As a tractor owner. Two things, the DPF & SCR (>=75hp) on a tractor is not a great idea --
1) Tractors are typically owned by low margin businesses (i.e. farmers) that need to be repaired in the field AND need to be repaired quickly, else you loose a crop. Adding complexity to tractors literally can cost the farm.
2) The actual emissions reduced is questionable. Tractors run significantly less than a truck, like 50-100x less often. Further there are at least 2x more trucks sold per year
3) To run the SCR system, the engine had to run hot for like 20 minutes burning extra fuel and required DEF (yet more input costs)
3) The emissions they are trying to reduce with the these are likely not excessively harmful from a tractor; largely because most tractors who need an SCR system is >75hp, which also means they're typically used on a large farm (100+ acres). Which dissipates the risks substantially.
For reference my 2022 Kubota tractor repeatedly had issues with the DPF / SCR system, mostly the software to enforce environmental rules. This lost us ~$20k one year due to the tractor being knocked out for a week (I was mid-cut for 140 acre hay, rained & rotted in the field post-cut).
For reference, I was very much ready to bypass the SCR system, but decided against it to keep the warranty. It had nothing to do about "right to repair", I figured out exactly how to bypass it.
Regarding "distracted driving" causing cyclist fatalities, my point was more that bicyclists have a higher risk rate. If we're already going down this path for safety, banning bicycling would have a higher impact than reducing fatalities ~10% which is what the EU claimed it could prevent.
Regarding the claim that "pretty much all fatalities on bikes are the result of being killed by drivers", that's simply not true. It also largely depends on laws and location.
We actually found the Mistral Small 4, quantized to 4bit was comparable to Qwen 3.6 27B and is roughly the same size. At least from our experience on our use cases, the quantization of the Mistral model worked far better than trying to quantize the Qwen family.
Fully agree to your point though, Mistral in general is far behind where I'd expect and Qwen in particular is crushing it at the smaller sizes.
Personally, I'd consider anything 20B params and above a "medium" model. Small being <20B and large >100B. I think obviously we can get to the huge 1-2T param models, but frankly the margin of accuracy improvement for the speed hit is kinda insane (1-2% for many metrics).
They're both considered medium tractors with similar weights for a similar purpose. I forget the official weights, but it's something like 100-175HP is mid-tier.
You can get a kubota M5-111 with a closed cab for $70k-100k, cheaper than these. Plus zero percent financing though then for 5-7 years. Well built and a comparable class in terms of weight and horse power.
People aren’t buying them for price, but the first sentence discusses it as if it’s relevant.
My assumption are farmers are trying to skirt the eco rules for vehicles in some way. Which by the way is insanely annoying and has caused issues for all the farmers I know at one point or another. Worse, you can’t fix the ecosystems on your own so you have to get them serviced costing quite a bit and importantly putting your tractor out of commission for a while. It’s why older tractors have a premium
If you ever chat with older folks pre-90's much of this information was accessible fairly easily. It only changed with the push by the government to crackdown on Waco, Oklahoma City bombing, militias and other related groups. There was then a campaign to make it "normal" to limit free speech on the subjects, where as these books were available before.
I think the whole thing where AI should make information less available is a difficult battle and one which I personally oppose, but do understand. Free speech and information isn't the problem, it's the people, actions and substances they create.
After the age of the internet, I think it's been a forever loosing battle to limit information, it's why we couldn't stop cryptography, nuclear weapon proliferation, gun distribution, drug distribution, etc. The AI is just another battle ground, one which, if they actually do manage to control could definitely create some walls to this information, but not stop it.
More scary, is that the AI as it becomes pervasive and stop people from asking certain questions, because they don't know they should ask... but that's unrelated to the risk of mass death.
When I read that I'm always personally confused. He had a commanding voice and had an aurora of being above it all. But when you listened and watched what he actually did, he seemed very political in my mind, though perhaps more of a moderate(?).
He even advocated for world government, endorsed politicians, etc.
Feels a little like clickbait "MAGA-themed", never heard of Converso.
That said, the analysis itself is interesting and worth a look, if nothing else it's a general pattern you can follow for many chat applications to see how secure it is.
Bourdain actually joked about killing himself in the exact manner and location in, which he did. When I heard it happened, my wife and I both recalled the same times he'd mentioned it. It wasn't a surprise really.
Bourdain had been referencing Hunter S Thompson and the way he went out for years. He'd also repeatedly mentioned wanting to go out in southern France after a great day. Bourdain generally had the same "vibe" as Thompson as well. Here's Thompson's last note to his wife:
> No More Games. No More Bombs. No More Walking. No More Fun. No More Swimming. 67. That is 17 years past 50. 17 more than I needed or wanted. Boring. I am always bitchy. No Fun—for anybody. 67. You are getting Greedy. Act your old age. Relax — This won't hurt.
To me, it wasn't a surprise at all. My wife and I even had discussed when we thought it would happen. The main thing about Bourdain was that people could relate to him and he wrote excellent prose. He seemed authentic and he went out on his terms, which is what he wanted and was the way he lived.
There’s a lot of indications that we’re currently brute forcing these models. There’s honestly not a reason they have to be 1T parameters and cost an insane amount to train and run on inference.
What we’re going to see is as energy becomes a problem; they’ll simply shift to more effective and efficient architectures on both physical hardware and model design. I suspect they can also simply charge more for the service, which reduces usage for senseless applications.
Back in 2016 - 2018 my work at Capital One resulted in a modified C-RNN style architecture that was producing gpt-2 level results. Using that model we were able to build a general purpose system that could generate data for any dataset (with minimal training, from scratch):
At the time it was clear to all on the team that RNNs, just like transformers later on, are general purpose frameworks that really only require more data and size to function. In the 2018-2020 era and probably today, they are slower to train. They also are less prone to certain pitfalls, but overall had the same characteristics.
In the 2019-2020 I was convinced that transformers would give way to better architecture. The RNNs in particular trained faster and required less data, particularly when combined with several architectural components I won’t get into. I believe that’s still true today, though I haven’t worked on it in the last 2-3 years.
That said, transformers “won” because they are better overall building blocks and don’t require the nuances of RNNs. Combined with the compute optimizations that are now present I don’t see that changing in the near term. Folks are even working to convert transformers to RNNs:
I suspect over time the other methods my team explored and other types of networks and nodes will continue to expand beyond transformers for state of the art LLMs
In 2016 & 2017 my team at Capital One built several >1B parameter models combining LSTMs with a few other tricks.
We were able to build generators that could replicate any dataset they were trained on, and would produce unique deviations, but match the statistical underpinnings of the original datasets.
We built several text generators for bots that similarly had very good results. The introduction of the transformer improved the speed and reduced the training / data requirements, but honestly the accuracy changed minimal.
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