Comments

You must log in or register to comment.

jsonathan OP t1_j3hwx5l wrote

Right now, this is just a simple demo of what’s possible with AI-driven debugging. But I’d like to build it out so that instead of just explaining errors, Adrenaline provided a ChatGPT-style assistant that can answer questions about your error, and teach you during the debugging process.

This is open-source, so if anyone’s interested in contributing, here’s the GitHub repository: https://github.com/shobrook/adrenaline

46

uoftsuxalot t1_j3hz4nm wrote

Not to take anything away from this project, but it’s just an api call to gpt3 with prompt “fix this error {error}”. I thought there was some training and fine tuning, but I guess LLMs can do it all now a days

144

cgk001 t1_j3i2vsa wrote

Limited by the 4k token max in api call?

27

GoofAckYoorsElf t1_j3ilywu wrote

This is all great. The only problem is that I can't use it due to non-disclosure and IP protection of my employer. As long as I have to send code over the web, it's a no-no.

13

naiq6236 t1_j3j0ofz wrote

This would be a game changer dude

2

IshKebab t1_j3j1gkz wrote

Yeah I imagine that will be an issue for lots of people. What's the SotA in open source LLMs?

I looked it up. Apparently it's BLOOM. Slightly bigger than GPT-3. No idea if it is better.

You need a DGX A100 to run it (only $150k!).

12

Accomplished-Low3305 t1_j3j6b0v wrote

It would be nice to have some metric to evaluate how good is GPT-3 solving bugs. In my experience it only works fine for simple bugs, such as using an incorrect variable.

9

yerop82726 t1_j3j6hp9 wrote

Nice one. Keen to see the vscode extension!

3

RKHS t1_j3k2134 wrote

This is a fairly useless example. It's simply a rewording of the error. Do you have any examples that are non trivial?

3

davidswelt t1_j3kg6vo wrote

OK, how did you evaluate it? How do you tell it's working well or not?

2

ksblur t1_j3kkft7 wrote

Just wait till you see what the rate for management will be after LLMs come for their jobs.

Managers are mostly people-interaction-managers, and LLMs are already 10x better at that than they are at creating novel code.

11

fnetma t1_j3kmesf wrote

An AI debugger would be very helpful. I actually see that as a use case...

2

Mikatron3000 t1_j3l71ns wrote

Not sure if this is already there but it might be worth adding some license information here since sending closed source code over an open sourced API / model might become a no no in the future legally. I guess that would be the problem of making this an Intellij / vscode plugin

2

devinhedge t1_j3lhp0h wrote

This is cool. How do we give feedback to the training engine so that it improves over time?

2

EarthAdmin t1_j3mgzbo wrote

Would love this to be a VSCode plug-in! Happy to drop our OpenAI api key in there.

2

TrueBirch t1_j3mw92i wrote

There are some things that are incredibly hard. Imagine you work on a farm. You toss the keys to the ATV to a 17yo farmhand who's never worked for you before. You say, "Head over to field 3 and tell me if it's dry enough to plow. You can see where it is on this paper map. Radio back using this handheld." The farmhand duly drives the ATV to field 3, sees that it's muddy, picks up the radio, and says, "Sorry boss, field 3's a no-go."

We're a long way from a robotic farmhand being able to perform those skills, certainly not for a price comparable to a farm laborer.

You could definitely train an application-specific AI to monitor fields and report on their moisture levels. You could even have an algorithm that schedules all of your farm equipment based on current conditions and other factors. So it's not that AI can't revolutionize how we work, it's just that it'll be different from true AGI.

2

2Punx2Furious t1_j3nzumw wrote

> We're a long way from a robotic farmhand being able to perform those skills, certainly not for a price comparable to a farm laborer.

If we get AGI, we automatically get that as well, by definition. Those you listed are all currently hard problems, yes, but an AGI would be able to do them, no problem.

The issue is, will AGI ever be achieved, and if yes, when?

I think the answer to the first one is simple, the second one not as much.

The answer (in very short) is: Most likely yes, unless we go extinct first. Because we know that general intelligence is possible, so I see no reason why it shouldn't be possible to replicate artificially, and even improve it, and several, very wealthy companies are actively working on it, and the incentive to achieve it is huge.

As for the when, it's impossible to know until it happens, and even then, some people will argue about it for a while. I have my predictions, but there are lots of disagreeing opinions.

I don't know how someone even remotely interested in the field could think it will never happen for sure.

As for my prediction/opinion, I actually give it a decent chance of it happening in the next 10-20 years, with probability increasing every year until the 2040s. I would be very surprised if it doesn't happen by then, but of course, there is no way to tell.

0

TradeApe t1_j3rra6b wrote

If they can automate huge chunks of super busy cargo harbors, they can automate valet parking...and they won't even need AGI for that. Hell, valet parking will likely become obsolete once full self driving is here.

People also didn't think AI will make artists obsolete...but here we are.

1

TrueBirch t1_j3xlp2e wrote

Artists are hardly obsolete. Photoshop didn't make them obsolete and generative AI won't either. And I say that as someone who has extensively used Stable Diffusion for work and personal projects.

Regarding valets, I'm referring to the ability to toss your keys to a robot and have it drive your car. Even when true self driving cars are first produced (which always seems to be ten years away), we'll be a long way away from a robot being able to park a non-automated car. That's just one example of a task that seems really easy for humans but is shockingly hard for robots. Folding laundry is another one, which is especially relevant since I'm ignoring the fact that my dryer just finished a load.

1

LucasLeroy19 t1_j4cj9v0 wrote

My question to you is, why the name Adrenaline? How did you come up with that?

1

TrueBirch t1_j4jd8af wrote

A true AGI has way too many edge cases to be possible in the timeframe you describe. It's also not necessary to create AGI in order to make a lot of money from AI. You can find the specific jobs that you want to replace and create a task-specific AI to do it.

1

eldenrim t1_j4kmltf wrote

I'm curious how you feel about the following:

There are humans that can't do the task you outlined. Why use it as a metric for AGI? Put in other words, what about a "less intelligent" AGI, that crawls before it walks? An AGI equivalent to a human with lower IQ, or some similar measurement that correlates with not being capable of the same things as those in your example?

Second, if an A.I can do 80% of what a human can, and a human can do 10% of what an A.I can, would you still claim the system isn't an AGI? As in, if humans can do X, A.I can do X * 100 things, but there's a venn diagram with some things unique to humans and many things unique to A.I, does it not count because you can point to human examples of tasks it cannot complete?

Finally, considering a human system has to account for things irrelevant to an AGI (body homeostasis with heart rate and such, immune system, etc) and an AGI can build on code before it, what do you see as the barrier to AGI? Is it not a matter of time?

1

TrueBirch t1_j4kv71p wrote

I think "AGI" is a silly concept overall and never really happening. Computers are good at doing things in different ways from humans. Rather than chasing AGI, you can make a lot more of an impact by leveraging a computer's strengths and avoiding its weaknesses.

For my example, I picked an occupation with an average salary south of $30,000/year (source). I'm not saying everybody can do it, but the market puts a price on this kind of labor that suggests many people can do it. A true AGI system could replicate how a low-salary human does a job. In reality, a computerized system would use a few wireless sensors that call home instead of physically driving around looking at fields.

Similarly, consider meter readers, another low-wage job. Imagine what it would take to create a robot that could drive from house to house, get out of the car, find the power meter, gently move anything blocking it, and take a reading. Instead, utilities use smart meters that call home. It's cheaper, more reliable, and simpler.

It's beyond hard to create a true AGI system, and there are plenty of ways to make tons of money with application-specific systems.

1

eldenrim t1_j4l7ilw wrote

I'm currently interested in ML to alleviate the suffering of my disabled partner and myself, I just enjoy theoretical discussion with AGI.

Maybe making money will come later. :)

1

2Punx2Furious t1_j4lbgjj wrote

Yes, I'm saying the fact that there are edge cases doesn't matter, because it's not us who have to address them. As we get closer and closer to AGI, it will get better at handling them, we won't have to find them, and code solutions for them. I think it will be an emergent quality of AGI.

1

TrueBirch t1_j4qdbf2 wrote

It'll be something entirely new, but not capable of doing everything that my toddler can do. Systems will be designed to avoid those weaknesses. Again, think about replacing meter readers with cheap sensors instead of expensive robots.

1

aminostfx t1_j51kpiw wrote

This is awesome. But i have an idea to make it even better. What if we train a RL agent to write code without errors and actually make sure there is no bugs in the code. The environment used to train the RL would be the compiler. We can start first with support python only and supporting other languages later on. DM if you’re interested to colab on this project.

1