Minutes ago I had the idea to do a Google search for a situation I was trying to remember from about 15 years ago (an investment in Green Mountain coffee by a Fidelity fund) instead of using an LLM. The results were abysmal: Querying {Fidelity Green Mountain} not only uncovered 0 relevant information, but many of the results didn't contain either "fidelity", or "green mountain", both of which are obviously critical to the query. Claude easily answered it, albeit with a full prompt, but Google would have returned relevant results very recently and no longer does.
It is no longer a search engine in the traditional sense, it's just returning a bad AI guess at what a full LLM prompt would have been for this query, followed by I guess just the most popular links containing one of the words. Nothing about the results is recognizable as a result that would have been returned even a year ago. This is evident from the "Ask Google" branding rather than "Search Google". Google Search is dead as we knew it.
Google's search has been bad for a long time, esp. with older content. I had to use Bing to find a bug report from 2014 I was trying to find around 2 years ago.
It's a number of things like:
1. not searching for exact quotes/text, only words in the query;
2. including other lexical forms in the results (e.g. searching for things like "how do you open the run dialog" returns results for running (the sport/exercise) as well as run (an application));
3. including synonyms, typos, etc.
It seems like while each change makes sense and can improve results in some cases, over time the combined effect has made Google's search near unusable.
I hooked up Google Maps to ChatGPT and that was definitely helpful for this; without them it would seemingly just guestimate driving distances, but with the MCP it was able to determine exact driving/walking times and also render maps with waypoints for activities around the lodging.
So the goalposts have moved to include continual learning.
In a sense I think no one will agree on a definition of AGI until it becomes impossible to construct any benchmark under which an AI underperforms "average" humans. That or it's defined retrospectively, after it's overwhelmingly obvious it met any such definition.
I hardly think it’s fair to label an objection so old that Turing included it (and discussed it at length) in the list of objections to thinking machines in 1950 “moving the goalposts.”
> These arguments take the form, “I grant you that you can make machines do all the things you have mentioned but you will never be able to make one to do X”. Numerous features X are suggested in this connexion. I offer a selection:
> Be kind, resourceful, beautiful, friendly (p. 448), have initiative, have a sense of humour, tell right from wrong, make mistakes (p. 448), fall in love, enjoy strawberries and cream (p. 448), make some one fall in love with it, learn from experience (pp. 456 f.), use words properly, be the subject of its own thought (p. 449), have as much diversity of behaviour as a man, do something really new (p. 450). (Some of these disabilities are given special consideration as indicated by the page numbers.)
Just because a condition is new to you doesn’t imply moving the goalpost. People have been putting forward continual learning and similar conditions like autonomy since 1950s.
I'm confused, is this a research device, hobby/tinker item, or consumer product?
I don't mean to be negative, but making a sustainable business on a hardware race to the bottom sounds challenging.
I'd be curious to to know from others here, what in-home tasks do people think they'll really be able to automate with any of the various bimanual or humanoid robot platforms that seem to be going around?
Nothing, none of these are good enough to do any home tasks reliably. These are all research/hobbyist devices that won't be interesting for another 5-10 years. Probably a bunch of fun to play around with though.
ASIC makes it sound like it's a single chip, but in reality serving trillion-param models on Cerebras requires a full cluster (as in multiple racks, MW of power).
Major tenants like OAI have near total control. These GW-class datacenters are built to spec for the tenant. Construction doesn't start until all the details are signed off.
If you are just paying the bills and leasing the space, is it really your fault a datacenter some other entity built is sucking the water table of Levelland dry?
If I just use and don't own the data center, then how can I be held accountable for it's impact others?
What I like about this, is that it significantly increases the probability of a sci-fi scenario where you're picking up a hot chip on the black market; rumor has it, Mythos 9 weights baked in...
Back in the kazaa and limewire days, you'd sometimes try to get a movie / episode from a series, wait hours / days for it to download, and when it was done you had a ~50/50 chance to actually watch what you wanted or an old german porn movie :/
> The only argument they have here is that they use GB300 GPU's which for some reason should not be available to chinese citizens
Note that Chinese companies are free to rent from GB300 clouds internationally. There are large datacenter hubs in Singapore and Malaysia serving chinese and other customers.
Though there is also reported [1] significant smuggling of Nvidia chips into China as well.
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