I recently had the opportunity to meet with a first year graphic design student and talk about what a day in the life of a Creative Director looks...
Artificial Intelligence and the Cyberpunk dream
I was always amazed at the way the media convinced me that Artificial Intelligences would think, behave and act like human beings. That made me look forward to our Cyberpunk future.
I was always amazed at the way the media convinced me that Artificial Intelligences would think, behave and act like human beings. That made me look forward to our Cyberpunk future. But at some point, I started to research more and realized that I wouldn't see robots walking down the street with their robotic dogs (also capable of thinking like dogs) too early.
For the eventual dreamers of a future in which human beings and machines live as a society in harmony who appears in this blog: cheer up! I'll give you a brief introduction to why our dream is still far away (or closer than you might think).
Ok, soo, first stop: what is Artificial Intelligence? Machine Learning? Deep Learning?
Artificial Intelligence is the biggest concept. Machine Learning is a subset of Artificial Intelligence, and Deep Learning is a subset of Machine Learning. Artificial Intelligence enables a machine to make its own decisions, with is the final goal of creating an AI application, such as a Self-driving car: it makes decisions while driving without human intervention.
As a subset of Artificial Intelligence, Machine Learning provides the tools to explore and understand particular data. By achieving this, machine learning has different approaches: supervised learning, unsupervised learning, reinforcement learning, and hybrid learning methods.
Ok, and what about Deep Learning? The main inspiration behind deep learning was to let machines learn like human brains (but unfortunately now we know they work differently). These days, Deep Learning is the most used technique and has replaced many other Machine Learning algorithms like regression and decision trees. The reason is that now we have much more computational power and a lot more data to run deep learning algorithms. In Deep Learning, we have techniques like CNN for image data, RNN for time series data, we also have techniques like transfer learning.
All those techniques in Deep Learning and Machine Learning are for achieving our main goal, which is to derive AI applications.
Second stop: so now we know Artificial Intelligence, Machine Learning, and Deep Learning, how does Data Science work?
Basically, Data Science is the technique that tries to apply all those particular approaches in Machine Learning and Deep Learning, and also uses some mathematical tools like statistics, probability, and linear algebra to solve business problems.
Data Science is the technique that tries to apply all those particular approaches in Machine Learning and Deep Learning, and also uses some mathematical tools like statistics, probability, and linear algebra to solve business problems. Attention on: "solve business problems"! That is our focus, as described in The Journal of Data Science:
"Data Science is almost everything thas has something to do with data: Collecting, analyzing, modeling... yet the most important part is its applications -- all sorts of applications"
Attention again: "all sorts"... like Machine Learning! So, at this point, all theoretical papers about recurring neural networks support vector machines became physical, in other words, there is something that can change the way we live and how we experience things in the world.
Last stop: Nowadays, Deep Learning became a tangible useful call of Machine Learning that would affect our everyday lives. But not like human robots as I said in the beginning, it is more like: "Alexa, turn the lights off". It is not hard to look around and find Artificial Intelligence, see that and comprehend what we are living with that is a vow of pride and hope: pride in how far our genius minds have come, and hope for much more to come (please, thinking robots...).