Well, looks like they sorted em out!
Boy would they only know 10 years later you don't even need to write tests anymore. Must feel like Sci-fi timeline if you warped one of these blog authors into our future
I've done slipshod work full of bugs and security problems and thrown it over the fence hoping it will stand up long enough to be someone else's problems like 20 years ago!
Also, a look at how our expectations / goalposts are moving. In 2010, one of the first "presentations" given at Deepmind by Hassabis, had a few slides on AGI (from the movie/documentary "The Thinking Game"):
Quote from Shane Legg: "Our mission was to build an AGI - an artificial general intelligence, and so that means that we need a system which is general - it doesn't learn to do one specific thing. That's really key part of human intelligence, learn to do many many things".
Quote from Hassabis: "So, what is our mission? We summarise it as <Build the world's first general learning machine>. So we always stress the word general and learning here the key things."
And the key slide (that I think cements the difference between what AGI stood for then, vs. now):
AI - one task vs. AGI - many tasks
at human level intelligence.
----
I'm pretty sure that if we go by that definition, we're already there. I wish I'd have a magic time traveling machine, to see Legg and Hassabis in front of gemini2.5/o3/whatever top model today, trained on "next token prediction" and performing on so many different levels - gold at IMO, gold at IoI, playing chess, writing code, debugging code, "solving" NLP, etc. I'm curious if they'd think the same.
But having a slow ramp up, seeing small models get bigger, getting to play with gpt2, then gpt3, then chatgpt, I think it has changed our expectations and our views on what is truly AGI. And there's a bit of that famous quote "AI is everything that hasn't been done before"...
Consider that LLM->TTS example's human equivalent: when you're talking, you naturally emphasize certain words, and part of that is knowing not just what you want to say but why you want to say it. If you had a machine learning model where the speech module had insight into why the language model picked the words it has, and also vision so it knows who it's talking to to pick the right tone, and also the motor system had access to that too for gesturing, etc. then at that point you'd have a single AI that was indeed generally solving a large variety of tasks. We have a little bit of that for some domains but as it stands most of what we have are lots of specific models that we've got talking to each other and falling a little short of human level when the interface between them is incomplete.
I remember the Thinkgeek PC EZ-Bake Oven that fit into a 5.25" bay in your PC - fitting for 2004! https://hoaxes.org/af_database/permalink/pc_ez-bake_oven
And my favourite: Microsoft's Alpine Legend for Xbox 360 in 2009 that caused a stir because so many people actually wanted that game to be real. https://www.youtube.com/watch?v=ZUBQknWUEYU
Coding tests (if done correctly) is basically defining the behaviour of a black box API using running code. So it is easy to imagine an AI generating the black box from the tests/behaviour spec.
Kuraj•5mo ago
hinkley•5mo ago
But iif you perfected it then it would also be the thing that actually kills software development. Because if I told you your whole job is now writing tests, you’d find another job.
nemomarx•5mo ago
hinkley•5mo ago
Their job is to make sure that the business people and the devs sort it out without coming to blows. When they do work like this it's generally as a template to be copied, not the entire project.
lazyasciiart•5mo ago
hinkley•5mo ago
But the only people who write code as bad as QA folks do are the DevOps people.
The paradox of SDETs is: QA makes less than dev, no matter what flavor. If you're good at poking holes in developer logic, and you can code yourself, there's a 40-60% raise for you if you can switch into security consulting, which takes the same foundational skills and some reading.
So there are at least two brain drains for "good coder in test", and we aren't even the most lucrative one.