and all that Jazz
It seems like AI/ML is the big thing in silicon valley. If you check out how the VC money is moving, what the TechCrunch articles say, and what’s being posted on HackerNews, it’s like ML is the greatest thing since social networks. If Machine Learning is so great, why don’t we use it everywhere? Why not get rich building prediction models for the stock market? According to this NVIDIA blogpost, Deep Learning, responsible for the current ML boom, wasn’t so great until recently:
Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) — images, text, transactions, mapping data, you name it. …
How Machines Learn
Like all things artificial, AI requires a solid definition for intelligence and design for how to get there. In 1485, Leonardo da Vinci studied the flight of birds in attempts to create artificial flight. In 2017, Google designs hardware and software to efficiently replicate artificial neurons in hopes of advancing Artificial Intelligence, prioritizing the pursuit of artificial learning.
How do you learn to play a board game? Do you like to study the directions until it all makes sense, or do you like to learn as you go? Just like humans have different ways of learning, Machines also learn through a variety of practices. Unlike humans (where the majority of knowledge is learned), there’s a lot more that a computer is programmed to directly know than a computer is programmed to learn. In fact, most functionalities of machines today are directly (formally and algorithmically) represented for the computer. …
Why must machines learn? Because humans can’t keep up.
The current AI/ML boom is a result of the advancements in a specific approach to learning, Deep Learning. Artificial Intelligence, Machine Learning, and Deep Learning are all responsible for some of the biggest advancements in the past year, and people celebrate these technologies interchangeably. Lately, tech conferences are swarming with people wanting to know more about Artificial Intelligence, Machine Learning, and Deep Learning as if they were the same thing. There’s a good amount of history behind each of these technologies, but a simpler way to keep track of the difference is by our motivations.
The Difference in Motivations
Machines learn because humans have better things to do. I’m half joking, but it’s a good start. Of course, there are more technical takes on how AI, ML, and Deep Learning relate. …
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