AI systems are vastly better than humans at a wide variety of tasks. Better at handwriting recognition, better at scheduling, better at playing games, better at speech recognition and transcription, etc.
I am skeptical on many of those. Speech recognition is not even close to human level. Whisper, and whatever Google uses will make a lot of mistakes on audio files that are trivial to any native speaker.
But we don’t solely rely on how well we hear since we have knowledge that allows us to correct for poor hearing based on what is being said rather than forging ahead with a nonsense transcription. Machine transcription is definitely faster and cheaper but the end product isn’t “better,” and anyone who has read it can attest to that.
> But we don’t solely rely on how well we hear since we have knowledge that allows us to correct for poor hearing based on what is being said rather than forging ahead with a nonsense transcription.
Good voice transcription AI already do that too; that's why they work best if they know which language they're operating in, as that means they can use the language to create a model of the most likely words.
I think the most recent WWDC from Apple even has a video about adding custom vocabulary for their speech engine to pick up on that covered some details in this exact topic, though I can't search right now.
Undoubtedly so but I have yet to see one that doesn't make mistakes a human would be unlikely to. It is not an easy capability to reproduce and wouldn't have been my first choice if I wanted to talk about things it can do better than people.
> Undoubtedly so but I have yet to see one that doesn't make mistakes a human would be unlikely to.
Absolutely, AI is very rarely human in its failure modes, and often has novel and exciting failure modes instead.
But, on average… or so the marketing claims… it makes fewer mistakes.
For a while, it was possible to improve upon super-human chess AI by pairing them with a human; the combination was called a centaur. Eventually the AI got too good even for that as they stopped making the sorts of mistakes humans could spot, but in the meantime, even though they were superhuman, they had failure modes that we could help out with.
The answer is that we have programmed these systems to do what we require. They cannot exceed but they fail becasue of errors that we have placed in these systems.
All of the tasks that you have mentioned have been programmed that way. It has taken human ingenuity to work out how to do this programming. The end result is a machine (non-sentient, non-intelligent) that is doing what we require.
If you look at game playing, a system was created to play Go and won and yet that same system fails to win against humans under many circumstances. The literature is there, yet not publicised for all the world to see. A result of keeping the hype in play.
If you look at speech recognition, these systems still fail when we humans work against them and yet, we humans still recognise what the machines fail at.
Just keep in mind that a tractor can move a greater amount of material than a human can, but it is still only a tool. A plane can travel faster and fly higher that a human can, but it is still only a tool.
We use these systems to augment our abilities and yet they are all limited in so many ways that we are not.
The upshot is that we can do amazing things with the things we create, but none of those things exist without us and all those things fail without us.
> All of the tasks that you have mentioned have been programmed that way. It has taken human ingenuity to work out how to do this programming.
The successful Go AI were programmed to learn; we still can't program a decent Go AI with rules humans come up with.
> The literature is there
Do you have a link? Two Minute Papers just had a video about an AI systematic finding ways to confound other AI, but I thought we'd passed the point where the best Go AI could be so manipulated by humans…
Your example of the Go AI being programmed to learn is not all that accurate for what has been achieved here. I didn't keep the link for the discussion on the confounding of the Go AI system. What the discussion covered though was that there were simple Go configurations that the GO AI failed abysmally on when playing a human - it didn't learn here.
I have spent forty years dealing with all sorts of computer systems - designing, building, maintaining, repairing, redesigning and rebuilding. One thing I have learnt over that time is that none of the systems ever built has been error free in terms of the logic entailed within them. All to often, I have seen systems that were used to make decisions with and those using them assuming that the outputs were correct or reasonable. Yet on investigation, the logic entailed in them was completely rubbish.
We make assumptions and often we do not carefully check that those assumptions are actually real. I don't trust anything I write until I have gone over it with a fine tooth comb and then I will try to document all my assumptions and this usually shows up various logic errors or conditions that I didn't think about. I don't see this happening much out in the real world.
> Your example of the Go AI being programmed to learn is not all that accurate for what has been achieved here.
What do you mean?
AlphaZero was trained entirely on self-play, and is a generic reinforcement learning algorithm. All it starts with are the rules (Chess, Go, Shogi) and a few million games later it beats — so far as I can see from a quick Google — all the humans, and most matches against AlphaGo Zero which learned the same way and which in turn beat AlphaGo Lee in every match, and that (unlike the aforementioned) was trained on examples of human matches in addition to self-play… but still learning from those examples as there's no known useful[0] set of rules that even says if a Go game is over let alone which moves are good.
There are AI which can find and exploit its weaknesses, but I've not seen anyone else suggest humans can defeat it.
> I didn't keep the link for the discussion on the confounding of the Go AI system. What the discussion covered though was that there were simple Go configurations that the GO AI failed abysmally on when playing a human - it didn't learn here.
Do you remember the name of the AI?
A bit of rummaging got me KataGo, but the humans had to use another AI to discover the weaknesses of KataGo rather than figuring it out for themselves.
And yes, KataGo absolutely does learn. The fact you can trivially stop the learning process is a feature not a bug for AI, precisely because it means any safety testing of the sort you're calling for is actually possible (albeit rather different than formal logic).
[0] pathological cases are easy — "board empty == not finished" — but not helpful.
They used a simple regular pattern and the system failed to beat the human. It didn't [learn] from this.
All such systems use a set of rules (whether specific or pattern based or mathematically based - there is some form of logic involved, even when using probabilistic functions), you and I can make choices based on illogical decisions - irrational decisions if you like. No computational system is capable of irrational decisions, the decisions may be surprising but of you look at the code then that option was always there somewhere, It cannot take a path that does not exist.
We can create a completely new path not previously available.
> They used a simple regular pattern and the system failed to beat the human. It didn't [learn] from this.
Anything written like that would struggle against an amateur.
The machine learning based Go AI don't do that, and do beat humans.
> All such systems use a set of rules (whether specific or pattern based or mathematically based - there is some form of logic involved, even when using probabilistic functions), you and I can make choices based on illogical decisions - irrational decisions if you like. No computational system is capable of irrational decisions, the decisions may be surprising but of you look at the code then that option was always there somewhere, It cannot take a path that does not exist.
We can create a completely new path not previously available.
Whatever standard I use for logical or illogical decisions, wherever I put that line, humans and AI seem to be on the same side.
We have electrical impulses flowing though messy networks, crossing tiny chemical barriers where they can be influenced by neurotransmitters; to me, that's not different enough from information flowing through an artificial neural network with weights and biases that have been automatically modified through feedback after winning and losing millions of games to say that the machine "isn't learning" — or that humans and machines aren't on the same side of "logical", at the fundamental lowest level we can't violate chemistry any more than transistors can violate physics, at the highest level the real logic of each can be random.
AI are inhuman, certainly, but still learning.
…
Just to check, you are aware that the weights and biases of an artificial neural network are basically never set by humans? That this process has to be automated?