Richard Dawkins is 85 to be fair, just like Bernie Sanders is 84 when he made similar comments.
The other guy worked on Google's AI safety team where one would expect he'd have a basic grasp of how the technology works before making outlandish claims.
You open with corporations want less regulations then give an example of corporations using law to protect their interests around jailbreaking? Just like they did with copyright/IP rules and the million rules around cars
You should ask the DIY diabetes community what they think of FDA regulations preventing modifications of medical devices.
Being reductive about this stuff is not a helpful framing.
A Chinese TV channel spent a bunch of money doing ADAS tests and Tesla came out on top of all the Chinese brands, including all the LIDAR systems. Although tests were all in the day time.
There's also been talk of companies pushing a hybrid LIDAR+vision approach using custom hardware since it's complex to merge the two datasets. So the answer might eventually be somewhere in between instead of companies choosing one or the other depending on costs.
- Tesla's vision only approach seems a lot more competent than the Lidar suites from smaller Chinese makers. Perhaps I misjudged how necessary Lidar was to achieve safe driving.
- Virtually all of the Chinese car infotainment were basically a 1:1 copy of Tesla's. I couldn't find any that genuinely tried something unique lol
> - Tesla's vision only approach seems a lot more competent than the Lidar suites from smaller Chinese makers. Perhaps I misjudged how necessary Lidar was to achieve safe driving.
Three things can be simultaneously true:
* Tesla's cameras are sufficient for some scenarios.
* Tesla's cameras are insufficient for other scenarios.
* A system with good data and bad algorithmic processing is still going to be bad. The Chinese vehicles almost always fail the tests because they see the obstacle but drive into it anyway.
Yeah it's interesting hearing their engineering logic, that fewer sensor types means less sensor collision and faster iteration, where iteration speed is really what matters. I also think people overhyped lidar because they don't understand it, and human behavior is to associate things we don't understand to magic. It's not magic, it performs poorly in inclination weather and can have issues with resolution over range and data processing (although lidar does do a lot of things well).
All of this said, once Karpathy left they have slowly looked at adding new sensors (recently radar), so who knows what the future for Tesla's sensor suite holds.
> I also think people overhyped lidar because they don't understand it
Speaking as a person who understands it extremely well and who has an advanced degree in computer vision, I'm sure that internet randos did, but I promise the people who actually know about the failure modes of the different modalities did not. I don't really expect you to take my word for it, but maybe this will spark an interest in investigating the failure scenarios of 3D reconstruction using cameras in computer vision. Just know that Google is an absolute top tier juggernaut in the CV/ML/AI research world, and they don't use lidar out of ignorance.
> less sensor collision
This isn't a real thing for anyone doing a good job. A sensor can be good for a scenario or it can be bad for a scenario. More sensors feeding input only gives you gradations of accuracy instead of binary accuracy. Having gradations of accuracy is an unambiguously good thing. When you only have one sensor, you have no way to know whether in the moment it is feeding you an optical illusion. That's what it means for something to be an optical illusion. But when you have multiple sensors of different modalities, then you have meaningful information about whether local disagreement between the different modalities means that one is better or worse than the other, because you can contextually characterize the failure scenarios of each.
> It's not magic, it performs poorly in inclination weather and can have issues with resolution over range and data processing (although lidar does do a lot of things well).
Inclement, not inclination. And I hate to be the bearer of bad news, but cameras also do poorly in inclement weather and have issues with resolution over range, and the solutions are identical for both (superresolution, temporal blending, alternate wavelengths, stereo correspondence, etc).
Tesla people always say (said?) things like "Well humans only drive with their eyes, so cars should be able to as well," but that's not a true statement about what humans have in relation to what Teslas have. Humans have many more different sensor modalities than what Tesla's cameras give. Teslas have single-view fixed-focus cameras that, for much of the FOV, can only reconstruct structure from shape assumptions (object detection and classification) and inter-frame changes (optical flow) coupled with sensation of the vehicle's motion. That's all they get. It's not bad at all, especially coupled with advanced machine learning, but you do have more than that coupled with even more advanced machine learning. When you as a human drive, in addition to what Teslas have (you do also have them), you also have binocular stereopsis cues, autofocus lens convergence cues, vehicle-independent motion parallax cues, and the ability to manipulate shade cover so you don't get blinded. Are all those extra cues necessary for every scenario? No, obviously not. Do they help though? Yes. Try driving with only one eye open and without moving your body or head at all. You can absolutely do it, but you won't be as good as you would with both eyes open and free movement.
The Tesla vehicles failed 4 of the 9 SAE Level 2 tests, during ideal weather conditions. How is it reasonable to operate an autonomous taxi service with these results?
Fun fact, for Tesla's FSD/AI reporting, it doesn't consider any incident where airbags didn't deploy to count for accident stats. This includes situations where the airbag system cannot deploy because of catastrophic damage.
It also, strangely, doesn't count fatality incidents.
China was the offshore haven and built their own domestic economy off the expertise while still maintaining very low income taxes and 15% corporate tax for tech companies.
That idea that you could have a) stopped globalism as lots of new giant markets opened up after the 1970s and b) be better off paying 2x for everything by banning everything foreign is just a fantasy. It’s just nostalgia fueled radical politics that both the far left and right latch on to.
The other part of the story that gets ignore is the administrative state exploding in the US/Canada post 1970s, where making new industry and development became very difficult making other countries more attractive while the cost of living exploded.
So instead of becoming competitive all we’re left with is these ideas of the government forcing domestic industry by using national security as an excuse to justify the backwards economics of it all.
> Ultimately, the OpenAI president had to read some of the most embarrassing entries aloud in front of a jury and a packed courthouse, as well as over a YouTube livestream that peaked at around 1,200 viewers.
Anyone know which livestream they are talking about?
I've tried these larger agent skillsets in the past and felt it was a waste of time because it was just doing too much. Just like vim it's often better to pick and choose from the community instead of installing skills like they are an IDE. Skills are way too personal because every dev and dev team is different. So better to treat these as a reference for your own config rather than bulk install someone else's config.
Same for MCPs and system instructions, there are a lot of people that just install everything without understanding it, cluttering their context, wasting >50k tokens for these tools they don't need and then complain that they need to pay >100$ per month because they reach their limits too fast.
The other guy worked on Google's AI safety team where one would expect he'd have a basic grasp of how the technology works before making outlandish claims.
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