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#neuralnetworks

5 posts4 participants0 posts today
Continued thread
datatofu

Edit: Why 6 months? え～ the short answer is, "because I started 3 years behind everyone else." At the time, #datascience was not well defined as a career direction, and #ai models were still trying to gain traction. I.e. as simple #neuralnetworks that provided a computational advantage over their statistical model counterparts. So, if I did not do at least that much, it would have been in vain

Zoomers of the Sunshine Coast 🇨🇦

🧠 Welcome to the
curved space of everything
buzzsprout.com/2405788/episode
helioxpodcast.substack.com/p/1

August 06, 2025 • (S5 E11) • 16:12
Heliox: Where Evidence Meets Empathy 🇨🇦‬

🧠💥 Just discovered how your brain might be hiding explosive secrets in curved spaces. New research reveals why AI suddenly "gets it" - and it's not what you think. The math that's reshaping memory itself. #NeuralNetworks #AI #brainscience

Thanks for listening today!

If you enjoy the show, please visit the podcast
On Apple Podcasts, please scroll to the bottom,
and give it a rating.
On Spotify, head to the show and click the three-dot icon to rate.
⭐⭐⭐⭐⭐
Thank you!

#ArtificialIntelligence#ScientificBreakthrough#HigherOrderInteractions
Zoomers of the Sunshine Coast 🇨🇦

AlphaGo Moment for
Model Architecture Discovery

buzzsprout.com/2405788/episode

helioxpodcast.substack.com/pub

August 02, 2025 • ( S5 E7) • 17:39
HELIOX: WHERE EVIDENCE MEETS EMPATHY 🇨🇦‬

We're living through what might be the last era where humans are the limiting factor in AI development. That's not hyperbole—it's the stark conclusion emerging from breakthrough research that should terrify and exhilarate us in equal measure.

________________________________________

#ArtificialIntelligence#MachineLearning#AIResearch
Tero Keski-Valkama

In-context learning has been consistently shown to exceed hand-crafted neural learning algorithms across the board.

But it's limited by the length of the context. Even with neural architectures allowing context to grow to infinity, these come with high costs and scaling problems.

Is there a way to incorporate new knowledge learned in-context back into neural network weights?

Of course there is!

Let's imagine we have a lot of data, sequences of instructions and outputs where in-context learning happens.

From this data we can produce a dataset of synthetic data which presents the new knowledge learned. We can continually train the model with this dataset.

Of course this is super slow and inconvenient. But as a result we'll get a dataset with in-context learning happening, and old model weights against new model weights.

We can use this data to train a neural programmer model directly!

That model would take in the context as such, and if in-context learning has happened in those interactions, it can predict the changes to the neural network weights which would happen if the long and heavy synthetic data pipeline had been run.

Instead of the heavy pipeline, we can just use the neural programmer model to directly update the large model weights based on the in-context learning it experienced, to crystallize the learnings into its long-term memory, not unlike what hippocampus does in the human brain.

#AI#LLMs#NeuralNetworks
Hidden Dragon ☑️

Your Wi-Fi may know who you are, literally. “WhoFi,” a new system from Rome’s La Sapienza University, identifies people with 95.5% accuracy using signals bouncing off their bodies. No cameras. No lights. Just basic routers and neural networks. It even works through walls. Groundbreaking tech, or surveillance nightmare? The line just got blurrier.

#WhoFi#WiFiTracking#PrivacyTech
Continued thread
datatofu

Edit: "But your work is #datascience about #neuralnetworks and #ai and data regulation is #cybersecurity so why would you need to be concerned about that?"

Dude. Bro. Dudebro. Broham. Ham slice. If you do not understand the relationship between cybersecurity, ai, and data processing, then there is nothing I can say to help you. Either you or your company are in some deep shit

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