I think the biggest systemic issue with middle management is that its mostly a political position that comes with political challenges. Middle managers have to be the political interfaces that try to implement and affect policy given to them by upper management, all the while giving feedback on said policy from the line workers to upper management. Good middle managers can really shield good workers from a lot of upper management idiocy. Bad middle managers exacerbate those issues. Its a thankless position that can really make things better, but often is stuck doing a mediocre job due to lack of training and bad circumstances.
I'll be honest, why even fucking with any other kind of geo-engineering other than high altitude sulfur dioxide injection. We literally have seen a big jump in warming from problably removing the sulfuric byproducts of cargoship fuel. At its heart global warming is an issue of energy in vs energy out. Its a lot harder to remove billions of tons of co2 to increase energy out versus using a couple thousand tons of sulphur dioxide to reduce energy in. Maybe not as a permanent fix, but a better fix than this nonesense.
There's a lot of really interesting work in neuroevolution that has the potential to make some really interesting unsupervised training regimes. I think theres some really interesting possibilities for unique encoding schemes like ACE encoding to speed up training and provide much smarter behavior out the other end. Especially, if "genes" can form reusable elements of neural topology that makes scaling networks faster. Reusing components all over body is how we fit such complexity in the relatively little unique DNA we have. The other interesting thing about using genetic algorithms for a portion of training/network mapping is that allows you to have heterogenous networks, so you can have simulations or representations of astrocyte/glial behaivor easily get integrated with neural networks. With traditional training methods it's a massive fucking pain to train a non-feed forward network.
I do think that languages like Elixir and other cpu concurrent strong tools can really be leveraged to make some dynamite libraries.
My pet project has been trying to use elixir with NEAT or HyperNEAT to try and make a spiking network, then when thats working decently drop some glial interactions I saw in a paper. It would be kinda bad at purely functional stuff, but idk seems fun. The biggest problems are time and having to do a lot of both the evolutionary stuff and the network stuff. But yeah the ubiquity of free datasets does make it easy to train.
Seconding Dyson Sphere Program its great, though it can be a bit of a tale of 2 games. Pre-ILs and Post-ILs which can get a bit samey since most of your production stacks become relatively samey. You can turn off the combat enemies, though honestly I'm enjoying my play through with them because they force a lot more consideration in not only how you expanded but why and when you expand.
We can not even get close to saying our current networks can be even close to synapses in performance or functional because architecturally we still use feedforward networks no recursion, no timing elements, very static connections. Transitors will definitely have some advantages in terms of being able to synchronize information and steps to an infinitely better degree than biological neurons, but as long as we stick with transformers it's the equivalent of trying to get to space by stacking sand, could you get there eventually? Yes, but there's better ways.
Just because papers are getting published doesn't mean its actually gaining any traction. I mean we have known that time series of signals recieves plays a huge role in how bio neurons functionally operate and yet we have nearly no examples of spiking networks being pushed beyond basic academic exploration. We have known glial cells play a critical role in biological neural and yet you can probably count the number of papers that examine using an abstraction of that activity in neural net, on both your hands and toes. Neuroevolution using genetic algorithms has been basically looking for a big break since NEAT. Its the height of hubris to say that we have peaked with transformers when the entire field is based on not getting trapped in local maxima's. Sorry to be snippy, but there is so much uncovered ground its not even funny.
This is amazing work, but to me it highlights some of the biggest problems in the current AI zeitgeist, we are not really trying to work on any neuron or ruleset that isnt much different from the perceptron thats just a sumnation function. Is it really that suprising that we just see this same structure repeated in the models. Just because feedforward topologies with single neuron steps are the easiest to train and run on graphics cards does that really make them the actual best at accomplishing tasks? We have all sorts of unique training methods and encoding schemes that don't ever get used because the big libraries don't support them. Until, we start seeing real varation in the fundamental rulesets of neuralnets we are always just going to be fighting against the fact these are just perceptrons with extra steps.
I'm fascinated by Elixir, but more so for using to do neural network experiments with more unique topologies, training methods, and activation functions. Being able to spin every neuron out into a process just seems too damn elegant for doing work with spiking neural networks. Will it be as fast as the python stacks? Probably not, but not being constrained to the implicit assumptions in a lot of those libraries will be worth a lot. Plus, there's already good neural network and genetic algorithm libraries for Elixir. I really think this langauge is gonna go places.