What’s the margin of error on a free return orbit burn though? Isn’t there a scenario of being pointed slightly in the wrong direction or burning for too long throwing them off?
Yea, I can't imagine being a woman and having to deal with some of these drivers.
This doesn't compare, but as a man I get really put off by the amount of invasive questions (where I work, where my family is from, etc) when I'm just trying to get from point A to point B.
I'm a mid-millenial FWIW, so I very much remember a world of only having old school taxis.
There are many cases when the owner wouldn’t be liable as well, like if the victim was performing an illegal act like attacking the owner or dog, or trespassing. If a child isn’t following the law or being supervised by a parent, some consequences are inevitable and the driver isn’t instantly liable. For example, if a student jumps in front of a car in an attempted suicide, it would be very hard for a driver to avoid that in certain situations.
In a technical sense, maybe, but it's all going to be about optics. They have a responsibility to handle the situation well even if it's not their fault, and the public will hold them accountable for what they deem the involvement was, which may not be the actual scenario.
> In a technical sense, maybe, but it's all going to be about optics.
Indeed, it is, and that is exactly why Waymo will have to accept some responsibility. I can bet that internally Waymo's PR and Legal teams are working overtime to coordinate the details with NHTSA. We, the general public, may or may not know the details at all, if ever.
However, Waymo's technical teams (Safety, etc) will also be working overtime to figure out what they could have done better.
As I mentioned, this is a standard test, and Waymo likely has 1000s of variations of this test in their simulation platforms; they will sweep across all possible parameters to make this test tighter, including the MER (minimum expected response from the AV) and perhaps raise the bar on MER (e.g. brake at max deceleration in some cases, trading off comfort metrics in those cases; etc.) and calculate the effects on local traffic (e.g. "did we endanger the rear vehicles by braking too hard? If so, by how much??" etc). All these are expected actions which the general public will never know (except, perhaps via some technical papers).
Regardless, the PR effects of this collision do not look good, especially as Waymo is expanding their service to other cities (Miami just announced; London by EOY2026). This PR coverage has potential to do more damage to the company than the actual physical damage to the poor traumatized kid and his family. THAT is the responsibility only the company will pay for.
To be sure, my intuition tells me this is not the last such collision. Expect to see some more, by other companies, as they commercialize their own services. It's a matter of statistics.
The performance of a human is inherently limited by biology, and the road rules are written with this in mind. Machines don't have this inherent limitation, so the rules for machines should be much stronger.
I think there is an argument for incentivising the technology to be pushed to its absolute limits by making the machine 100% liable. It's not to say the accident rate has to be zero in practice, but it has to be so low that any remaining accidents can be economically covered by insurance.
At least in the interim, wouldn’t doing what you propose cause more deaths if robot drivers are less harmful than humans, but the rules require stronger than that? (I can see the point in making rules stronger as better options become available, but by that logic, shouldn't we already be moving towards requiring robots and outlawing human drivers if it's safer?)
That is capitalism capitializing. I sorta think it is also the computer going from a geek toy to mass adoption and incentives changing. 3D printers for example are good but if they go mainstream they'll become like HP 2D printers on the enshittification axis
Disagree; this is completely taxpayer funded and we deserve to know every detail relevant to mission status. In this scenario knowing what illness and why it's grounds for a return is very relevant. That said, I can see NASA delaying information release to figure out a good strategy for it while still respecting any wishes of the sick astronaut with regards to disclosure.
When you drive on a taxpayer funded road, should you disclose publicly your medical history? When the taxpayer funded US military kidnap a foreign president on your name, should you disclose publicly your medical history ? When you use the taxpayer funded GPS etc...
What a strange take. Does this also apply to every soldier in the armed forces? Seems your criteria is equally applicable there.
The relevant people that can do the research and write future policies based on the data obviously will have the information. Not sure what good you think that you personally having it can do.
> What a strange take. Does this also apply to every soldier in the armed forces? Seems your criteria is equally applicable there.
Why? There are different rules for different endeavors, specializations, and roles. NASA is ostensibly for exploration, in an expansive sense. Hiding information of any kind, seems antithetical to the over-arching mission.
> The relevant people that can do the research and write future policies based on the data obviously will have the information.
Given recent events, this assumption of fidelity is not something I can subscribe to, for the rest of my days.
A single soldier having a medical issue generally doesn't cancel a multi-month mission costing some X large sum of money, requiring another Y large sum of money to even finish cancelling it (returning their unit home).
Therefore it's not relevant and not needed for the public to know.
Yes, I’m sure aircrew never get so violently sick as to affect millions or billions of dollars in crew and and supporting assets due to an emergency, and armed service members are never transported by emergency transportations for eye-watering costs. Technical inequity that ignores facts is the argument of those without arguments.
The specifics of “who” has zero relevance to what is necessary for an ongoing situation; you don’t get to dictate your access and timeline to information just because you contributed a fraction of a penny to something.
+1, though I'll add it doesn't need to be a completely "non-chill" job.
Your job should be manageable day-to-day with certain period of stress or extra work where you need to push yourself. This is where growth occurs.
Example: You need to learn a new language/framework and maybe spend some extra hours outside of work for a few weeks to ramp up for a new project. But once you have a handle on it, you go back to your regular schedule. And that might even mean now you can relax and work less than your regular schedule. It's all about managing when you grind and when you "coast".
Given their per user pricing model, who's going to pay for this for any given community? Discord is mostly popular in communities where self-organized funding would be realistic.
Zulip's product lead here. Communities are eligible for a free Community plan if they self-host, and there are discounts and sponsorships available for Zulip Cloud as well.
I think most addicts when not high/drunk/fucked up off their substance of choice would admit that they they are more impaired and not good to drive when they are on their substance of choice.
But you'll still find some small percentage of them claiming they wouldn't be impaired.
Source: have actually spend a lot of time around addicts.
DeepSeek and other Chinese companies. Not only do they publish research, they also put their resources where their mouth (research) is. They actually use it and prove it through their open models.
Most research coming out of big US labs is counter indicative of practical performance. If it worked (too) well in practice, it wouldn't have been published.
You were asked pretty precise question. Instead of addressing it directly your proof is that China in general does do economic espionage. So does fucking every other developed country, US including.
"some elements of the indictment concern cyber-snooping in connection with trade disputes, which at least sounds a lot like the kind of cyber-snooping on firms that the United States does."
Your comment seems to imply "these views aren't valid" without any evidence for that claim. Of course the theft claim was a strong one to make without evidence too. So, to that point--it's pretty widely accepted as fact that DeepSeek was at its core a distillation of ChatGPT. The question is whether that counts as theft. As to evidence, to my knowledge it's a combination of circumstantial factors which add up to paint a pretty damning picture:
(1) Large-scale exfiltration of data from ChatGPT when DeepSeek was being developed, and which Microsoft linked to DeepSeek
(2) DeepSeek's claim of training a cutting-edge LLM using a fraction of the compute that is typically needed, without providing a plausible, reproducible method
> Large-scale exfiltration of data from ChatGPT when DeepSeek was being developed, and which Microsoft linked to DeepSeek
This is not the same thing at all. Current legal doctrine is that ChatGPT output is not copyrightable, so at most Deepseek violated the terms of use of ChatGPT.
That isn't IP theft.
To add to that example, there are numerous open-source datasets that are derived from ChatGPT data. Famously, the Alpaca dataset kick-started the open source LLM movement by fine tuning Llama on a GPT-derived dataset:
https://huggingface.co/datasets/tatsu-lab/alpaca
That’s an argument made about training the initial model. But the comment stated that DeepSeek stole its research from the US which is a much stronger allegation without any evidence to it.
For starters ChatGPT was pretty much trained on "stolen" data. However I actually do support it. I think both cases - ChatGPT preying on world wide data and Deepseek using such data by partially "borrowing" it from ChatGPT are fair game.
That's a fair point. I suspect that to one outside the field, their touting major breakthroughs while trying to conceal that their first model was a distillation may cause a sense of skepticism as to the quality of their research. From what I've gathered, their research actually has added meaningfully to understandings of optimal model scaling and faster training.
Here's an umbrella doc from the USTR, and the good stuff:
China used foreign ownership restrictions, such as joint venture (JV) requirements and foreign equity limitations, and various administrative review and licensing processes, to require or pressure technology transfer from U.S. companies.
2. China’s regime of technology regulations forced U.S. companies seeking to license technologies to Chinese entities to do so on non-market-based terms that favor Chinese recipients.
3. China directed and unfairly facilitated the systematic investment in, and acquisition of, U.S. companies and assets by Chinese companies to obtain cutting-edge technologies and IP and generate the transfer of technology to Chinese companies.
4. China conducted and supported unauthorized intrusions into, and theft from, the computer networks of U.S. companies to access their IP, including trade secrets, and confidential business information.
As mentioned - no one has claimed that DeepSeek in its entirety was stolen from the U.S.
It is almost a certainty based on decades of historical precedent of systematic theft that techniques, research, and other IP was also systematically stolen for this critical technology.
Don't close your eyes when the evidence, both rigorously proven and common sense, is staring you in the face.
>Your comment seems to imply "these views aren't valid" without any evidence for that claim.
No, your comment seems to be a deflection. You made an outstanding claim, that DS stole some IP, and have been asked for outstanding evidence, or at least some evidence. You need to provide it if you want to be taken seriously.
>Large-scale exfiltration of data from ChatGPT when DeepSeek was being developed, and which Microsoft linked to DeepSeek
Where's the evidence for that? I also have a claim that I can't back up with anything more than XLab's report: before the release of R1, there were multiple attempts to hack DS's systems, which nobody noticed. [1]
You really seem to have no idea what you're talking about. R1 was an experiment on teaching the model to reason on its own, exactly to avoid large amounts of data in post-training. It also partially failed, they called the failed snapshot R1-Zero. And it's pretty different from any OpenAI or Anthropic model.
>DeepSeek's claim of training a cutting-edge LLM using a fraction of the compute that is typically needed, without providing a plausible, reproducible method
DeepSeek published a lot more about their models than any top tier US lab before them, including their production code. And they're continuing doing so. All their findings in R1 are highly plausible and most are replicated to some degree and adopted in the research and industry. Moonshot AI trained their K2 on DeepSeek's architecture with minor tweaks (not to diminish their novel findings). That's a really solid model.
Moreover, they released their DeepSeek-Math-7B-RL back in April 2024. [2] It was a tiny model that outperformed huge then-SOTA LLMs like Claude 3 Opus in math, and validated their training technique (GPRO). Basically, they made the first reasoning model worth talking about. Their other optimizations (MLA) can be traced back to DeepSeek v2.
That's n=1 nonsense, not evidence. GPT contamination was everywhere, even Claude used to claim to be GPT-3 occasionally, or Reddit Anti-Evil Team. (yes, really) All models have overlapping datasets that are also contaminated with previous models outputs, and mode collapse makes them converge on similar patterns which seem to come and go with each generation.
corporate espionage was my first thought back then. unfolding events since indicate that it wasn't theft but part of a deal. the magic math seems to check out, too
Well it's cool that they released a paper, but at this point it's been 11 months and you can't download a Titans-architecture model code or weights anywhere. That would put a lot of companies up ahead of them (Meta's Llama, Qwen, DeepSeek).
Closest you can get is an unofficial implementation of the paper https://github.com/lucidrains/titans-pytorch
The hardest part about making a new architecture is that even if it is just better than transformers in every way, it’s very difficult to both prove a significant improvement at scale and gain traction. Until google puts in a lot of resources into training a scaled up version of this architecture, I believe there’s plenty of low hanging fruit with improving existing architectures such that it’ll always take the back seat.
Do you think there might be an approval process to navigate when experiments costs might run seven or eight digits and months of reserved resources?
While they do have lots of money and many people, they don't have infinite money and specifically only have so much hot infrastructure to spread around. You'd expect they have to gradually build up the case that a large scale experiment is likely enough to yield a big enough advantage over what's already claiming those resources.
I would imagine they do not want their researchers unnecessarily wasting time fighting for resources - within reason. And at Google, "within reason" can be pretty big.
But, it's companies like Google that made tools like Jax and TPU's saying we can throw together models with cheap, easy scaling. Their paper's math is probably harder to put together than an alpha-level prototype which they need anyway.
So, I think they could default on doing it for small demonstrators.
At the same time, there is now a ton of data for training models to act as useful assistants, and benchmarks to compare different assistant models. The wide availability and ease of obtaining new RLHF training data will make it more feasible to build models on new architectures I think.
I don't think the comparison is valid. Releasing code and weights for an architecture that is widely known is a lot different than releasing research about an architecture that could mitigate fundamental problems that are common to all LLM products.
I don't think model code is a big deal compared to the idea. If public can recognize the value of idea 11 months ago, they could implement the code quickly because there are so much smart engineers in AI field.
If the hundred dollar bill was in an accessible place and the fact of its existence had been transmitted to interested parties worldwide, then yeah, the economist would probably be right.
Student: Look, a well known financial expert placed what could potentially be a hundred dollar bill on the ground, other well-known financial experts just leave it there!
Well we have the idea and the next best thing to official code, but if this was a big revelation where are all of the Titan models? If this were public, I think we'd have a few attempts at variants (all of the Mamba SSMs, etc.) and get a better sense if this is valuable or not.
I've read many very positive reviews about Gemini 3. I tried using it including Pro and to me it looks very inferior to ChatGPT. What was very interesting though was when I caught it bullshitting me I called its BS and Gemini expressed very human like behavior. It did try to weasel its way out, degenerated down to "true Scotsman" level but finally admitted that it was full of it. this is kind of impressive / scary.
Every Google publication goes through multiple review. If anyone thinks the publication is a competitor risk it gets squashed.
It's very likely no one is using this architecture at Google for any production work loads. There are a lot of student researchers doing fun proof of concept papers, they're allowed to publish because it's good PR and it's good for their careers.
Underrated comment, IMHO. There is such a gulf between what Google does on its own part, and the papers and source code they publish, that I always think about their motivations before I read or adopt it. Think Borg vs. Kubernetes, Stubby vs. gRPC.
The amazing thing about this is the first author has published multiple high-impact papers with Google Research VPs! And he is just a 2nd-year PhD student. Very few L7/L8 RS/SWEs can even do this.
Meta just published Segment Anything 3 and along with a truly amazing version that can create 3D models posing like the people in a photo. It is very impressive.
"What's some frontier research Meta has shared in the last couple years?"
the current Meta outlook is embarassing tbh, the fact they have largest data of social media in planet and they cant even produce a decent model is quiet "scary" position
Yann was a researcher not a productization expert. His departure signals the end of Meta being open about their work and the start of more commercial focus.
Just because they are not leading current sprint of maximizing transformers doesn't mean they're not doing anything.
It's not impossible that they asses it as local maximum / dead end and are evaluating/training something completely different - and if it'll work, it'll work big time.
As a counterpoint, I found GPT 4.5 by far the most interesting model from OpenAI in terms of depth and width of knowledge, ability to make connections and inferences and apply those in novel ways.
It didn't bench well against the other benchmaxxed models, and it was too expensive to run, but it was a glimpse of the future where more capable hardware will lead to appreciably smarter models.
A very common thing people do is assume a) all corporations are evil b) all corporations never follow any laws c) any evil action you can imagine would work or be profitable if they did it.
b is mostly not true but c is especially not true. I doubt they do it because it wouldn't work; it's not high quality data.
But it would also obviously leak a lot of personal info, and that really gets you in danger. Meta and Google are able to serve you ads with your personal info /because they don't leak it/.
(Also data privacy laws forbid it anyway, because you can't use personal info for new uses not previously agreed to.)
I’ve long predicted that this game is going to be won with product design rather than having the winning model; we now seem to be hitting the phase of “[new tech] mania” where we remember that companies have to make things that people want to pay more money for than it costs to make them. I remember (maybe in the mid aughts) when people were thinking Google might not ever be able to convert their enthusiasm into profitability…then they figured out what people actually wanted to buy, and focused on that obsessively as a product. Failing to do that will lead to failure go for the companies like open AI.
Sinking a bazillion dollars into models alone doesn’t get you shit except a gold star for being the valley’s biggest smartypants, because in the product world, model improvements only significantly improve all-purpose chatbots. The whole veg-o-matic “step right up folks— it slices, it dices, it makes julienne fries!” approach to product design almost never yields something focused enough to be an automatic goto for specific tasks, or simple/reliable enough to be a general purpose tool for a whole category of tasks. Once the novelty wears off, people largely abandon it for more focused tools that more effectively solve specific problems (e.g. blender, vegetable peeler) or simpler everyday tools that you don’t have to think about as much even if they might not be the most efficient tool for half your tasks (e.g. paring knife.) Professionals might have enough need and reason to go for a really great in-between tool (e.g mandolin) but that’s a different market, and you only tend to get a limited set of prosumers outside of that. Companies more focused on specific products, like coding, will have way more longevity than companies that try to be everything to everyone.
Meta, Google, Microsoft, and even Apple have more pressure to make products that sanely fit into their existing product lines. While that seems like a handicap if you’re looking at it from the “AI company” perspective, I predict the restriction will enforce the discipline to create tools that solve specific problems for people rather than spending exorbitant sums making benchmark go up in pursuit of some nebulous information revolution.
Meta seems to have a much tougher job trying to make tools that people trust them to be good at. Most of the highest-visibility things like the AI Instagram accounts were disasters. Nobody thinks of Meta as a serious, general-purpose business ecosystem, and privacy-wise, I trust them even less than Google and Microsoft: there’s no way I’m trusting them with my work code bases. I think the smart move by Meta would be to ditch the sunk costs worries, stop burning money on this, focus on their core products (and new ones that fit their expertise) and design these LLM features in when they’ll actually be useful to users. Microsoft and Google both have existing tools that they’ve already bolstered with these features, and have a lot of room within their areas of expertise to develop more.
Who knows— I’m no expert— but I think meta would be smart to try and opt out as much as possible without making too many waves.
My thesis is the game is going to be won - if you define winning as a long term profitable business - by Google because they have their own infrastructure and technology not dependent on Nvidia, they have real businesses that can leverage AI - Google Search, YouTube and GCP - and they aren’t burning money they don’t have.
2nd tier winner is Amazon for the same reasons between being able to leverage AI with both Amazon Retail and AWS where they can sell shovels. I’ve also found their internal Nova models to be pretty good for my projects.
Microsoft will be okay because of Azure and maybe Office if they get their AI story right.
I just don’t see any world where OpenAI comes out ahead from a business standpoint as long as they are sharecroppers on other people’s hardware. ChatGPT alone will never make it worth the trillion dollar capitalization long term unless it becomes a meme stock like Tesla
never seen I say this but X(twitter) has more success in integrate their business product with AI (Grok)
I know I know that Elon is crazy etc but Grok example and way to integrate with core product is actually the only ways I can even came up tbh (other than character.ai flavor)
If I was a Meta shareholder I might well agree with you. But as someone with very little interest in their products so far, I’m very happy for them to sink huge amounts of money into AI research and publishing it all.
I’m just calling balls and strikes. For all I care, the whole lot of them can get sucked down a storm drain. Frankly I think there’s way too much effort and resources being put into this stuff regardless of who’s doing it. We’ve got a bunch of agentic job stealers, a bunch of magic spam/slop generators, and a bunch of asinine toys with the big name LLM stuff: I don’t think that’s a net gain for humanity. Then there’s a bunch of genuinely useful things made by people who are more interested in solving real problems. I’ll care about the first category when it consistently brings more value than garbage “content” and job anxiety to average people’s lives.
It was not always like this. Google was very secretive in the early days. We did not start to see things until the GFS, BigTable and Borg (or Chubby) papers in 2006 timeframe.
> Is there any other company that's openly publishing their research on AI at this level? Google should get a lot of credit for this.
80% of the ecosystem is built on top of companies, groups and individuals publishing their research openly, not sure why Google would get more credit for this than others...
Working with 1M context windows daily - the real limitation isn't storage but retrieval. You can feed massive context but knowing WHICH part to reference at the right moment is hard. Effective long-term memory needs both capacity and intelligent indexing.
Arxiv is flooded with ML papers. Github has a lot of prototypes for them. I'd say it's pretty normal with some companies not sharing for perceived, competitive advantage. Perceived because it may or may not be real vs published prototypes.
We post a lot of research on mlscaling sub if you want to look back through them.
reply