QIPSPHAMLS

I was asked to write an introduction, so welcome everyone to my very first blog, ever.

I’m currently in the dissertation phase of my PhD in Computer Science so it’s been beaten into my head over the past few months that I really don’t know anything about anything (just kidding; it’s actually far worse than that). However, despite being completely unqualified, I’m going to write about my favorite topic, Machine Learning and Artificial Intelligence. Actually, favorite topics, I guess.

Rather than start with blah blah blah about blah blah and how important blah blah blah is, I thought I’d begin this episode with some Frequently Asked Questions (FAQs). The more astute among you are thinking, “How can there be FAQs if this is the first one? For that matter, how can there be any questions at all, frequent or otherwise?” Honestly, these are just Questions I’m Pretty Sure People Have About Machine Learning, but QIPSPHAMLs doesn’t roll off the tongue quite as easily as FAQs.qipsphamls

So anyway, let’s get started with some FAQs (QIPSPHAMLs).

  1. What is Machine Learning?

Machine Learning is the name given to a collection of algorithms used to “give computers the ability to learn without being explicitly programmed,” according to Arthur Samuel, who is generally regarded as the father of Machine Learning. The algorithms generally fall into two categories: supervised and unsupervised learning. Supervised learning typically requires large data sets to train the algorithm properly; with unsupervised, you just run the algorithm and hope nothing blows up.

Just kidding. That hardly ever happens.

Unsupervised techniques are used when there is no training data available and are good at putting unknown things into categories. Imagine you have a pile of weird, nonsensical phrases that you don’t know what to do with. Unsupervised learning algorithms could divide them into categories of legal terms and medical terms, even though no one understands either of them.

There exists a large collection of Machine Learning algorithms to do everything from sorting fruit to diagnosing disease to deciding what you’d like to buy next on Amazon. I’m not going to go through them all now because there’s a lot—and frankly, the names are awful, like convolutional neural network and self-organizing feature map. They really just serve as a reminder for why computer scientists shouldn’t be allowed to name things.

  1. Is a robot going to take my job?

Probably. The question you should’ve asked in this imaginary conversation is, “When?”

Pepper the job stealing robot
The future is here to take your job and his name is Pepper.

Well, it depends on what you do. If you’re a nurse or physical therapist you’ve most likely got 10 or 20 years. If you’re a truck driver you should probably head back to school next semester. More immediately, there’s a lot of good that can be done by combining human intuition and expertise with artificial intelligence (AI).

It’s a very exciting time for this field, and if I get to write another one of these, I’ll go into more detail in a future episode.

  1. I heard Elon Musk and Stephen Hawking both said that AI is a threat to mankind. Are computers about to take over?

Wow, great question. The short answer: Probably not.

Right now, programming languages are brittle. In the context of programming, that means the code is easily broken, which, as I think about it, is actually pretty similar to what brittle means in other contexts.

In other words, the army of killer robots would probably be done in by a missing semicolon or a Windows update.

There are some real threats from AI including issues with our current economic model due to the combination of AI and robotics taking over more of the job market. I plan to write a post that focuses on the actual dangers of AI in the future. The post will be in the future, the AI concerns are current.

  1. What’s the difference between AI and Machine Learning?

I knew you were going to ask that.

The terms are often used interchangeably, but there are some subtle differences.

AI is the overarching field of trying to make computers think like people…although there is some current research that says thinking like people may not be best thing for computers. Machine learning is a subset of AI that typically focuses on data, basically recognizing patterns such as speech or image recognition, categorization problems, checking if you cheated on your taxes, etc.

Pretty much everything we have done so far is considered narrow AI, meaning focused on a specific problem like playing a game or recommending friends on Facebook. General AI is the term for a computer that thinks like us. We’re not there yet, but it’s coming sooner than most people think. I also plan to write about that in an upcoming episode.

I was told to make this entry a 5- or 10-minute read, which is difficult because I have no idea how fast you read. I’m guessing this is close to enough for most of you so I’ll stop here.

If you have any questions about AI or Machine Learning, or just need help naming something, drop me a line.

Thanks!

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