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AlphaCode as a dog speaking mediocre English

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Re: AlphaCode as a dog speaking mediocre English

#141

Post by mark_l_watson »

skybrian wrote: post #129
mark_l_watson wrote: post #88

Earlier quoted context omitted.

I installed the VSCode and PyCharm CoPilot plugins, and signed in with my GitHub account (you first need to request CoPilot access and wait until you get it). As you type in comments or code, CoPilot will sometime autocomplete up to about 10 lines of code, based on the content of the file you are editing (maybe just the code close to the edit point?). My other tools? I use LispWorks Professional, Emacs with Haskell s…

Do you usually find the autocomplete from CoPilot useful enough to keep without editing, or is it wrong often enough to be annoying?

If it is wrong, I hit the escape key and the suggested code disappears.

Re: AlphaCode as a dog speaking mediocre English

#143

Post by randcraw »

Jack000 wrote: post #101

I think this type of model will have a massive impact on the software industry. 99% of programming tasks in the wild don't involve any kind of algorithmic design, but are more like making a CRUD pattern, writing SQL queries etc. This kind of work is easier to automate but more difficult to source the training data. If and when these models are applied to more mundane problems, I'd expect immediately better performanc…

I think the validation phase of auto-coding fullblown apps is much more complex than AutoCode is ready for. When coding up a specific function, it's pretty easy to assess whether it maps input to output as intended. But composing functions into modules is much harder to validate, much less entire programs.

And specifying a full app to be autocoded is most certainly NOT a solved problem.

Until AutoCode can build an app that employs compound AND complex behaviors, like Angry Birds, or a browser, I'll continue to see it as little more than a write-only copy/paste/derive-driven macro generator.

Re: AlphaCode as a dog speaking mediocre English

#144

Post by danielovichdk »

Why do we write code ?

Code is not a goal. It's a tool. The tool can get better. But it will always be a tool and someone has to control the tool.

Writing code is super easy. It's the easiest part of our job.

The hard part of our jobs is to understand why we need to write code and right along that, what we write code for ?

Maintenance Structuring Domain knowledge Refactoring

Those are hard things to do. And they are not solved by having a program writing code for you.

Re: AlphaCode as a dog speaking mediocre English

#145

Post by hawkice »

CTmystery wrote: post #87

Amazing stuff for sure. Looking at the example on page 59, though, I certainly see a description that contains sufficient information to implement against. I read this, and then I jump back into the tech spec that I'm writing to find: (1) The product specification and use cases are so poorly defined that I need to anticipate the use cases, design a system that is general enough to accommodate them, and implement it i…

Dog speaking mediocre English won't take my job (yet).

Re: AlphaCode as a dog speaking mediocre English

#146

Post by accountLost »

Jack000 wrote: post #101

I think this type of model will have a massive impact on the software industry. 99% of programming tasks in the wild don't involve any kind of algorithmic design, but are more like making a CRUD pattern, writing SQL queries etc. This kind of work is easier to automate but more difficult to source the training data. If and when these models are applied to more mundane problems, I'd expect immediately better performanc…

Considering that even futur users of crud applications cannot come up with decent requirements for these applications, I am not sure AI will help.

Re: AlphaCode as a dog speaking mediocre English

#147

Post by killerstorm »

omnicognate wrote: post #18

As someone who is skeptical, but open minded, about the impact these technologies will have on practical programming I think I'm one of the "people" in "people are complaining..." The article makes some assumptions about what such people think that certainly aren't true for me: 1. That we are unimpressed. I'm gobsmacked. 2. That we don't think these are significant advances. They're obviously huge advances. 3. That w…

AlphaCode is a piece of academic research. It's about demonstrating a possibility of using a particular type of a model to solve a particular type of tasks. It's not about making a practical tool.

People can certainly take this approach and build tools. That might take different shapes and forms. There are many ways these models can be specialized, fine-tuned, combined with other approaches, etc.

For example, somebody can try to apply them to a narrow use case, e.g. generate front-end code in React/JS and CSS from a backend API. They can fine-tune it on best example of React code, add a way to signal uncertainty so that specification can be clarified, etc.

Nobody expects these models to be able to write an entire OS kernel any time soon. But a lot of types of programming are far more regular, repetitive and can be verified a lot easier.

Re: AlphaCode as a dog speaking mediocre English

#148

Post by visarga »

password321 wrote: post #54
stephc_int13 wrote: post #10

Earlier quoted context omitted.

AI has been over-hyped, that's all. The machine learning techniques that were developed and enhanced during the last decade are not magical, like any other machines/software. But people have different and irrational expectations about AI.

If anything is overhyped in AI it's deep reinforcement learning and its achievements in video games or the millionth GAN that can generate some image. But when it solves a big scientific problem that was considered a decade away, that's pretty magical.

I believe modelling the space of images deserves a bit more appreciation, and the approach is so unexpected - the generator never gets to see a real image.

Re: AlphaCode as a dog speaking mediocre English

#149

Post by notahacker »

stephc_int13 wrote: post #131
globalise83 wrote: post #122

Earlier quoted context omitted.

What is the limit you foresee on computation, especially when such computations can optimise themselves without human intervention?

The underlying rule here, in my opinion, is the law of diminishing returns. (log shaped curve) AlphaZero is already capable of optimizing itself in the limited problem space of Chess. Infinitely increasing the computing power of this system won't give it properties it does not already have, there is no singularity point to be found ahead. And I am not sure that there are any singularities lying ahead in any other dom…

And building on that, the real bottleneck in most domains isn't going to be computer power, it's going to be human understanding of how to curate data or tweak parameters.

We've already seen in games with simple rules and win conditions that giving computers data on what we think are good human games can make them perform worse than not giving them data. Most problems aren't possible for humans to ebncapsulate perfectly in a set of rules and win conditions to just leave the processing power to fill in the details, and whilst curating data and calibrating learning processes is an area we've improved hugely on to get where we are with ML, it's not something where human knowledge seems more likely to reach an inflection point than hit diminishing returns.

Re: AlphaCode as a dog speaking mediocre English

#150

Post by visarga »

runarberg wrote: post #112
asdfasgasdgasdg wrote: post #5

Earlier quoted context omitted.

It's rarely productive to take internet criticism into account, but it feels like AI is an especially strong instance of this. It seems like a lot of folks just want to pooh pooh any possible outcome. I'm not sure why this is. Possibly because of animosity toward big tech, given big tech is driving a lot of the research and practical implementation in this area?

> but it feels like AI is an especially strong instance of this. It seems like a lot of folks just want to pooh pooh any possible outcome. I'm not sure why this is. I presume the amount of hype AI research has been getting for the past 4 decades might be at least part of the reason. I also think AI is terribly named. We are assigning “intelligence” to basically a statistical inference model before philosophers and ps…

It doesn't matter if we call it "intelligent" or "general", the real test is if it is useful. A rose by any other name...
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