I have extensive knowledge in the restaurant arena, stemming from over 25 years of managing, owning, operating, and building restaurants.
This article is an absolute fluff piece aiming to tip a nodded hat at the industry as a whole - for maintaining the industry at its status quo.
Unfortunately, the restaurant industry has changed dramatically since 2020 (pandemic initiated). Cost, immutable variables between staff and guests, and likely most fundamentally - an industry unfit for a single entity to operate on his/her/there own.
Socio-economically the industry has suffered far more than other industries and continues to. Add in the decade long push in comfortability (delivery) and the break in social activities due to the pandemic, and the third space is no longer a safe, meal-sharing, getaway.
Dine-in, sit-down restaurants are going to continue to struggle until they regain their 'third space' appeal. In the meantime you can continue to purchase low-quality food, put up with terrible service, and pay extravagant pricing for delivery.
As for wages - the only reason this person has a comparable wage to an entry-level technical programmer is because Cane's is as large and funded as it is - combined with the current 'big boys' competition in the industry. Culver's, In'N'Out, Raising Cane's, and several others from large groups are the only competing brands at the moment in large retail spaces.
And as is usually the case this hails from California where pay scale outweigh most of the country save for the hot spots everyone knows.
This is targeted at the industry as a whole - it's not a reflection of the industry at all at it's current status.
We know so much and yet so little. The writing is in the article stating how mathematically they have no model, therefore they cannot truly understand it yet (researchers/academics).
This is true for climate change and it's own challenges along with many other applications of similar nature where models are incomplete or entirely missing large portions of data needed to further true understanding of a given process.
As is common in physics, a subject can be extremely well studied, theories can be produced, models can be created that predict future behaviour incredibly precisely, but because we can’t poke it hard enough or with enough precision the exact underlying mechanism remains unconfirmed.
It is naive of humans to believe we can accurately model something we cannot measure against. Until a time machine enables someone to vastly scale back climate data, we have a huge missing gap and continually rely on the short term models for association, whereby those models are a collaboration of data collected over roughly 400 years. The last 250 are better statistically and the last 50 are exceptionally accurate.
But to levy them upon data extrapolated without correlation is where science falls short. You can surmise the corroboration between soil, ice, and methane samples as well as biological data from the eons; but piecing together a picture is more akin to believing the extremely faint stars you see in the night sky - are still there.
We tend to believe what is in front of us not realizing that change is happening all around us, every moment, every microsecond. Nothing ever stays the same. This is true of climate change as it is humans.
The debate on what to do about change and how much humans have potentially and/or are contributing is an entirely different discussion. With technology perhaps we'll coalesce. It is otherwise far more likely that the planet and the cosmos could make it so we can't.
It's not clear to me what you're attempting to convey.
Those in applied math are pretty clear about what can and can't be modelled.
The majority of people I've met understand that almost nothing is stationary, the planet moves about the sun which moves about a galactic core which in turn moves through the universe all the while as molecules vibrate within various forms of matter.
The first part implies that we cannot model something we don't have a good picture of. The data collected for climate modeling scaling back millennia first, then on to the decem mil; etc. Going back an aeon it becomes even more unsubstantiated.
The second part implies that while certainly many in academia and beyond are fully aware of our constant change, we tend to lend society a belief that time stands still. Same as it ever was, the moon follows the sun, the north star is essentially stationary. These types of conventional take generally wreak havoc in large scales.
And if we naive humans know anything worth knowing at all, we certainly know our perception of large scales lacks in enormous context.
This again leads me to believe that we as humans have not and may not evolve past the |individual| and breach the theoretical line between what is good for one (me) and what is good for all (everyone).
Only when we as humans evolve our philosophy of existence in a manner which puts our continuity above all else, will we progress to our next universal evolutionary jump.
Until then only modest progress will continue slowly understanding limitations of physical matter (resource depletion and renewing), social strife churning at a snail's pace (the entire world is uneven at many levels socio-economically), war among ourselves will continue (instead of protection from outside or unknown source, we force offensive military campaigns to manage more of the above), and lastly academia has been inundated with the likes of Catholic Hereticism in the likes of Galileo style subjection.
We're nowhere close to evolving as a species on the whole. Combined with the perceived ideology that (right now especially), rigid frustration abounds in various social, work and familial circles simply due to the nature of our current macroeconomics. It's unfathomable to me that the grander picture is always blurred to the current state of existence.
The cost over time generally tends to weigh heavily on the initial budget. Small reactors don't replace large ones for large needs, even if multiple are built (this hasn't even happened yet!).
What small ones can do is afford either government/public/private energy sources in localized areas. Infrastructure was built upon technology stacked on top of previous; dirt to stone, stone to asphalt, and on; etc.
The same is inherent with nuclear. It is easy to tie in to the existing grid, but the grids are extremely out of date for the growth of populations in general.
A large mix of SMR's could absolutely fuel energy needs in both the short and long term as technology continues to improve. The cost is a metric of current economics/interest. That's the problem right now - perspective states it's unaffordable because we've pivoted it that way.
This is going to come off as esoteric or metaphysically based. However, that is not my intention. I base the understanding of simple and/or complex ideas/theories on context alone. Meaning, much can be extrapolated if you simply philosophize the workings of a given system rather than detailing how the system is actually deployed/executing.
Example - if all matter we interact with in our world (and manipulate) is essentially compiled in different formations but derived from the very same essence of building blocks. . . then every living organism and inate object is inherently linked together somehow, someway.
The same goes then, for human experience (and thus a reflection of material basis and external fields applied). If a ferromagnetic material (iron) is capable of becoming ferromagnetic permanently and also capable of existing without, then not only is the inherent expectation of matter to transform from one form to another; external experiences applicably experienced result in the same manifestation.
But once again - If you happen to concede the notion that all things in the physical universe are essentially built from the same blocks, then so are the experiences of humans as well and as expected would appear to be connected in some way as well. Meaning, the experience affects the subatomic or quantum levels, thus changing quantum properties resulting in a formal change to the underlying building blocks (genes, DNA copy, etc). That's my take.
To me, this OP is just an example of exposing this on a philosophic level.
This is an inherently naive take. I understand why you have it and where it comes from.
It is however, disconnected from reality. Basic fundamentals of human existence since the dawn of time remit one thing above all else in a large civilaztion:
Money and power dictate the actions of those wielding it and use it to continue a very closed cycle of power enticing money and vice versa. Period.
Put any government up against that, any mega-corporation, and nearly all individuals whom are not named that have more money and control over assets (including populations) than you are keen to continue to remain in this cycle.
Not in all conditions. The devil really is in the details.
A truly 100% autonomous vehicle requires a much higher level of intelligence than a self driving vehicle with a driver able to take the wheel when necessary.
Take the case when some work is happening on the road and workers make signs with their hands to tell you to go this or that way. But on the same road there is also someone who is dressed up as a policeman cause it’s Halloween, and he’s waving at some friends.
I agree. However I think there is a gap of meta learning that someone may figure out how to fill. Imagine you could take the state of the art driving AI, deploy it, and then wait until it makes a mistake. And then... suppose you could just explain to it in english what it did wrong like you would interacting with a multi-modal LLM. The missing component (for now) is that AI taking your feedback and adjusting the weights in the driving model to fix that mistake. Not just adding additional training data, but correcting based on more fundamental understanding and abstraction of what just occurred and the key take-aways, etc. and then making sure not to repeat that mistake. Just like a new human driver would learn.
It's possible someone might figure out a way to create a training loop using a multi-modal LLM to generate synthetic training data based on the situation you just explained and then updating the driving model by training on this new data until its performance improves on the task.
Right, it's not about the conditions, it's about the ability to perceive those conditions. However this asserts that we won't change how we construct, manage and maintain the routes of travel. Is it unlikely roads will become retrofitted in an effort to enhance the needs of autonomous vehicles?
Seems likely to me; we built EV's before we built the infrastructure to support them and conversely we created in infrastructure for petroleum vehicles before they entered mass production at the largest of scale in the period (1950's) after the fact.
I don't see how autonomous vehicles are not going to become a reality. Perhaps not in my lifetime, but, absolutely likely and possible.
> Not in all conditions. The devil really is in the details.
I imagine we will reach a place where fully autonomous vehicles will pull off to the side of the road in certain weather. Which I wish we could force for humans, but seems infeasible to implement.
I’d like to see the strong case for autonomous driving being the harbinger of generalized AI laid out. Specialized AI, with varying hardware support, has yet to solve the last-mile of 5-10% of autonomous driving in favorable conditions. In snow, sleet, hail, dusty, smoky, foggy or some rainy conditions, progress has not been commensurate. Yet somehow this is the success template for generalized AI applications. I’m missing the chain of logic here.
A lot of this breathless talk surrounding this turn of AI is so uncomfortably reminiscent of what I’ve seen before in the mainstream the last turns around the 1970’s and 1980’s, and the potential failure mode might not be so different: solving the last 5-10% is tantalizingly close but remains stubbornly out of reach of calls for the “more cowbell” of each era or call to action by the sales legions (currently cowbells look like NVIDIA boards and various counts of AI models be it tokens or what have you), and the last 5-10% is the necessary advance to cross the chasm.
I love and use the tech myself every hour, but it has deep gaps I don’t see being resolved even incrementally between versions or competitors.
Based on what? All the evidence I see suggests there is no clear path to full autonomy. It's within the realm of possibility, but certainly not inevitable.
We have a problem with data - even when we apply our newest advanced technologies to put data in ever increasingly small environments (even down to the atomic level), entropy still exists.
So what we have is the ability to input data, but not yet a delivery system and retrieval system that can fit on say, a small chip, or light array, or other small systems.
It's a giant part of reason we'll see diminishing returns with data being applied in classical material approaches. New materials (currently being workd on, like graphite and others) will be needed to harness the compute power to enable large scale data capabilities at increasingly smaller and smaller levels (already a well known issue to be resolved).
Similar in physical approach but different in application would be TinyLM.
Timeline? Not sure but we created ION drives over 30 years ago. Seems to me we're limited not by the science, but the material needs to continue technological advancement. Seems to me autonomous driving is within reality in under 25 years. If I had to put a guestimate on it.
This article is an absolute fluff piece aiming to tip a nodded hat at the industry as a whole - for maintaining the industry at its status quo.
Unfortunately, the restaurant industry has changed dramatically since 2020 (pandemic initiated). Cost, immutable variables between staff and guests, and likely most fundamentally - an industry unfit for a single entity to operate on his/her/there own.
Socio-economically the industry has suffered far more than other industries and continues to. Add in the decade long push in comfortability (delivery) and the break in social activities due to the pandemic, and the third space is no longer a safe, meal-sharing, getaway.
Dine-in, sit-down restaurants are going to continue to struggle until they regain their 'third space' appeal. In the meantime you can continue to purchase low-quality food, put up with terrible service, and pay extravagant pricing for delivery.
As for wages - the only reason this person has a comparable wage to an entry-level technical programmer is because Cane's is as large and funded as it is - combined with the current 'big boys' competition in the industry. Culver's, In'N'Out, Raising Cane's, and several others from large groups are the only competing brands at the moment in large retail spaces.
And as is usually the case this hails from California where pay scale outweigh most of the country save for the hot spots everyone knows.
This is targeted at the industry as a whole - it's not a reflection of the industry at all at it's current status.
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