this is one of the many strategies used in autogluon that enables it to outperform other autoML tools on most datasets:
https://arxiv.org/abs/2003.06505

https://arxiv.org/abs/2207.12560

One complaint people raise is regarding latency & complexity of deploying ensemble models, but there are many easy options to deal with this:
https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-indepth.html#accelerating-inference

[removed]

I don't know for certain that the CLIP was distilled also, that is an assumption on my part. Also EMAD has been fuzzy about exactly when the release would be.

Seems like a fairly contrived take. The bitter lesson is about a general principle---algorithms that scale well with more data and compute win---whereas the foundation model regime---pre-train a model on a large dataset, and then either fine-tune it or use the features of the foundation model for down-stream---is a very specific way of leveraging data and compute. I see little reason why other regimes of using large amount of data and compute might not be better.

Based on my own research, my prediction is that foundation models will die out for robotics once we have scalable online continual learners. Extremely large models that are always learning in real-time would replace the foundation models paradigm.

Expert knowledge could be encoded as rules whose output is used as features for a machine learning system. These rules would accept data you already have, and produce new data as conclusions which would be fed as extra variables to a modeling algorithm.

This is why I hang my hat on software engineering. You guys can fight over who has the better data or algorithms or more servers. Ultimately yall need stuff to be built, and that's where I get paid.

Machine learning is about learning from data. How you do that is wide open, but usually there is an optimisation algorithm involved somewhere or another. The optimisation algorithm itself does not learn from data, it is a tool that is applied in some scheme to learn from data.

It exactly matches Mitchell's definition of ml though.

> A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

https://towardsdatascience.com/what-is-machine-learning-and-types-of-machine-learning-andrews-machine-learning-part-1-9cd9755bc647#:~:text=Tom%20Mitchell%20provides%20a%20more,simple%20example%20to%20understand%20better%20.

Localisation error decreases the more data you have.

yeah i'm lost because i literally don't understand the distinction

Nope. By me. I guess it could habdle this hyper-specific futurist nerd humor?

It's a joke.

> If we could magically combine the reasoning ability of symbolic systems with the pattern recognition and generalization of neural networks, we would be getting very close to AGI imo.

I must be misunderstanding your meaning, because I don't see why this is particularly difficult. Train an AI to recognize deductive/mathematical reasoning and translate it into symbolic or mathematical logic. Run an automated proof assistant or computer algebra system on the result. Use the AI to translate back into natural language. Shouldn't be much more difficult than creating code, which ChatGPT can already do, and it would instantly eliminate 95% of the goofy problems LLMs get wrong.

It is satire, classify under news/theOnion

2 mins ago (12:30PM CST)

When did you get this?

I got this :

Thank you for applying for the Apple AIML residency program (July 2023–July 2024 cohort). Apple will be hosting an information session for selected applicants to learn more about the residency program and the interview timeline. This will also be a chance to meet the host managers and ask questions about our research teams and open residency positions.

Why is this tagged as news when it’s false, speculation, and no sources cited are cited?

On hands of Big Tech , ChatGPT is best user data harvesting tool.Users are more willing toask away most intimate details , their ideas , deep secrets, relationship problems to an ChatGPT. That is biggest treasure trove that google missed and MS gonna get it soon.

Written by ChatGPT right?

This guy makes a similar comparison in his blog but goes into a bit more detail than the tweet.

https://trees.substack.com/p/false-dichotomy-and-disillusion-in

Is it worth creating your own models or extensively fine-tuning foundational models? Probably not.

Literally just the history of all technology, which suggests saturation given enough time.

Mods can we ban this kind of garbage.

I'm guessing this is unintentional, but you talk like XetHub has been a thing for a while. I even went to see what had I missed, and for what I gathered it's a startup that just emerged from it's stealth status like, five days ago (from its twitter status it's more like three weeks, but still). They'll probably opensource the core tech as a freemium like almost everything else in the current convoluted MLOps landscape.

how do you stay up to date on ML/AI research? what are good feeds to follow?

For me this doesn’t really make all that much sense. Stacking is not a tool in itself, it’s rather a modelling approach. Also from what I can tell in the h2o docs it seems that their Stacked Ensemble has only one level to it and no meta-model. Thus rather than using e. g. the outputs (or probability distribution) of each model to train a new model, it will only use the output for any given input directly to return its final prediction, doesn’t it?