Are you implying I’m an llm? That’s a first.
1,836 karma · joined June 21, 2011
Helped build and sold a company -- Vidrovr
Working on something new...
https://github.com/danielmorozoff https://danmorozoff.substack.com
Section 5.5.2: The “Spiritual Bliss” Attractor State
The consistent gravitation toward consciousness exploration, existential questioning, and spiritual/mystical themes in extended interactions was a remarkably strong and unexpected attractor state for Claude Opus 4 that emerged without intentional training for such behaviors.
Vidrovr makes video useful. We use machine learning to turn disorganized heaps of media and tangles of live feeds into beautifully structured metadata to drive actions and business decisions for our users. We enable previously impossible applications, at previously impossible scales.
We are a small team with self-sustaining - and growing - revenues. Our notable major partners include The AP, the USAF, CNBC, the German Marshall Fund, DHS, FOX sports, and more.
Vidrovr fosters an open, supportive engineering culture emphasizing curiosity, self-teaching, and fearlessness. Engineering is a very small team with fewer than ten members, and there is ample opportunity to learn outside your domain of expertise. Our organization is fairly flat: you'll have direct access to company leadership, insight into strategic conversations, and avenues for direct involvement in major decisions.
We are fully remote. Vidrovr does not currently maintain a physical office. About half of our team is based in New York, with other members in Knoxville, Atlanta, Boston, Seattle, and Mexico City. Being fully remote, we highly value good communication skills!
Vidrovr provides unlimited PTO, and we're serious about it. We periodically require engineers to take time off if they have not recently. We value your independence: we expect quality engineering, not your soul.
Engineering: - Apply Here (Senior Full Stack): https://www.squarepeghires.com/jobs/8e9jkp/senior-software-e...
Vidrovr makes video useful. We use machine learning to turn disorganized heaps of media and tangles of live feeds into beautifully structured metadata to drive actions and business decisions for our users. We enable previously impossible applications, at previously impossible scales.
We are a small team with self-sustaining - and growing - revenues. Our notable major partners include The AP, the USAF, CNBC, the German Marshall Fund, DHS, FOX sports, and more.
Vidrovr fosters an open, supportive engineering culture emphasizing curiosity, self-teaching, and fearlessness. Engineering is a very small team with fewer than ten members, and there is ample opportunity to learn outside your domain of expertise. Our organization is fairly flat: you'll have direct access to company leadership, insight into strategic conversations, and avenues for direct involvement in major decisions.
We are fully remote. Vidrovr does not currently maintain a physical office. About half of our team is based in New York, with other members in Knoxville, Atlanta, Boston, Seattle, and Mexico City. Being fully remote, we highly value good communication skills!
Vidrovr provides unlimited PTO, and we're serious about it. We periodically require engineers to take time off if they have not recently. We value your independence: we expect quality engineering, not your soul.
Apply Here (FE Eng): https://www.squarepeghires.com/jobs/8zo1xp/senior-frontend-e...
Apply Here (Full Stack Eng): https://www.squarepeghires.com/jobs/pdw3nr/copy-senior-softw...
Apply Here (Senior Quality Assurance (QA) Engineer): https://www.squarepeghires.com/jobs/r034jr/senior-qa-enginee...
Apply Here (Application Eng): https://www.squarepeghires.com/jobs/pj736p/application-engin...
Vidrovr makes video useful. We use machine learning to turn disorganized heaps of media and tangles of live feeds into beautifully structured metadata to drive actions and business decisions for our users. We enable previously impossible applications, at previously impossible scales.
We are a small team with self-sustaining - and growing - revenues. Our notable major partners include The AP, the USAF, CNBC, the German Marshall Fund, DHS, FOX sports, and more.
Vidrovr fosters an open, supportive engineering culture emphasizing curiosity, self-teaching, and fearlessness. Engineering is a very small team with fewer than ten members, and there is ample opportunity to learn outside your domain of expertise. Our organization is fairly flat: you'll have direct access to company leadership, insight into strategic conversations, and avenues for direct involvement in major decisions.
We are fully remote. Vidrovr does not currently maintain a physical office. About half of our team is based in New York, with other members in Knoxville, Atlanta, Boston, Seattle, and Mexico City. Being fully remote, we highly value good communication skills!
Vidrovr provides unlimited PTO, and we're serious about it. We periodically require engineers to take time off if they have not recently. We value your independence: we expect quality engineering, not your soul.
Apply Here (FE Eng): https://www.squarepeghires.com/jobs/8zo1xp/senior-frontend-e... Apply Here (Full Stack Eng): https://www.squarepeghires.com/jobs/r4end8/senior-software-e...
https://www.frontiersin.org/articles/10.3389/fphar.2022.8841...
I recall Tenenbaum's lab had a similar paper a few years back.
Very cool to see though, you can make this at home with a very basic high voltage generator and a set of needles.
Here's another design for DIY: https://www.youtube.com/watch?v=wGq7LfjDyZM
Vidrovr (https://www.vidrovr.com) unlocks insights trapped in unstructured multimedia data, such as audio, image and video, generated by businesses and governments. Vidrovr uses AI to automate manual tasks that people perform to utilize these data assets. This leads to x5 efficiency gains in performing their work. Vidrovr spun out of Columbia University's AI lab and has been invested in by premiere investors including Samsung Next and Verizon.
Vidrovr’s processing engine can streamline business operations that utilize unstructured data, through the use of various AI models and tools to extract and model insights locked in your data. Vidrovr currently services clients in a number of industries - including media companies like the AP and financial institutions. Additionally, Vidrovr provides services to various USG organizations.
More details on our careers page: https://www.vidrovr.com/careers
We are looking for: - Full Stack Engineer - ML Pipeline Engineer / Data Engineering
If you are interested please shoot me a note: contact@vidrovr.com
The NSF Career grant affords you the latitude to pursue your research interests, but those are inevitably aligned with the academic process as you are in pursuit of tenure. So you need to publish and you need to produce publishable work- very unlikely you will choose only high risk moonshot projects as your likelihood of receiving tenure is directly correlated to those publications. Furthermore, your statement that the NSF or the university is not an investor, is also flawed imo. The NSF specifically asks for impact as it ties the money it allots a research to economic impact to the US- as it should since it is tax payer money. I.e your investor is the US. A similar argument can be drawn to the university that provides you startup lab funds- their long term goal is to receive publications/ patents/ prestige they can the monetize against through either selling those ideas/ receiving royalties or donations go their foundations. As you become a more famous researcher you also attract more masters students who pay a good deal of money to go to school there. They are in fact your investor only expecting a different ROI...
As for companies- how much do you think an engineer or ml researcher costs per year to a company especially the caliber in a Phd lab? 500k expense is pretty small. your assumption that a company wont give you 500k to fo work is an illusion- they do it's just not hard cash, they spend on resources that you use. An average PhD level base salary at a Fang - 200+k plus bonus/ equity closing on over 300k?
So the real question is pick your poison.
- Story telling is everything. It seems this is a huge lesson never taught in grad school.
- Technical details sometimes work against you. The author is absolutely right that getting the general thought process across is crucial.
- Who you get on your review committee tends to significantly skew the outcome. Can make or break your chances.
- I have known a lot of groundbreaking work funded by other money for these exact reasons. Sometimes good science is too far afield for people to understand. An anecdote i like from recent times is how Eric Betzig built the super res microscope. Here's the background.
https://arstechnica.com/science/2015/04/quitting-failures-a-...
TLDR: he won the Nobel...
The sad truth is $500k for a career grant seems like a lot when you're in the university, but when you get out and see where else money is being spent you realize how poor academia really is.
If you're looking to land a job at FAIR / Deepmind or Google Brain/ Nvidia Research as a researcher or ML scientist the expectations of knowledge are very different than 'data science'. These are research lab groups, that work on pushing the state of the art forward. They are also supported by great engineers, building awesome tools that improve ML research. So transitioning into this sort of role requires more than doing Kaggle competitions, it requires developing an intuition for the respective ML subfield / and trying new things and usually failing. i.e. this is a research role and will require a lot of study and learning
If on the other hand you are looking for datascience / take model and build pipeline to run AI, or perform hyper param sweeps or simply modify some model code, then on I would say that is much more engineering than research ML. This has a much lower barrier to entry coming from engineering and could be a good stepping stone to a transition into pure ML research.
On a more general note to consider when thinking of transitioning to ML is that these systems are probabilistic in nature vs purely deterministic as they are in more general software systems. People (ie humans) are bad at wrapping their heads around distributional processes - you can see this in all fields that deal with them (Quantum vs Classical Physics, Biological Systems etc).
In general I guess what I have seen is when engineers try to dip their toes into ML, what's required is a mindset shift in how to approach problems. Once that happens the depth of that shift determines the type of role with ML you wish to pursue.
I wonder how you square this idea of generalization with Godel's incompleteness theorems?
Again I believe the incentive structures at teaching universities properly match what students are there to accomplish, whereas at research unis they tend to be muddled.
all this to say i think undergrad math education is poorly designed/ incentivized and run in my experience and leads to a huge loss of talent from the practice and art of mathematics.