at that time all the compute resources in the world would not have been enough to train the models from even the last ~6 years or so, probably more.

H100 to GB200 saw a 50x increase in efficiency, for example.

VR anyone?

It's much closer to Salem and Bend than it is Portland, for instance.

Nectar/Green Cross are my gotos, both of them are well below the prices yall are discussing. Green cross in particular has better stuff than the other venues as well. Maybe try shopping in Salem instead of Portland? Dunno, I never go into the city for weed.

try this place in salem, really nice stuff and great prices. "Reserve" is their top-of-the-plant pickings that go for $160 oz. Not a big difference between their bulk (75-100) and reserve.

https://www.greencrosscanna.com/commercial-street

Portland's dewpoint I think peaked around 65F.

given a bunch of reads of short(50-300bp) or long (1000-100,000 bp) lengths (we use different algorithms for each) you need to resolve a 1D sequence that holds all of those continguous subsequences for each chromosome. Genome assembly is often described as finding a hamiltonian cycle among the data. For short reads, we use debrujin graphs to avoid the hardness of a complete hamiltonian. for long reads, MinHash has become a popular heuristic.

But this can be tricky, for instance, in species that isn't haploid -- you actually have two possible genomes that you need to correctly assemble. Sometimes the difference is a base or two, but sometimes its much longer stretches that can appear as large 'bubbles' in the assembly graph.

The assembly problem can be harder or easier depending on the organism, for instance, Wheat is notoriously hard to assemble due to the fact that it has 3 mostly (but not completely) distinct genomes. The Norway Spruce is composed of ~80B basepairs, which will put strain on even the biggest machines. Oh and its mostly repeats.

Repeats are _everywhere_, and they can be really long (sometimes on the order of millions of nucleotides). Also -- depending on the species youre assembling, the repeat content and the kind of repeats can change. You also can get different errors from the library preparation, again related to the species of origin.

Using lab techniques is particularly helpful, as we can leverage molecular and genetic information. BACs (baceterial artificial choromosomes) can be used to break the genome into smaller continguous chunks and to act as 'anchors' for the larger assembly problem. Recombination rates of different SNPs can be used to infer spatially local segments of DNA, and optical tags can be added and imaged to provide physical anchors for certain sequences. Some organisms can be bred into pure lines, with limited heterozygosity. Expressed RNA transcripts can be used in a similar manner, as they are the concatenation of ordered exons. Combining these methods is typically the best way to get a good genome assembly.

Some of the bubble resolution heuristics could probably be improved with deep learning, but getting the right data for that is probably more effective with traditional graph algorithms. Also -- you really only need a good genome assembly once, and sometimes it doesnt even need to be all that good.

Genome assembly is a _really_ fascinating area of research, and honestly, the right direction is probably to represent genomes as something other than 1D sequence that more accurately reflects biology. Deep learning is still looking to make its mark on genomics, and unfortunately its not well suited for much of the discovery efforts such as genome assembly.