766 karma · joined February 18, 2015
I wrote my own site parsing toolkit which removed the friction I have with the current ones. It's now easy to add new locations quickly to scale.
Methods like formula driven supervised learning exist to arrive a good pretrained weight state, but could this procedure be generalized for specific datasets or flavors of input data.
Remote: Yes, 3 years remotely working
Willing to relocate: No
Technologies: Physics-informed AI, interpretable networks, fundamental & applied research
Résumé/CV: https://gergltd.com/cv_isaac_gerg.pdf
Email: first.last at gergltd.com
Former research professor. Worked for DARPA, IC, ONR.
High on thinking, low on theater.
NNs can certainly learn beyond what they were trained for as new architectures have demonstrated. See benchmarks for any if the dan hendryks imagenet corruption dataset benchmarks (imagenet c r and a). Data + architecture work together to enable.
I have work using neural networks trained on synthetic aperture sonar SAS imagery using language only. The SAS datsets are extremely hard to come by. The dataset I used was from parts if world I can assure you we're never in the training set. My approach exceeds what clip models can do zero shot wise. Therefore, the network classified imagery if a modality and environment 100 percent not guaranteed to be in the training data.
Now I have 20 YOE in software too before the booon of AI.
https://www.linkedin.com/search/results/all/?keywords=%23ape...
Too many codes or old or gate kept behind proprietary walls. Many are old and don't use the newest acceleration techniquea to make the simulation fast. Additionally, none of them scale using aws. I want SAS/SAR image to be easy to generate for anyone.
Not a chance. The DoD has massive pockers which and INCREDIBLY SPREAD OUT. You can't underestimate how spread this money is. The DoD has maybe a 64 GPU cluster and ALMOST NO ONE USES IT FOR DEEP MODEL TRAINING. Even contractors end up working with DGX boxes to do all their training.
As of 2023, I was doing the largest Deep learning training runs out of anyone I have known in the industry and I've been in the industry for 20 yeras. The second best groups behind mine were using 4 GPU locally machines that they had to purchase on contract.
There's no way the DoD can train these models themselves, not even close. They are COMPLETELY DEPENDENT ON INDUSTRY. I was the PM for a DARPA program in 2023 and SAME PROBLEM. They had no compute or would rely on university compute if a program had a university partner. YOU HAVE NO IDEA HOW FAR BEHIND THE DOD IS IN THIS SPACE.