by rcshubhadeep on 12/5/22, 1:20 PM with 34 comments
1.> What will happen when evil players will get hold of this tech? Are we going to witness another cold-war or something, just this time the threat will be who develops better models?
2.> This incredibly powerful models are built and maintained by private orgs. Isn't that scary in itself?
3.> How do we adapt ourselves so that we can co-exist with such techs (and the more that are yet to come in the near future)
Maybe some more points can be added. What are your thoughts?
by kossTKR on 12/5/22, 2:39 PM
It first made a basic admin interface in Html+Css i could copy paste into Codepen, then made it interactive with pure JS, then refactored it to Vue3, then refactored it into the now obscure Angular 1.4 just to test it, then back to Vue 3, then added Pinia for persistent state in the browser, and then converted the application to PHP Laravel+Livewire, and then to Python with Django+Htmx for backend
EDIT: Wow, then i had it make graph with Canvas showing a sinus wave that i could speed control via an input with the prompt "make a an application in JS and Html that shows a sinus curve that you can speed up or down via an input".
Absolutely mind boggling! It means it can create already create a simple working application and transform it between most known stacks, even older ones.
I have no idea why some people here on HN say that it doesn't understand logic when it can refactor like that?
This is honestly making me reconsider being a developer as a career choice just a little.
by tsukikage on 12/5/22, 1:30 PM
Interesting. IME even for toy problems it generally spits out code which fails to do what I requested for many inputs or at all. Using this tool to solve a problem requires not only that I understand what it spits out but also that I understand how to actually solve the problem, so that I can work out which parts of the rubbish it spits out are broken and ask it to iterate on those.
It doesn't seem frightening or particularly transformative; I'm not even convinced that using it could save more time than not. It's not doing anything radically different to https://github.com/drathier/stack-overflow-import and the latter works better.
The worst part is that just as with tools like Grammarly, the people who would most benefit from a properly working version of such a tool are exactly the people least well placed to understand when and how the output of the tool is wrong.
I welcome evil players attempting to use it: their evil plans will self-destruct in hilarious ways.
by seydor on 12/5/22, 4:06 PM
1) We are forever in 'cold war', nuclear weapons can destroy the eart multiple times over since 70 years ago. Not sure how a less potent weapon supercedes this fact
2) Those models are successful with computer code because programmers have been very pro-open source and open-data since forever. We 'll have open source versions of those things soon -- we ll still need to find the hardware but i think this can be done too. Much easier to do than a nuclear bomb so i expect these systems to become ubiquitous
I wish biology and medicine have had a similar attitude to open data and open science. Imagine if you could run similar queries in genome databases or neuroscience images.
3. First we get excited, then some people will turn that fun tech to billions as before
I really don't remember people being such doomers when the internet came about. What happened?
by colingmathews on 12/5/22, 4:46 PM
Don't get me wrong, I do think there are dangers to runaway progress. But we can't stop the internet now. I think our best bet is opening ourselves up to what comes and help skew attention towards tech that leads to more compassion.
by balaji1 on 12/5/22, 2:27 PM
My friend wrote a "remote c2c server, basically a mutating malware" using ChatGPT. He had no bad intentions, he just works in the security domain.
People with malicious intentions will be the biggest and earliest adopters of AI. Somehow that is my first thought.
by lmarcos on 12/6/22, 4:22 PM
In the past, software engineers were dealing with punched cards (few engineers), later on they were dealing with assembler (more demand), then with low level programming languages like C (demand starts to increase), and nowadays engineers deal with high level prog. languages like Python, JS, etc. (high demand). As the technology makes software more ubiquitous and reachable to any aspect of life, the demand for people who know about software increases.
Maybe in the future software engineers will have to deal with even higher level languages (prompts?), but that would only mean that making software is easier than before and you'll see software even more ubiquitous than it is right now. Demand will go up for people who know about software even if the tooling seems like it requires less people with such knowledge.
by jstx1 on 12/5/22, 1:26 PM
Even before large language models we had the option to copy-paste working code for rnn based time series forecasting for years.
by hnthrowaway0328 on 12/5/22, 5:25 PM
by solardev on 12/5/22, 3:55 PM
by sdfgdfghj on 12/5/22, 2:12 PM
It scares me that in few year I wouldn't be able to tell if something is fake or not most of the time and global mind set will be controlled by even more fake content.
We may need to figure out a way to identify genuine human-generated content.
by gardenhedge on 12/5/22, 3:21 PM
by ActorNightly on 12/5/22, 10:01 PM
1. There are a good subset of jobs across multiple industries that are simply "decision tree lookup" operations. These types of job will most certainly be replaced. For example, I worked for an aerospace company, we hired a consultant for advising on a manufacturing process. He basically looked at what we are trying to make, and advised on the tooling, process, e.t.c. This is the type of job that can be easily done by a future version ChatGPT that is sufficiently trained on both text and mathematical contexts. Software jobs often fall into above category, replicating common patterns that developers have learned. ChatGPT right now is even smart enough to take an input json and output json and write code to transform one into the other.
2. The actual "compute" operations jobs (like making software that requires figuring out a new pattern of transforming data or interfacing with a new piece of hardware like a 3D display) won't be replaced, but the skill will shift to a lot more computer science centric in being able to either a) additive train generic models on specific tasks, or b) use state of the art AI assisted tools effectively.
3. Overall, quality of life is going to improve, as it will get a lot cheaper to do things.
TLDR; if are a software dev and you haven't already, get super familiar with ML concepts, Pytorch, etc.
https://github.com/karpathy/micrograd is a very good primer to start with once you understand the basic concepts.
by johlits on 12/6/22, 11:05 AM
by cleerline on 12/5/22, 11:46 PM
from the following table create a php crud application : CREATE TABLE `rating` ( `rating_id` int(11) NOT NULL, `review` varchar(4048) DEFAULT NULL, `rating` tinyint(4) NOT NULL )
by evilbob93 on 12/5/22, 5:20 PM
by xchip on 12/5/22, 1:34 PM