> Anthropic’s stance is fundamentally incompatible with American principles.
I don't think that Secretary Hegseth is qualified to speak on American principles.
Cheating on multiple spouses[1], being an active alcoholic, and being accused of multiple sexual assaults and paying off the accusers[3] is fundamentally incompatible with being a Secretary of Defense and a good leader.
Also, this violates freedom of speech and will probably get shot down in the courts.
Little Snitch is a user-friendly, software-level blocker, only – use with caution.
Just FYI: LittleSnitch pre-resolves DNS entries BEFORE you click `Accept/Deny`, if you care & understand this potential security issue. Your upstream provider still knows whether you denied a query. Easily verifiable with a PiHole (&c).
I liken the comparison to disk RAIDs: a RAID is not a true backup; LittleSnitch is not a true firewall.
You need isolated hardware for true inbound/outbound protection.
>Just FYI: LittleSnitch pre-resolves DNS entries BEFORE you click `Accept/Deny`, if you care & understand this potential security issue. Your upstream provider still knows whether you denied a query. Easily verifiable with a PiHole (&c).
This also feels like an exfil route? Are DNS queries (no tcp connect) logged/blocked?
When you see the LittleSnitch dialogue (asking to `Accept/Deny`), whatever hostname is there has already been pre-resolved by upstream DNS provider (does not matter which option you select). This software pares well with a PiHole (for easy layperson installs), but even then is insufficient for OP's attack.
Can’t help but feel like you’re overestimating the competency of the average voter in these effected areas; a breath after yours—though not necessarily your own—may condemn these people for being undereducated, out of touch with culture or subject to corporate grifters.
As a human who grew up during the Cold War, nuclear conflict is horrifying.
From an AI standpoint, a nuclear strike likely has several benefits:
- It reduces friendly casualties and probably overall enemy casualties.
- It shortens conflict time.
- Reduces damage to infrastructure. (Rebuild costs)
- Is likely cheaper to deploy overall, compared to conventional weapons. This assumes the stated parameters indicate the nuclear weapons are already manufactured.
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Edit: blibble brings up good counterpoints below. I was thinking in 1945 terms, which is flawed.
Exactly. The AI just does the math based on the goals you've given it. AI would have happily nuked Hiroshima and Nagasaki because it would have estimated that doing so would save the lives of X number of US soldiers in a land invasion, and given a goal of achieving "unconditional surrender now", it wouldn't have considered that a land invasion wasn't imminently necessary and therefore killing 200,000 civilians wasn't the right moral choice.
Nuclear weapons are war deterrent, not an actual weapon unless used against a country which is not a nuclear country. Using nuclear weapons pretty much guarantees both sides will be wiped down so it most definitely nowhere near logical
Except real life is not a program, and the input data is flawed (human and machines' errors). The acceptance tests are just predictions, based on, again, fallible analyses of the flawed data from history. So many layers of errors that compound
Depends on the audience. If it's entrenched enterprise business software? Probably.
Generally, I'd avoid using legacy DotNet and Windows servers for anything I care about. But the reality is that you often can't, outside of the startup space.
I even occasionally use DotNet on Linux and it's very good. I get the sense that the tier 1 platform for DotNet (non-legacy) is Linux, as far as Microsoft engineers are concerned.
My experience was very similar to yours. I was definitely not prodigious. I was slightly gifted, for the most part, and exceptionally gifted in other non-notable ways.
I think one thing that hurt me, in particular, is that I repeatedly got told "you're really going to go places some day!". And, so I waited for things to happen.
It took me way too long to realize I had to make things happen.
If you're reading this and you're young and gifted, you need to make things happen. You'll have people help you along the way, but odds are, you will never be discovered and have riches lavished upon you.
This comment really resonates with me (I’m old now, for context). I was put in the gifted program, but the truth is I wasn’t very good at math while I was wildly strong in language. I was a pretty solitary kid who read a lot of quality literature and was endlessly curious about the world. I ended up getting kicked out of the program early in elementary school for being "immature," but the gifted label stuck and I kept hearing how smart I was.
By high school, I was a 1.4 GPA student who was also on the Academic Decathlon team winning state-level medals. My upbringing was extremely abusive, which definitely contributed to the academic problems, but what I really needed was someone in my corner pushing me to explore and actually try. Being told I was smart wasn’t just useless, it was actively harmful. I became afraid to try and leaned on what came naturally (hence doing well on Decathlon tests) while consistently failing to finish things (no homework, no papers... and the GPA shows it).
What would have made a huge difference for me was being explicitly taught how and why to study, how to take and review notes, and how to manage my time. And just as importantly, someone consistently emphasizing that effort matters a lot more than being "smart."
Even now, I’m honestly surprised by how many people I work with in tech equate being "smart" with being good at math, algorithms, or pattern recognition, while seeming almost oblivious to some pretty big gaps in other areas. That mindset isn’t doing anyone any favors.
Based on that list it boils down to 2 things it seems:
- cost (no longer a problem)
- too much code needed and it bloats the data pipelines. Does anyone have any actual evidence of this being the case? Like yes, code would be needed, but why is that innately a bad thing? Bloated data pipelines feels like another hand-wave when I think if you do it right it’s fine. As proven by Waymo.
Really curious if any Tesla engineers feel like this is still the best way forward or if it’s just a matter of having to listen to the big guy musk.
I’ve always felt that relying on vision only would be a detriment because even humans with good vision get into circumstances where they get hurt because of temporary vision hindrances. Think heavy snow, heavy rain, heavy fog, even just when you crest a hill at a certain time of day and the sun flashes you
Just for the record though, Musk isn't blindly anti-LIDAR. He has said (and I think this is an objective fact) that all existing roads and driving are based on vision (which is what all humans do). So that should technically be sufficient. SpaceX uses LIDAR for their docking systems.
I would argue that yes, we do use vision but we get that "lidar depth" from our stereo vision. And that used to be why I thought cameras weren't enough.
But then look at all the work with gaussian splatting (where you can take multiple 2d samples and build a 3d world out of it). So you could probably get 80% there with just that.
The ethos of many Musk companies (you'll hear this from many engineers that work there) is simplify, simplify, simplify. If something isn't needed, take it out. Question everything that might be needed.
To me, LIDAR is just one of those things in that general pattern of "if it isn't absolutely needed, take it out" – and the fact that FSD works so well without it proves that it isn't required. It's probably a nice to have, but maybe not required.
Humans aren't using only fixed vision for driving. This is such a tiresome thing to see repeated in every discussion about self driving.
You're listening to the road and car sounds around you. You're feeling vibration on the road. You're feeling feedback on the steering wheel. You're using a combination of monocular and binocular depth perception - plus, your eyes are not a fixed focal length "cameras". You're moving your head to change the perspective you see the road at. Your inner ear is telling you about your acceleration and orientation.
And also, even with the suite of sensors that humans have, their vision perception is frequently inadequate and leads to crashes. If vision was good enough, "SMIDSY" wouldn't be such an infamous acronym in vehicle injury cases.
the issue is clearly attention not vision when it comes to humans. if we could actually process 100% of the visual information in our field of view, then accidents would probably go down a shit load.
Humans have both issues. There are many human failures which are distinctly a vision issue and not attention related, e.g. misestimation of depth/speed, obscured or obstructed vision, optical focus issues, insufficient contrast or exposure, etc.
But how many of those crashes not caused by inattention could have been avoided with less idiocy and more defensive driving? I mean, yes, we can’t see as well in fog, but that’s why you should slow down
Again, I'm still not saying that humans don't make bad decisions. I'm saying that, unequivocally, they also get into accidents while paying attention and being careful, as a result of misinterpretation or failure of their senses. These accidents are also common, for example:
* someone parking carefully, misjudges depth perception, bumps an object
* person driving at night, their eyes failed to perceive a poorly lit feature of the road/markings/obstacles
* person driving and suddenly blinded by bright object (the sun, bright lights at night)
* person pulling out in traffic who misinterprets their depth perception and therefore misjudges the speed of approaching traffic
* people can only focus their eyes at one distance at a time, and it takes time to focus at a different distance. It is neither unsafe nor unexpected for humans to check their instruments while driving -- but it can take the human eye hundreds of milliseconds to focus under normal circumstances -- If you look down, focus, look back up, and focus, as quick as you can at highway speeds, you will have travelled quite a long distance.
These type of failures can happen not as a result of poor decision making, but of poor perception.
> But how many of those crashes not caused by inattention could have been avoided with less idiocy and more defensive driving?
Most of them.
We can lump together "inattention" and "idiocy" for the purposes of this conversation, because both could be massively alleviated by a good self-driving car without lidar.
If you look at the parallel comments, you'll see that the majority of accidents and fatalities indeed come from these two factors combined (two-thirds coming from distraction, speeding, and impaired driving), and that kube-system is having to resort to ridiculous fallacies to try to dispute the empirical data that is available.
> There are many human failures which are distinctly a vision issue and not attention related
Which are a tiny minority. The largest causes of crashes in the US are attention/cognition problems, not vision problems. Most traffic systems in western countries (probably in others, too, but I don't have personal experience), and in particular the US, are designed to limit visibility problems and do so very effectively.
> That sounds more like a personal opinion, because I don’t think that data is particularly easy to objectively collect.
That sounds like a personal opinion?
Maybe do the bare minimum of research before spouting yours.
DOT says that only 5% of crashes are caused by low visibility during weather events.[1]
In 2023, the combined causes of alcohol, speeding, and distracted driving (all cognitive/attention issues) caused 67% of highway deaths. [2]
I was able to find these in 30 seconds. You did zero research to confirm whether your belief was correct before asserting that my claim was opinion. That's pathetic.
> Regardless it is irrelevant to the point.
And your point is therefore irrelevant to the discussion at hand, because the person you were replying to did not claim that vision had no safety impact, but that it had little safety impact:
> the issue is clearly attention not vision when it comes to humans. if we could actually process 100% of the visual information in our field of view, then accidents would probably go down a shit load.
...and, as we can clearly see, the issue is attention (and some bad decision making), not vision.
None of those things you cited is “human vision or perception”
“Low visibility during weather events” is a small subset of this.
A ridiculously common example of the limitations of human vision is when people hit curbs parallel parking because of the inherent limitations of relying on depth perception to estimate the exact location of the vehicle when it cannot otherwise be directly seen. Go look in a parking lot and see how common curbed wheels are.
Also, NHTSA estimates that they don’t have any information for 60% of incidents, because they go unreported.
> None of those things you cited is “human vision or perception”
> “Low visibility during weather events” is a small subset of this.
You're still refusing to do the most basic research or even read my comment:
> In 2023, the combined causes of alcohol, speeding, and distracted driving (all cognitive/attention issues) caused 67% of highway deaths.
Do the math. 100% - 67% is 33%. Even literally not opening Google, you can already deduce that the maximum fraction of fatalities caused by vision is 33%.
Given that you aren't interested in reading or researching and instead just want to push your opinion as fact, I think your claims can be safely discarded.
Edit: Because you're editing your comment because you realize that you're making an absolute fool of yourself:
> A ridiculously common example of the limitations of human vision is when people hit curbs parallel parking
A completely irrelevant distraction - this causes virtually zero accidents and even fewer fatalities, and you know it.
> Also, NHTSA estimates that they don’t have any information for 60% of incidents, because they go unreported.
Aha, so now you actually did research, and found that all of the available data supports my claims, so you're attempting to undermine it. Nice try. "Estimates" vs. actual numbers isn't really a contest.
Come back when you have actual data - until then, you're just continuing to undermine your own point with your ridiculous fallacies and misdirections - because if you actually had a defensible claim, you'd be able to instantly pull out supporting evidence.
I'm not arguing about fatalities or relative percentages of contributing factors, nor am I arguing that alcohol/speeding/attention are not all also issues. They are, you're right.
The only thing I argued is that "lapses in human visual perception are responsible for some crashes", which is a fact.
Attention is perhaps the limiting factor, but being able to look in two directions at once would help, and would help greatly if we had more attention capacity. E.g. anytime you change lanes you have to alternate between looking behind, beside, and in front and that greatly reduces reaction time should something unexpected happen in the direction you aren't currently looking...
In theory, a computer should be able to do the same. It could do sensor fusion with even more sense modalities than we have. It could have an array of cameras and potentially out-do our stereo vision, or perhaps even use some lightfield magic to (virtually) analyze the same scene with multiple optical paths.
However, there is also a lot of interaction between our perceptual system and cognition. Just for depth perception, we're doing a lot of temporal analysis. We track moving objects and infer distance from assumptions about scale and object permanence. We don't just repeatedly make depth maps from 2D imagery.
The brute-force approach is something like training visual language models (VLMs). E.g. you could train on lots of movies and be able to predict "what happens next" in the imaging world.
But, compared to LLMs, there is a bigger gap between the model and the application domain with VLMs. It may seem like LLMs are being applied to lots of domains, but most are just tiny variations on the same task of "writing what comes next", which is exactly what they were trained on. Unfortunately, driving is not "painting what comes next" in the same way as all these LLM writing hacks. There is still a big gap between that predictive layer, planning, and executing. Our giant corpus of movies does not really provide the ready-made training data to go after those bigger problems.
Putting your point another way, in order to replicate an average human driver’s competence you would need to make several strong advancements in the state of the art in computer vision _and_ digital optics.
In India (among others), honking is essential to reducing crashes
We often greatly underestimate / undervalue the role of our ears relative to vision. As my film director friend says, 80% of the impact in a movie is in the sound
I'm positive that Teslas have gyroscopes and accelerometers in them. Our eyes actually have a fairly small focal length range due to the fixed nature of our cornea and only being able to change focal length by flexing the crystalline lens.
What I said has to do with "vision only systems" (what Musk has claimed will be enough to do FSD) with sensor fusion systems (what everybody else having success in this space does)
Mentioning gaussian splatting for why we don't need lidar depth is a great example of Musk-esque technobabble; surface level seemingly correct, but nonsense to any practitioner. Because one of the biggest problems of all SfM techniques is that the results are scale ambiguous, so they do not in fact recover that crucial real-world depth measurement you get from lidar.
Now you might say "use a depth model to estimate metric depth" and I think if you spend 5 minutes thinking about why a magic math box that pretends to recover real depth from a single 2D image is a very very sketchy proposition when you need it to be correct for emergency braking versus some TikTok bokeh filter you will see that also doesn't get you far.
This is not really true if you have multiple cameras with a known baseline, or well known motion characteristics like you get with an accelerometer+ wheel speed.
Sufficient to build something close to human performance. But self driving cars will be held to a much higher standard by society. A standard only achievable by having sensors like LiDAR.
if a self driving car had the exact vision of humans it would still be better because it has better reaction times. never mind the fact that humans cant actually process all the visual information in our field of view because we dont have the broad attention to be able to do that. its very obvious that you can get super human performance with just cameras.
Whether thats worth completely throwing away LiDAR is a different question, but your argument is just obviously false.
This reminds me of the time I was distantly following a Waymo car at speed on 101 in Mountain View during rush hour. The Waymo brake lights came on first followed a second or two later by the rest of the traffic.
Even if they weren’t going to be held to a higher standard for widespread acceptance, tens of thousands of people a year in the us die due to humans driving badly. Why would we not try to do better than that?
Sufficient if all else were equal. But the human brain and artificial neural networks are clearly not equal. This is setting aside the whole question of whether we hope to equal human performance or exceed it.
That doesn't matter. It's not like we use 100% of our brain capacity for driving.
In fact, that's why radio/music/podcasts thrive. Because we're bored when we drive. We have conversations, etc. We daydream.
As long as the skills relevant to actually driving are on parity with humans, the rest doesn't matter.
In fact, in a recent podcast, Musk mused that you actually may have a limit of how smart you want a vehicle model to be, because what if IT starts to get bored? What will it do? I found that to be an interesting (and amusing) thought exercise.
To do gaussian splatting anywhere near in real time, you need good depth data to initialize the gaussian positions. This can of course come from monocular depth but then you are back to monocular depth vs lidar.
> and the fact that FSD works so well without it proves that it isn't required
The reports that Tesla submits on Austin Robotaxis include several of them hitting fixed objects. This is the same behavior that has been reported on for prior versions of their software of Teslas not seeing objects, including for the incident for which they had a $250M verdict against them reaffirmed this past week. That this is occurring in an extensively mapped environment and with a safety driver on board leads me to the opposite conclusion that you have reached.
They aren't. Tesla has logged some 800k total miles with their robotaxi vehicles, including miles with safety drivers. Waymo has logged 200M driverless miles. That's 0.4% of the mileage, with the most generous possible framing.
My understanding is that there's more data processing required with cameras because you need to estimate distance from stereoscopic vision. And as it happens, the required chips for that have shot up in price because of the AI boom.
But I think costs were just part of the reason why Elon decided against Lidar. Apparently, they interfere with each other once the market saturates and you have many such cars on the same streets at the same time. Haven't heard yet how the Lidar proponents are planning to address that.
Lidar critics like to pretend that anti-collision is not a well-studied branch of Computer Science and telecoms. Wifi, Ethernet and cellphones all work well simultaneously, despite participants all sharing the same physical medium.
The points linked repeatedly focus on cost and complexity as justification, even explicitly stating musks desire to minimise
components in Kaparthy’s list.
They don’t focus on safety or effectiveness except to say that vision should be ‘sufficient’. Which is damning with faint praise imho.
If that link was to try and argue that the removal of sensors makes perfect sense i have to point out that anyone that reads that would likely have their negative viewpoint hardened. It was done to reduce cost (back when the sensors were 1000’s) and out of a ridiculous desire by Musk for minimalism. It’s the same desire that removed the indicator stalk i might add.
Instead of betting on RADAR and LIDAR HW getting better and cost going down, they went with vision only approach. Everybody in this field knows the strengths and weakness of each system. Multi-modal sensor fusion is the way to go for L4 autonomy. There is no other way to reduce the risk. Vision only will never be able to achieve L4 in all the weather conditions. Tesla may try to demonstrate L4 in limited geography and in good weather conditions but it won't scale.
Karpathy’s main points:
Extra sensors add cost to the system, and more importantly complexity. They make the software task harder, and increase the cost of all the data pipelines. They add risk and complexity to the supply chain and manufacturing.
Elon Musk pushes a philosophy of “the best part is no part” which can be seen throughout the car in things like doing everything through the touchscreen. This is an expression of this philosophy.
Vision is necessary to the task (which almost all agree on) and it should also be sufficient as well. If it is sufficient, the cost of extra sensors and tools outweighs their benefit.
Sensors change as parts change or become available and unavailable. They must be maintained and software adapted to these changes. They must also be calibrated to make fusion work properly.
Having a fleet gathering more data is more important than having more sensors.
Having to process LIDAR and radar produces a lot of bloat in the code and data pipelines. He predicts other companies will also drop these sensors in time.
Mapping the world and keeping it up to date is much too expensive. You won’t change the world with this limitation, you need to focus on vision which is the most important. The roads are designed to be interpreted with vision.
You’re missing the point. None of those have the same integrations into other software and APIs that the OpenClaw plugins provide. And not everyone wants to write their own minimal implementation. This is why OpenClaw is popular.
I don't think that Secretary Hegseth is qualified to speak on American principles.
Cheating on multiple spouses[1], being an active alcoholic, and being accused of multiple sexual assaults and paying off the accusers[3] is fundamentally incompatible with being a Secretary of Defense and a good leader.
Also, this violates freedom of speech and will probably get shot down in the courts.
1. https://en.wikipedia.org/wiki/Pete_Hegseth#Marriages
2. https://en.wikipedia.org/wiki/Pete_Hegseth plus multiple recent media pieces
3. https://en.wikipedia.org/wiki/Pete_Hegseth#Abuse_and_sexual_...
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