This is the first part of what I'm hoping will be a series on the ideas, discoveries, and algorithms that led to the development of modern artificial intelligence. I'd love to hear if this is something you're interested in - I have a list of topics already that I'm hoping to cover, and if there are particular topics you'd like to see discussed, let me know!
Man, I can't wait for this series! I've watched videos about machine learning in the past and different kinds of reward functions - but it's never really clicked because of the terminology involved and the way that information is portrayed
Woah, weird timing. I just started re-watching some of your old videos yesterday and this morning. I hadn't watched any in months. And now there's a new one!
"[The Analytical Engine] might act upon other things besides number, were objects found whose mutual fundamental relations could be expressed by those of the abstract science of operations, and which should be also susceptible of adaptations to the action of the operating notation and mechanism of the engine…Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent" -Ada Lovelace, 1843
I know it sounds amazing that she knew about this in 1843, but literally all she said here is: "If we had a machine that can perform mathematics, and we could write musical theory as a set of mathematical rules, then that machine could perhaps write music". It's a "duh" statement. yes; if you had a machine that can execute instructions and if you can write music as a set of instructions then the machine can execute those instructions and create music. Ofcourse today's AI doesn't understand musical theory, it just creates sounds that it has heard before. It's allmost literally averaging what's in it's database, there is no creativity, no reasoning, nothing.
@@vinny142not exactly accurate, its not searching a database, thats its special part. in loose terms, it transforms its "learnt data" into a number of patterns. then, when you input something new, it shapes the result based on the patterns it has learnt, + some randomless. its more similar to probability distribution than a static search.
8:05 - was this NOT in their original paper, or did you extrapolate it? Based on your description of their motivation, I wouldn't have expected a threshold of 0 to fire anything in a biological model. Maybe this stems from a simplification they made to use combinatorial logic, whereas the biological neurons are not combinatorial.
There isn't some superposition we know about that would lead the machine to deliver a "You're on the right track in thinking about A, B, and C, but I'm not completely sure about the final answer, so be more specific about the following: X, Y, and Z". It sounds like a need software engineers need to anticipate to not seem "politically motivated", even though the decisions about what prompts need an "I'm not sure" answer would itself be politically motivated.
This is the first part of what I'm hoping will be a series on the ideas, discoveries, and algorithms that led to the development of modern artificial intelligence. I'd love to hear if this is something you're interested in - I have a list of topics already that I'm hoping to cover, and if there are particular topics you'd like to see discussed, let me know!
❤🎉 looking forward to it
will you show some pseudocode steps?
I have yet to find a video that can explain bayesian networks to me. I would love to see your attempt
Symbolic AI
Absolutely!!
you are getting all of us through computer science, now even the artifical intellegence classes
Man, I can't wait for this series! I've watched videos about machine learning in the past and different kinds of reward functions - but it's never really clicked because of the terminology involved and the way that information is portrayed
Woah, weird timing. I just started re-watching some of your old videos yesterday and this morning. I hadn't watched any in months. And now there's a new one!
I can't wait to see the next video on this topic !
PART 2!! PART 2!! PART 2!!
Ooh, really looking forward to this series!
very cool. I'm starting intro to machine learning in a few weeks. Love your other videos and I'm excited to see where this series goes.
LFG! This should be an awesome series, thank you!
soooo good keep up the great work!!!
Great fan of your content.
Please do a video on multithreading vs multiprocessing vs multitasking vs parallelism
NOOOO this was 4 hours ago… I got soo exited as I thought you already had a full series out. Well will just have to wait I guess… impatiently😂
Great video bro, I'm looking for the next videos
underrated video!!! i love this!! subscribed!!
Very excited for this series!
Amazing video! Waiting for future ones 🥳
I am sorry man you really deserve a million subscriber great videos .❤❤
This video was amazing, thank you 🙏
This channel have potential to get more followers if only you do more videos. Thanks man. You got great knowledge
"[The Analytical Engine] might act upon other things besides number, were objects found whose mutual fundamental relations could be expressed by those of the abstract science of operations, and which should be also susceptible of adaptations to the action of the operating notation and mechanism of the engine…Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent"
-Ada Lovelace, 1843
I know it sounds amazing that she knew about this in 1843, but literally all she said here is: "If we had a machine that can perform mathematics, and we could write musical theory as a set of mathematical rules, then that machine could perhaps write music".
It's a "duh" statement. yes; if you had a machine that can execute instructions and if you can write music as a set of instructions then the machine can execute those instructions and create music.
Ofcourse today's AI doesn't understand musical theory, it just creates sounds that it has heard before. It's allmost literally averaging what's in it's database, there is no creativity, no reasoning, nothing.
@@vinny142not exactly accurate, its not searching a database, thats its special part. in loose terms, it transforms its "learnt data" into a number of patterns. then, when you input something new, it shapes the result based on the patterns it has learnt, + some randomless. its more similar to probability distribution than a static search.
Brilliant, thank you!
THANX , BRIAN!!! AWESOME!
Rellay its very interesting and full of knowledge i am enjoying these
Love your explanation
Interesting 👍👍👍
exicted for this machine learning video
excited for this one (:
Spanning tree I love your videos thank you for the series ❤ I love studying AI
great start!
Nice vid can’t wait for the next one
can't wait for part 2😢
Awesome, so is the 2nd part coming out tomorrow?
❤❤
So underated
Wtf ?
8:05 - was this NOT in their original paper, or did you extrapolate it? Based on your description of their motivation, I wouldn't have expected a threshold of 0 to fire anything in a biological model. Maybe this stems from a simplification they made to use combinatorial logic, whereas the biological neurons are not combinatorial.
1 quick question from another video how u make not gate with dominoes?
There isn't some superposition we know about that would lead the machine to deliver a "You're on the right track in thinking about A, B, and C, but I'm not completely sure about the final answer, so be more specific about the following: X, Y, and Z". It sounds like a need software engineers need to anticipate to not seem "politically motivated", even though the decisions about what prompts need an "I'm not sure" answer would itself be politically motivated.
We've come a long way.
What that maze mouse did was to create a flow map.
Couldn't understand the neurons part..
First Commant :))
Second
Is any job opportunity produces due to ai field ? 2. Could u pls make a video about charisma using science
Today on Robots Ask: Will humans ever learn?
Why has the black knight a white mane, while the white knight does not have a black mane?
Ahhh, finally🤌 us peasants get one new video after months of waiting..