Good riddance, Tom Bombadil. I don't care how merry a fellow he was, those were my least favorite chapters of Fellowship.
In grad school I worked with MRI data (hence the username). I had to upload ~500GB to our supercomputing cluster. Somewhere around 100,000 MRI images, and wrote 20 or so different machine learning algorithms to process them. All said and done, I ended up with about 2.5TB on the supercomputer. About 500MB ended up being useful and made it into my thesis.
Don't stay in school, kids.
Well if you liked PoE I doubt you'll like D4. It's a much simpler game. Sadly my only advice is to try GD and Last Epoch again. I've got hundreds of hours in the former and I just got 10 hours into the latter.
Last Epoch feels like a more approachable PoE. I thoroughly enjoy how the skills interplay with one another, but I still prefer the itemization in Grim Dawn.
The only reason I'm not playing GD currently is because I have too many QoL mods installed so my cloud saving doesn't work, but I can cloud save for Last Epoch for my steam deck lmao.
They raised my rent 20% over two years and priced me out of two apartments. Glad to see progress.
Believe it or not, I studied this in school. There's some niche applications for alternative computers like this. My favorite is the way you can use DNA to solve the traveling salesman problem (https://en.wikipedia.org/wiki/DNA_computing?wprov=sfla1)
There have been other "bioprocessors" before this one, some of which have used neurons for simple image detection, e.g https://ieeexplore.ieee.org/abstract/document/1396377?casa_token=-gOCNaYaKZIAAAAA:Z0pSQkyDBjv6ITghDSt5YnbvrkA88fAfQV_ISknUF_5XURVI5N995YNaTVLUtacS7cTsOs7o. But this seems to be the first commercial application. Yes, it'll use less energy, but the applications will probably be equally as niche. Artificial neural networks can do most of the important parts (like "learn" and "rememeber") and are less finicky to work with.
Ooh, I know this one. It comes from old English, meaning "small thing"
We've got some really good theories, though. Neurons make new connections and prune them over time. We know about two types of ion channels within the synapse - AMPA and NMDA. AMPA channels open within the post-synapse neuron when glutamate is released by the pre-synapse neuron. And the AMPA receptor allows sodium ions into the dell, causing it to activate.
If the post-synapse cell fires for a long enough time, i.e. recieves strong enough input from another cells/enough AMPA receptors open, the NMDA receptor opens and calcium enters the cell. Typically an ion of magnesium keeps it closed. Once opened, it triggers a series of cellular mechanisms that cause the connection between the neurons to get stronger.
This is how Donald Hebb's theory of learning works. https://en.wikipedia.org/wiki/Hebbian_theory?wprov=sfla1
Cells that fire together, wire together.
Actually, neuron-based machine learning models can handle this. The connections between the fake neurons can be modeled as a "strength", or the probability that activating neuron A leads to activation of neuron B. Advanced learning models just change the strength of these connections. If the probability is zero, that's a "lost" connection.
Those models don't have physical connections between neurons, but mathematical/programmed connections. Those are easy to change.
Heck, we barely know how neurons work. Sure, we've got the important stuff down like action potentials and ion channels, but there's all sorts of stuff we don't fully understand yet. For example, we know the huntingtin protein is critical to neuron growth (maybe for axons?), and we know if the gene has too many mutations it causes Huntington's disease. But we don't know why huntingtin is essential, or how it actually effects neuron growth. We just know that cells die without it, or when it is misformed.
Now, take that uncertainty and multiply it by the sheer number of genes and proteins we haven't fully figured out and baby, you've got a stew going.
I loved FFTA so much, and did not care as much for FFTA2 on DS. Easily sunk 1000 hours into my GBA copy.
siRNA and miRNA: Are we a joke to you?
Devastating loss for the science community. I used this database in my PhD, and didn't expect it to shut down ever.