Is Artificial Intelligence the Next Dot-Com Bubble?

Is #ArtificialIntelligence the Next Dot-Com Bubble? 

 #fintech #VC #AI @nanalyzetweets

  • We’ll stick to playing rock-paper-scissors with AI, but all of this is leading to some serious visibility for AI as an emerging technology – which as investors make us want to get some skin in the game.
  • The recent news that artificial intelligence (AI) startup Afiniti may have confidentially filed for IPO was the first time that we started to feel like this thing might be revving up for real now.
  • It wasn’t just because this would be the first pure-play AI startup to see an IPO, it was because we really couldn’t believe how effective Afiniti’s technology was in adding value.
  • Secondly we’re also seeing a very large number of AI startups getting into the game (over 1,500) which is the same sort of pile-on mentality we saw in the dot-com era.
  • Now you just add a tagline to that same website that says “powered by AI” and now you’re an “AI company”.

If you’ve played Texas Hold’em, then you know how tough it is to be a good poker player. Lots of venture capitalists like to play poker, so it wasn’t surprising to see one who thought to himself “let’s see how good artificial intelligence (AI) really is“. He consulted a team of engineers and computer scientists to see where they might be able to exploit the AI agent named Lengpudashi. They then played 36,000 hands over 5 days and the AI agent kicked the isht out of them. We’ll stick to playing rock-paper-scissors with AI, but all of this is leading to some serious visibility for AI as an emerging technology – which as investors make us want to get some skin in the game. The problem is, there aren’t many ways to do that yet.
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Artificial Intelligence, Machine Learning and Deep Learning

Artificial Intelligence, Machine Learning and Deep Learning | #MachineLearning #Artificiali…

  • Deep learning is a subset of machine learning, which is a subset of AI.
  • Machine learning, as others have said, is a subset of AI.
  • The “learning” part of machine learning means that ML algorithms attempt to optimize along a certain dimension; i.e. they usually try to minimize error or maximize the likelihood of their predictions being true.
  • Deep learning is part of DeepMind’s notorious AlphaGo algorithm, which beat the former world champion Lee Sedol at Go in early 2016.
  • The initial guesses are quite wrong, and if you are lucky enough to have ground-truth labels pertaining to the input, you can measure how wrong your guesses are by contrasting them with the truth, and then use that error to modify your algorithm.

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@Ronald_vanLoon: “Artificial Intelligence, Machine Learning and Deep Learning | #MachineLearning #Artificiali…”


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Artificial Intelligence, Machine Learning and Deep Learning