It only didn’t help with the first incident of accidental injuries (makes sense, almost everyone has one early on regardless) but it was still also associated with reduced recurrence
fwiw (and I am not an expert) in my understanding, Swift also has guaranteed memory safety without a GC (using automatic reference counting). not sure how it compares to Rust in that aspect
I think actually 4o image generation in ChatGPT is still a tool call with a prompt to an “image_gen” tool, I don’t think the generator receives the full context of the conversation. If you do a ChatGPT data export and inspect the record of a conversation using 4o image gen, you’ll see it’s a tool call with a distinct prompt, much like it was with dalle. And if you pass an image in as context, it’ll pass that to the tool as well.
I imagine this is for anti-jailbreak moderation reasons, which is understandable
I was going to suggest chess position recognition, AFAIK it's a completely unsolved computer vision task (once a position is recognized, I think analysis is well solved by, say, a stockfish tool for the LLM, but there is interesting work going on with language models themselves understanding chess)
The sample output is very poor. Cool demo, but really just emphasizes how much of a hit product the NotebookLM team has managed to come up with, ostensibly with more or less the same foundation models already available.
I feel similarly about NotebookLM, but have noticed one odd thing - occasionally Host A will be speaking, and suddenly Host B will complete their sentence. And usually when this happens, it's in a way that doesn't make sense, because Host A was just explaining something to or answering a question of Host B.
I'm actually not sure what to make of that, but it's interesting to note
the EM dataset for this connectome, FAFB, is only a few hundred TB. as a rule of thumb volume electron microscopy datasets are on the order of 1 PB / cubic millimeter, and the fly brain is much smaller than 1 mm3
Looks like Princeton tops the Ivy League (and perhaps all universities?) at $27M per faculty member. I often like to call it "a hedge fund with hobbies"
We are so far ahead in the case of these models - we already have the complete wiring diagram! In biological systems we have only just begun to be able to create the complete neuronal wiring diagrams - currently worms, flies, perhaps soon mice
one thing i noticed, the first sentence on the page says: "Keeping the internet open is crucial, and part of being open means Reddit content needs to be accessible... "
but if you use the text-to-speech reader it reads "Keeping the internet open is crucial, and part of being open means public Reddit content... " (emphasis mine)
I remember in high school precalc, we learned about the Taylor series, and my teacher told us that it was how trig functions were actually implemented on calculators. Well I looked it up and found that it was actually CORDIC, and went and had some fun implementing it in TI Basic
There are extremely effective techniques, but it is not really solved. The current techniques still require human proofreading to correct errors. Only a fraction of this particular dataset is proofread.
we are nowhere near whole human brain volume EM. the next major milestone in the field is a whole mouse brain in the next 5-10 years, which is possible but ambitious
The methods used here are state of the art. The problem is not just turning 2D slices into a 3D volume, the problem is, given the 3D volume, determining boundaries between (and therefore the 3d shape of) objects (i.e. neurons, glia, etc) and identifying synapses
Nice to see what was probably already an internal resource now published and open for comment. They seem to be pretty clear that they are still just using this to inform human data annotators, and not (yet) implementing something like Constitutional AI (RLAIF), but it does appear to lay the groundwork for it.