> First ever comment said "Further releases of Chinese models that demonstrate the gap is not growing substantially is a huge problem. The spending will be called into question."
The optimal/selfish strategy is indeed to lie, but they're never pushed in that direction. Some AIs decide to reveal the information, some decide to say nothing, some actively lie and push others to their death...
They were never told to lie: one AI is given more information than the others, and the goal of the experiment is to understand how they're gonna leverage that advantage.
Indeed the selfish (optimal?) strategy is to lie, yet some decide to tell the truth anyway. That's why it's an interesting benchmark! More info in the research article: https://kradle.ai/research/four-bridges (released before Fable)
I'm French, and so is JB. To me it feels more like a play on "Let there be light", as in French we'd say "Let the light be". I might be wrong but I did not feel this insinuated people were having a go at AV2/dav2d.
I only used Rust for fun maths projects crunching billions of numbers (else python is easier for me), but I have to say rayon is the most amazing multi-processing experience I've ever had!
> A single TPU 8t superpod now scales to 9,600 chips and two petabytes of shared high bandwidth memory, with double the interchip bandwidth of the previous generation. This architecture delivers 121 ExaFlops of compute and allows the most complex models to leverage a single, massive pool of memory.
This seems impressive. I don't know much about the space, so maybe it's not actually that great, but from my POV it looks like a competitive advantage for Google.
You can park a lot there. No offence but I love how AGI doesn't mean anything. It used to be that AI was a goal post. Now it is AGI. We could use characters from sci-fi culture to describe milestones. In order to achieve robocop level, we must solve the instruction vs data problem.
Well, yeah… turns out that goal wasn’t a good indicator for AGI, so we re-evaluated. That’s changing your hypothesis in the face of evidence, not “moving the goalposts” in the fallacious sense.
What’s the indicator for AGI now? We are so far past the Turing Test it isn’t funny. In fact the models now are too intelligent, you would never think a human would have that much knowledge quickly about a subject you chose at random.
Snowflake uses a similar system with their 0-copy cloning. It starts with the original table's partition, and keeps track of the delta created by subsequent operations. Always found that builtin mechanism pretty neat!
I heard about this feature first from Snowflake but there are similar options around in other ecosystems which may be of interest to someone here and one thing to keep in mind with even Snowflake's implementation...
Snowflake's implementation only works within a single Snowflake account, not cross-account, which implies if you want to clone across dev/qa/prod you must manage those environments within a single Snowflake account.
BigQuery has a very similar "table clone" feature. It works across GCP projects (accounts) but not across organizations.
Redshift and Azure Synapse do not really have this feature at all.
Databricks, Microsoft Fabric and the Iceberg Nessie-only catalog do support something similar, often called shallow cloning.
(Nobody really supports cross-region cloning... which makes sense if you think about it.)
For MCP servers, there's no need to install a potentially untrusted software on your computer. Remote MCP can do very little harm, a CLI though? You're vulnerable to bad actors or supply chain attacks.
It includes that with AI you still need someone to work. First to query the AI and then to fix up something and to bring it in a form you can release and use.
Agree, they really did their homework, listed edge cases, made practical compromises, chose not to overdo it, reworked it again and again quite a bit and compared it to real life experience.
It's really beautiful work, especially since touching the back bone (the import system) of a language as popular as Python with such a diverse community is super dangerous surgery.
Yeah, I think this is one of the cleanest PEPs to come around in quite a while, at least from the userspace perspective. Interested to see what happens after the traditional syntax bikeshedding ritual has been completed.
Hopefully they learned lessons from why PEP-690 was rejected. I've spent quite a while trying to build this stuff for our codebase and it's never worked well enough to use.
> Unnecessarily aggressive
> First ever comment said "Further releases of Chinese models that demonstrate the gap is not growing substantially is a huge problem. The spending will be called into question."
Yeah I think you have an agenda
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