Technically, yes, but it's a similar relationship of humans being animals. If you say animals, the audience will assume you're not talking about humans.
This comment got me wondering whether loss of farmland in the US is a serious issue.
It looks like there's about 800 million acres of farmland in the US and we're losing about 2 million acres per year to the land being repurposed. Despite that, crop production has more than tripled in the past 70 years due to technological advances.
That said, economic effects, loss of farmland, and climate change have contributed to slower growth and higher variability of crop yields recently.
In the past decade there's been a modest 0.8% annual increase in crop production despite losing about 2 million acres per year.
MOE as an idea specific to neural networks has been around since 1991[1] . OP is probably aware, but adding for others following along, while MoE has roots in ensembling, there are some important differences: Traditional ensembles run all models in parallel and combine their outputs, whereas MoE uses a gating mechanism to activate only a subset of experts per input. This enables efficient scaling via conditional computation and expert specialization, rather than redundancy.
Emissions are trending downward because of shift from coal to natural gas, growth in renewable energy, energy efficiencies, among other things. Major oil and gas companies in the US like Chevron and ExxonMobil have spent millions on lobbying efforts to resist stricter climate regulations and fight against the changes that led to this trend, so I'd say they are the closest to these evil corporations OP described. Additionally, the current administration refers to doing anything about climate change a "climate religion", so this downward trend will likely slow.
The climate regulations are still quite weak. Without a proper carbon tax, a US company can externalize the costs of carbon emissions and get rich by maximizing their own emissions.
I'm most interested in building cool projects, and I have found AI to be a major multiplier to that effort. One of those cool projects was a custom coding tool, which I now use with all my projects, and continue to polish as I use it.
As far as productivity, it's hard for me to quantify, but most of these projects would not be feasible for me to pursue with my limited free time without the force multiplier of AI.
rust + wasm simulation of organisms in an ecosystem, with evolving neural networks and genes. super fun to build and watch.
>which AI you are using?
using chatgpt/claude/gemini with a custom tool i built similar to aider / claude code, except it's very interactive, like chatting with the AI as it suggests changes that I approve/decline.
>No sign so far of AI's usefulness slowing down as the complexity increases?
The AI is not perfect, there are some cases where it is unable so solve a challenging issue and i must help it solve the issue. this usually happens for big sweeping changes that touch all over the codebase. It introduces bugs, but it can also debug them easily, especially with the increased compile-time checking in rust. runtime bugs are harder, because i have to tell the ai the behavior i observe. iterating on UI design is clumsy and it's often faster for me to just iterate by making changes myself instead.
This has been my bible for performance work internally at Google. Kind of surprised they released it publicly, but I guess they removed all the Gemini-specific details.
I'm building this for myself because I want it too. I recommend you do the same, because it's been really fun to build and teaches you a lot about what LLMs can and cannot do well.
I plan to share it on Github, but waiting for my employer's internal review process to let me open source it as my own project, since they can legally claim all IP developed by me while employed there.