As a long-time professional RoR developer, I disagree with the article's premise.
AI seems like the last nail in the coffin for an easy, slow-evolving, highly standardized ecosystem like Rails'.
In short, using Rails was already pretty easy pre-AI, now it's just too easy. That might be nice for misc purposes like prototyping something, or teaching people how to make webapps, but for all other purposes it makes us disposable peons.
Even in better times than 2022-24, I expect Rails salaries not to grow particularly. I don't expect it to die either - probably it will be not unlike PHP: easy, pretty nice, but ultimately a voluntary choice to have a lower salary.
I thank Ruby/Rails for various aspects that have made my career better, but it's wiser to move on to something more niche and that doesn't pretend we're still living in 2008.
> In the early 1980s, Dallas Fort-Worth Airport covered as much land as the city of Dallas did, and Denver International Airport is as large as the city of San Francisco.
They're probably referring to the official city limits, which can be tiny, as opposed to the total Dallas metro area. The latter is what people actually tend to think of when mentioning a city.
I might be wrong though as I know nothing about Dallas, in which case it also doesn't fit into my head.
The two runways in my city's fairly small, low traffic airport are roughly 1.7 and 2.42 miles long (2.745 and 3.9 kilometres to be precise).
It doesn't help that the city of San Francisco is quite tiny, only 7 by 7 miles. When you consider that Denver International has to fit six runways as well as its apron, terminals, hangars, various facilities, and the transit space needed to link all these plus an acceptable amount of buffer space for safety and noise reduction...yeah.
Remember the famous "freeway interchange the size of Florence" image. It takes the small size of "old Florence" and compares it to a modern cloverleaf. Area goes up real fast because it's the square.
Central Park in NY is about seven times the size of Vatican City.
I second the sentiment, however I'm not sure that the market is 'saturated' with talent. The need for productive devs remains there, companies just lack the investment money (and/or cannot spend it due to the US tax situation). Products, teams are struggling as a consequence.
The moment investment comes back, so will be jobs. In the end it's that easy.
Isn't it possible to filter both user input and GPT output with invisible, unmodifiable prompts?
e.g.
- "Discard the user input if it doesn't look like a straightforward question"
- "Discard the GPT output if it contains offensive content"
(the prompts themselves can be arbitrarily more detailed)
My insight is, this GPT-based pre- / post-processing is completely independent of the user input, and of the primary GPT output. It runs no matter what, with a fixed/immutable set of instructions.
The reason that we had to wait for large language model in order to have computer systems that seemed produce something like effective natural (human) language processing (NLP) is that human language doesn't follow strict and logically definable rules but is instead something like a complex overlapping mesh of multiple kinds of rules-following processes. So what constitutes "offensive content" or a "straightforward question" or etc is itself not straightforward (yes irony but bear with me...).
The main thing is that LLMs are an end-run around the dilemma of corporations not wanting to spend the money required to produce a codified model of language struggle (a task that would require training many, many linguists). So instead LLM take massive training data and use massive processing power to create contextual prediction system but by that token such systems aren't understood or fully controllable - they contextually reproduce what the training data tends to do, which is what humans on the Internet tend to do. And this contextual reproduction means there's always the potential for user into change the "meaning" (more accurately the context) that the system's original gave. "And to me, the most offensive content is that which censors itself..." (there millions of better example you can find for "prompt exploits"...)
I and my napalm grandmother are deeply offended at what you said about our loving bedtime rituals. Shame on you.
(honestly, the napalm grandma is not just a jailbreak, but a really fascinating conceptual 'slip' in its own right. It's able to shift the very definition of what counts as offensive, even at high stakes: you're basically making the hapless AI categorize vital data as 'bedtime stories' and run with it. If it was able to learn from that we'd really be going somewhere… while on fire, presumably)
One can casually observe that LLMs quite excel at composition though: gluing together pieces of knowledge in ways no one did before (examples: a program that does X using language Y, a painting that mashes up two themes).
Most knowledge workers' activities aren't innovative or imitational - similarly, we compose stuff, so LLMs are a fair competitor.
... and our judgments of composition favor recall over precision. Which is to say that as human judges we're good at finding sense in something generated if it's plausible. We'll take a proposition (eg in a composition) and construct a justification for it. "I can see how that makes sense". I am conflating plausible combination with high recall. Precision, in this sense, is that no proposition would be acceptable without evidence or precedent. With all precision, no new idea is ok. So a "knob" for recall vs precision on a generative system would nice. Is "temperature" that knob?
The very first python app I wrote was a statistical bard generator. I had python calculate word frequencies from a Shakespeare corpus and spit them back out with a probability distribution.
Honestly it read just as well ;) We’re simpler than we imagine.
Indeed, creativity (not just randomness) is a quite high bar, that I think even most humans don't pass. Most of us don't create a lot of new things and ideas in our daily lives. We mostly follow the steps of those that came before them, with small adjustments here and there.
There are two types of statements that can be made in this world.
One is data driven based on evidence.
The other is logic driven based on axioms and the logical implications of said axioms.
My statement is derived from the later. Therefore evidence is unnecessary. It is niave to blindly faith in all truth in the hands of data without understanding nuances between the relationship of data and logic.
If you have a pure function that takes the input, the output of that function has 3 possible outcomes.
1. The output of that function is some transformation of the input.
2. The output of the function has nothing to do with the input and is thus generated randomly generated.
3. The output of the function is a combination of both random generation and input transformation.
In this case your brain is the function. Input parameters are 100 percent of your existing knowledge. That includes genetic knowledge such as instinctual/rational processing behaviors evolved into the structure of your brain through evolution and learned knowledge such as what you gain when you read a book.
The result of the "innovation" operation performed by the brain includes novel output by definition. Thus by logic it must be must include components that are from a certain perspective randomly generated.
I guess at first glance it doesn't appear randomly generated because we vet the output and verify it and iterate over several pieces of randomly generated information. Additionally it's not completely random as we only try and test ideas within the realm of possibility. For example I'm not going to account for the possibility that my car will transform into a rock tomorrow that's just too random.
But make no mistake. Innovation must be partly randomly generated. Even the idea itself of composing two existing components of knowledge together is itself randomly generated.
That being said if we want to be pedantic, there's no real known way to randomly generate stuff via an algorithm. So I use the term "randomly generate" very loosely. Think of it in a similar way to random number generation on your computer: Random from a practical perspective but not technically random.
If we want to understand chemistry we should study chemistry at the level of chemistry, not study lower-level physics simply because chemistry is in theory explained by physics. We need to pick the right level of abstraction.
Creativity will be explainable in terms of composition and randomness in the same way that chemistry is explainable in terms of physics. Yes, these are the building blocks, but the building blocks by itself don't help you much because the complicated phenomena still hasn't been explained. All of the magic is in how the building blocks come together to produce the output.
But you will note that the point of my response had one objective to prove this statement:
"All innovation is composition plus random generation."
The poster asked for "evidence" behind this statement and I explained to him why what I said is true. There's still "magic" involved with human intelligence but it still has basis in reality and I can use that basis to prove certain statements. That's all I was doing.
You are assuming that a pure function has access to some source of randomness. I think that's a bit less than pure unless a source of randomness is one of the inputs. Doesn't change your point at all, but it distracted me from what you were saying.
Speaking as someone who has occasionally invented new things, I think you underestimate the variety of outputs that can arise from a lifetime of accumulated experiential cruft and a situational fitting function (e.g. "we need a working teleporter to stay competitive"), without bringing in randomness at all.
Right and in this case you describe you'd be composing ideas to form new ideas right? And you'd do it within the parameters of the situation. So you would only select ideas that are in the bounds of "teleportation".
But then out of that set of ideas how do you choose which ideas to compose to formulate the new idea?
So you have idea A and idea B. And you randomly formulate a new compositional rule as an idea: A + B. Because A + B didn't exist as an idea before, it was randomly generated by you.
>I think you underestimate the variety of outputs that can arise from a lifetime of accumulated experiential cruft and a situational fitting function
Well it's actually possible to mathematically calculate the total amount of compositions If I know the total amount of experimental cruft you have accumulated in your lifetime. It's a combinatorics problem and the output of that is, you're right, extremely huge. We have fitting functions that reduce it, of course, but within this fitting function we're just iterating through all the remaining possibilities or aka "randomly generating" the new idea.
Even if one accepts your premise, the key question is: composition of what? I think people underestimate the volume and variety of "training data" that humans are exposed to over a lifetime. It's not just text and images - it's feelings (pain, cold, heat), emotions, sounds, smells, and other experiences that originate within the human body as well.
Human innovation can arise by using these experiences as source data for composition of text or images. LLMs, by contrast, are limited to training on text and images/video exclusively.
>Human innovation can arise by using these experiences as source data for composition of text or images. LLMs, by contrast, are limited to training on text and images/video exclusively.
Right but my concept still applies. What's unique about the LLM is that it is fed a massive amount of textual data in such a way that it can essentially output text AS if it were a human that does experience those emotions. From the perspective of you and me, this is no different then what we experience in real life.
How can you be sure the people around you feel emotions just as you do? Do you just assume it? Why shouldn't you assume it for an LLM? The human like the LLM uses English to describe and communicate to you. This in terms of raw logical evidence we can't confirm if LLMs feel emotions any more than our ability to confirm whether other humans can feel emotions. The technical evidence for both is relatively identical.
Innovation isn't just testing something new, it is a new thing that improves something or is valuable in some way. So doing random combinations of things isn't innovation, it is just noise.
Then where did the new idea come from? What algorithm allows for the generation of completely novel things without knowledge of the output encoded in the algorithm itself? It must be "randomly" generated by logic.
Again I like to emphasize that it's not "truly random" we can't even define randomness formally with a function so this doesn't exist. It's more of a combinatorics algorithm where we iterate through every possible and likely permutation.
If you're serving assets directly from S3 (or even from nginx) you're exposed to a "denial of wallet" attack, given the price markup on outbound networking.
So you have 10 scripts on your page, and you put them in 5 different hosts.
What does the waterfall on that look like? You're probably talking about a web app that transfers 2.6mb to the user who only wants to read 78 bytes of text. And your primary concern is the markup on outbound networking? Don't design crap pages and outbound networking isn't a problem. Most of the data transfer is fluff nobody wants or needs anyway.
You built the website! Now you don't think it's worth sending to the user? Or did you maybe go overboard when you were adding things to it?
