dcl

I have to explain this to managers, execs and stakeholders all the time. ML models work great for systems where the rules/dynamics do not change over time. With Forecasting, in a lot of domains where you want a forecast, everything is subject to change - laws, policies, regs, customers appetites, competitors behaviours, etc.

That is going to be absolutely wild for whoever can access/afford it.

A CPU only benchmark for typical data-science and ML tasks that I can run on a few different systems that will help me figure out what the next system I should buy is. I have no idea what Apple silicon is like for these tasks.

Deliberately producing misaligned and deceitful AI systems now. Great.

Ferrari uses cars like this to test loyalty. If you want to get 'on the list' buying cars like this is one of the ways to do it, especially if you haven't spent considerable $ with them before.

imagine no more: https://chatgpt.com/s/m_6a14fc70630c8191bedf8e06913f4d34

This. If you want to get on the list to buy the new supercars, you're going to have to start here. And you better add some expensive options.

This is the car you will need to buy to get on the list to buy the Ferrari you kind of want - but not the Ferrari you really, really want, that will cost you a lot more.

Anyone else think Britain is going to eject the Royal Family within 100 years?

Because it's a 4gb download?

S&P gets you dividends though, so the interpretation of that chart is tricky. Holding the S&P, you can still do better than holding gold, even when the GOLD/S&P ratio is positive.

How much am I missing out by using the standard launcher my Pixel comes with? I haven't played with different launchers since the Nexus 4 and Android 2/3 (I think).

Are you getting confused with the photoelectric effect experiment?

What happens when you prompt one of these kind of models with de_dust? Will it autocomplete the rest of the map?

edit: Just tried it and it doesn't, but it does a good job of creating something like a CS map.

Is this why I cannot seem to fine tune YOLO models on a Apple M4? The loss hits nan after a few batches. Same code using Windows PC and Google Colab CPU and GPU is fine...

At a previous $dayjob at a very large financial institution, it's however many clusters are present in the strategy that was agreed to by the exec team and their highly paid consultants.

You find that many clusters and shoehorn the consultant provided categories on to the k clusters you obtain.

Is there any 'small' OCR models around?

Say I only care about reading serial numbers from photos in a manufacturing process, not whole document parsing. Using a 3B param model to do this seems like a bit of overkill...

A friend doing bioinformatics told me about this at uni, it was definitely one of those "i can't believe this is doable" sort of things.

Anyone tried the 20B param model on a mac with 24gb of ram?

https://en.wikipedia.org/wiki/Thinking_Machines_Corporation for the reference

ML algorithms are compression algorithms, the trained models are compressed data.

Ahh rightio. That's a shame.

"Bring your own AI" or "Provide your AI API access key" will probably be coming to a lot of services/apps that we want 'our' AI's to interact with.

I can see this also bringing strongly tiered AI's, there will be commodity/free AI's a and expensive ones for rich people/power users.

Doesn't support NVIDIA GPU's!? Is this a display or gaming specific thing?

All the ML people are using NVIDIA GPU's on Linux.

That is what made me click.

You have awakened some incredible memories. I know exactly what you are talking about.

Rearden metal...?

thank you for this laugh

Wouldn't have even been a believable April fools joke even 5 years ago.

Search engines are still required for me. LLM's still get lots of very important things wrong.

Last night, I asked Claude 3.7 Sonnet to obtain historical gold prices in AUD and the ASX200 TR index values and plot the ratio of them, it got all of the tickers wrong - I had to google (it then got a bunch of other stuff wrong in the code).

Also yesterday, I was preparing a brief summary of forecasting metrics/measures for a stakeholder and it incorrectly described the properties of SMAPE (easily validated by checking Wikipedia).

I constantly have issues with my direct reports writing code using LLM's. They constantly hallucinate things for some of the SDK's we use.