Title: Understanding the Four Types of #AI
Source: Twitter, via Vala Afshar
We need to do more than teach machines to learn. We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.
My Impressions: I often marvel at the attempts from Hollywood to portray AI as a foregone conclusion of using computers, and how simple it is to do things if you are a computer. My favorite depiction continues to be the Star Trek computer – if you ever worked in AI you know how far we are from something like that… but I digress.
I often find myself having the debate on whether that model will become reality, whether the singularity is possible, and how we get there. While most people get hung on feelings and emotions as the differentiating factor between us and them, I favor what I call the three I’s (feelings and emotions can be recognized, measured, and replicated — the key is to identify the few variables that affect them – like sarcasm, but we can talk about that some other time).
The three I’s are Intuition, Innovation, and Imagination; they are not measurable or easy to replicate (yet). As I often use as an example – Monsieur Fleming would’ve never found penicillin with the search parameters her was using if he was a computer. It’s a fungus growing on food — not sure how to tell a computer to test that (especially since they would not bring their lunch, where the mold would grow on).
This article goes one further examining how the real barrier between computers and people is memories – and how they work. If you think about it, the most complex process we have as humans is memory, not emotions. What, how, where, and why something is stored, recalled, blocked, used, or discarded is far more complex that crying when you see a commercial with puppies (not that I do that, but I’ve been told some of you do).
Read this article so you can see what are the major challenges we have to focus on, not for advanced analytics only (although the ability to hold a conversation that is coherent requires memory) but for machine learning.
(Cross-posted @ thinkJar)