Nice but i am scared people write in their resume that they train a GPT model from scratch. And when asked in detail they will accept just ran minGPT without understanding it.
This is the AI story now a days.
Best solution is to ignore minGPT and write your own version.
That seems fine. How many reddit clones have we seen?
For what it's worth, as a hiring manager who hires technical people, but not software engineers, these kinds of side projects can really help folks with a less formal education.
The upside is that if they DO understand it or at the very least learned something interesting or useful while working on the project it will help them a lot in an interview.
Folks who do what you're worried about exist, and they just don't get hired by people who aren't impressed with the shallow use of some new technology. There are also plenty of companies where that person will be fine, not really need to use GPT to do their job, and everyone will be happy anyways :)
How you feel if somebody claim they know java just because they can call elastic search api (or put any tool written in java). Are you going to hire them and put them on a project which involve coding in java?? the issue is false claim on resume which shows no integrity.
On other extreme do you go to a doctor for surgery who make false claim about being done surgery.
Some companies are looking for someone who knows enough Java to use an ElasticSearch API - it's about knowing your audience, and I think a lot of new folks just don't know how to target their applications.
For what it's worth I've gone back and forth over this in my career, and I do think it has a bit to do with the level of your assertion, but there's an amount of naïveté that creeps into resumes especially since advice is so widely varied, do you brag, sell yourself, show real projects, or your past titles.
Anyways, there's no right way, and I've decided that the job seeker is in a position of weakness to employers and the industry in general, and if people are seeking to better themselves - great.
When I interview and hire people, I'm the one who screens out people who lie or are incapable of doing their job, what they put on their resume is just part of the process.
Your medical example is a bad one, sorry, I'm not going to engage with it, there are gatekeepers in almost all industries, and I'm not proposing changing them by saying that someone putting Java on their resume is not the same as a doctor lying about their qualifications. One, because systems exist to vet those qualifications, and two because they're simply not on the same spectrum.
How many people have you screened, hired, and for how many roles? Is this a problem you've run into or experienced professionally, or just something that annoys you?
I've experienced both. When I ran a satellite, I employed interns and most were CS majors who claimed to know C, C++, and Java - I asked them to write strcpy in C as my interview, none of them could do it, and these people were juniors in college, so no, I'm not too worried about what people put on their resume because it's just not representative of their actual abilities ever.
If someone claims to know Java and gets a job with no actual test of their skill, that's the manager's fault.
Granted, that's that case with any machine learning project on a resume. People were using scikit-learn/NLTK for abstracting/simplifying training long before TensorFlow and PyTorch were the new hotness.
Those projects are good things to press in a phone interview.
Does just training a model get you a good job nowadays? I don't think anyone competent hiring people would be impressed by that, except at garbage places with garbage hiring managers
I would expect an entry level person to be able to verbally answer basic questions such as:
- Explain why an NN with n nodes across multiple hidden layers can model a more complex structure than an NN with n nodes but only one hidden layer.
- When using an NN, when is it appropriate and not appropriate to utilize a cross-entropy cost function?
- Why can a single perceptron not approximate an XOR operation?
- Why is neural network (NN) training data divided into three sets: training, generalisation, and validation? What is the purpose of each? Must the three sets be mutually exclusive?
