If the government audited the company's tax records and disagreed that the fiber bars should've been expensed, they'd most likely charge tax, interest, and a penalty proportional to the $5 cost. Fraud requires intentional wrongdoing; no court or auditor is going to find that a company intentionally schemed to defraud the government of a couple dollars by sneaking fiber bars into a travel expense report.
The problem is that 25% lower risk of all-cause mortality is too big to be explained solely by the vaccine. The reduction is similar when excluding deaths due to COVID-19, and is probably driven by people who got the vaccine being different in some ways that the observational study isn’t controlling for.
If you're not on social media, you might be missing part of what has changed people's minds. Social media is the biggest consumer-facing technological innovation of the last two decades, and it's financially lucrative, but also net-bad for society. I have a sense that we should do something about it, but as you say, doing something would require repudiating the values I held when I was younger.
Per https://www.fda.gov/inspections-compliance-enforcement-and-c..., it looks like the FDA is unhappy that Cue did something to identify whether the cartridge went outside of the allowed temperature parameters in transit (CP-4166 says that the seller is responsible if a device malfunctions due to damage in transit) and didn’t test each lot as much as they were supposed to or maybe used a lower accuracy threshold than stated in their claims.
There's nothing super-special about the host. The accelerators are the special part (and, as described elsewhere, they are orders of magnitude more powerful than the Edge TPU). However, if you're an academic/independent researcher, being able to access a system with that much system memory/CPU cores for free through TPU Research Cloud is potentially appealing even without the accelerators.
A single TPUv2 host has 8 TPU cores with 64GB of total HBM (8GB per core), but like GPUs, TPUs can't directly access a network, so the host also needs CPUs and standard RAM to send data to them. They are fast, and the host has to be fast enough to keep them fed with data, so the host is pretty beefy. But FWIW, a TPUv2 host has somewhere around 330GB of RAM, not 1.4TB.
According to the paper, "the success of our attack when applied to Claude may be lowered owing to what appears to be an initial content filter applied to the text prior to evaluating the LLM." The authors are skeptical that this defense would be effective if it were explicitly targeted, but it seems like it does stop attacks generated using Vicuna from transferring.
In ML, no one is going to police your citation list. I've cited some weird stuff in my papers, including ideas from tweets and random quotes from Jeff Dean. It's never been a problem.
It's interesting, because as a scientist who reads and writes these kinds of papers, my first impression was: This guy has a pretty big ego or is otherwise badly miscalibrated if he believes his genius idea has a "99.44%" chance of preventing outlier activations without doing any experiments.
Yes, there are more obese and elderly people now than there were in 1957. No, we can't "adjust" death numbers to place less weight on the deaths of those obese and elderly people in this context. Perhaps it would make some sense to do so if our goal was to measure virulence of the virus, but policy decisions have to take into account the composition of the population as it is today.
If this is true (https://en.wikipedia.org/wiki/Legacy_preferences#Economic_im... suggests it's disputed), the effect is probably marginal, and in any case, Harvard has the largest academic endowment in the world. It seems unlikely that it needs legacy admissions to stay afloat.
At some point in high school, I realized that the reason I was unhappy was that I was suppressing my feelings and ignoring what they were telling me. Instead, I needed to learn how to predict my feelings in advance and guide my behavior and thoughts proactively to avoid feeling unhappy.
The kind of "confidence" that is important to happiness is very specific. Overall, I'm less confident and more anxious than the average person. What I can do is convince myself that I'm making the best decisions for myself under the circumstances I find myself in. As long as I can make optimal decisions without feeling strong negative emotions, I simply don't have to feel those emotions. For me, happiness is not "lack of a persistent itch to do things differently" — what is important is that, if I have a persistent itch to do things differently, I actually follow it.
There are two steps to building a conversational LLM. The first is pretraining on an enormous amount of text. The second is fine-tuning, which usually involves a combination of a small amount of high-quality human data and reinforcement learning from human feedback (in practice, from another neural net trained to model human feedback).
This paper is about the quality of the pretraining. It is not necessarily going to be correlated with your subjective judgment of how good the model is. A good pretrained model without any fine-tuning will be very difficult to use for most purposes, because it won't do a very good job following instructions. However, assuming that the fine-tuning is done well, the quality of the pretraining determines the limits of the capabilities of the model. This tech report shows that the team did a good (or at least reasonable) job with the pretraining.
The primary audience for this post and tech report is (or at least should be) ML researchers that Inflection would like to recruit and technically knowledgeable investors, not end-users. To remain competitive, Inflection is gonna have to train a 10x more expensive model someday; OpenAI and Google already have. They need talent and investor $ to do that.
It means that the market believes they are unlikely to be on the hook for that amount, or else the market cap would be near-zero. Given 3M's current profits, assets, and liabilities, a 142.7B payout would bankrupt it.
Excess deaths are the number of deaths above the expected number of deaths. The expected number of deaths usually comes from a model that takes into account how the number of deaths would normally change from one year to the next. This model would incorporate the effect of changes in the composition of the population that are occurring over long timescales.
I know that there are many people at OpenAI who worry about the risks posed by AI and support real regulation to mitigate these risks. That said, given that Sam Altman’s position on climate change is something along the lines of “we shouldn’t reduce emissions now because we can develop tech to fix whatever we’ve done later” (e.g. https://twitter.com/sama/status/1445059564114563080), I’m skeptical that he personally sees AI regulation as anything other than a means to regulatory capture.
I guess my hope was that I could get people who disagree with my views to engage substantively with them by hinting that I have sufficient knowledge to weigh in here. However, that doesn't seem to have been very effective, and your point that posting here may simply be a waste of time is well-taken.
I’m not sure I understand what you’re getting at. It doesn’t seem hard for a top AI lab to get extremely detailed data regarding how top AI researchers perform research. It’s probably significantly easier than collecting data from experts in other fields.