Peek into the future of AI with us as we explore its uncanny resemblance to the human brain, guided by the wisdom of a neural network pioneer. This episode promises to stretch your understanding of not just artificial intelligence, but also of our own cognitive processes. We discuss the fascinating history of neural networks and how they mirror our brain's complex structure of synapses and neurons. Through a riveting conversation, our esteemed guest illustrates the process of learning - both human and machine - using a rich tapestry of examples to show how repetition and exposure are critical for mastering new skills.
Prepare to have your curiosity piqued as we compare the training of human cognition to the programming of artificial intelligence. Our expert guest unpacks the nuances that both differentiate and liken the two, shedding light on why neural networks need a deluge of data to 'learn' effectively. We also venture into the metrics that could measure learning efficiency between the organic and the artificial, a discussion that's bound to captivate anyone intrigued by the crossover of human psychology and machine learning. If you're eager to understand the future AI holds for the workforce and beyond, this conversation is your gateway to a world where biology and technology converge.
Exactly kind of what we're talking about is how that shit is going to take my job. So tell me how it's different to train a neural network. It sounds like we're talking about is training a neural network and what's different about training a neural network versus training the human brain, for instance? Just as a fun comparison.
Speaker 2:
Well, I would say they are very similar approaches because neural networks was invented back in the middle of 20th century, so it was invented in 1940s that the concept of neural networks and I would say that to train a human and train artificial neural network is something similar. So the overall idea is that you need to show different examples and say something like hey, these are good examples, these are bad examples. And if you multiply it by millions or trillions examples, then any human can understand what to do. And the same goes with neural networks networks. So neural neural networks was invented as a kind of way how we can uh simulate uh our brain inside the computers. So they are pretty much uh similar to what we have in our brain. So in fact I can show you the picture here which is like quite common.
Speaker 2:
So here how neural networks looks in our brain. Uh, it's uh contains uh synapses and neurons. So these big guys are neurons and these uh like threads or like connections, they are synapses. And in like mathematical world or in computer world, it's uh becomes a kind of a complex equation where we just sum and multiply some numbers and get some results. So all in all, to answer your question, how, like, human training is different from neural network training. They are quite similar to each other. Similar to each other. The question is because human brains at least right now they are much more advanced than existing neural networks. Neural networks, they just need yeah, they just need more like examples, more training. That's maybe the main difference, I would say.
Speaker 1:
That's fascinating the way you said that. So may I ask a follow up question to how you just compare them? What is the metric that you would use to compare them? So if we're going to compare,