Haha lol, so just once a year, think a bit ahead so you save one grocery trip, and you're already better off

Yeah, doesn't an ICE exhaust that in like 30 miles?

I don't think it implies that all datasets are equally likely. I think it only implies that given all possible datasets, there is no best approach to modelling them. All possible != All are equally likely

But I don't have my book with me, and I do t trust the internet since it seems to lead to random blogposts instead of the original paper (Wikipedia gave a 404 in the footnotes)

We use gradient boosting at quite a big scale. Not LLM big, but still big. It's just not NLP or CV at all. It's for fraud detection in large transactional tabular datasets. And it outperforms basically all neural network, shallow or deep, approaches.

Define scale

Language models? Sure. Images? Sure. Huge amounts of transaction data to search for fraud? Xgboost all the way lol.

Church no free lunch theorem: there is no single approach best for every possible problem. Djeezes I hate it when marketing takes over. You learn this principle in the first chapter of literally every data course

Yeah OpenAI was founded to be... Well... open.

It's the most closed ai company in existence probably

GPT is largely built on Google research

It's smart by Google to wait until Microsoft burns the 10 billion, then easily surpass it.

The hype is so painful at the moment, non technical people and sales idiots are way overselling chatgpt.

A little bit dangerous, because the A100 is a beast when you look at performance/kWh. If you run a lot of heavy workloads, it's your best option, but on this chart it looks like the worst

TCO != Purchase price

Slam is pure Mechatronics which I don't consider ml.

AI is basically decision making. Giving information, how does a machine learn from its environment, take decisions, without human oversight. How does a machine adapt itself with more experience.

ML is just a way to create models.

For example the SLAM algorithm is an important algorithm in AI, because it allows robots to map their environment. However, this is not ML at all.

Another example of AI is knowledge graphs, like the earliest chess engines. A perfect chess AI can be made without any machine learning at all.

It's important to keep making the distinction.

I view ML as a way to create an accurate model, and AI as a way to have a model take decisions in a complex environment without humans in the loop.

There is a large overlap, but you can easily have one without the other.

arxiv is really planting your flag on the topic. I dont trust it because its not peer reviewed, but its definitely a good way to call dibbs

> there’s some commentary on those early papers in the Neurocomputing 1 and 2 collections

could you link or give a doi or something?

First thought: decide for yourself who your target audience is

If you hope to sell this to companies, or even start-ups, be prepared for a lot of questions around data governance, security, ....

Second: do you have an idea how many users you need for break even and how the infrastructure needs to scale to cope with that? Gpu's aren't cheap of course, neither is electricity or cloud providers

xgboost or lightgbm are the goto for classic tabular business data

Don't waste your time. Check datarobot (and H2O is the closest competition).

Everybody else plainly sucks at automl, sorry to put it so bluntly but it's true

I am a happy customer of them, and it took a mountain of effort to convince our it teams to move away from Microsoft and databricks etc..., But the results were just in another ballpark, so we had a strong business case

Honestly, if you want decent automl results, you should only consider datarobot. Everything else is noticeably worse

We are a customer of them and it's a game changer. Yes it's expensive and not aimed at hobbyists, and it's like super expensive. But it's good

If I find the time, I shall upload this dataset into our system and check the results. Remind me later if I forget

Ok yeah well that's stupid. Because I am actually in favour of column names instead of indexes. Indexes are pain in the ass when your incoming dataframe changes, it creates an implicit dependency.

But your last line is my point. You shouldn't be concerned about MLops stuff, but if your models is already in the right framework, it saves soooo much time

As someone who sometimes has to hire people, perhaps this is the issue:

Imagine how difficult it is for big companies to get a MLOps framework going, with all the red tape and scattered IT systems. It was very painful where I work. In the end we got something working using a python platform that really needs you to use pandas and sklearn type interfaces.

Let's hypothetically say you are a great data scientist using R, or Sas or MATLAB or ... If I don't have a lot of options I'd hire you and put you on a training program for our framework. But if I have multiple decent candidates, and some don't require retraining, yeah imma gonna pick one of them. I am not spending 2 months trying to get compliance and cybersec to approve your docker container with R code in it, if I can have a similar model in our pre-approved workflow.

Would you ask a doctor for a few online resources on your medical treatment? You are completely disrespectful towards the architecture profession if you think a few online resources are all you need to get started, and that you'll just fix it in the future by hiring an architect to clean up your mess.

I will give you free advice, for once: don't trust any of the online simple architecture articles and take free advice. It will cost you much more in the end

Designing an architecture for your company is a multi week project, with lots of nuances and decisions. There is no best option, and especially without your business context it's literally impossible to recommend something good. From your question, it's clear your architecture expertise is very low

Do yourself a favour and get an architect, preferably from a dedicated local data specific consulting company and not a big box all-rounder like Accenture or TCS. Expect day rates of 1200-1500 if it's a short term project.

What were you expecting, someone to say 'just use Google cloud', and just go with it?

Don't fool yourself, if you are not ready to build an architecture, don't. Youll have to start over next year. You were hired for the wrong job then