Comments URL: https://news.ycombinator.com/item?id=47598403

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=47510255

Points: 4

# Comments: 0

Separately, I just had another terrible interaction with my own data through Google Labs Flow. That is the site that is serving the latest Nano Banana image and Veo video generations. My takeaways on the quality and value and issues with those "world models" are for another time. Here I'm pointing out another unique "dark pattern" that product managers seem compelled to apply: "if you want us to save your data in a database, then you have to let us view that data and train models on it". It's ridiculous, either I can have my data automatically deleted and have sessions be completely "dumb", or I can submit my soul for eternity and "allow any reviewer to analyze".

Beware, founders and developers relying on Google. Don't be surprised if you wake up with your data held hostage. Don't be surprised when you realize your intellectual property can either be deleted or stolen, but not simply saved.

Thanks Google.

Comments URL: https://news.ycombinator.com/item?id=47483249

Points: 9

# Comments: 1

Now it means company is stupid.

Why are we still interviewing like its 1999?

In his Nobel Prize winning speech, Demis Hassabis ends by discussing how he sees all of intelligence as a big tree-like search process.

https://youtube.com/watch?v=YtPaZsasmNA&t=1218

Reversion to the past is not preparation for the future.

The training objective is clever.

The 50+ filters at Ecodash.ai for 90,000 plants came from a custom RAG model on top of 800,000 raw web pages. Because LLM’s are expensive, chunking and semantic search for figuring out what to feed into the LLM for inference is a key part of the pipeline nobody talks about. I think what I did was: run all text through the cheapest OpenAI embeddings API… then, I recall that nearest neighbor vector search wasn’t enough to catch all relevant information, for a given query to be answered by an LLM. So, I remember generating a large number of diverse queries, which mean the same thing (e.g. “plant prefers full sun”, “plant thrives in direct sunlight”, “… requires at least 6 hours of light per day”, …) and then doing nearest neighbor vector search on all queries, and using the statistics to choose what to semantically feed into RAG.

His comments are relatively humble and based on public prior work, but it’s clear he’s working on big things today and also has a big imagination.

I’ll also just say that at this point “the cat is out of the bag”, and probably it will be a new generation of leaders — let us all hope they are as humanitarian — who drive the future of AI.

Side note: previously I was funded by NSF and NASA to study such questions from biophysics and astrobiology.

That said, this was a delightful read. I did not realize or conceive of mitochondria as, like bacteria in our bodies, independent living networks with unique genomes, evolution, and flows of information and energy.

Reading about the health benefits of “external mitochondria” made me think about when I hug my dog: are we exchanging mitochondria, perhaps?

Comments URL: https://news.ycombinator.com/item?id=41862400

Points: 2

# Comments: 0

I remember in 2012 for my MS thesis on Deep Neural Networks spending several pages on Boltzmann Machines and the physics-inspired theories of Geoffrey Hinton.

My undergraduate degree was in physics.

So, yes, I think this is an absolutely stunning award. The connections between statistical entropy (inspired by thermodynamics) and also of course from biophysics of human neural networks should not be lost here.

Anyways, congratulations to Geoffrey Hinton. And also, since physics is the language of physical systems, why not expand the definition of the field to include the "physics of intelligence"?

From their official explanation page (https://www.nobelprize.org/uploads/2024/09/advanced-physicsp...): "With ANNs the boundaries of physics are extended to host phenomena of life as well as computation."

Comments URL: https://news.ycombinator.com/item?id=41636445

Points: 1

# Comments: 0

As a fellow deep learning modeler of Earth systems, I can also say that what they're doing really is 100% top notch. Congrats to the team and YC.

The counter proposal was indeed funded by the City of Miami, to point out how ridiculous it would be to have a 20 foot concrete wall around the city.

As a local resident, I loved seeing this sad 3D render in particular, which even has a graffiti on it nearly spelling "Berlin": https://i0.wp.com/dirt.asla.org/wp-content/uploads/2022/09/0...

In seriousness, it was really cool to see the counter proposal's "nature-based solution" which would design 39 acres of distributed barrier islands around the coastline, to block storm surge naturally.

Being trained to say words like “Alternatively”, “But…”, “Wait!”, “So,” … based on some metric of value in focusing / switching elsewhere / … is basically brilliant.