You mention a basic understanding but forgive me if I rehash things you already know:
Jev is good for simple fast decisions, in their own words - system one thinking. Anything that can be broken down into yes/no, a confidence %, or a set list of answers provided prior to the question. As you say, lots of specific use cases sure, but what's the big deal?
Well, right now most other models are purely system two thinking, big slow thoughts. Put an open ended question in, get an open ended answer out (plus hallucinations when confidence is low!).
When you ask ChatGPT or Claude a simple question, that system two thinking burns tokens and takes time to answer you.
Jev is a proposition to the whole ecosystem to make our current AI systems more efficient.
Going further than just the simple idea of "Get Jev to answer the yes/no questions, reserve Claude for bigger questions" is the concept that LLMs are making tonnes of their own questions as they go during their thought processes and agentic runs that Jev could be inserted as a tool call to speed it up.
You ask for research on a topic, Claude fans out and builds a list of possible resources, Jev selects the ones to pursue, Claude needs to determine if the user prompt was asking for something specific or broad, a joke or a serious query, deserving a long or short response, etc etc, it just gives Jev those options and asks it to pick. So no more having a big bulky frontier model making small decisions, Jev does it for a fraction of the cost and time and Claude gets on with the system two bits.
You can extend that logic moreso to coding applications where Jev could be essentially auto-completing basic work.
All the demos you are seeing now are people mucking about with the concept before we see OpenAI or Anthropic release an update.
Hell, maybe Opus 5.5 is doing some Jev-style stuff to be 40% more efficient.
"Oil drill manufacturer says there is 0% chance global warming is real"
"Gun retailer says guns kill people not guns"
Why are we listening to Jensen Huang's opinion on this? The bias is palpable.
Of course AI has dangerous aspects (the gun metaphor is perhaps apt here), but asking the man who stands to gain the most from unregulated AI growth is not the way.
Opposite, AI has let me delve into resources that were hard to find, compare and confirm approaches to problems, and make progress on dozens more projects that I'd had sitting around.
I now have Home Assistant established at home. LibreElec for a home media server. Time lapse camera for home mushroom growing. Wine steeping projects. And much more.
Can you give an example? Not sure what you're referring to.
'gates' can be used in several ways.
gate - door - "the garden gate"
gate - controversy - "gamergate""
gate - metaphorical barrier - "this site is age gated", "This gates the product from universal adoption"
1 - Make a prompt in Google AI Studio
2 - Delete it
3 - Go to Drive > Trash and delete the trash file ("forever") something that will happen on its own after 30 days
4 - Go to a support page for recovering lost files and try to recover it (Google themselves state this needs to be done within 25 days)
5 - Conclude Google are being naughty?
Everything is working as intended. You made content, deleted it, deleted it from trash, then used the final failsafe within the described 25 day window to recover that content.
If you broaden the process from "immediately recover[ing]" the file to doing so after 25 days and it still works then I'd say something unwanted is happening.
Also posting this to a "Fuck AI" community with the opener "I am asking everyone who uses Google AI Studio to help with something critical" is unlikely to get you many volunteers. They don't use AI at all in there.
I came into this post wondering what on earth an 'AI control key' could possibly be. I really did not expect to see a cheap Aliexpress macro keypad plastered with buzzwords.
Did you write this post with AI because I could say the same about your choice to says ChatGPT "Emitted" something. Why not 'made', 'produced', 'created' etc?
It looks like you thesaurused a less common word for a job done by more common ones.
I'm not saying you are wrong, just pointing out that we all have idiolects that make us make word choices others wouldn't. Also, I didn't think ChatGPT were doing text watermarking. Isn't it just Claude and Gemini so far?
Made produced and created, wrote? imply agency... Anyways my point is about replacing widely used jargon with made up jargon (ok new jargon) that makes you squint and obfuscates the connection to industry standard.
That glossary also reads like this:
Why it matters
Without a fabric, integration complexity grows quadratically with each new system. A capability fabric linearizes this growth by providing shared infrastructure for discovery, governance, and composition – turning integration from a liability into an organizational asset.
Anyways, I'm pretty sure the only reason you'd do this (watermarking) is to be able separate your own outputs from the internet. And if you can make it a law so all the other AI companies collude even better.
"Why AGI is impossible" is our premise. Let's remember that when looking at the argument in the post.
Opening statement: "My point is that intelligence can be perceived in things that are not intelligent." - We already have a circular defeat here. If something is perceived as intelligent, it's either intelligent or our perception is wrong. I think that's what the OP means, but they just state AI is not intelligent instead of focussing on the perception issue.
"AGI might never be achieved with current architecture. Sure, depends on the definition of AGI." So you've moved from 'impossible' to 'might be possible' already. And you've stated it depends on the definition. Feel free to define it fairly, instead of prepping for moving goalposts.
"I envision it as remarkable as described by the AI lab CEOs… basically as a God inside a GPU." Oh. You've gone for one of the harder definitions. But since you've invited goalposts to be moved how about we judge AGI on just general human intelligence levels, not a god's. That's the broader consensus on an AGI definition, and no god has stepped up to have their intelligence tested in a standardised manner before so that's a bad metric.
"LLMs don’t know what they are doing." Do we? Soft and hard determinist models imply we don't have much control over our thoughts and impulses. My brain is a black box to me as an AI's working is to itself. I'd even say an AI has a better understanding of it's inner workings. An AI can tell me its token usage, I can only estimate my calorie expenditure for a thought, and I'd probably get it wrong. I think you have some unstated definition of consciousness, and that you think humans qualify it, and AIs don't. I'm not saying AI is conscious, just that we need to define it, and check it applies to humans objectively before we say humans can do it and AI can't.
"LLMs don’t care about the why of anything." Demonstrably false. Though interesting on your phrasing, unsure an LLM can be accused of not knowing what it is doing in one breath, and then be said to have 'cares' for thing in another. But onto the refutation - an LLM is a collection of 'why' statement. Vectorised information is the collection of answers to why, what, how, when, where statements. You're literally talking about training data as a prerequisite for intelligence. LLMs have tonnes of that. If you're saying an entity must consciously gather training data by itself before being called intelligent that's different. And also wrong. Humans are born with intelligence before they start gathering their training data, we have plenty of baked in intelligence. And LLMs can gather more data too, they can ask why, they can 'care' about why. Agentic LLMs especially do this constantly.
"But that why isn’t the major driver in its output, it is just statically there. Actually it is not even “there”, it was deconstructed into numbers / activation functions" The why is literally the driver of the output, in sum, under constraint of other whys, and random temperature controls. And this is literally the mechanism humans use during sleep to transfer memories from short to long term memory. We take external data, condense it, then condense it further into representations of those experiences. The exact mechanics/processes differ between silicon and neuron, but this is why we perceive intelligence in LLMs, they were modelled after our perceived intelligence.
Galton board example - sure, you're highlighting that its the perception that's wrong. If you don't understand what's going in the middle, you can perceive it wrong. And that'd be the point of my response above. Even when we look in the human mind we still don't see or understand all of it. What we do understand we have replicated in machines. To make a clear statement about intelligence we need more understanding. It's not as easy to say an LLM is closer to a Galton board than a human mind.
And none of these arguments go near affirming that AGI is impossible. If they have any value, they'd just say our current approach is wrong. They don't rule out others. So the title argument is wrong, even if there are merits in the content about current LLM perceptions of intelligence.
The trip up point will be that businesses sending emails to individuals still need to provide a method by which users can object to the tracking. The tracking itself is fine though, as tracking pixels in emails are all for statistical purposes.
Jev is good for simple fast decisions, in their own words - system one thinking. Anything that can be broken down into yes/no, a confidence %, or a set list of answers provided prior to the question. As you say, lots of specific use cases sure, but what's the big deal?
Well, right now most other models are purely system two thinking, big slow thoughts. Put an open ended question in, get an open ended answer out (plus hallucinations when confidence is low!).
When you ask ChatGPT or Claude a simple question, that system two thinking burns tokens and takes time to answer you.
Jev is a proposition to the whole ecosystem to make our current AI systems more efficient.
Going further than just the simple idea of "Get Jev to answer the yes/no questions, reserve Claude for bigger questions" is the concept that LLMs are making tonnes of their own questions as they go during their thought processes and agentic runs that Jev could be inserted as a tool call to speed it up.
You ask for research on a topic, Claude fans out and builds a list of possible resources, Jev selects the ones to pursue, Claude needs to determine if the user prompt was asking for something specific or broad, a joke or a serious query, deserving a long or short response, etc etc, it just gives Jev those options and asks it to pick. So no more having a big bulky frontier model making small decisions, Jev does it for a fraction of the cost and time and Claude gets on with the system two bits.
You can extend that logic moreso to coding applications where Jev could be essentially auto-completing basic work.
All the demos you are seeing now are people mucking about with the concept before we see OpenAI or Anthropic release an update.
Hell, maybe Opus 5.5 is doing some Jev-style stuff to be 40% more efficient.
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