you have a point about ML/AI. To my mind, judging by the hype, there seem to be a lot of solutions looking for a problem. Having said that I feel I should also jump on the bandwagon and get my ML/AI credentials as an insurance against future demand for the skillset ;-)
"I wish we could leave the framework and name-dropping behind and talk more about what it takes to evaluate predictions, how to cope with biases, etc."
We can, can't we? "We" as in professional software developers. I always thought of this word-hyping of a management thing and a buzzword pool for non-techies.
I know this influences our work, but that doesn't keep us concentrate on what's really up... or am i wrong?
By the way, I tried getting into ML but i'm really poor at maths and at that time was not willing to put time into maths. And nearly every tutorial back then threw formula after formula at my face... So a bit of mathematical education could not hurt. Doesn't have to be a PhD though.
> We can, can't we? "We" as in professional software developers. I always thought of this word-hyping of a management thing and a buzzword pool for non-techies. I know this influences our work, but that doesn't keep us concentrate on what's really up... or am i wrong?
Its just tiresome because it is tiring to refute BS or to explain why a certain approach is not viable.
> So a bit of mathematical education could not hurt
definitely but the word "PhD" is often welded as if you'd have to have secret knowledge that is otherwise not accessible to you, which isn't true.
Well the hype is required to get your grandmom, older execs, or a strategy/biz dev team at a brick and mortar firm who can afford only 1 dev to gain confidence that they too can use ML.
Its true that with todays Frameworks and easy API calls almost everyone with a little technological background can deploy a ML/AI model and get sufficiant results. But Bootcamps cannot replace an academic education. As soon as you are not able to understand and review new released papers and insights and have to wait for an high level blog entry or video course on that topic you are worth nothing. Without a deeper understanding you can just guess what is going on inside that blackbox NN or ML model and have to rely on blindly change parameters and even worse you are not able to understand your results or compare someoneleses results with yours using statistical tests and so on. So in the end people (maybe not everyone) without academic background are just API callers that will struggle on the long term.
Overall, working with people with different backgrounds, a ML-related PhD is usually not correlated nor anti-correlated to these people having a good understanding of the relationship between models and and their applications.
I wish we could leave the framework and name-dropping behind and talk more about what it takes to evaluate predictions, how to cope with biases, etc.