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#foundationmodels

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#wwdc25 #FoundationModels #Xcode #LLM

Has anyone seen the Foundation Models actually working? Am I holding it wrong? I am not getting any responses either in the playground or app, and there is a bunch of this crap in the console when running the app.

Running Xcode 26 beta on macOS 26 beta.

Are there regional restrictions? Xcode AI chat also is “unavailable in my region”.

ChatGPT & Co für sicherheitsrelevante Einsatzfelder nutzbar machen
Die @Cyberagentur hat #Forschungsprogramm #HEGEMON zur Bewertung und Anpassung generativer #FoundationModels für sicherheitskritische Anwendungen gestartet.
Entwicklung neuer Benchmarks für KI-Modelle und Anwendung auf komplexe Aufgaben aus dem Geoinformationswesen.
Mehr Infos zur Vergabe: t1p.de/qcuq4
#Cyberagentur #HEGEMON #KI #Sicherheit #Benchmarking #PCP #Geoinformation #Forschung #KITransparenz

Can time series (TS) #FoundationModels (FM) like Chronos zero-shot generalize to unseen #DynamicalSystems (DS)? #AI

No, they cannot!

But *DynaMix* can, the first TS/DS foundation model based on principles of DS reconstruction, capturing the long-term evolution of out-of-domain DS: arxiv.org/pdf/2505.13192v1

Unlike TS foundation models, DynaMix exhibits #ZeroShotLearning of long-term stats of unseen DS, incl. attractor geometry & power spectrum, w/o *any* re-training, just from a context signal.
It does so with only 0.1% of the parameters of Chronos & 10x faster inference times than the closest competitor.

It often even outperforms TS FMs on forecasting diverse empirical time series, like weather, traffic, or medical data, typically used to train TS FMs.
This is surprising, cos DynaMix’ training corpus consists *solely* of simulated limit cycles & chaotic systems, no empirical data at all!

And no, it’s neither based on Transformers nor Mamba – it’s a new type of mixture-of-experts architecture based on the recently introduced AL-RNN (proceedings.neurips.cc/paper_f), specifically trained for DS reconstruction.

Remarkably, DynaMix not only generalizes zero-shot to novel DS, but it can even generalize to new initial conditions and regions of state space not covered by the in-context information.

We dive a bit into the reasons why current time series FMs not trained for DS reconstruction fail, and conclude that a DS perspective on time series forecasting & models may help to advance the #TimeSeriesAnalysis field.

Replied in thread

@Techmeme This is the danger of closed source

These are knowledge models, and they only output what they are fed with

And no, they won’t magically develop ’reasoning skills’ and be able to sift through propaganda. NOT when it’s part of the training

To think otherwise means you don’t know shit how they work

They obey statistics. Training data for #ai #foundationmodels should be subject to public #academic scrutiny. Otherwise the models are bound to fall for flooding attacks

Check out the new Helmholtz Foundation Model Initiative (#HFMI) website at hfmi.helmholtz.de/ #FoundationModels

The Helmholtz Association provides ideal conditions for developing such forward-looking applications: an abundance of data, powerful supercomputers for training the models, and extensive expertise in artificial intelligence.

Our goal is to develop Foundation Models across a wide spectrum of research fields to address the major questions of our time.