792 karma · joined June 21, 2023
Drax in the UK [1] is a quite good case study for this (assuming they get it all up and running), though they're not using algae. Right now they grow trees, and burn those in pellet form. It's currently considered sustainable as it's not adding new carbon to the above-ground system (whereas coal/gas/oil is adding to the above-ground carbon). Their next phase is to attempt to capture the post-combustion emissions from their chimney stacks, at which point they have a non-biodegradable mass of carbon to bury somewhere.
[1] https://www.drax.com/sustainability/sustainable-bioenergy/
What I'm about to say obviously pales in comparison to raising a child with autism, but entering an ultramarathon/triathalon is a quite good way to experience something like this first hand in a safe environment. The amount a human can actually "go through" when it's asked of them is entirely remarkable.
It highlighted both to the colonised and the colonisers that the empire was way over-extended.
I'm pretty sure for the overwhelming majority of (successful) SaaS businesses, the most expensive part is the marketing & advertising budget. 30-50% isn't uncommon, because the returns on successful sign-ups are enormous.
That the models can't see a corpus of 1-5 digit addition then generalise that out to n-digit addition is an indicator that their reasoning capacities are very poor and inefficient.
Young children take a single textbook & couple of days worth of tuition to achieve generalised understanding of addition. Models train for the equivalent of hundreds of years, across (nearly) the totality of human achievement in mathematics, and struggle with 10-digit addition.
This is not suggestive of an underlying capacity to draw conclusions from general patterns.
> There are some cases where big data is very useful. The number of situations where it is useful is limited
Even though there are some great use-cases, the overwhelming majority organisations, institutions, and projects will never have a "let's query ten petabytes" scenario that forces them away from platforms like Postgres.
Most datasets, even at very large companies, fit comfortably into RAM on a server - which is now cost-effective, even in the dozens of terabytes.
[2]https://motherduck.com/_next/image/?url=https%3A%2F%2Fweb-as...
https://www.scotsman.com/webimg/legacy_elm_28724349.jpg?crop...
The link provided as proof for this comment is Wayve receiving a $1bn injection from Microsoft and Nvidia [1].
The $1bn raise is not the concern of a budding 23 year old graduate leaving Imperial/Cambridge/Oxford. They're looking at the first £100k capital to see them through the first few months. In the UK, the scene for the first capital injection is far weaker than in the US, which has an inevitable downstream impact.
Looking through Antrhopic's publication history, their work on alignment & safety has been pretty out in the open, and collaborative with the other major AI labs.
I'm not certain your view is especially contrarian here, as it mostly aligns with research Anthropic are already doing, openly talking about, and publishing. Some of the points you've made are addressed in detail in the post you've replied to.
There's very few people who can lead in frontier AI research domains - maybe a few dozen worldwide - and there are many active research niches. Applying an NDA to a very senior researcher would be such a massive net-negative for the industry, that it'd be a net-negative for the applying organisation too.
I could see some kind of product-based NDA, like "don't discuss the target release dates for the new models", but "stop working on your field of research" isn't going to happen.
An AI being able to consistently outperform us in recalling the syntax for switch statements, is a world away from "all of our basic needs being taken care of by automation". The former is going to take a few more weeks/months, while the latter is going to take a few more decades/centuries.
In the interim, there will be some winners, and many losers from this innovation. Wealth will concentrate significantly towards the winners, while the losers will be out of work with a valueless skillset, and their basic needs going unmet. While this may be true for most high-skill professions in the coming decades, there's a unique irony for programmers - who will be the losers, having invented and then fueled the engine of their own demise on behalf of the winners.
It's not necessarily a value-judgement based comment. It's just noting the irony, and highlighting that it's a specific genre of irony that economists absolutely salivate over.
Though you can access these techniques now, in the weeks after Spectre attacks were discovered, the browsers all consolidated on "make timing less accurate across the board" as an immediate-term fix[1]. All browsers now give automatic access to imprecise timing by default, but have some technique to opt-in for near-precise timing.
Similarly, Swift has SuspendingClock and ContinuousClock, which you can use without informing Apple. Meanwhile mach_absolute_time & similarly precise timing methods require developers to disclose the reasons for its use before Apple will approve your app on the store[2].
[1] https://blog.mozilla.org/security/2018/01/03/mitigations-lan...
[2] https://developer.apple.com/documentation/kernel/1462446-mac...
To mitigate this threat, javascript engine developers simply added a random fuzzy delay to all of the precision timing techniques. Swift's large volume of calls to unrequired methods is, almost certainly, Apple's implementation of this mitigation.
[1] https://en.wikipedia.org/wiki/Spectre_(security_vulnerabilit...
The author talks about the productivity losses rising from social-issue disagreement in the workplace, but it's rare that you can point to a press release from a C-suite employee and say "this specific document caused one in twenty staff members to leave immediately". The productivity destruction at Coinbase from that press release was enormous.
https://www.coinbase.com/en-gb/blog/a-follow-up-to-coinbase-...