AI is freaky to think about. Right now Mythic is making low-cost analog machine learning chips that can support neural networks with 1 trillion parameters, and the human brain has 1,000 trillion parameters. It is not going to be long before we get there.,(reply>), @IAN 4000 using lot of data as a quick  way to give an IA a baseline "inteligence" doesn't seems that far fetch given human take years to learn basic concept. However, after that first learning humain continue to learn where IA have yet to improved there network on the fly. Without this, only short term conversation will work with IA,(reply>), @theparijat1000 One of the dominant theories on how general AI might be achieved is by dramatically increasing the number of neurons/parameters to (or past) the quantity found in human brains. Small neural networks (like basic perceptrons) take lots of training and produce not great results, but convolutional neural networks are all the rage right now because computing power has exploded and computers can train huge neural networks that are producing better and better results. Basically the idea is that massive neural networks may solve the problems you pointed out with training time / creativity. Also worth mentioning that humans actually have enormous amounts of training data since they have already learned how to recognize countless different types of objects.<br><br>So if that theory is correct, that the main difference between current neural network technology and "real" intelligence is the number of parameters in the neural network, things are going to get very weird over the next few decades.,(reply>), @theparijat1000 but we already have years of data our senses have collected, even though you don't know what may happen exactly, you're using your past experiences to plan for it. I'm not saying neural nets and brains are 1:1, but there are definitely similarities.,(reply>), As an anatomy professor who teaches about brains, I would like to disagree with the idea that neural networks and brains are all that similar. Sure, I mean the network is "neural" that is its been reverse engineered from the structure of neurones, but in general we dont take up as much data and dont go through all that many trials and errors to learn something. For example, humans can possibly make plans for something upcoming, for which they have no data before, or act on instinct, with the bottom line being it does not always need millions of trial and error and huge datasets to practice something to act. I feel the two works pretty differently even though there may be similarities.,(reply>), ​@IAN 4000 Another software engineer chiming in. I pretty much agree. they are different structures but have similar inputs and outputs if you know what i mean