This seems like what should be a killer feature: Copilot having access to configuration and logs and being able to identify where a failure is coming from. This stuff is tedious manually since I basically run through a checklist of where the failure could occur and there’s no great way to automate that plus sometimes there’s subtle typo type issues. Copilot can generate the checklist reasonably well but can’t execute on it, even from Copilot within Azure. Why not??
I have had the experience of approaching or completing something potentially dangerous (merging on a busy street for example) and thinking I should “save” and sub consciously visualizing doing so internally. Very fleeting sensation and doesn’t happen consistently at all but it’s interesting when I notice it.
I don't think this is unique to churches, or even non-profits. Plenty of non-church non-profits rely on a few large donors for much or their funding (in fact plenty are designed that way out the gate - they're founded by one very wealthy individual to work on the projects they care about) and plenty of for profit businesses rely on a few large dollar clients for much of their revenue. Both could potentially be seen extensions of the same economic system that concentrates wealth for both individuals and businesses at top.
We don't really have a true test that means "if we pass this test we have AGI" but we have a variety of tests (like ARC) that we believe any true AGI would be able to pass. It's a "necessary but not sufficient" situation. Also ties directly to the challenge in defining what AGI really means. You see a lot of discussions of "moving the goal posts" around AGI, but as I see it we've never had goal posts, we've just got a bunch of lines we'd expect to cross before reaching them.
I don't think we actually even have a good definition of "This is what AGI is, and here are the stationary goal posts that, when these thresholds are met, then we will have AGI".
If you judged human intelligence by our AI standards, then would humans even pass as Natural General Intelligence? Human intelligence tests are constantly changing, being invalidated, and rerolled as well.
I maintain that today's modern LLMs would pass sufficiently for AGI and is also very close to passing a Turing Test, if measured in 1950 when the test was proposed.
>I don't think we actually even have a good definition of "This is what AGI is, and here are the stationary goal posts that, when these thresholds are met, then we will have AGI".
Not only do we not have that, I don't think it's possible to have it.
Philosophers have known about this problem for centuries. Wittgenstein recognized that most concepts don't have precise definitions but instead behave more like family resemblances. When we look at a family we recognize that they share physical characteristics, even if there's no single characteristic shared by all of them. They don't need to unanimously share hair color, skin complexion, mannerisms, etc. in order to have a family resemblance.
Outside of a few well-defined things in logic and mathematics, concepts operate in the same way. Intelligence isn't a well-defined concept, but that doesn't mean we can't talk about different types of human intelligence, non-human animal intelligence, or machine intelligence in terms of family resemblances.
Benchmarks are useful tools for assessing relative progress on well-defined tasks. But the decision of what counts as AGI will always come down to fuzzy comparisons and qualitative judgments.
The current definition and goal of AGI is “Artificial intelligence good enough to replace every employee for cheaper” and much of the difficulty people have in defining it is cognitive dissonance about the goal.
I’d remove the “for cheaper” part? (And also, only necessary for the employees whose jobs are “cognitive tasks”, not ones that are based on their bodies. So like, doesn’t need to be able to lift boxes or have a nice smile.)
If something would be better at every cognitive task than every human, if it ran a trillion times faster, I would consider that to be AGI even if it isn’t that useful at its actual speed.
Because an important part of being a Natural general Intelligence is having a body and interacting with the world. Data from Star Trek is a good example of an AGI.
Turing test is not really that meaningful anymore because you can always detect the AI by text and timing patterns rather than actual intelligence. In fact the most reliable way to test for AI is probably to ask trivia questions on various niche topics, I don't think any human has as much breath of general knowledge as current AIs.
> you can always detect the AI by text and timing patterns
I see no reason why an AI couldn't be trained on human data to fake all of that.
If noone has bothered so far, that's because pretty much all commercial applications of this would be illegal or at least leading to major reputational damage when exposed.
One of the very first slides of François’ presentation is about defining AGI. Do you have anything that opposes his synthesis of the two (50 years old) takes on this definition?
I have graduated with a degree in Software engineering and i am bilingual (Bulgarian and English). Currently AI is better than me in everything except adding big numbers or writing code in really niche topics - for example code golfing a Brainfuck interpreter or writing a Rubiks cube solver.
I believe AGI has been here for at least a year now.
I suggest you to try to let the AI think through race conditions scenarios in asynchronous programs; it is not that good at these abstract reasoning tasks.
Can the AI wash your dishes, fold your laundry, take out your trash, meet a friend for dinner or the other thousand things you might do in an average day when you're not interacting with text on a screen?
You know stuff that humans have done way before there were computers and screens.
Yeah, I'm convinced that the biggest difference between the current generation of AIs we have and humans is that AIs don't have the range of tool use and interaction with the physical environment that humans do. And that's what's actually holding AGI back not access to more data.
Related - there needs to be individuals and businesses that want/need and can afford upgrades and repairs. If office workers are getting replaced with AI we don't need to build and maintain offices and the ecosystems that support them (see also WFH/Covid) and those workers won't have income to pay for plumbers, electricians, roofers, etc. for their personal property. A worst case scenario AI workforce revolution would attack trades from both supply and demand.
It's worth noting that for those edge cases all the productivity monitoring in the world won't make that employee any more effective, and you won't need those tools to see that they're not cutting it (assuming you're engaged with your team as the other commenter describes). You'll likely lose more in annoying the rest of your team and burning your own cycles with surveillance than you'll gain from it.
> It's worth noting that for those edge cases all the productivity monitoring in the world won't make that employee any more effective, and you won't need those tools to see that they're not cutting it (assuming you're engaged with your team as the other commenter describes).
The main purpose of the tracking of the “edge cases” is basically insurance in the event of a law suit.
Yes, it irritates the folks with good intentions, but a good manager will keep the tracking tax as light as possible for the folks who are actually working.
The amount of headache it saves when the lawsuit or threat of a lawsuit comes around is quite a bit.
I've long maintained that kids must learn end to end what it takes to put content on the web themselves (registering a domain, writing some html, exposing it on a server, etc.) so they understand that _truly anyone can do this_. Learning both that creating "authoritative" looking content is trivial and that they are _not_ beholden to a specific walled garden owner in order to share content on the web.
I don't know if AI books are or will be as good or better than human written, but to me this is the problem - "Even though artificial intelligence is still in its infancy, AI-made books are already flooding the market." There is no scarcity problem in books. There are already way more that I would enjoy reading than I will ever actually read. It's already tough to prioritize which ones to get to without having vastly more to sort through. And people _enjoy_ writing books. I don't want to support automating something away that people enjoy doing, is produced in abundance, and is very low cost to obtain already.
Seriously, we have a Haagen-bot[0] problem ("the robot that eats ice cream so you don't have to") widespread across the field where people are trying to figure out how they can get their piece of the new ML world.
Part of it is that people aren't thinking about what's actually scarce. Let alone what a world more optimized for people might be like.
> I don't want to support automating something away that people enjoy doing, is produced in abundance, and is very low cost to obtain already.
I was surprised at the time how cheap the original Pebbles were, they were nearly exactly what I wanted and I would have been willing to pay more for mine. In fact I ultimately paid more to replace mine with a watch I like less. When Pebble folded I wondered if having too low of price ultimately hurt them - if they didn't pick up enough customers to make up on volume what they left off the table on per-unit revenue? I hope the relaunch is successful, and I assume they have all manner of internal data that says I'm wrong, but my initial reaction to the listed prices is the same as it was to the originals - they seem too low. (I'm setting aside the caveat about a potential price change due to tariffs and assuming they launch at current list price.)
There's a big difference: it's 2025 and there are no shortage of competitors that look better and have more features than a $150 Pebble 2 or $225 Time 2. Unlike 2015 the market already has a $200 Apple Watch, $60 Amazfit Bip, $55 CMF Watch Pro, and a $220 Coros Pace which will track an ultramarathon. All these devices are made by mature companies and have multiple revisions.
I liked my Pebbles, but I won't spend $300 on one because the chance of failure (again) is so great.
I'm not sure why manufacturers would care - it's a ten year old device with limited appeal. Chinese manufacturers already make better, cheaper watches.
> I'm setting aside the caveat about a potential price change due to tariffs and assuming they launch at current list price
As you should, because if they raise the price because of tariffs they won't see a dime of it. It's less raising the price and more that they don't yet know how much tax they'll be expected to collect and remit.
PhD itself is an abbreviation for "Doctor of Philosophy." The title is more about the original Greek "lover of wisdom" than about the modern academic discipline of philosophy. https://en.wikipedia.org/wiki/Doctor_of_Philosophy
Doctor is similar - in the US, when someone says "Doctor" they usually mean "Medical Doctor" but "Doctor" just comes from the Greek "teacher" / "scholar" which is more broad and the title can still be used officially and correctly for PhDs. https://en.wikipedia.org/wiki/Doctor_(title)
Just a little correction. Doctor is Latin and roughly means "someone who has learned a lot."
Science also originally referred to knowledge. What we think of as "science" used to be called the natural sciences. Sometimes people get confused because I have a B.S. in Classics because science has lost that broader meaning.
Indeed, in the Summa Theologica, Thomas Aquinas asks if theology is a science, and concludes that it is. He also gave lip service to logical rigor and falsifiability, in the latter case by encouraging the discipline of asking contrary questions and answering them. What he didn't do was appeal to empirical data, to any great extent.
I think the reasoning behind "doctor of philosophy" may be lost to history. All knowing Wikipedia suggests that it didn't happen at once. My take was that the requirements for a modern PhD were added long after the title was adopted.
I suspect there was a time when a person could be well versed in multiple of what are now separated fields, and that you had to be a philosopher to make sense of science and math. Also, as science was flexing its own wings, claiming to be a philosopher might have been a way to gain an air of respectability, just like calling a physician "doctor" when the main impact of medicine was to kill rich people.
It is still called natural science, but it used to be called natural philosophy.
And it is interesting, as you say, that when it comes to Bachelor/Master/Doctor of Science/Art/Philosophy (even professor), these are all titles formed from arbitrary terms that have been enshrined by the institutions that give people these titles.
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