Bubbles of bitcoin, blockchain, machine learning, deep learning, artificial intelligence and more
People usually appreciate me for my predictions on technologies. As I already noted our team will quickly hit the edge case for any use cases and that's supposed to be the reason for my better prediction rate. And so, here are my predictions…
Bitcoin bubbleWhen Bitcoin became talk of the town/industry, I informed my boss and colleagues that I find no real use case for this digital currency--except in underground markets like drugs, etc. In fiat currencies, there is at least a way to take legal route, in case, if you've transferred the money and not received the goods. But, in Bitcoin, this is not possible at all as it is not a legal currency. So, if there is no trust, the chances that other person can cheat you by not sending the goods and or not transferring bitcoins after receiving goods.
My stand above vindicated by the article Ten years in, nobody has come up with a use for blockchain
Blockchain bubbleSome overwhelmed people even told me that the Blockchain, the underlying technology behind Bitcoin will become huge hit. I'm highly skeptical from the initial stage itself. The only advantage of Blockchain in my opinion is its ability to provide immutable public ledger/database--but at very high cost (of mining). I think, people who're betting on Blockchain for such security feature will move back to PGP kind of public-private key encryption mechanism.
Machine learning, deep learning, artificial intelligence bubblesIn 2017, these buzz words became popular. These technologies were projected as serious human replacements. When I checked these technologies, I have noticed that these are far from perfect and at least now they can't replace people. My stand is vindicated by the interview of Ryan Dahl, creator of Node.js and Software Engineer working at Google Brain, in which he said:
I mean, this is my opinion. We are nowhere near matching human intelligence. I mean, the machine learning systems that we're using are very, very simplistic, and don't work at all. In fact, I have a blog post about my residency, in which I enumerate all the difficulties there are in developing these models. I think people who don't work in the field have this idea that you can kind of take this model and push some data through it, and it's just going to work. But that's not the case. These things are very finicky and not well understood, and it takes many, many months of careful tweaking and experiments to get even the most modest results.