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Comment by amrx101 | Hacker News Reader

Have a simple rant here. All these BIG $ companies every now and then come out with statements and what not, that doing AI ML is very easy and every one including their cats should do AI, ML courses and training(preferably on their platform). Once that is done the job market is yours. Reality is far from this.

- Today AI|ML does not have the capability marketed by these big companies. Incidentally marketing is targeted at governments, big non tech companies and gullible undergraduates.

- Undergrads many a times take these training courses wherein they acquire the skill set to call these APIs and flood the job market wherein a data-entry or data-analyst job is tagged as AI|ML job.

- High paying jobs in AI|ML still require a Masters or PhD or a mathematical background.

In conclusion, the current hype around AI|ML is misguiding gullible undergrads and governments(I dont mind the government being cheated THOUGH).

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I’d like to add to that list all the “learn AI” blogs etc. that gloss over the details and show you how to call some libraries to do cool stuff. Everyone and their cat can learn how to invoke some APIs, which is cool, but the reality is that data is hard, and if you want to do good, sound work, you need to have an understanding of the math and fundamental principles behind it.

It might not save you from making stupid mistakes or wasting your time, but at least you will know what mistakes you can - and will - make.

Another side of this that I've seen is people with PhDs in applied mathematics on a research team where a PhD is a hard requirement, spending all of their time passing dataframes around and convincing the sales team that the bar charts in the product are in fact correct.

Have we been working on the same team? :P The worst part (or best part of the joke) is that with a mathematical PhD you're very ill equiped for a discussion with your typical sales rep.

i am doing quite some deep learning work as part of my consultancy practice. And its all hand made stuff , together with lots of trial and error while trying to replicate papers that might be relevant fkr the task at hand.

so i totally agree with your statement. big corps overhype the shit out of it in order to sell and lots of n00bs fail for it.

regarding your last points. even if its super easy nowadays to deploy YOLO, and everyone and his mother can do it, making actually something that works and provides business value is hardcore. and without scientific skills_> no chance

if you don't mind me asking, what do you mean by "hand made". I'm currently wworking on a information science degree and I'm trying to focus on machine learning and data science. Could you go into a little more detail on what gives something business value?

Not the OP, but ultimately something that increases revenue or decreases costs by some measurable amount.

The best thing you can do to make yourself good at this is to practice. Get some Kaggle data and try to fit a model. Realise your data is crap, clean data, repeat.

Every useful system is hand made in the sense that there's a vast amount of set up and operational code. Mostly the model's the easy part (although it will take so much time to run).

hand-make as in:

- looking very carefully into the very specific challenge of your client

- figuring out how (and if) ML can help

- figuring if its still economically feasible (costs of research vs perceived(!) benefit)

- deriving a solution.

- tinkering tinkering tinkering. usually more with the data than with the models :-)

All my A.I. projects are essentially outsourced R&D projects where we deliver the brain and computing power. So far, it never was as easy like installing YOLO or any other off the shelf product.

Edit: You also need very often custom software to create custom datasets. AI models are often only tested on academic datasets but I observed empirically that their performance transfers badly to real world datasets. So you need to create your own datasets etc. This is often a non-trivial problem. So I wrote a lot of dataset creation tools in my AI practice.

Actually, making some things with ML has become really easy, google auto ml image recognition, or the equivalent with azure and amazon gives you a website to upload tagged images and builds a model based on it. If the problem to solve it's not very complicated (let say, room type recognition) it just works. The only problem is if you try to apply that model in volume, prepare for the costs.

In my case, I train my model with them and download to use on our machines without having to touch any ML related code.

Do big companies, offering ML/AI courses hire people with just some of their certificates? I mean, do these courses skip the whiteboarding?

To the best of my knowledge never.

So lets see. Suppose you do a Google Certification for AI|ML. This makes you an apt user of Google's AI platform. Your value lies not with Google but with other companies that want to use Google's platform for AI, ML work.

For specific AI,ML work(like developing the API that you are using), Google will hire PhDs and grad students who specialise in AI|ML. For engineering solutions of those products Google will hire software and distributed systems engineer. You will be hired by someone who wants to use Googles platform.

Hey, but ML is the new electricity and you are certified. /s

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