Had a math prof in undergrad that once said, “this guy” 61 times in a 50 minute lecture!
915 karma · joined December 14, 2017
I’ve had friends who have been on cocktails like these, and one of them once said something like, “I’ve been depressed before, and this is not that. I’m not depressed. I don’t have the emotional capacity to be depressed. This is more like a total emotional blank slate.”
She was basically a robot for a few months. Incapable of really any emotions, including sadness, anxiety, frustration, etc. Suffice to say, she also didn’t have the emotional drive to push her towards positive things like deciding on how to spend her weekend free time.
Thankfully she’s changed her meds and is feeling overall better (if, admittedly, at the price of some emotional stability).
> I have an image of a hand drawn workflow diagram. I’d like to turn it into a mermaid.js file.
(with the image attached)
1. Draw a DAG of whatever pipeline I’m working on with pen and paper.
2. Take a photo of the graph, mistakes and all.
3. Ask ChatGPT to translate the image into mermaid.js
Given how complicated the pipelines are that I’m working with and the sloppiness of the hand drawn image, it’s truly amazing how well this workflow works.
https://chatgpt.com/share/66ff28e2-ea74-800b-a230-86d562f60f...
It's tricky. On the one hand, it's obviously not appropriate to be flippant about patient privacy. On the other, it's clearly that advancements in human health are being hindered by our current approach to (dis)allowing researchers access to data.
You gave the example of unstructured PDF, but there are challenges with structured docs as well. We’ve run into docs that are hard to chunk because of this deeply nested and repeated structure. For example, there might be a long experimental protocol with multiple steps; at the end of each step, there’s a table “Debugging” for troubleshooting anything that might have gone wrong in that step. The debugging table is a natural chunk, except that once chunked there are a dozen such tables that are semantically similar when decoupled from their original context and position in the tree structure of the document.
This is one example, but there are many other cases where key context for a chunk is nearby in a structured sense, but far away in the flattened document, and therefore completely lost when chunking.
"SFARI’s mission is to improve the understanding, diagnosis and treatment of autism spectrum disorders by funding innovative research of the highest quality and relevance."
SFARI in turn funds a lot of foundational neurological and rare disease research, since autism is such a common phenotype.
In addition to the _quality_ of any proposed alternative(s), computational speed also has to be a consideration. I've run into multiple situations where you want to measure similarities on the order of millions/billions of times. Especially for realtime applications (like RAG?) speed may even out weight quality.
It really makes me frustrated about the ways I was introduced to statistics: brute force memorization of seeming arbitrary formulas.
We are addressing challenges faced by families of children with rare diseases who are seeking a diagnosis, and our solution is a two-sided marketplace for rare disease genomic insights.
On one side, we will offer children who have a rare disease—and an inconclusive whole genome assay—another chance at a diagnosis. A majority of families who order a whole genome test do not receive their much needed diagnosis and are rarely provided with clear followup options. On the other side of the market, we will use the genomic data we collect to identify orphan drug leads, which we will sell to biopharma clients who are creating personalized medicines.
I'm happy to chat about any questions or comments you have!
Here’s a recent attempt at quantifying the costs across all rare disease:
https://chiesirarediseases.com/assets/pdf/chiesiglobalraredi...
https://news.ycombinator.com/item?id=36014796
It seems like his graduate student did him a great disservice by feeding the questions to 3.5
What’s interesting about the game is that, at first pass, there’s no ambiguity. All questions need to be answered with “Yes” or “No”. But many questions asked during the game actually have answers of “it depends”.
For example, I was thinking of “peanut butter” and chatGPT asked me “Does it fit in your hand?” as well as “Is it used in the kitchen?”. Given my answers, chatGPT spent the back half of its questions on different kitchen utensils. It never once considered backing up and verifying that there wasn’t some misunderstanding.
I played three games with it, and it made the same mistake each time.
Of course, playing the game via text loses a lot of information relative to playing IRL with your friends. In person, the answerer would pause, hum, and otherwise demonstrate that the question asked was ambiguous given the restrictions of the game.
Regardless, it was clear that chatGPT wasn’t accounting for ambiguity.
But as someone who feels most at home with Python, I always love to see new competition in this space.
Out [2]: 'https://github.com/crisdosyago<p>cris@dosycorp.com'
It looks like your about changed more recently than my BQ data pull?