This does exactly that.
I would say that the more a program thinks on its own which actions to take to maximize its chances of success, the closer to AI it is.
If it's doing exactly what it's explicitly told, then it's not really intelligent, is it?
This does exactly that.
I would say that the more a program thinks on its own which actions to take to maximize its chances of success, the closer to AI it is.
If it's doing exactly what it's explicitly told, then it's not really intelligent, is it?
You seem to acknowledge a distinction between ML and AI, but I'm not able to understand in your categorization, what would AI be?
We don't need mathematical optimization to call it "AI", but there SHOULD be more than a simple if-then.
At least show me that you're path-finding. That's not even being done here - this is just path-following.
"I leave a trail, you follow it." Explain to me how that qualifies as AI. Simple BFS/DFS achieves a lot more than this - which is considered by most to not even really be AI.
People simply have no clue what AI actually means and think it is only about sci-fi robots and ML.
>In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
Hence the 'or' in my statement. Neither are present here.
For years (but even now) Artificial Intelligence meant understanding how intelligent behaviors worked, and then understand what is the sequence of "if-then"s that could express those behaviors in an artificial setting. Use statistical inference is a "hack" (unavoidable, often) to cover the cases in which such behaviors seem to be too complex to be grasped - and then expressed - by one simple and/or comprehensive algorithm, but the (possibly unattainable) ideal would be having everything expressed as "pretty much an if-then statement" indeed.
This is flatly incorrect - the point of AI is to have a machine achieve intelligent behaviors without explicit programming.
If an "if-then" must be written by a programmer for every single behavior, then this is called "programming". It is not called "artificial intelligence".
You leave a trail for the enemy to follow, and they follow it.
It's not even path-finding, it's path-following. Which is pretty much an if-then statement.
It's a neat, simple approach, and fun to watch. But there isn't any learning, or knowledge, or other AI.
Or maybe this is limited to little personal projects, and not major libraries ?
Take, for example, the definition of the operation s[i:j] in the Python docs: the result is "slice of s from i to j". Is that the same type as the input? Is it an iterator? No information.
I doubt most people are confused about the output, but a reference is specifically for people who are uncertain about how a function works.
But almost every major library follows a convention that's very similar to the cpython docs.
If you're making a language reference, shouldn't it look like the language ?
It looks more like an XML cheatsheet at first glance.
I work in python full-time for a living, and found myself having to re-examine several times to make sense of this wacky, non-standard notation.
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Edit:
Keep in mind there's a standard way to represent these options in a clear, consistent manner (lists, sequences, optional args).
No need to re-invent something that's hard to read and understand.
See: https://docs.python.org/3/library/stdtypes.html#sequence-typ...
Yes, there are many explicit if-else style programs in Russel & Norvig, & other books - but those are the 'training wheels', until better methods are developed. For actual AI, the training wheels are supposed to come off, and the agent learns and acts on its own.